Gender differences in entrepreneurial traits, perceptions and usage of information and communication technologies.

Ndubisi, Nelson Oly

INTRODUCTION

The benefits of deploying information and communication technology (ICT) in business cannot be over stated. There is a growing understanding of how businesses should operate using ICT to achieve optimal effectiveness. Information technology in general has become the major facilitators of business activities in the world today (Tapscott & Caston, 1993; Mankin, 1996) hence, business organization investments in ICT have increased significantly in the past two decades. Albeit, advances in technology continue at a fast pace, the use of emerging information and communication technologies has not been commensurate (Ndubisi & Richardson, 2002) or has fallen below expectations (Johansen & Swigart, 1996; Wiener, 1993; Moore, 1991). Landauer (1995) and Sichel (1997) had argued that low usage of systems is a plausible explanation for the 'productivity paradox'. As such, an understanding of the salient factors that determine ICT usage among male and female entrepreneurs is important for researchers, system designers, and vendors.

The aim of this research is to increase understanding of the fundamental issues of technology adoption decisions by focusing on differences in the decision making process of men and women entrepreneurs in Malaysia. The increasing number of women-owned enterprises (Ndubisi et al., 2001), and the extensive role of technology in business (Gill, 1996) and enterprise performance, create important impetuses for this study. The outcome of this study will inform strategies for increasing technology up-take and greater usage of existing technologies as well as assist in change management in male and female entrepreneurship businesses. The study will also add to the existing body of knowledge in this area by unveiling differences in traits, perceptions and usage of ICT among male and female entrepreneurs.

ICT USAGE

This section discusses the theory underlying the key constructs in the study's model. The technology acceptance model (TAM) (Davis, 1989) was adapted in this study to examine the differences in perceived usefulness, perceived ease of use, and ICT usage between male and female entrepreneurs in Malaysia. TAM was adapted from the Theory of Reasoned Action (Fishbein & Ajzen, 1975; Ajzen & Fishbein, 1980) to understand the causal chain linking external variables to technology usage intention and actual use in a workplace. Among the different theoretical models, the Technology Acceptance Model (TAM) is chosen for this study as it helps to further the understanding of technology acceptance and usage behavior, users' perceptions of the system's usefulness and ease of use, as well as their associations with entrepreneurial traits. Moreover, the TAM provides valid instrument which has been extensively used to investigate a range of issues in the area of user acceptance and usage of technologies (Moore & Benbasat 1991; Sjazna, 1994; Ndubisi et al., 2001).

TAM defines relationships among perceived usefulness (U), perceived ease of use (EOU), behavioral intention (BI), and behavior (B). Specifically, that certain external variables influence behavioural intention to use, and actual usage, indirectly through their influence on perceived usefulness and perceived ease of use. Davis (1989), 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". Ndubisi and Richardson (2002) adapting the TAM examined the influence of entrepreneurs' traits on technology usage, indirectly through their influence on perceived usefulness and perceived ease of use. Entrepreneurial traits that were found to determine usage were innovativeness, risk-taking propensity, perseverance, and flexibility.

ENTREPRENEURIAL TRAITS

Some of the traits suggested by previous research that describe entrepreneurs are reviewed below. Hornaday and Aboud (1971) reported the following traits among entrepreneurs: high need for independence and effective leadership, internal locus of control, and high need for achievement. McGaffey and Christy (1975) found a high information processing capability. Decarlo and Lyons (1979) found that entrepreneurs have a high need for achievement, high need for independence and effective leadership, high need for autonomy, low conformity, and exhibit aggression, support, and benevolence. Miller (1983) reported that internal locus of control is a dominant entrepreneur trait. Other traits are: high need for autonomy; low conformity; high energy level, risk-taking, and change (Sexton & Bowman, 1983); dominance, endurance, innovation, self-esteem, low anxiety level, and cognitive structure (Sexton & Bowman, 1983); and low interpersonal effect, social adroitness, low harm avoidance, and low succorance (Sexton & Bowman, 1983).

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, and others. McBer & Co. (1986) unveiled that entrepreneurs have preference for intermediate level risks. Burch (1986) mentioned nine salient traits, which dictated a high propensity for one to behave entrepreneurially: a desire to achieve, hard work, nurturing quality, able to accept responsibilities, reward oriented, optimistic, excellence-oriented, an organiser, and money oriented. Wells (1994) found the following traits: they are proactive, they are motivated by a need for high achievement, and they demonstrate commitment. Ndubisi and Richardson (2002) summarized the list into four major traits: innovativeness, risk-taking propensity, perseverance or persistence, and flexibility and found associations between them and usage via perceived usefulness or ease of use. Since entrepreneurial traits are important determinants of ICT usage via perceived usefulness and ease of use, it is important to understand the mean differences in these explanatory variables as well as their explanatory power on usage based on sex typing. This is because sex typing may help identify attributes and behaviours salient to women and men respectively (Bem & Allen, 1974) that will benefit market segmentation efforts as well as gender-based technology market niches.

GENDER

Gender in this study refers to "biological sex" which differs from another view of gender by Bem (1981) as a psychological construct. There is a number of evidence of gender differences in decision-making processes of individuals. For instance, there are research evidence supporting decision processing differences between man and women in financial decision making (Powell & Ansic, 1997), hospital problem solving (Steffen & Nystrom, 1988), retirement decisions (Talaga & Beehr, 1995), preference for work schedule (where the employee has preschool children) (Kantrowitz et al., 1989; Shellenbarger, 1991), absenteeism (Leigh, 1995; Scott & McClellan, 1990), college course and major selection (Wilson et al., 1994; Gianakos & Subich, 1988), what is perceived or processed as being "ethical" (Franke et al., 1997; Dawson, 1995; Galbraith & Stephenson, 1993), attributes important in determining self-esteem (Tashakkori, 1993), emotional expression (Deaux, 1985; Kring & Gordon, 1998), leadership style (Eagly & Johnson, 1990; Helgesen, 1990; Rosener, 1990), and communication or conversational style (Tannen, 1995).

Research on gender differences has suggested that for men, job/work activity is typically their most important role (Barnett & Marshall, 1991), while working mothers prefer part-time work, flexible work schedules, and telecommuting in order to accommodate their family responsibilities (Shellenbarger, 1991; Kantrowitz et al., 1989). O'Niel (1982) suggested that men are greatly preoccupied with work, accomplishments, and eminence. Similarly, Hoffman (1972) reported that men, more than women are motivated by achievement needs, while Hennig and Jardim (1977) stated that men adopt strategies focused on bottom-line results versus methods used to achieve those results. Hence, men tend to be more directed toward impersonal and individualistic tasks and goals, compared to women (Gill et al., 1987). Other reports, for example, Rosenkrantz et al. (1968) suggested that "objective" and "logical" are more male-valued traits, Minton and Schneider (1980) is convinced that men may be more task-oriented than women, a finding consistent with Sargent (1981), which reported that men have been socialized to value having an impact and therefore, tend to engage in task-oriented or instrumental behaviour.

In relation to technology usage, Bozionelos (1996), Morrow et al., (1986) suggested that women display somewhat higher levels of computer anxiety; and lower computer aptitude (Felter, 1985) compared to men (Chen, 1985). Both computer anxiety and computer aptitude have been related to perceptions of effort (Venkatesh et al., 2000), thus suggesting that constraints or ease of technology use (perceived difficulty or perceived ease of use) will be more salient to women compared to men. Women tend to focus on the methods used to accomplish a task--suggesting a greater process orientation (Hennig & Jardim, 1977; Rotter & Portugal, 1969). Given the outcome orientation (instrumentality) of men and process orientation of women, it is expected 'ceteris peribus' that usefulness will be a stronger determinant of ICT usage among male while ease of use will be stronger determinant among female. This speculation is worth probing in the light of previous findings (such as, Decarlo & Lyons, 1979; Hornaday & Aboud, 1971; among many others) that both male and female entrepreneurs have high need for achievement.

METHOD

Participants and Procedure

A total of 295 questionnaires were sent out and 177 usable responses were received, which translates to 60% response rate. Respondents were drawn from members of the Entrepreneur Development Unit of the Malaysian Prime Minister's Department or members of the National Association of Women Entrepreneurs in Malaysia. It was ensured that entrepreneurs who belonged to the two associations were not double-counted. The primary business activities of the respondents' organizations range from manufacturing, to sales, education, designing, construction, etc.

Entrepreneurs were surveyed using structured questionnaire made up of four parts. Part 1 measures the actual system usage with three indicators (such as use of a wide variety of software packages in CBIS environment; the number of business task performed using systems; and frequency of system usage) taken from ICOLC (1998). ICT usage was measured in terms of current usage or actual usage behaviour of entrepreneurs unlike most previous research (e.g. Davis et al., 1989), which have measured usage based on intention. Straub et al. (1995) had questioned intention as a predictor of actual behaviour. Bentler and Speckar (1979), and Songer-Nocks (1976) earlier disagreed with Fishbein and Ajzen's (1975) assertion that attitudes and norms can influence behaviour only indirectly through behavioural intention. Venkatesh (2000) called for future research using actual usage instead of usage intention to test the TAM, hence based on Szajna (1994) actual usage was used in the present study.

Parts 2 and 3 respectively measure perceived usefulness and perceived ease of use with items taken from Davis et al. (1989) and Ndubisi et al. (2001). Measures of perceived usefulness in this study are perceptions that using IT will increase productivity, improve job performance, enhance job effectiveness, and be useful in the job; and perceived ease of use is measured in terms of how clear and understandable is the interaction with system, ease of getting the system to do what is required, mental effort required to interact with the system, and ease of use of the system. Part 4 measures the traits of entrepreneurs (such as innovativeness risk-taking propensity, perseverance, and flexibility) using items adapted from Harper (1996) and Kitchel (1997). Test of Differences were applied and the results presented and discussed in the ensuing section.

RESULTS

Table 1 shows the varieties of systems investigated, the specific job tasks where systems are applied, as well as the usage rate by entrepreneurs.

Differences in Traits, Perceived Usefulness and Ease of Use, and ICT Usage.

Table 2 shows the summarized results of the test of differences in mean traits, perceptions, and ICT usage by male and female entrepreneurs.

Findings show that male entrepreneurs show significantly higher traits of perseverance (t-value = 2.406; p-value = .017) and flexibility (t-value = 3.280; p-value = .001) as compared to female entrepreneurs. As shown in Table 2, scores for the two constructs are much higher for male entrepreneurs than for females. There are no significant differences in the mean scores of innovativeness and risk-taking propensity.

With regards to perceived usefulness and ease of use, the study unveils significant differences based on gender. Mean perceived usefulness of ICT for female is 17.66 and for male is 16.14, while mean ease of use for female is 16.93 and for male is 14.25. Female entrepreneurs have stronger perceptions of the usefulness (t-value = 3.633; p-value = .000) and ease of use (t-value = 5.861; p-value = .000) of the systems compared to male entrepreneurs. Comparing this result with Hennig and Jardim, (1997) and Rotter and Portugal (1969), which suggested that women tend to focus on the methods used to accomplish a task while men focus on outcome, there is a mixed result. Women entrepreneurs focus on both outcome and process. Perception of usefulness and ease of use of technologies were more salient for female than for male entrepreneurs. As observed from the previous paragraph that female entrepreneurs are less flexible than the male ones, it is suspected that such relative inflexibility or rigidity could lead to better perceptions of existing systems. For male entrepreneurs who seem to be more flexible, frequent replacement of existing applications could affect how they appreciate existing system's characteristics, such as ease of use and usefulness.

There is no significant difference in overall usage of ICT (t-value = .530; p-value = .597) between male and female entrepreneurs. To investigate usage differences further, usage components (e.g. varieties of systems used and various job tasks where systems are applied) were regrouped. Varieties of systems were combined into two groups as follows: Basic systems (which include, word processing, electronic mail, spreadsheets, graphics, and database), and advanced systems (e.g. application packages, and programming languages). Specific job tasks were also grouped into those for administrative purposes (e.g. producing reports, letters and 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). The result still points to non-significant difference in usage based on gender. Specifically, there is no difference in usage of basic systems (t-value = .988; p-value = .325) or advanced systems (t-value = -.626; p-value = .533) based on gender. Also no differences were found in the usage of systems for administrative tasks (t-value = .118; p-value = .907), for planning purposes (t-value = -1.199; p-value = .232), or for control purposes (t-value = -1.823; p-value = .070) between male and female entrepreneurs.

ICT Usage Determinants: Male and Female Entrepreneurs Comparison

Table 3 compares the explanatory power of traits, perceived usefulness and ease of use on ICT usage between male and female entrepreneurs.

The results shown in Table 3 indicate that variations in ICT usage explained by entrepreneurs' traits (such as innovativeness, risk propensity, perseverance, and flexibility) 63 percent and 20 percent respectively for female and male entrepreneurs. This implies that traits are much more salient in explaining technology usage among women entrepreneurs than they are among their male counterparts. Although innovativeness is a robust determinant of usage among both male and female entrepreneurs, the strength of the coefficients is greater for female entrepreneurs than for males. Risk-taking propensity is an important determinant among female entrepreneurs but not among male entrepreneurs. Because females generally have higher risk-aversion than males, it is logical that the amount of risk the entrepreneur is comfortable with tends to affect technology usage by females than males. Perseverance and flexibility show no significant impact on usage of ICT in both categories of entrepreneurs.

Perceptions show a different result in that the variation in usage explained by perceived usefulness and perceived ease of use is greater among male entrepreneurs (32%) as compared to female entrepreneurs (18%). The results clearly demonstrate the strong impact of perceived usefulness on system usage by male and female entrepreneurs as well as a dearth of significant influence of perceived ease of use on usage. In sum, the findings are that the variations in ICT usage accounted for by entrepreneurial traits and usefulness and ease of use perceptions differ among male and female entrepreneurs. Moreover, although the direction of the beta coefficients for all trait elements and perception elements are the same for male and female entrepreneurs, the strength of the coefficients differs.

POTENTIAL CONFOUNDING FACTORS

There are some important demographic variables that could potentially confound observed gender differences. Three potential confound associated with gender include income, occupation, and education (Venkatesh et al., 2000). It is widely believed that men are often more educated than women, and are thus found at the higher levels in the organizational hierarchy, with higher income. Thus it is deemed important to first evaluate (and control, if necessary) the effects of income level, occupation level, and education level (Kite, 1996; Praeger, 1986). In the current research, the issue of the confounding of occupation is not critical as all respondents are owners (entrepreneurs) of their business (similar occupation). In addition, income and education do not confound the observed relationships. Moreover, no past research as shown that either education or income is significantly associated with entrepreneurial propensity.

IMPLICATIONS AND CONCLUSIONS

Does gender matter when examining information and communication technology usage by entrepreneurs? This research suggests that although there are no differences in overall usage based on gender, usage frequency, usage determinants (such as perceived usefulness and perceived ease of use), and traits (such as perseverance and flexibility) do show differences based on gender. Male entrepreneurs recorded higher usage frequency than females. The difference in usage rate between males and females may have to do with the needs of each, propensity to experiment with the different features and functions of the system (males are known to be more adventurous with technologies), amount of time spent at work (females due to family and work related activities that compete for their time, tend to work overtime less frequently compared to males). Part of the longer working hours could be spent interacting with systems. The differences in mean scores for perseverance and flexibility favor male entrepreneurs. Systems' perceived usefulness and ease of use are higher for female entrepreneurs. The t-test results show that personal traits of perseverance and flexibility are higher for male entrepreneurs, while perceptions of systems' usefulness and ease of use are higher for females.

Traits explain technology usage by women entrepreneurs better than it explains usage by males--60 percent and 20 percent respectively. The results further indicate that innovativeness is a robust determinant of usage among both male and female entrepreneurs, although the strength of the association is greater for females than for males. Thus, 1 unit change in innovativeness will produce higher rate of usage by females compared to males. Risk-taking propensity is an important determinant among female entrepreneurs but not among male entrepreneurs. For females, a unit increase in risk-taking propensity will result in significant increase in usage rate, but not so with males. Perseverance and flexibility show no significant impact on usage of ICT in both categories of entrepreneurs.

Perceptions are other important determinants of technology usage by male and female entrepreneurs in Malaysia besides traits. There is a strong impact of perceived usefulness on system usage by male and female entrepreneurs as well as non--significant influence of perceived ease of use on usage. Based on the beta estimates, perceived usefulness is a stronger influence on usage among females than it is among males. This indicates that per unit increase in usefulness perception by females culminates to a greater increase in usage compared to their male counterparts. Thus, both males and females are outcome oriented in their adoption of information and communication technologies. Instrumentality is deemed an important driver for both, albeit more so for female entrepreneurs (based on the size of the beta estimates). Perceived ease of use is not a significant driver of usage for both males and females. Thus, entrepreneurs are not process oriented in their technology adoption. This may be because of their high need for achievement, which might compel them to continue to adopt systems that are deemed useful in achieving their goals, even though there may be slight difficulty in use.

These findings are important for technology management in small firms as well as in designing strategies that would enhance technology uptakes and usage of existing technologies by systems designers and vendors. For example, since more and more women are setting up entrepreneurial ventures in this male dominated sector, designers and vendors of new technologies must understand the factors that are salient to each group of entrepreneurs that are likely to lead to acceptance and greater usage. This information will help in formulating sound marketing strategies. For example, since risk-taking propensity is an important driver for women entrepreneurs, systems designers should ensure that uncertainty is minimized. One way to do this is to provide comprehensive, user friendly manuals that users can rely on in figuring out any usage difficulty. Moreover, reducing user anxiety and enhancing self efficacy and perceived behavioural control can help to reduce perceived risk and uncertainty. Vendors can achieve such reduction through training and other confidence building coaching techniques.

Theoretically, the research offers a better understanding of the relevant drivers of information and communication technologies among entrepreneurs in Malaysia. While certain traits offer good explanations for technology usage by both male and female entrepreneurs (e.g. innovativeness), others (e.g. risk-taking propensity) offer explanation for usage by females only. Yet, other traits (namely perseverance and flexibility) have no significant association with usage by males and females. With respect to perceptions, both male and female entrepreneurs have shown that usefulness is an important predictor of usage while ease of use is not. Perceived usefulness is an important driver of technology usage by both male and female entrepreneurs contrary to the general belief that outcome orientation is more of men's technology usage determinant than it is for women. Perceived ease of use has no significant influence on technology usage by entrepreneurs contrary to the postulation of the technology acceptance model. Future research may attempt to validate these findings among entrepreneurs in develop nations, as well as examine the moderation effect of culture in these relationships.

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Nelson Oly Ndubisi, Monash University

Table 1: ICT Usage

System Variety Usage (%) Specific Job Tasks Usage
 (%)

Word processing 91.5 Letters and memos 85.9
Electronic mail 78 Producing report 75.1
Spreadsheets 55.9 Communication with others 73.4
Application packages 53.6 Data storage/retrieval 59.9
Graphics 44.6 Planning/Forecasting 46.3
Database 37.3 Budgeting 44.1
Programming languages 26 Controlling & guiding 38.4
 activities
Statistical analysis 25.4 Analyzing trends 34.5
 Making decisions 34.5
 Analyzing problems/ 23.2
 alternatives

Table 2: Mean Differences in Traits, Perceptions and IT Usage

 FEMALE
 Mean S/D t-value

Traits
Innovativeness 14.0811 3.8986 1.342
Risk-taking propensity 13.9324 3.2322 0.139
Perseverance 15.1622 3.0879 2.406 *
Flexibility 10.7568 2.9229 3.280 **

Perceptions
System's Usefulness 17.66 1.9604 3.633 **
System's Ease of Use 16.93 2.4289 5.861 **

Technology Usage
Overall usage (OU) .1265 2.9727 .530

OU components
System Varieties (SV) 4.0676 2.0827 0.324
Job Tasks (JT) 5.4324 3.2565 1.056
Usage Frequency 4.86 1.30 2.157 *

SV components
Basic Systems usage 2.9595 1.2761 0.988
Advanced Systems 1.1081 1.1535 0.626

JT components
For Admin purposes 2.9324 1.1626 .118
For Planning purposes 1.5405 1.5632 1.199
For Control purposes .9595 .8827 1.823

 MALE
 Mean S/D

Traits
Innovativeness 14.8085 2.9838
Risk-taking propensity 13.8641 3.2299
Perseverance 16.2233 2.6006
Flexibility 12.0485 2.0213

Perceptions
System's Usefulness 16.14 3.5811
System's Ease of Use 14.25 3.6507

Technology Usage
Overall usage (OU) .0909 2.2410

OU components
System Varieties (SV) 4.165 1.8948
Job Tasks (JT) 4.9515 2.5682
Usage Frequency 5.26 1.08

SV components
Basic Systems usage 3.1553 1.3192
Advanced Systems 1.0097 0.8343

JT components
For Admin purposes 2.9515 .9840
For Planning purposes 1.2718 1.3299
For Control purposes .7282 .7945

* p < 0.05

** p < 0.01

OU = overall usage

SV = system variety

JT = job tasks

Table 3: Summarized Regression Results of the Impact of Traits
and Perceptions on ICT Usage

Variables Male Female
 Standardized Beta Standardized Beta

Traits
Innovativeness .465 *** .774 ***
Risk-taking propensity .129 .347 **
Perseverance .075 .067
Flexibility .005 .354
[R.sub.2] 0.20 0.63

Perception
Perceived Usefulness .430 *** .525 ***
Perceived Ease of Use .173 .229
[R.sub.2] 0.32 0.18

* p < 0.01

** p < 0.001

Dependent Variable = Usage