Evaluating the direct and indirect impact of traits and perceptions on technology adoption by women entrepreneurs in Malaysia.

Ndubisi, Nelson Oly

ABSTRACT

The current research adopts the technology acceptance model (TAM) in examining the relationship between IT adoption, perceived system's ease of use, and the entrepreneurial traits (such as, innovativeness, risk-taking propensity, perseverance, and flexibility) of Malaysia women entrepreneurs. The results show that perceived ease of use has an indirect influence (via perceived usefulness) on adoption. Contrary to TAM, no significant direct relationship was found between perceived ease of use and adoption. Innovativeness and risk-taking propensity were found to determine perceived ease of use and adoption. The findings show that women entrepreneurs are driven by instrumentality in technology adoption. Contrary to the process orientation reported in previous studies for women in general, women entrepreneurs are outcome oriented in technology adoption. Important implications on theory and practice are discussed.

INTRODUCTION

While advances in technology continue with rapidity, the use of these upcoming technologies has fallen below expectations (Ndubisi, Gupta & Massoud, 2003; Johansen & Swigart, 1996; Wiener, 1993; Moore, 1991) and has been identified as one of the plausible explanations for the productivity paradox (Sichel, 1997; Landauer, 1995). A number of studies have shown that successful investment in technology can reap immense benefits for the adopting individuals and organisations (Doms et al 2003; Gretton et al. 2002; Bennett et al. 2003). On the basis of these benefits, various governments have been motivating their business communities particularly entrepreneurs, to avail themselves of the benefits of these technological advances. Despite these significant technological advances and increasing governmental investments in promoting IT usage at individual and organizational levels, it is still unclear, the extent of IT usage among women entrepreneurs, the determinants of usage, and the role of personal traits. Clearly, understanding the determinant structure of these key variables is critical for researchers, entrepreneurs, as well as systems developers and vendors targeting entrepreneurs.

The focus of this research on women entrepreneurs reflects the growing number and importance of women owned businesses (Michaels 2006) around the globe. Michaels (2006) reported that the number of women-owned businesses in the US grew at twice the rate of all firms between 1997 and 2002, jumping 14 percent to 6.2 million. Cowling and Taylor (2001) reported that proportionately, three times as many male self-employed in 1991 had gone on to become job creating self-employed by 1995. The research is precipitated by the fact that entrepreneurs (especially women) are a distinct and important group, which unfortunately has not received well-deserved research attention in Malaysia. Moreover, entrepreneurs have been reported in personality and psychological research as exhibiting unique traits that distinguish them from other user groups, which traits could have different implications on their usefulness and ease of use perceptions and adoption of computer technologies.

In this study, perceived ease of use is investigated to understand its determinants (namely users' traits), and its impact on adoption of computer technologies by women entrepreneurs. Studies comparing the salience of perceived usefulness and ease of use between male and female users of technology have shown that perceived usefulness is more important for male users while female users emphasize ease of use in technology usage decisions. For example, Venkatesh et al., (2000) reported higher instrumentality (i.e. outcome) for men and higher process orientation (ease of use/difficulty) for women in technology adoption decisions. Hennig and Jardim (1977), Rotter and Portugal (1969) have earlier shown that women tend to focus on the methods used to accomplish a task--suggesting a greater process orientation. Given the process-orientation of women, it is the aim of this study to examine the preponderance of ease of use over usefulness with respect to computer technology adoption by women entrepreneurs.

LITERATURE REVIEW

A number of models have been developed to investigate and understand the factors affecting the acceptance of computer technology in organisations such as the Theory of Reasoned Action--TRA (e.g. Fishbein & Ajzen 1975; Ajzen & Fishbein 1980), the Technology Acceptance Model--TAM (e.g. Davis 1989; Davis et al., 1989), the Theory of Planned Behaviour--TPB (e.g. Ajzen 1991; Mathieson 1991), the Model of PC Utilisation (Thompson, Higgins, & Howell 1991), the Decomposed Theory of Planned Behaviour (e.g. Taylor & Todd 1995a), Innovation Diffusion Theory (e.g. Agarwal & Prasad, 1997; Brancheau & Wetherbe, 1990; Rogers, 1995), and recently The Entrepreneurs' Technology Acceptance Model (Ndubisi & Richardson, 2002). Some of these studies were carried out at the individual level (e.g. Agarwal & Prasad, 1998), and some at the organisational level (e.g. Cooper & Zmud, 1990).

The theoretical model employed in this research is the technology acceptance model (TAM). The study focuses on the TAM because it helps to understand the role of perceptions such as usefulness and ease of use in determining technology adoption. TAM theorises that external variables influence behavioural intention to use, and actual usage of technologies, indirectly through their influence on perceived usefulness and perceived ease of use. Davis (1989, p320), defines perceived usefulness as "the degree to which a person believes that using a particular system would enhance his or her productivity", and perceived ease of use as "the degree to which a person believes that using a particular system would be free of effort". Although TAM has been recognized for its parsimony and predictive power, it has also been reported that while parsimony is TAM's strength, it is also the models salient constraint. Venkatesh (2000) asserts that while TAM is very powerful in helping to predict acceptance, it does not help understand and explain acceptance in ways that guide development beyond suggesting that system characteristics impact usefulness and ease of use, thereby placing a limitation on the ability to meaningfully design interventions to promote acceptance. Mathieson (1991) believes that TAM is predictive but its generality does not provide sufficient understanding from the standpoint of providing system designers with the information necessary to create user acceptance of new systems. Furthermore, there has been some concern about the predictive ability of TAM. Straub et al. (1995) questioned intention as a predictor of actual behaviour. Bentler and Speckart (1979), and Songer-Nocks, (1976) earlier disagreed with Fishbein and Ajzen's assertion (on which TAM is based) that attitudes and norms can influence behaviour only indirectly through behavioural intention. Nevertheless, TAM researchers have called for future research using actual usage instead of usage intention to test the TAM. Present research has towed this line of suggestion by investigating actual or current usage as the dependent variable.

Two important TAM constructs are perceived usefulness and perceived ease of use. Perceived usefulness is defined as the extent to which a person believes that using a particular technology will enhance her/his job performance, while Perceived ease of use is the degree to which using IT is free of effort for the user (Davis 1989). A significant body of TAM studies has shown that perceived usefulness and perceived ease of use are determinants of usage (e.g. Davis 1989; Mathieson 1991; Adams et al. 1992; Segars & Groover 1993; Szajna 1994; Igbaria et al. 1997). Technology adoption decisions have been typically characterised by a strong productivity orientation (Venkatesh and Brown, 2001). In many studies (e.g. Mathieson 1991; Agarwal and Prasad 1997; Igbaria et al. 1997), perceived usefulness, one of the constructs related to the use-productivity contingency has emerged as one of the strongest predictors of adoption and usage behaviour. Some past studies have claimed that perceived usefulness is more important to male users, while perceived ease of use is more salient for female. It is germane therefore to see if this process orientation of women in general also applies to women entrepreneurs, given the latter's unique personal traits.

Women and IT Usage Decisions

Research has shown that women exhibit more "feminine" traits (e.g. tenderness) (Bem, 1981), which distinguishes them from other user groups. The meta-analysis of Taylor and Hall (1982) suggested that these feminine traits correlate with "expressive" behaviors. There is substantial evidence in organizational behavior and management information systems research (e.g. Davis, 1989; Davis et al., 1989; Mathieson, 1991; Tailor & Todd, 1995) suggesting that the key underlying cognition determining an individual's attitude toward the behavior of adopting and using a new technology in the workplace is her/his perceptions about the usefulness of the technology. Specifically, the link between usefulness perceptions and attitude toward using a new technology has been shown to have path coefficients ranging from .50 (Davis et al., 1989) to .79 (Taylor & Todd, 1995). Given these strong results, it could be concluded that an individual's attitude toward using a technology in the workplace reflects instrumentality and intrinsic motivation to use technology. Venkatesh et al., (2000) reported higher instrumentality (i.e. outcome) for men and higher process orientation (ease of use/difficulty) for women as determinants of technology adoption. Their finding supports the notion of earlier research (such as Hennig & Jardim, 1977; Rotter & Portugal, 1969) that women tend to focus on the methods used to accomplish a task--suggesting a greater process orientation. Given the process-orientation of women and the lower levels of control (see Mirowsky & Ross, 1990) generally perceived by women in the work environment, the perceived ease of use or difficulty of using technology is expected to have an important influence over their decisions to adopt or reject a new technology (Venkatesh et al., 2000). Further, there is evidence to suggest that women display somewhat higher levels of computer anxiety (Bozionelos, 1996; Morrow, et al., 1986) and lower computer aptitude (Guriting et al. 2007). Both computer anxiety and computer aptitude have been related to perceptions of effort, thus suggesting that constraints to technology use (perceived difficulty) will be more salient to women. It is implicit therefore, that ease of use is more important than usefulness to women in technology adoption and usage if women are more interested in process than outcome. However, a body of research (DeCarlo & Lyons, 1979; Hornaday & Aboud, 1971) has shown that entrepreneurs have high need of achievement. Because of the achievement needs of entrepreneurs and other entrepreneur's traits, it is expected that women entrepreneurs will be influenced by instrumentality in decision-making processes about a new system. It is expected that the traits of women entrepreneurs may play a determinant role in their perceptions of systems' usefulness and ease of use, and adoption.

Entrepreneurial Traits

The traits suggested by previous empirical research which describe entrepreneurs are: (1) high need for achievement (Decarlo & Lyons, 1979; Hornaday & Aboud, 1971; among many others); (2) internal locus of control (Hornaday & Aboud, 1971; Miller, 1983); (3) high need for independence and effective leadership (DeCarlo & Lyons, 1979; Hornaday & Aboud, 1971); (4) high need for autonomy (DeCarlo & Lyons, 1979; Sexton & Bowman, 1983, 1984); (5) information processing capability (McGaffey & Christy, 1975); (6) preference for moderate level of risks (McBer & Co., 1986); (7) low conformity (DeCarlo & Lyons, 1979; Sexton & Bowman, 1983, 1984); (8) aggression, support, and benevolence (DeCarlo & Lyons, 1979); (9) energy level, risk-taking, and change (Sexton & Bowman, 1983, 1984); (10) dominance, endurance, innovation, self-esteem, low anxiety level, and cognitive structure (Sexton & Bowman 1983); and (11) low interpersonal effect, social adroitness, low harm avoidance, and low succorance (Sexton and Bowman, 1984).

Lee (1996) used the Need Theory as a theoretical framework to study the motivation of women entrepreneurs. She hypothesized that business ownership is a manifestation of four needs--achievement; affiliation; autonomy; and dominance. The research concluded that women entrepreneurs are motivated by a high need for achievement, a slightly high need for dominance and moderate needs for affiliation and autonomy. Finds women entrepreneurs demonstrate a higher need for achievement and dominance than women employees but significant difference in the needs for affiliation and autonomy.

Earlier, Yonekura (1984) in the discussion paper on "Entrepreneurship and Innovative Behaviour of Kawasaki Steel" suggested the following traits: assertiveness, insistence, Forward-looking, critical thinking, creativity, innovation, continuity, preparedness, responsibility, open-mindedness, etc. Burch (1986) mentioned nine salient traits, which dictated a high propensity for one to behave entrepreneurially. They are: a desire to achieve, hard work, nurturing quality, able to accept responsibilities, reward oriented, optimistic, excellence-oriented, an organiser, and money oriented. These traits influence one's self-efficacy which Ajzen in his Theory of Planned Behaviour believes to influence intention and usage behaviours. Table 1 is a summary of some of the traits reported in prior studies.

From the review of literature it is observed that innovation, risk-taking propensity, perseverance, and flexibility are more common and consistently reported traits among entrepreneurs. These traits were studied further to explore their influence on perceived usefulness and perceived ease of use.

The entrepreneurial role has long been recognized as a prime source of innovation or creativity. For many entrepreneurs, the basic drive is creativity and innovation to build something out of nothing. They are always looking for something unique to fill a need or want. Thus the more innovative the entrepreneur is, the stronger and more positive her perceptions of the system's ease of use will be, and in turn her IT usage, as she continues to experiment with new and better ways of solving needs.

Risk here refers to the uncertainty of outcomes of an organisations resource commitment. Entrepreneurs who have very high risk propensity are more likely to meddle with matters of uncertain outcomes; they are not too keen at enormous data collection before making decisions because of the short decision window confronting them therefore, technology adoption is likely to be faster. In the other hand more risk-averse adopters are likely to collect a lot of information that might help to make adoption outcomes more certain. This process is likely to slow down the speed and extent of adoption. It has been reported that organisational innovations result from, among other factors, risk taking in organisations. According to Nohria and Gulati (1997) and Singh (1986), innovation can often result from successful risk taking, hence, the high risk-taking entrepreneurs will perceive the system as easy to use.

Perseverance is the ability to continue doing something one believes in for an extended period, enduring difficulties, and finding a solution when facing a barrier. A CEO whose perseverance level is high keeps on working on achieving goals despite repeated failures (Kitchel, 1997). Thus, perceived usefulness, ease of use, and subsequently adoption of IT, will be greater in view of strong user perseverance.

More flexible entrepreneurs are likely to adapt more easily to rapid technological obsolescence. Depending on the frequency of technology replacement or upgrading need, the more flexible entrepreneurs may have a more rapid adoption. McCalman and Paton (1992) asserted that technological change due to its dynamic impact on existing system and also its threatening image can create many challenges for the change agent. While such challenges may deter less flexible users, more flexible entrepreneurs may even flow with technology fad, thereby making adoption a continuous exercise.

METHODOLOGY

Participants & Procedure

The population of study consists of women entrepreneurs that are members of the National Association of Women Entrepreneurs of Malaysia--(NAWEM). These are current IT users. The list of members of NAWEM was taken from the NAWEM Business Directory. Entrepreneurs were surveyed using structured questionnaire. All the one hundred and twenty-five members of NAWEM were contacted to participate in the survey. Each was sent a copy of the questionnaire, and seventy-four (59.2%) usable responses were received. Respondents are engaged in various activities, from manufacturing, to sales, education, interior decoration, fashion designing, etc. Seventy-three percent of the entrepreneurships have been established for over five years, 20.3% and 79.7% are respectively in the manufacturing and service sectors, 89.2% are employing less than one hundred staffs, and 84.6% are owner-managed. A total of 58.1% of the entrepreneurs are graduates, 43.2% are forty years or below while the rest are forty-one years or more. There are more Chinese (64.9%) than Malays (32.4%) and Indians (2.7%).

The design of the questionnaire basically takes the approach of that by Davis et al. (1989), which has been adapted by many other researchers (such as Venkatesh and Davis 1996, Igbaria et al. 1995; 1997; Ndubisi et al 2003), but in this study with modifications to capture the hypothesised effect of entrepreneurial traits. Part 1 measures the actual system usage with two indicators, the number of computer supported business tasks performed and the number of different software applications used. In line with International Coalition of Library Consortia (1998), the indicators used in enhancing the reliability of measuring the system usage in this study are specifically: (1) use of a wide variety of software packages in CBIS environment (e.g. spread sheet, word processing, graphic, data processing, etc); and (2) the number of business task performed using systems such as budgeting, planning, analysis and forecasting. Achieved reliability measure was Cronbach's Alpha 0.83. Parts 2 and 3 respectively measure perceived usefulness and perceived ease of use. Perceived usefulness indicators are improvement on job performance, increase in productivity, enhancement of job effectiveness, and system usefulness in the job. Indicators of perceived ease of use include; clear and understandable interaction with system, system compliance to commands, minimal mental effort in interacting with the system, finding the system easy to use. These indicators are similar to that used by Davis et al. (1989), Ndubisi and Richardson (2002) and their respective inter-item reliability achieved in this study are a = .90 for perceived usefulness and a = .88 for perceived ease of use. Part 4 measures the traits of the entrepreneur. Entrepreneurial traits in this study include innovativeness, risk-taking propensity, persistence/perseverance, and flexibility. Indicators measuring these entrepreneurial traits were adapted from Kitchel (1997) and Harper (1996). The measures are reliable with the following alpha values: innovativeness (.92), risk-taking propensity (.83), perseverance (.70), and flexibility (.82). Part 5 measures the demographic variable using single items such as: age, educational background and job function of the respondent, and the profile of the organisation such as primary business activity, period of establishment, and number of employees in the organisation, prior computer experience (Ndubisi et al 2003). For parts 2-4, respondents were asked to indicate the extent of agreement and disagreement on a five- point Likert scale ranging from (1) "strongly disagree to (5) "strongly agree".

RESULTS

There are no significant changes in the observed relationships based on demographic data. Greene at al. (2003) had earlier argued that research shows similarities in the personal demographics of entrepreneurs, but there are differences in business choices, financing strategies, growth patterns, and governance structures of female-led ventures. According to Greene, these differences (not demography) provide compelling reasons to study female entrepreneurship--looking specifically at women founders, their ventures, and their entrepreneurial behaviours as a unique subset of entrepreneurship (Greene et al. 2003).

IT Usage Pattern

The results show that all respondents (100%) are using word processor, 73% are using electronic mail, 57% are using application packages. Other systems are graphics (42%), spreadsheets (41%), databases (41%), and programming languages (31%). Job tasks where systems are used are Letters and memos (88%), producing reports (77%), internal communication (66%), data storage/retrieval (62%), budgeting (49%), controlling & guiding activities (47%), planning & forecasting (45%), making decisions (43%), analyzing trends (42%), and analyzing problems & alternatives (24%). It is observed that 59.5% of respondents are using a minimum of four out of the seven varieties of systems presented, and 54.1% are using a system for at least five out of the ten job tasks.

System variety was subsequently combined into two larger groups as follows: Basic Systems (which include, word processing, electronic mail, spreadsheets, graphics, & databases), and Advanced Systems (e.g. application packages & programming languages). Specific job tasks were grouped into those for administrative purposes (such as producing reports, letters & memos, data storage/retrieval, & communication with others), planning purposes (e.g. analyzing trends, planning/forecasting, analyzing problems/alternatives, & making decisions), and control purposes (e.g. budgeting, controlling & guiding activities). All the respondents are using at least one basic system, and 58.1% of respondents are using a minimum of one advanced system. A computer system is in use for at least one administrative task by all respondents, 59.5% of respondents are using a system for a minimum of one planning, or control task.

Descriptive statistics of perceived ease of use show that 87.8% of respondents strongly agree or agree that system interaction is clear and understandable, 78.4% strongly agree or agree that it is easy to get the system to do what is wanted, 96% strongly agree or agree that interaction with the system does not require a lot of mental effort, and 89.2% strongly agree or agree that the system is easy to use. With respect to perceived usefulness, 94.5% of respondents strongly agree or agree that the system is useful in their job, 96% strongly agree or agree that the system improves their job performance, increases their productivity, or enhances their job effectiveness. The mean and standard deviation of perceived usefulness are respectively 17.66 and 1.96, while that of perceived ease of use are 16.93 and 2.43. On the whole, respondents find the system useful and easy to use.

Hypotheses Testing

The hierarchical multiple regression model (Abrams, 2006) was employed to analyse the relationships in the model and the results are summarised and schematised in Figure 1 below.

[FIGURE 1 OMITTED]

Perceived ease of use and usefulness contribute significantly (F = 8.53; p < .001) and predict 19.4% variation in technology adoption by women entrepreneurs. Details of the results show that perceived usefulness has significant positive relationship with technology adoption (t-value = 3.93; p < .001), while perceived ease of use does not (t-value = -1.55; p < .126). The above values for usefulness and ease of use indicate that perceived usefulness is more salient than perceived ease of use in technology adoption by women entrepreneurs.

However, there is an indirect relationship between perceived ease of use and adoption via perceived usefulness. In other words, perceived usefulness mediates the relationship between ease of use and adoption. According to Baron and Kenney (1986, p. 1176), a variable functions as a mediator when it meets the following conditions: (a) variations in levels of the independent variable significantly account for variations in the presumed mediator, (b) variations in the mediator significantly account for variations in the dependent variable, and (c) when a and b are controlled, a previously significant relation between the independent and dependent variables is no longer significant or it is significantly decreased. Table 2 shows the result of the test for the mediator effect of perceived usefulness in the relationship between ease of use and usage.

The beta coefficient for model 1 is significantly higher than that of model 2. In addition, the increase in [R.sup.2] of .18 between models 1 and 2, explain the mediation effect of usefulness in the relationship between ease of use and adoption. Thus, perceived usefulness mediates the relationship between ease of use and adoption.

Entrepreneurial Traits, Perceived Ease of Use, and Adoption

Table 3 below summarizes the regression analysis of the relationship between traits, ease of use, and adoption.

Entrepreneurial traits namely innovativeness, risk-taking propensity, perseverance, and flexibility contribute significantly to perceived ease of use (F = 4.28; p < .05) and adoption (F = 24.03; p < .001). The traits also predict 19.9% and 58% variation in ease of use and adoption respectively. It is further observed that risk-taking propensity is significantly associated with system's perceived ease of use, while innovativeness and risk-taking propensity are important determinants of adoption (see Table 3).

Ease of use also mediates the relationship between traits and adoption. The increase in the coefficient of determination between model 1 and 2 is as a result of the mediator effect of ease of use. It is also observed from Table 4 that the beta coefficients of innovativeness and risk-taking propensity are significantly reduced between model 1 and model 2. This reduction coupled with the increase in coefficient of determination indicates that ease of use mediates the relationship between innovativeness and risk-taking propensity in one hand and technology adoption in the other. There is neither a direct nor an indirect relationship between perseverance, flexibility and technology adoption.

DISCUSSION

The findings show that Malaysian women entrepreneurs' adoption of IT is driven directly by their perception of the system's usefulness and indirectly (via perceived usefulness) by perceived ease of use. Women entrepreneurs in this study deem easy to use systems as useful systems and in turn adopt. In fact, ease of use in itself is not a determinant of adoption, but becomes influential when easy to use systems are perceived as useful systems. These findings are also consistent with Ndubisi et al., (2003) and Ndubisi et al (2005).

The lack of direct influence of ease of use on adoption is contrary to the postulation of the technology acceptance model, but plausibly explained by the outcome orientation of entrepreneurs. As shown in the literature, entrepreneurs have a high need for achievement (Decarlo & Lyons 1979; Hornaday & Aboud 1971; Burch 1986, etc), and such desire to achieve coupled with their low risk-aversion and low anxiety level (Sexton & Bowman 1984) could minimize the influence of perceived difficulty of systems, provided such systems are beneficial. In other words, the need to achieve will cause perceived usefulness to overshadow system's difficulty in use, thereby making sure that such systems are deployed even with some measure of difficulty in use. Secondly, at the stage of adoption, users may be aware of the system's benefits but not necessarily its ease/difficulty of use. Unlike usefulness, which can be described to an adopter, it takes a hands-on-experience to appreciate whether a system is easy or difficult to use. At the point of adoption, such hands-on-experience may not be available in many instances, and even where they are available, their sketchy nature as often provided by systems vendors may not reveal all its encumbrances. Moreover, even where all encumbrances are unveiled, at the point of adoption, an adopter may rationalize that such difficult is common with first encounters, which will gradually disappear as familiarity with the system increases. These reasoning can make a user to buy a system deemed useful and yet not easy to use, which explains why perceived usefulness is preponderant over perceived ease of use in determining technology adoption among women entrepreneurs.

Another interesting finding of this research is the difference in antecedents of adoption between women entrepreneurs and other female (non-entrepreneurs) technology users. Venkatesh et al (2000) reported higher process orientation (ease of use) for women in technology adoption. Earlier, Hennig and Jardim (1977); Rotter and Portugal (1969) reported that women tend to focus on the methods used to accomplish a task. The evidence from the present research contradicts such notion. It is clear from the current study that women entrepreneurs are somewhat different from other women in the earlier studies in that they are outcome oriented more than process oriented. In fact women entrepreneurs focus on outcomes rather than processes in making technology adoption decisions. It has also been reported that women display somewhat higher levels of anxiety (Bozionelos 1996), which have been found to inversely correlate with technology adoption. However, women entrepreneurs are different. Just like other entrepreneurs, women entrepreneurs exhibit a low anxiety level (Sexton & Bowman 1983; 1984), which could result in greater adoption.

Two important traits that bear on women entrepreneurs' perception of systems ease of use and systems adoption are innovativeness and risk-taking propensity. Clearly, both traits are directly associated with adoption. Specifically, the higher the risk-taking propensity of women entrepreneurs, the greater their level of adoption. Similarly, the more innovative an entrepreneur is, the greater her/his technology adoption. Rogers (1995) in his innovation diffusion theory described innovators as initiators or originators of technologies or ideas. These often adopt more than anyone else since others follow their footsteps, even when there are no followers, innovators move on. Innovativeness has also been associated with high risk-taking propensity. Since innovators are always at the forefront, they shoulder a higher risk of uncertainty of outcomes, which others may not experience eventually. Therefore, adopting new technologies is not surprisingly a function of innovativeness and risk-taking propensity of women entrepreneurs.

With regards to ease of use, risk-taking propensity is positively correlated with it. The greater the amount of risk that users are at home with, the more favourable their perception of the ease of use of the particular system will be. This is because low risk aversion has the potential to create a favourable atmosphere by eliminating anxiety and phobia for uncertainty, thereby making adopters more willing and ready to tryout new technologies. In addition, as trial rate increases, so does usability.

IMPLICATIONS

Theoretically, this work supports the theorization of the technology acceptance model that perceived usefulness is directly related to technology adoption, and perceived ease of use is indirectly (via perceived usefulness) associated with adoption. Further, contrary to the second TAM relationship, the study found no evidence for a direct relationship between perceived ease of use and adoption among women entrepreneurs. Other interesting findings of the study that support or challenge current theory are the process orientation of women with respect to technology adoption as well as the focus of women on the methods used to accomplish a task as against the outcome of undertaking the task. Clearly, the findings of this research shows that for women entrepreneurs, perceived usefulness is much more important than perceived ease of use. Thus, women entrepreneurs are outcome oriented (not process oriented) and also focus on the result rather than the method used to accomplish a task. The end is more important than the means. This result may have been accounted for by entrepreneurs high need for achievement, low risk aversion and doggedness, which may move them to overlook some difficulties or complexities in use so long as the system is beneficial.

Implications of the research on practice are two prolonged. Firstly, is with regards to the management of technology in entrepreneurial ventures, and secondly is with respect to systems development and marketing. Entrepreneurs should invest in useful systems; such investment should not be hindered by slight system's complexity or difficulty, which have been found to fade away with time as users gain more and more experience with the specific system. Further, entrepreneurs should be more innovative and assume greater risk, since these traits are crucial in forming a favourable perception of systems usability and system's adoption.

Systems developers and marketers on their part should supply more value added systems. The strong impact of system's perceived usefulness on adoption shows that those marketers that are market oriented, who desire to deliver superior value to users will eventually be rewarded. Also important, beside usefulness is system's user friendliness. Since easy to use systems are deemed useful systems and consequently adopted, designers and vendors must not make a toy of the ease of use factor. This is because albeit this factor has no direct influence on adoption, it anchors perceived usefulness, which directly predicts adoption.

STRENGTHS OF THE CURRENT RESEARCH

Some of the strengths of this research are highlighted. Firstly, the data are based on a poll of entrepreneurs who are officially recognised as Malaysian entrepreneurs by their membership of the national association of women entrepreneurs in Malaysia (NAWEM). Secondly, the model is based on theory grounded on existing management information system studies. Moreover, actual IT usage was used rather than usage intention (as a predictor of usage behaviour), which has been questioned by some scholars.

FUTURE RESEARCH DIRECTIONS

This study deliberately studied only women entrepreneurs because of the small amount of research in this sector compared to their male counterparts. Future research should be geared towards a comparative study of male and female entrepreneurs in Malaysia to examine if there are any differences in their IT usage and usage drivers. It is also necessary to examine the moderating effects of gender on the following relationships: (1) perceived usefulness and adoption, (2) perceived ease of use and adoption, and (3) perceived ease of use and perceived usefulness.

CONCLUSIONS

Women entrepreneurs are outcome oriented in their technology adoption decisions. They focus more on the beneficial outcomes rather than on ease or difficulty of use process. They emphasize the end rather than the means to the end, which has been reported for other women (non-entrepreneurs) in previous research.

Innovativeness and risk-taking propensity are influential traits in technology adoption decisions. These traits also influence the ease of use perceptions of systems, which determines adoption indirectly through perceived usefulness. Hence, entrepreneurial traits, user's perceptions of system's usability and usefulness are potent keys to understanding the technology adoption decision processes of women entrepreneurs.

REFERENCES

Abrams, D. R. (2006). Introduction to Regression 1. Princeton University Data and Statistical Services, Retrieved January 29, 2007, from http://www.data@princeton.edu.

Adams, D., Nelson R.R., & Todd, P. (1992). Perceived usefulness, ease of use, and usage of information technology: A replication. MIS Quarterly, 16(2), 227-248.

Agarwal, R. & Prasad, J. (1997). The role of innovation characteristics and perceived voluntariness in the acceptance of information technologies. Decision Sciences, 28, 557-582.

Agarwal, R. & Prasad, J. (1998). A conceptual and operational definition of personal innovativeness in the domain of information technology. Information Systems Research, 9, 204-215.

Ajzen, I. (1991). The Theory of Planned Behaviour. Organisational Behaviour and Human Decision Processes, 50, 179-211.

Ajzen, I. & Fishbein, M. (1980). Understanding attitudes and predicting social behaviour, Englewood Cliffs, New Jersey: Prentice-Hall Inc.

Akkeren, J.V. & Cavaye, A.L.M. (1999). Factors affecting the adoption of e-commerce technologies by small business in Australia: An empirical study. Retrieved January 29, 2007 from http://www.acs.org.au/act/events/io1999/akkern.html.

Baron, R.M., & Kenney, D.A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic and statistical considerations. Journal of Personality and Social Psychology, 51, (6), 1173-1182.

Bem, S.L. (1981). The BSRI and gender schema theory: A reply to Spence and Helmreich. Psychological Review, 88, 369-371.

Bennett, B. Cobbold, T. & Phillips, M. (2003). ICT use and firm performance in Australia--Evidence from firm interviews. Department of Industry, Tourism and Resources Working Paper (8), Canberra, 1-39.

Bentler, P.M. & Speckart, G. (1979). Models of attitude-behaviour relations. Psychological Review, 86, 542-464.

Bozionelos, N. (1996). Psychology of computer use: XXXIX. prevalence of computer anxiety in British managers and professionals. Psychological Reports, 78(3), 995-1002.

Burch, J.G. (1986). Entrepreneurship. New York: Wiley.

Brancheau, J.C. & Wetherbe, J.C. (1990). The adoption of spreadsheet technology: Testing innovation diffusion theory in the context of end-user computing. Information Systems Research, 1(2), 115-143.

Cooper, R.B. & Zmud, R.W. (1990). Information technology implementation research: A technological diffusion approach. Management Science, 36(2), 123-139.

Cowling, M. & Taylor, M. (2001). Entrepreneurial women and men: Two different species? Small Business Economics, 16(3), 167-75.

Davis, F.D., (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-339.

Davis, F.D., Bagozzi, R. & Warshaw, P.R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982-1003.

Davis, F.D. & Venkatesh V. (1996). A critical assessment of potential measurement biases in the technology acceptance model: three experiments. International Journal of Human-Computer Studies, 45, 19-45.

DeCarlo, J.F. & Lyons, P.R. (1979). A comparison of selected personality characteristics of minority and Non-minority female entrepreneurs. Journal of Small Business Management, 17, 22-28.

Doms, M.E., Jarmin, R.S. & Klimek, S.D. (2003). IT investment and firm performance in US retail trade, FRBSF Working Paper, 2003-19, Retrieved January 26, 2007 from http://www.frbsf.org/publications/economics/papers/2003/wp03-19bk.pdf.

Fishbein, M. & Ajzen, I. (1975). Belief, attitude, intentions and behaviour: An introduction to theory and research. Reading, MA: Addison-Wesley.

Glick-Smith, J.L. (1999). What are the Characteristics of a Successful Entrepreneur? Retrieved January 26, 2006 from http://207.17.212.203/FAQ/characteristics_successful_entre.html.

Greene, P.G., Hart, M.M., Gatewood, E.J., Brush, C.G. & Carter, N.M. (2003). Women entrepreneurs: Moving front and centre: An overview of research and theory. Coleman White Paper Series, Retrieved January 29, 2007 from http://www.usasbe.org/knowledge/whitepapers/greene2003.pdf.

Gretton, P., Gali, J. & Parham, D. (2002). Uptake and impacts of the ICTs in the Australian economy: Evidence from aggregate, sectoral and firm levels, Proceedings of Workshop on ICT and Business Performance, OECD Paris, December 9, 1-68.

Guriting, P., Chunwe, G. & Ndu, N.N.O (2007). Computer self-efficacy levels, perceptions and adoption of online banking. International Journal of Services Technology and Management, 8(1), 54-61.

Harper, David. (1996). Entrepreneurship and the market process. London: Routledge.

Hennig, M. & Jardim, A. (1977). The managerial woman. Garden City, NY: Anchor press.

Hornaday, J.A. & Aboud, J. (1971). Characteristics of successful entrepreneurs. Personnel Psychology, 24, 141-153.

Igbaria, M., Guimaraes, T., & Davis, G.B. (1995). Testing the determinants of microcomputer usage via a structural equation model. Journal of Management Information Systems, 11(4), 87-114.

Igbaria, M., Zinatelli, N., Cragg, P., & Cavaye, A.L.M. (1997). Personal Computing Acceptance Factors in Small Firms: A Structural Equation Model. MIS Quarterly, 21(3), 279-305.

International Coalition of Library Consortia, (1998), Guidelines For Statistical Measures of Usage Of Web-Based Indexed. Retrieved Jan 29, 2007 from http://www.library.yale.edu/consortia/webstats.html.

Johansen, R. & Swigart, R. (1996). Upsizing the individual in the downsized organisation: Managing in the wake of reengineering, globalisation, and overwhelming technological change. Reading, MA: Addison -Wesley.

Julien, P.A. & Raymond, L. (1994). Factors of new technology adoption in the retail sector. Entrepreneurship Theory and Practice, 18(4), 79-87.

Kitchell, Susan (1997). CEO characteristics and technological innovativeness: A Canadian perspective. Journal of Administrative Sciences, 14(2), 111-125.

Landauer, T.K. (1995). The trouble with computers: Usefulness, usability, and productivity. Cambridge, MA: MIT Press.

Lee. J. (1996). The motivation of women entrepreneurs in Singapore. Women in Management Review, 11(2), 18-29.

Mathieson, K. (1991). Predicting user intentions: Comparing the technology acceptance model with the theory of planned behaviour. Information Systems Research, 2(3), 173-191.

McBer and Co, (1986). Entrepreneurship and small-enterprise development. Second Annual Report to the United States Agency for International Development, March 25.

McCalman J. & Paton R.A. (1992). Change management A guide to effective implementation. London: Paul Chapman Publishing.

McGaffey, T.N. & Christy, R. (1975). Information processing capability as a predictor of entrepreneurial effectiveness. Academy of Management Journal, 18, 857-863.

Michaels, N. (2006). Women entrepreneurs growing in numbers & importance. Retrieved Jan 29, 2007 from http://www.score.org/m_pr_20.html.

Miller, D. (1983). The correlates of entrepreneurship in three types of firms. Management Science, 29, 770-791.

Mirowsky, J. & Ross, C.E. (1990). Control or defense? Depression and the sense of control over good and bad outcomes. Journal of Health and Social Behaviour, 31, 71-86.

Moore, G.A. (1991). Crossing the chasm: Marketing and selling technology products to mainstream customers. New York: HarperCollings Publishers.

Morrow, P.C., Presll, E.R., & McElroy, J.C. (1986). Attitudinal and behavioural correlates of computer anxiety. Psychological Reports, 59, 1199-1204.

Ndubisi, N.O. & Richardson, S. (2002). The entrepreneurs technology acceptance model, Academy of Entrepreneurship Journal, 8(2), 95-113.

Ndubisi, N.O., Gupta, O. K. & Massoud, S. (2003). Organizational learning and vendor support quality by the usage of application software packages: A study of Asian entrepreneurs. Journal of Systems Science and Systems Engineering, 12(3), 314-331.

Ndubisi, N.O. Gupta, O. K., & Ndubisi G.C. (2005). The moguls model of computing: Integrating the moderating impact of users' persona into the technology acceptance model, Journal of Global Information Technology Management, 8(1), 27-47.

Nohria, N. & Gulati, R. (1997). What is the optimum amount of organisational slack? A study of the relationship between slack and innovation in multinational firms. European Management Journal, 15(6), 603-611.

Rogers, E.M. (1995). Diffusion of innovations. New York: Free Press.

Rotter, G.S. & Portugal, S.M. (1969). Group and individual effects in problem solving. Journal of Applied Psychology, 31, 537-547.

Segars, A.H. & Groover, V. (1993). Re-examining perceived ease of use and usefulness: A confirmatory factor analysis. MIS Quarterly, 17, 517-725.

Sexton, D.L., & Bowman, N. (1983). Determining entrepreneurial potential of students. Academy of Management Proceedings, 42, 408-412.

Sexton, D.L. & Bowman, N. (1984). Personality inventory for potential entrepreneurs: Evaluation of a modified JPI/PRF-E test instrument. In Hornaday et al. (1984) Eds. Frontiers of Entrepreneurial Research, Wesley, MA: Centre for Entrepreneurial Studies, Babson college.

Sichel, D.E. (1997). The computer revolution: An economic perspective. Washington, DC: The Brookings Institution.

Singh, J.V. (1986). Performance, slack and risk-taking in organisational decision making. Academy of Management Journal, 29(3), 562-585.

Sjazna, B. (1994). Software evaluation and choice predictive validation of the technology acceptance instrument. MIS Quarterly, 18, 319-324.

Songer-Nocks, E. (1976). Situational factors affecting the weighting of predictor components in the Fishbein model. Journal of Experimental Social Psychology, 12, 56-69.

Straub, D. Limayem, M. & Karahannan-Evaristo, E. (1995). Measuring system usage: Implications for IS theory testing. Management Science, 41, 1328-1342.

Taylor, M.C. & Hall, J.A. (1982). Psychological androgony: Theories, methods, and conclusions. Psychological Bulletin, 92, 347-366.

Taylor, S. & Todd, P. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144-176.

Thompson, R.L., Higgins, C. & Howell, J.M. (1991). Personal computing: toward a Conceptual Model of Utilisation. MIS Quarterly, 15(1), 125-143.

Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11, 342-365.

Venkatesh, V. & Brown, A.S., (2001). A longitudinal investigation of personal computers in homes: Adoption determinants and emerging challenges. MIS Quarterly, 25, 71-102.

Venkatesh, V., Morris, M.G., & Ackerman, P.L. (2000). A longitudinal field investigation of gender differences in individual technology adoption decision making processes. Organisational Behaviour and Human Decision Processes, 83, 33-60.

Webster, J. (1998). Desktop videoconferencing: Experiences of complete users, wary Users, and non-users. MIS Quarterly, 16(2), 201-226.

Weiner, L.R. (1993). Digital woes: Why we should not depend on software. Reading MA: Addison-Wesley.

Yonekura, S. (1984). Entrepreneurship and innovative behaviour of Kawasaki Steel: The post world war II period. Discussion paper no. 120, Institute of Business Research, Hitotsubashi University, Kunitachi, Tokyo, Japan.

Nelson Oly Ndubisi, Monash University, Malaysia

Table 1: Entrepreneurial Traits Reported in Previous Research

Entrepreneurial
Traits Author Comment

High need for Decarlo & Lyons (1979); Albeit, there is
achievement Hornaday & Aboud reasonable evidence
 (1971); Burch (1986); favouring this trait,
 Jacobson (1993); Wells it is not one of the
 (1994) most common ones.

Internal locus of Hornaday & Aboud More evidence is needed
control (1971); Miller (1983) to support this trait,
 hence it was not
 included in the present
 study.

High need for Decarlo & Lyons (1979); This trait also does
independence/ Hornaday & Aboud not represent the more
autonomy/low (1971); Sexton & Bowman common traits of
succorance and (1983;1984) entrepreneurs as the
effective Table shows.
leadership

Information McGaffey & Christy More evidence is needed
processing (1975); Yonekura to support this trait,
capability (1984); hence it was not
 included in the present
 study.

Risk taking, low McClelland (1961); Some of the authors
harm avoidance Ansoff (1972); Sexton & argued for high
 Bowman (1983; 1984); risk-taking propensity,
 McBer & Co. (1986); others favour only a
 Jantan et al. (2001) moderate risk, yet
 others say
 entrepreneurs only take
 calculated risks.

Innovativeness or McClelland (1961); This Table shows that
Low conformity Decarlo & Lyons (1979); innovativeness is one
 Stevenson (1983); of the most common
 Sexton & Bowman (1983; traits of entrepreneurs
 1984); Yonekura (1984), going by the number of
 McBer & Co (1986); authors listed.
 Jacobson (1993); Harper
 (1996); Kitchel (1997);
 Schumpeter (2000);
 Jantan et al. (2001);
 Ndubisi & Richardson
 (2002).

Aggression, Decarlo & Lyons (1979); More evidence is needed
support, & McBer & Co (1986) to support this trait,
benevolence hence it was not
 included in the present
 study.

Flexibility or Sexton & Bowman (1983; Flexibility has
Change 1984); Kitchel (1997); received much evidence
 Jantan et al. (2001); as an entrepreneurial
 Ndubisi & Richardson trait as did
 (2002); Ndubisi & innovativeness,
 Jantan (2003); Jantan risk-taking and
 Jantan (2003); Jantan perseverance. Yet it is
 still attracting more
 research attention.

Perseverance/ McClelland (1961); Another common trait of
endurance, High Stevenson (1983); entrepreneurs is
energy level Sexton & Bowman (1983; perseverance. With
 1984); Yonekura (1984); innovativeness,
 Burch (1986); McBer & risk-taking and
 Co (1986); Wells flexibility, they form
 (1994); Henzel (1995); the set of most common
 Kitchel (1997); entrepreneur traits.
 Glick-Smith (1999); Hence, justifying their
 Jantan et al. (2001); selection for the
 Ndubisi & Jantan (2003) purpose of the current
 research.

Table 2 summarises the demography of the respondents.

Demography Sub-demography Response rate (%)

Primary activity Manufacturing 20.3
 Service 79.7
Year of establishment 5 years or less 27
 Over 5 years 73
Years of computer experience 5 years or less 47.3
 6-10 years 50
 11 years or more 2.7
No of employees Below 5 41.9
 5-100 47.3
 101 or more 10.8
Highest educational Non-graduate 41.9
 qualification Graduate 58.1
Age 40 years or less 43.2
 41 years or more 56.8

Table 2: Perceived Ease of use and IT Usage (via Perceived Usefulness)

 Beta coefficients without Beta coefficients with
Perception usefulness (model 1) usefulness (model 2)

Ease of Use .136 -.215
Usefulness -- .546 **
 [R.sup.2] = .02 [R.sup.2] = .20

** = Significance at .01 level

Table 3: Entrepreneurial Traits on Perceived Ease of Use & Adoption

Drivers Ease of Use Adoption

 t-value p-value t-value p-value

Innovativeness .712 .479 3.69 .000
Risk-taking propensity 3.214 .002 2.02 .048
Perseverance -.437 .664 -.160 .873
Flexibility -1.395 .168 .483 .631
 [R.sup.2] = .199; [R.sup.2] = .582;
 F = 4.28; F = 24.03;
 sig. = .004 sig. = .000

Table 4: Traits and IT Adoption (via Perceived Ease of use)

 Beta coefficients Beta coefficients
 without ease of use with ease of use
Perception (model 1) (model 2)

Innovativeness .894 *** 876 **
Risk-taking propensity -.270 * -.546 *
Perseverance -.038 -.027
Flexibility .109 .141
Ease of use -- .075
 [R.sup.2] = .582 [R.sup.2] = .587

*** p < .001 ** p < .01 * p < .05