Programs in Information Science, Claremont Graduate School, 130 East 9th Street, Claremont, CA 91711, ... Why do individuals decide to use computer tech-.
ELSEVIER
Information & Management 29 (1995) 227-238
Research
Why do individuals use computer technology? A Finnish case study Magid Igbaria a,*, Juhani Iivari b, Hazem Maragahh
c
Programs in Information Science, Claremont Graduate School, 130 East 9th Street, Claremont, CA 91711, USA b Department of Computer Science and Information Systems, University of Jyvaskyla, P.O. Box 35, SF - 40351, Jyvaskyla, Finland ¢ Department of Quantitative Methods, College of Business & Administration, Drexel University, 32nd & Chesmut Streets, Philadelphia, PA 19104, USA
Abstract
Here we discuss the motivators for computer usage in Finland. IS and non-IS researchers have reported that perceived usefulness is a major determinant in a United States workplace. This study focuses on two aspects of motivation: extrinsic and intrinsic. Perceived usefulness is an example of extrinsic motivation, whereas perceived enjoyment is intrinsic. We found that extrinsic motivation plays a greater role in individuals' behavior and that perceived ease of use affects both perceived enjoyment and usefulness, as well as usage. Moreover, they were found to mediate fully the relationship between perceived ease of use and computer usage. Keywords: Usage; Perceived usefulness; Enjoyment; Ease of use; Finland; International; Cross-culture
1, I n t r o d u c t i o n W h y do individuals decide to use computer technologies? It is widely accepted that computer technologies have some benefits for individuals and organizations, but, it is recognized that the potential gains are not fully realized due to some lack of acceptance. Individuals are sometimes unwilling to use systems, even if they appear to increase productivity: " M a n y workers are suspicious o f new technology, even hostile to i t " [19]. It is evident that the acceptance and use of computers by individuals is limited due to this low motivation. Researchers have provided various answers to
* Corresponding author
what motivates computer use, and several models have been developed. Fishbein and A j z e n ' s [18] theory o f reasoned action (TRA), for example, was developed to predict and understand human behavior. It is based on the assumption that human beings are usually rational and make systematic use of information available to them. They are concerned with the determinants of behavior and relations among beliefs, attitudes, subjective norms, intentions, and behavior. Davis, Bagozzi and W a r s h a w ' s [12] technology acceptance model ( T A M ) adapted the generic T R A model to the particular domain of computer technology, replacing the T R A ' s attitudinal determinants with two variables: perceived ease of use and perceived usefulness. Although both T R A and T A M provided insights, more research is needed to determine what motivates individuals to use computer technologies; software
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engineers can then use the knowledge during system development. Most consideration has focused on the question of utility, and most internal marketing efforts seem to be effective when addressing practical aspects, such as: Are tasks done better a n d / o r quicker with a computer than without one? Less thought is given to the individual's intrinsic reason for accepting computer technology. Adams, Nelson and Todd [1] and Mathieson [33], for example, focused only on the impact of perceived usefulness and social norms on acceptance, with less concentration on the beneficial effects of pleasure and enjoyment in the use of computers and software [7]. Deci's [14] theory proposes that people expend effort due to both intrinsic and extrinsic motivation. Perceived usefulness is an example of extrinsic motivation, whereas perceived enjoyment is an example of intrinsic motivation. Although the first has begun to gain recognition as an important aspect of human motivation and, though theories have been developed, the methods have seldom been incorporated into IS implementation. In the case of computer technology, however, Davis, et al. [13] examined two motivators: perceived usefulness and enjoyment. They reported that these factors had significant effect on intentions to use a word processing program. Further, they reported that they mediated the effect of ease of use and quality on intentions. Although their model provided insights into user acceptance of computer technology, full-time MBA students in the U.S. were used, and the applicability of the findings to the population of employed adults is problematic. In addition, they studied a specific word processing system rather than microcomputer applications in general. Our study seeks to extend previous research by comparing the influence of perceived usefulness and enjoyment on computer technology acceptance, based on a sample of users in real organizational settings. Further, our study examines other determinants of computer acceptance among professionals and managers in Finland. A search for papers on motivational research indicates that almost all existing empirical IS efforts have been conducted in North America using American subjects. The applicability of this body of IS research findings in other cultures is unknown. Liebenau and Smithson [31 ] in the European Journal of Information Systems indicated that "the social
and cultural characteristics of European institutions can be studies as distinct from, or perhaps in contrast to, North American or Japanese institutions," and the applicability of research conducted in U.S. business schools and companies to European businesses may be challenged. Our study examines the motivators of computer technologies in a non-North American country that can be expected to have at least a partially different culture. It tests the model using a survey targeted to professional and managerial users in Finland. 1.1. Cultural context and external validity
A central concern in scientific research is external validity and " a key dimension of external validity is international" [43]. Hofstede [24,25] recognizes that many popular management and motivation theories, such as Herzberg's two-factor theory [21], Maslow's hierarchy of needs [32], and McGregor's theories [34] reflect the North American culture and argues that their applicability in other cultures is questionable. There has been frequent discussion about international generalizability. Aharoni and Burton [2], in a special issue of Management Science, suggested that more research is needed to address the generalizability of management science, where our knowledge is specific and limited to a given country or a culture. They concluded that since "[T]he world has increasingly become a global village, and large, multinational enterprises operate in a globally integrated fashion", we need to examine the findings of these studies vis-a-vis other cultures. The world is increasingly moving toward greater international exchange of knowledge and individuals, and therefore many people will find that their future work careers involve more than a single culture.In every culture certain factors act as motivators while others act as hygiene factors. The specific factors and their relative importance appears particular to each culture. Hofstede proposes that national cultures can be classified along four largely independent dimensions: (1) Power Distance - "the extent to which the less powerful members of institutions and organizations within a country expect and accept that power is distributed unequally;" (2) Individualism, i.e., "the degree to which people in a country prefer to act as individuals rather than as members of
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groups;" (3) Masculinity for societies in which social gender roles are clearly distinct; and (4) Uncertainty Avoidance, or "the extent to which the members of a culture feel threatened by uncertain or unknown situations." Lately, he has added a fifth dimension: Long-Term Orientation - "the fostering of virtues oriented towards future rewards." He argues that popular motivation theories are culture-bound. This leads to an interesting question as to what extent the motivational model may be valid in more collective societies than USA. In his analysis of 50 countries and 3 regions, he found that both the USA and Finland have small power distances (scores 40 and 33, and ranks 38 and 46), but differ in individualism (scores 91 and 63, and ranks 1 and 17), uncertainty avoidance (scores 46 and 59, and ranks 43 and 31/32) and especially masculinity (scores 62 and 26, and ranks 15 and 47). Because the empirical analysis of the long-term orientation of 23 countries did not include Finland, it is omitted here. The theory of motivation developed in the USA, for example, incorporates factors from other countries. Social needs tend to dominate the motivation of workers in Scandinavian countries that stress the quality of life (Hofstede's femininity dimension) over productivity (masculinity dimension). Therefore, American culture has focused on job enrichment (to increase productivity), whereas, Finland developed socio-technical systems and approaches to the quality of working life (the restructuring of employees into work groups to achieve the same ends). In countries high on uncertainty avoidance (such as Finland), security motivates most workers more strongly than does self-actualization. These workers consider job security and life-time employment as more important than a very interesting or challenging job. Additionally, workers in more collectivist countries tend to stress social needs over ego and self-actualization needs. Lachman, Nedd and Hinings [29] propose that any comprehensive cross-national research on organizations and management should incorporate resource availability since they affect organizational structure and behavior. It has been suggested that Americans have more access to new technology, particularly to microcomputers. The Scandinavian countries (Denmark, Finland, Norway and Sweden) form an interesting contrast to the United States. According an
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OECD report for 1989 [37], Scandinavia and Switzerland had the highest per capita IT spending, with the United States sixth. Finland was clearly the highest among the OECD countries.
2. Research hypotheses The theoretical grounding includes also the work of Vroom [48]. Intrinsic motivation theorists argue that behavior (usage) is determined by intrinsic as well as extrinsic motivation. People make an effort because a task is enjoyable and offers external rewards. Individuals accept technology because its use is fun and because it is useful and beneficial. Maybe usefulness, pleasure, and enjoyment are the motivators for accepting or rejecting a new technology. Perceived usefulness is "the degree to which a person believes that using a particular system would enhance his or her job performance" [ 11 ]. Its significance derives from the TAM model hypotheses that perceived usefulness affects behavior which thus affects the use of computer systems. Thus, if an individual perceives an activity to be instrumental in achieving valued outcomes, he or she will be likely to use that technology. It appears to exhibit a stronger and more consistent relationship with usage behavior than others, such as attitudes, satisfaction, and perception. This also is positively associated with system usage [26,39,40]. Therefore, we state Hypothesis 1: Perceived usefulness will be positively related to the use o f computer technology. Also individuals probably engage in activities because they lead to extrinsic rewards and bring about enjoyment and pleasure, e.g., children's decisions to play games. Triandis [46,47] proposed that affect - " t h e feelings of joy, elation, or pleasure, or depression, disgust, displeasure, or hate associated by an individual with a particular act" - have an impact on behavior. Individuals who believe that computers are useful may also accept fun as a fringe benefit. Thus, Hypothesis 2 states: Perceived f u n will have a positive effect on the use o f computer technology. Perceived usefulness should also play a major role in usage. Ease of use and usefulness are thought to
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be potentially important determinants of system use. Perceived complexity of innovations, similar to perceived unease of use, has also been recognized as a factor inhibiting technology diffusion [22,28,41]. In general, it has been suggested that increased complexity requires increased effort, thus decreasing the likelihood of adoption of new technology [16,23]. Additionally, the importance of perceived ease of use is supported by the self-efficacy theory developed by Bandura [4,5]. There appears to be a relationship between the role of (lack of) self efficacy (i.e, the belief that one is able to master a particular behavior) and phobia. Also personal efficacy is one of the major factors theorized as underlying intrinsic motivation. Webster and Martocchio [49] reported a positive relationship between computer efficacy and playfulness, which is similar to enjoyment. Therefore, Hypothesis 3 states that: Perceived ease of use will affect the use of computer technology directly and indirectly through perceived usefulness and enjoyment~pleasure.
3. Method
3.1. Sample and procedure The data for this study ware gathered by means of a Finnish language survey administered during Spring 1993. Initially, a sample of the major 109 companies in Finland [44] were selected. Because of fusions, bankruptcies, and problems in making contact, the activated participants were drawn from a sample of 86 corporations and 81 agreed to participate. The number of employees varied between 89 and 28,859 (the average being 4913); and the net sales ranged from 639 million Finnish marks ($127 million) to 57 billion Finnish marks ($11.4 billion) [45]. Small companies were mainly in the merchandising industry. If size is measured in terms of number of employees, there were 23 manufacturing and only 2 merchandising companies among the top 25 of the initial sample, whereas the smallest 25 companies consisted of 17 merchandising, 4 manufacturing, and
Table 1 Profile of the participants Age: Organizational tenure: Job tenure: Gender:
Average = 38.9 Average = 10.5 Average = 5.5 Male = 53.6%
S.D. = 8.3 S.D. = 8.3 S.D. = 5.3
Education:
Less than high school Trade school High school Some college Bachelor's Degree Graduate Degree Other N o n - m a n a g e m e n t / P r o f e s s i o n a l staff First level supervisor Middle management Top m a n a g e m e n t / E x e c u t i v e s Unclassified Accounting and Finance Marketing and Sales Technical areas General management and Personnel Others Manufacturing Merchandising Other
9.8% 4.9% 4.7% 45.3% 10.3% 23.9% 1.1% 51.9% 16.7% 25.2% 3.1% 3.1% 30.3% 14.1% 17.7% 13.6% 24.2% 50.0% 30.0% 20.0%
Organizational level:
D i v i s i o n / F u n c t i o n a l area:
Industry:
Median = Median = Median = Female =
39.0 7.5 3.0 46.4%
M. Igbaria et al./ lnformation & Management 29 (1995) 227-238
4 other industries. Manufacturing companies had an average of 6,469 employees and merchandising had an average of 1,580. Within each company, contact persons, usually IS managers, were identified and called by telephone to inform them of the purpose of the study. Individuals who would be participating were then identified. These contacts were asked to distribute the surveys to people other than computer professionals but who used computers in the execution of their job; secretaries were excluded. Participation was voluntary and people were assured of confidentiality. Ten questionnaires were sent to each contact person, except for two companies where fewer than 10 potential participants were identified. The total of 806 questionnaires were mailed. The exclusion of incomplete and returned questionnaires resulted in a final sample of 450 users, a response rate of 55.8%. The high response rate may be due to the fact that the managers encouraged participation. The respondents held professional and no supervisory responsibility (51.9%) and managerial positionss. The majority of the managers were middle level, with some first level supervisors and executives. Of the 450 participants, 53.6% were males and 46.4% were females. Age ranged from 21-61 and the mean age was 38.9 years (with a S.D. of 8.3). Approximately 45.3% of the participants had completed some college work, and 35% were college graduates. The average length of service in the current organization was 10.4 years (with a S.D. of 8.3) and the tenure in their current job averaged 5.5 years (with a S.D. of 5.3). Table 1 summarizes the demographic characteristics. The success of our sample is reflected in the non-significant differences between users in different industries, organizational levels, and functional areas. In view of the high association between size and industry, the non-significance of industry suggests that the company size is not a significant confounding factor in the sample. Further analyses indicated that there are no significant differences among users employed in different industries (manufacturing, merchandising, and others) and functional areas (technical vs. non-technical) in terms of their demographic characteristics. The only demographic variables on which users differed significantly were gender and education in
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technical vs. non-technical functional areas (F = 17, 11, p < 0.001 and F = 5.21, p < 0.05, respectively). A greater number of females and a smaller number of educated participants were non-technical users.
3.2. Measures Respondents were required to complete a survey that gathered information regarding demographic variables, the use of computer technology, and the reasons for using it. Most of the responses were Liken and semantic differential scales. Composite scales were created for all the study variables. Usage. Based on several studies (e.g., [9,15]), three indicators of usage were included: (1) perceived daily use; (2) perceived frequency of use; and (3) the number of business tasks for which the system is used . Following Igbaria et, al. [27] and Lee [30], , individuals were asked to indicate the amount of time spent on the system per day, using a six-point scale ranging from "almost never" to "more than 3 hours per day." Frequency of use has been suggested as important by Raymond [38] and it provides a slightly different perspective than time of use. It was measured on a six point scale ranging from "less than once a month" to "several times a day." In a microcomputer environment, the number of business tasks performed by the participants can be another indicator of user acceptance. For the purpose of this study, eight tasks were defined and the participants were asked to indicate the extent of their use for these tasks, as reported on a five-point scale ranging from "not at all" to "to a great extent." The number of tasks was calculated to represent another dimension of usage. These indicators are typical of the kinds of self-reported measures often used when objective use and acceptance metrics are not available. Objective use logs were not practical in our study since participants used different microcomputers and different applications for different tasks. Self-reported usage should not be regarded as precise measures of actual usage, although previous research suggests they are appropriate as relative measures [8]. The items used to construct the perceived usefulness scale were adapted from prior research with appropriate modifications to make them specifically relevant to microcomputers. Individuals were asked
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to indicate their agreement or disagreement with five statements using a five-point Likert-type scale (See Appendix A). The internal consistency reliability (coefficient alpha) of the scale was 0.93. Seven different pairs from the evaluation dimension were used on seven-point semantic differential items to assess perceived enjoyment. Individuals were asked to rate the items according to how they feel about using computers and to mark the place that best describes their feeling: Using a computer in my job is: fun-frustrating, pleasant-unpleasant, negative-positive, pleasurable-painful, exciting-dull, foolish-wise, and enjoyable-unenjoyable) (See Appendix A). This scale had an internal consistency reliability of 0.86. Perceived ease of use refers to the degree to which computer technology is relatively easy to understand and use. Individuals were asked to indicate the extent of agreement or disagreement with four statements using a five-point scale (See Appendix A). The internal consistency reliability of the scale is 0.91. Factor analysis was used to confirm the three distinct perception constructs. A factor analysis confirmed the existence of three factors with eigenvalues > 1.0 that accounted for 68.3% of the total variance. All three factors were examined against the three perceptions. The criteria used to identify and interpret factors were that a given item should load 0.50 or higher on a specific factor and have a loading no higher than 0.35 on other factors. All three factors were identical to, or corresponded very closely to, the three perceptions. Table 2 shows that perceived usefulness consisted of five items. Factor 2 consisted of seven items reflecting perceived enjoyment and pleasure. Finally, a third factor, perceived ease of use, consisted of four items. This suggests that usefulness, enjoyment, and ease of use were found to be unidimensional and factorially distinct. All the items used to operationalize a given construct loaded on a single factor.
3.3. Data analyses There was a significant relationship between the study variables and three of the demographic variables: gender, age, and education. Therefore, it was necessary to control for demographic variables in all
Table 2 Factor analysis results Item Ease- 1 Ease-2 Ease-3 Ease -4 Useful-1 Useful-2 Useful-3 Useful-4 Useful-5 Enjoy- 1 Enjoy-2 Enjoy-3 Enjoy-4 Enjoy-5 Enjoy-6 Enjoy-7 Eigenvalue Cumulative % of explained variance
Factor 1
Factor 2
Factor 3 0.85 0.81 0.84 0.86
0.85 0.86 0.87 0.90 0.74
6.48 40.50
0.74 0.78 0.72 0.80 0.76 0.64 0.61 2.80 58.00
1.65 68.31
Note: Loadings less than .30 are not shown. Factor 1. Perceived usefulness. Factor 2. Perceived enjoyment. Factor 3. Perceived ease of use.
analyses so that conclusions on usage were not confounded by demographics. The technique of path analysis with least squares multiple regression was used to determine whether the observed pattern of relationships was consistent with the hypotheses. We took several steps to check for possible violations of the assumptions underlying path analysis [6,20]. First, an examination of the alpha coefficients indicated satisfactory levels of internal consistency reliability among all the multi-item scales (alpha coefficients ranged from .86 to 0.93) [36]. Furthermore, the intercorrelations among the study variables were also examined. Examination of the intercorrelations revealed no evidence of extreme multicollinearity (i.e., r's < 0.80) and factor analysis confirmed the existence of three distinct measures. In addition, the residuals of the endogenous variables were tested for autocorrelation using the DurbinWatson test [17]. The results indicated the absence of correlated residuals. Hierarchical multiple regression analyses [10] were used to test the hypothesized relationships. We first regressed perceived usefulness on demographic
M. lgbaria et al./ Information & Management 29 (1995) 227-238
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Table 3 Matrix of intercorrelations among study variables Variables
1
2
3
4
5
6
7
8
Demographic variables: 1. G e n d e r ( 1 = F; 2 = M ) 2. A g e 3. E d u c a t i o n
0.17 ~ 0.24 ~
4. P e r c e i v e d ease o f use
- 0.02
5. P e r c e i v e d u s e f u l n e s s
0.04
6. P e r c e i v e d e n j o y m e n t
- 0.21 a
-0.13
a
- 0.28
a
-0.07 - 0.17 ~
0.15
a
0.18 a - 0.09
0.52 a 0.35 a
0.29 ~
S y s t e m usage: 7. F r e q u e n c y o f use
0.16 a
- 0.06
0.35 a
0.25 ~
0.43 "
0.09
8. T i m e o f use
0.03
- 0.09
0.24
0,30
a
0.37 a
0.16 ~
0.78 a
9. N u m b e r o f tasks
0.33 ~
- 0.05
0.35 ~
0.26 ~
0.32 ~
0.09
0.44 a
a
a
0.30 ~
p < .01
variables as control variables, adding perceived ease of use in step 2. In a similar manner, perceived enjoyment was regressed on demographic variables as control variables, adding perceived ease of use in step 2. Finally, the three indicators of use (i.e., frequency of use, time of use, and number of tasks) were regressed on demographic variables in step 1, perceived ease of use in step 2, and perceived usefulness and perceived enjoyment were added in step 3. In each analysis, the significance of the beta weight for the hypothesized independent variable was examined to determine support for the hypothesis. An indirect effect represents those interpreted by the intervening variables: the product of the path coefficients along an indirect route from cause to effect following arrows in one direction only. When
more than one indirect path exists, the total indirect effect is their sum. The sum of the direct and indirect effect reflects the total effect of the variable on the endogenous variable [3,42].
4. Results Table 3 shows that perceived ease of use and perceived usefulness are positively correlated with all system usage dimensions (i.e., frequency of use, time of use, and number of tasks). Further, perceived enjoyment is positively only correlated with time of use. Additionally, perceived ease of use was also found to be strongly correlated with perceived usefulness and perceived enjoyment.
Table 4 P r e d i c t i o n o f p e r c e i v e d u s e f u l n e s s and e n j o y m e n t Variables
Perceived usefulness Effect (/3 )
Perceived enjoyment r
Effect (/3 )
r
Control variables: G e n d e r ( 1 = F; 2 = M ) Age
0.01
0.04
- - 0 . 1 9 '~
-0.21
a
- 0.05
-- 0.07
--0.15 a
-0.17
~
Education
0.15 "
P e r c e i v e d ease o f use R~
0.54 b 0.29 b
a b
p _< .01 p _< .001
0.18 a 0.52 b
-- 0.07 0.35 b 0.19 b
-
0.09 0.35 b
M. Igbaria et al./ Information & Management 29 (1995) 227-238
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variables, education has strong positive effects on frequency of use, time of use, and number of tasks. Gender had also positive direct effects on frequency of use and number of tasks. However, males reported using the system more extensively and for more tasks. Comparison of the estimated correlations as represented by the sum of the direct and indirect effects (i.e., total effects) with the original correlations between the independent variables and the dependent variables provides further evidence of the " g o o d n e s s of f i t " of the model. With the criterion that the absolute difference between the reproduced (i.e., total effects) and original correlations does not exceed 0.10 [35]. In summary, the picture that emerges is that perceived usefulness had a much stronger direct effect than both perceived enjoyment and perceived ease of use on system usage.
The results reported in Table 4 show that the model variables explained significant variation in perceived usefulness. Consistent with Hypothesis 3, perceived ease of use had a strong direct effect on perceived usefulness. Additionally, education had a positive direct effect on perceived usefulness. It also shows that the demographic variables and perceived ease of use explained 19% of the variance of perceived enjoyment. Perceived ease of use had a very strong direct effect on perceived enjoyment. Further, gender and age were also found to have negative effects on perceived enjoyment, where males and older individuals reported lower level of enjoyment with the system. Consistent with Hypothesis 1, perceived usefulness had a strong direct effect on all system usage dimensions (frequency of use, time of use, and number of task. Table 5 shows that perceived enjoyment had nonsignificant effects on the three dimensions of system usage, which is inconsistent with Hypothesis 2. The results showing the direct and indirect effects of perceived ease of use on computer usage are also presented. Perceived ease of use had very strong indirect effects on usage mainly through perceived usefulness. Note that its indirect effect is much stronger than its direct effect on usage. It had a direct effect on one dimension of system usage (time of use), only. Finally, Table 5 shows that 28, 20, and 26 percent of the variance of frequency of use, time of use and number of tasks, respectively, were explained. It also shows that, among the demographic
5.
Discussion
What motivates individuals to use computer technologies? The findings of this study contributed to the understanding of two interrelated motivators relevant to the design and use of information systems: perceived usefulness and perceived enjoyment. Our study indicated that Finns professional and managers use computer technology mainly because they perceive them as useful tools to improve their job
Table 5 Prediction of system usage Variables
Direct
Indirect Total
Control variables: Gender(1 = F; 2 = M) .10 a .00 Age -.02 -.02 Education .27 c .06 Perceived ease of use .01 .21 Perceived usefulness .38 c Perceived enjoyment .02 R2 .28 c a
p < .o5 p < .Ol c p _
