Research
The in¯uence of attitudes on personal computer utilization
among knowledge workers: the case of Saudi Arabia
Muhammad A. Al-Khaldi
*, R.S. Olusegun Wallace
Department of Accounting and MIS, College of Industrial Management, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
Received 22 April 1998; accepted 24 March 1999
Abstract
Since the introduction of personal computers (PCs) in the early 1980s, Saudi Arabia has made major investments in PCs to match its rapidly growing economy. As a result, the PC business has become one of the fastest growing sectors in the Kingdom of Saudi Arabia.
Our paper reports on the results of a study which investigates the relationships between end-users' attitudes and PC utiliza-tion among knowledge workers in the context of Saudi Arabia. To gain a better understanding of the factors that in¯uence the use of PCs, we adopted Triandis' theory which suggests that behavior is determined by attitudes, social norms, habits and expected consequences of behavior. Our study is based on previous efforts to test the theory's validity in Saudi Arabia.
Our results suggest that PC utilization is determined by individual attitudes, personal characteristics, such as PC experience, facilitating conditions, such as PC access and social factors. We also observed that respondents to our questionnaire differ in the level of importance they attribute to the factors hypothesized as in¯uencing PC utilization compared to Canadian respondents in a previous study.#1999 Elsevier Science B.V. All rights reserved.
Keywords:PC utilization; User attitudes; Social factors; Knowledge workers; Saudi Arabia
1. Introduction
One aim of our study is to ascertain the attitudes of knowledge workers in Saudi Arabia [34] toward the use of PCs, and to determine whether those attitudes are similar to those reported by Thompson et al. [41, 42] for knowledge workers in Canada. There are reasons for replicating the Canadian study in the Kingdom of Saudi Arabia. Essentially, knowledge about the factors that promote the usage of PCs is useful to the global economyÐfar too important to be
limited to the ®ndings from one national study. Dif-ferent contexts allow us to understand variation across the world. The Canadian context is different from the Saudi context both from the point of view of economic development and cultural orientation. First, Canada is more developed than Saudi Arabia. In addition, Saudi Arabia has only recently started to develop the educa-tional, organizational and institutional systems that have long existed in developed countries, such as Canada. Second, Canada is essentially a secular com-munity, while Saudi Arabia is an Islamic society with a culture that manifests high power distance, uncer-tainty avoidant, collectivist, and femininity character-istics (along Hofstede's [23] cultural dimensions (see *Corresponding author. Tel.: 860-2656; fax:
+966-3-860-3489; e-mail: [email protected]
[4, 5, 12]). Although the potential importance of PCs in the social and economic development of Saudi Arabia is recognized by Saudi authorities [11], the need to uphold the tenets of Islam was probably responsible for the delay (until January 1999) in allowing public access through the PC to the enormous potential of the internet within Saudi Arabia. Given these societal differences, we would expect the PC utilization attitudes of the respondents in Saudi Arabia to differ from those of Canadian respondents. Another aim of the study is to explore the importance of factors (such as attitudesÐaffect, beliefs, behavior; societal and facilitating conditions) in promoting the usage of PCs in Saudi Arabia.
The Kingdom of Saudi Arabia has a population of 12.3 million citizens and 4.6 million foreign nationals, mainly from the Indian sub-continent and East Africa. More than 50 percent of the Saudi population are under the age of 16. The country has a stable economy and is a conducive environment for commerce and business. Its commercial sector is relatively unregu-lated and the ®nancial sector is liberalized. Saudi Arabia's microcomputer and minicomputer market exhibits growth rates similar to the American market [2]. As a result, it would be interesting to learn whether the relative strength of attitudes towards PC usage in Canada holds in Saudi Arabia. It is also interesting because, in contrast to previous studies of the factors capable of in¯uencing PC utilization in the Kingdom (e.g., [1, 9, 10]), our study used practitioners rather students as subjects of enquiry.
2. Conceptual framework and research questions
The link between end-user attitudes and PC utiliza-tion has occupied the attenutiliza-tion of scholars in recent years. The different frameworks include:
1. The theory of reasoned action (proposed by Fishbein and Ajzen [18] and adopted by
Thomp-son et al.) which seeks to investigate the reaThomp-soning that lies behind the decision to utilize PCs. This fact and the theory of perceived behavioral control [6, 7, 8] suggest that PC usage can be predicted by an individual's intention to use it. This intention is determined by some weighted combination of the individual's attitudes toward PC-related objects (hardware, software, etc.).;
2. The theory of perceived behavioral control, based on the individual's intention to perform a given task, is an extension of the theory of reasoned action to take account of behaviors over which people have incomplete control;
3. Technology acceptance model proposed and adopted by Davis et al. [14]. This model highlights perceived ease of use and perceived usefulness of the PC;
4. Expectancy model (proposed by Vroom [45] and adopted by DeSanctis [16]) based on the belief that the use of PCs leads to good performance, which in turn leads to desired outcomes or the valence of available outcomes;
5. Job diagnostic survey (utilized by Yaverbaum [47]); and
6. Computer attitude scale (developed by Loyd and Gressard [29] and used in experiments [20, 21, 30, 31] by them, Jabri [3] and Khaldi and Al-Jabri [9]). This framework examines four different factorsÐanxiety or fear of PCs, con®dence in ability to use or learn them, liking them, and their usefulness.
Our study draws upon all these. Attitudes are deter-mined by the individual's beliefs about the conse-quences of his or her behavior, based on the social norms and mores. The relationship is illustrated in Fig. 1.
In this context, user attitude is construed as a learned predisposition toward PC-related objects. Once the concept is de®ned thus, its operationalization requires the identi®cation of all PC-related objects
Fig. 1. End-user attitude as a determinant of PC usage.
affecting end-user's attitudes and the development of a measurement instrument to gauge the user's reaction. While Fishbein and Ajzen considered all beliefs about an act or behavior, Triandis [43, 44], in a modi®cation of the Fishbein and Ajzen's theory, makes a clear distinction between beliefs that link emotion to the act (occurring at the moment of action) and beliefs that link the act to future consequences. Triandis argues that an individual's intended behavior is determined by his or her feelings toward the object (Affect), expected consequences of the behavior (cognition) and what she/he should do (social factors).
The desire to understand end-users' behaviorÐ their attitudes and perceptions toward the availability of the PC, its costs and its ease of useÐcompels the adoption of a multi-disciplinary approach. This requires the use of a cumulative research tradition by which theories and models borrowed from other disciplines serve as a foundation [19, 27, 38]. For example, a cognitive account of PC utilization is a theory about its functional capacitiesÐthe things it can doÐthat are involved during the completion of the questionnaire. There are numerous ways of describing the range and kinds of functional capacities involved in any aspect of PC utilization but most scholars have found it useful to describe these capacities in terms of attitudes. However, probably because of the minimal use of the knowledge accumulated in the social psy-chology literature, the empirical evidence on whether PC utilization is in¯uenced by end-users' attitudes is mixed and inconclusive; (e.g., see [40]). In addition, little evidence relates to developing countries in gen-eral, and the Arabian Gulf Region in particular. To facilitate our understanding of attitudinal factors of PC utilization, we proposed the `working model' depicted in Fig. 2.
The proposed model is a simpli®cation, as we are not attempting to cover all relevant aspects of PC utilization. The model groups many of the individual/ organizational motives for PC utilization into four parts: (1) individual attitudes toward the PC; (2) social factors capable of affecting PC usage; (3) individual pro®les (age, experience, education, access and train-ing of the respondents); and (4) organizational factors which facilitate its use. The motives that are described in the model as attitudes are disaggregated for analy-tical convenience into three portions, along the lines suggested by Fishbein and Ajzen: affect, behavior, and
cognition. A theory of PC utilization would not be a complete theory of attitude if the emotional and intentional dimensions are excluded, thus previous studies have examined the role of the affective and cognitive faculties. In our study, these are referred to as affectandbehavior, respectively.
We separated the cognition motive into short-term and long-term consequences. The ®rst has two dimen-sionsÐcomplexity (anxiety or fear of the PC) and usefulness (how it ®ts the tasks and job of the end-user). Speci®cally, the study addresses two sets of questions:
1. What are the factors that have the highest in¯uence on the end-users' utilization of PCs in Saudi Arabia? and what is the comparative importance of attitudinal factors in different countries? For example, do end-users in different countries have similar attitudes? Do country-speci®c conditions imply different PC usage? 2. Are there within-country differences in the
impor-tance of the attitudinal factors? For example, Cul-pan [13] has shown that there are perceptual differences in the importance of attitudinal factors across industries in south-central Pennsylvania. On this basis, we investigate whether our respondents differ in their perceptions of the degree of impor-tance of the factors which may in¯uence PC uti-lization. We also investigate whether any differences in the respondents' perceptions are due to the differences in their pro®les.
3. Operationalization of constructs
To operationalize the constructs we adopted and amended the instrument designed by Thompson et al. The constructs are:
they should do. They deal with an individual's inter-nalization of work ethics and mores in organizations and capture what individuals perceive as an acceptable mode of conduct, given their generally accepted orga-nizational culture. In line with Thompson et al., we hypothesize that:
H1: There is a positive relationship between social factors concerning the use of PCs and PC utilization.
Each of the four factors was measured by a simple questionnaire item. For the ®rst three, the items were
scaled by a ®ve-point Likert-type scale (1strongly disagree to 5strongly agree). The fourth item was scaled using a ®ve-point scale representing different proportions (110% or less to 590% or more).
(2) Affect factors represent respondents' affection and disaffection for the PC. While we recognize that affective and cognitive components of attitude may overlap, we have adopted the strategy of separ-ating the two as suggested by Thompson et al. Affection for the PC is a function of an individual's previous training and awareness of the PC. We hypothesize that:
Fig. 2. Factors in¯uencing PC utilization.
H2: There is a positive relationship between affect and PC utilization.
Affect relates to three items: (1) PCs make work more interesting; (2) working with PCs is fun; and (3) PCs are good for some tasks but not for the kind of task I want. A ®ve-point Likert-type scale was used (1strongly disagree to 5strongly agree).
(3) Cognitive factors. This set of factors deals with the perceived consequences of adopting a PC for the individual, such as `working with the PC is complicated or it takes too long to learn how to use a PC to make it worth the effort.' As Thompson et al. suggest, the perceived consequences construct is consistent with the expectancy theory of motiva-tion, proposed by Vroom, which suggests that indi-viduals evaluate the consequences of their behavior in terms of potential rewards and base their actions on the attractiveness of the rewards. Perceived consequences may be positive or negative, just as they can be high or low. The consequences may be felt immediately on the use of a PC or may produce a valence whose effect may extend beyond the short-term to the long-short-term. We examine two perceived short-term consequences and one perceived long-term consequence.
3.1. Short-term consequences
(a) Complexity. Because complexity refers to the dif®culty that an individual may experience while using the PC, we hypothesize that:
H3: There is a negative relationship between per-ceived complexity and PC utilization.
Four complexity items were given to the respon-dents and a ®ve-point Likert-type scale was used to capture their opinions on each (1strongly disagree to 5strongly agree).
(b) Usefulness or job performance facilitation
(JPF). This set of perceived behavioral control factors was captured, using six items that describe the poten-tial outcomes and valences from using a PC; e.g.,the use of a PC can decrease the time needed for my important job responsibilities. These refer to the PCs potential for enhancing the individual's job perfor-mance. We hypothesize that:
H4: There is a positive relationship between per-ceived job ®t and PC utilization.
3.2. Long-term consequences
The positive and negative effects of PC utilization can often be felt on work performed long after the PC was introduced. Such factors were captured with six items, using a ®ve-point Likert-type scale. Respon-dents were asked for their opinion on the perceived understanding of the long-term effect of using the PC. We hypothesize that:
H5: There is a positive relationship between per-ceived long-term consequences of PC use and PC utilization.
The six types of effect on job characteristics are: the level of challenge of the respondents' job; the oppor-tunity for preferred future jobs; the amount of variety on the job; the opportunity for more meaningful work in the future; the ¯exibility of changing jobs in the future; and the opportunity for job security. The effects that the respondents were asked to consider include the potential for increase, decrease, or no change in each of the six job characteristics.
(4) Facilitating factors refer to the availability of support systems. Six items were also used to oper-ationalize these factors. We adopt Triandis's de®nition of facilitating factors as the ``objective factors, `out there' in the environment, that several judges or observers can agree make an act easy to do.'' We assumed that there was a positive association between supporting systems and PC utilization. We did not factor into our study conditions which impede PC utilization. As a result, we hypothesize that:
H6: There is a positive relationship between sup-porting facilitating conditions and PC utilization.
packages optionally used at work, which ranging from 1 to 5.
(6)Other factorsinvestigated include PC training, PC experience, and age. Training was indicated by Thompson et al. as an element that needs to be investigated with regard to PC attitude and it was later investigated by them. Respondents that are trained in the use of the PC would most probably tend to attribute greater importance to PC utilization motivators than those who are not. The more exposed to PC training, the more likely that there would be perceived positive long-term consequences. As a result, we hypothesize that:
H7: There is a positive relationship between PC training and PC utilization.
Other studies have found a signi®cant effect of PC access and experience on reducing PC anxiety and enhancing its utilization. We predict PC utilization to increase as the individual respondent gains more access to the PC, or as he gains more experience working with the PC. PC familiarity and experience can reduce fears and negative attitudes toward the PC and can encourage PC utilization. We hypothesize that:
H8: There is a positive relationship between the degree of access to the PC and PC utilization.
H9: There is a positive relationship between the extend of PC experience and PC utilization.
Given the relatively nascent usage of PCs in the Kingdom of Saudi Arabia, it is possible that the level of education and age may be major factors in the utilization of the PC. We predict that the level of education would be positively associated with PC utilization and that age would be negatively related to PC utilization. Younger employees are likely to have had some training in the use of PCs in their education, while older people would have gone to school at a time when PC education was not available. These speculations form the bases of following hypotheses:
H10: There is a positive relationship between edu-cation level and PC utilization.
H11: There is a negative relationship between age and PC utilization.
3.3. Organization size
Organization size has been suggested as a predictor of PC adoption in organizations [15, 17, 25, 35, 36]. On the basis of empirical research on ®rms, we suggest that organization size may be equally signi®cant for PC utilization and that it may have a stronger moderating in¯uence than individual attitudes on PC utilization by knowledge workers. The larger a ®rm is, the greater are the prospects of computeriza-tion. If this is true, this challenges one of the most central orthodoxies of PC utilization studies, and implies that those seeking to promote PC utilization ought, in choosing the method of motivating PC utilization, to give as much, or more weight to organization size, as to other motivating factors. Firms with substantial size can attain more effective transition from manual to PC systems through invest-ment in training. Growth in size generates pressures for computerization.
According to Raymond [37], smaller ®rms can hardly afford the enormous costs of PC utilization (employment of persons with specialized knowledge, training staff, transformation of manual to PC systems, acquisition). The activities of small ®rms are often below the level that is optimal for computerization. While previous studies have examined the factors that in¯uence PC utilization in small ®rms within a country (e.g., New Zealand, Saudi Arabia and Taiwan [24]), the studies have often assumed that the factors that promote PC utilization in small ®rms would be dif-ferent from those in large ®rms. This assumption is based on intuitive and reasoned conclusion rather than empirical investigation. We use number of employees to measure organization size. This has been used as a measure of organization size in several studies in accounting (e.g., [46]).
4. Methodology
4.1. Sample
country. It is the headquarters of the dominant ®rms in the Kingdom and has a great number of organizations (light industries, retail outlets and many government administrative of®ces). Most of the respondents work in semi-government pro®t-seeking ®rms and private ®rms located in Dhahran and Dammam, the cities with the largest concentration of knowledge workers in the Eastern Province. A sample of 200 knowledge work-ers was targeted. Knowledge workwork-ers whose opinions are pooled for this study include accountants, treas-urers and controllers, general managers, engineers, production analysts, and corporate planners. They generally use low-end applications software and not decision support or expert systems. However, the knowledge workers who participated in the study as end-users come within the ®rst three categories of the six end-user categories identi®ed by Rockart and Flannery [39]:
1. non-programming end-users, who access data through predeveloped menu-driven software packages;
2. command level end-users, who generate unique reports for their own purposes, usually with simple query languages; and
3. end-user programmers, who utilize command and procedural languages to access, manipulate, and process data for their personal information needs.
In total, after one follow-up (and telephone commu-nication) with the contact persons in each participating ®rm, 151 (75.5%) useful responses to the question-naire were collected from the participants. Every questionnaire which achieves less than 100 percent response rate has a potential non-response bias pro-blem. Like most researchers, we have addressed this problem in two ways.
1. By attempting to obtain as high a response rate as possible. However, questionnaires dealing with sensitive personal issues may be more likely to result in non-response. Users who are not comfortable with the PC may be reluctant to respond, while competent users may be particu-larly interested in the subject and willing to participate in the study. Here, we attempted to reduce non-response bias by using a follow-up procedure and the coordinator within a participat-ing ®rm to chase potential non-respondents.
2. By estimating qualitatively the potential effect of non-response on the ®ndings. On this basis, we conducted a comparative analysis of responses by date of response (or date of receipt of responses). This analysis is based on the presumption that late responders are reasonable `surrogates' of non-respondents. The response rate of 75.5% is the average of responses from the entire sample; the response rates from participating ®rms vary from a low of 60 percent to a high of 100 percent. Every effort was made to minimize the occurrence of non-response within a returned questionnaire. Where one was noticed, the individual ®rm from where the incomplete questionnaire came was consulted and the contact person in that ®rm was asked to trace the individual respondent to get the required response.
The questionnaire used to elicit opinions from respon-dents is a slightly modi®ed version of an instrument used by Thompson, et al. for Canadian surveys. The present study differs from these studies in the follow-ing ways. First, it differs in terms of the spread of respondents. Whereas the Canadian study collected responses from participants drawn from a large multi-national manufacturing ®rm, the respondents to our study were drawn from 10 of the leading ®rms in the Eastern Province of Saudi Arabia. Second, the ques-tionnaires were not completed, using the DISKQ technique adopted by Thompson et al. Instead, we employed a paper and pencil technique. We concluded that an interactive questionnaire was not suitable for the present environment of Saudi Arabia. The use of the method would affect the response rate in an environment where people are not used to it.
5. Procedure
would not refer to individual responses but would be published in summary form only.
6. Aggregation of responses
The instrument is structured in a way that asked the respondents (see Table 2 for their pro®le) to indicate
the level of their agreement with perceptual anchors on the level of importance of each of the suggested factors capable of promoting PC utilization. The survey instrument used a Likert scale (varying from `strongly agree'5 to `strongly disagree'1, with room for equivocation in the middle, i.e., `neutral'3). While factors are not independent, respondents were asked to treat each factor as inde-pendent of the others and to assign as many `5s', `4s', `3s' etc., as they felt were warranted by the factors included in the survey instrument; i.e., not to rank them. The responses to the items within each group were aggregated to produce indexes for each grouped variable. For example, the PC utilization index was derived by averaging the means of the responses to each of the three items that make up PC utilization.
The validity of the survey instrument was assessed by a factor analysis. Table 3 contains rotated factor matrix (varimax) with eight speci®ed factor loadings on their own constructs than on others. This is the case with rotated structure. However, there is one excep-tion. Item JPF1 fails to load highly on Factor 2, which represents the construct job performance facilitation but loads highly on Factor 8, which captures the affect items. As a result, the rotated structure appears to be Table 1
Characteristics of respondent companies
Number of employees
Type of firm
No. of firms
(%)
Under 50 Small 100 66.67
51±200 Medium 24 16.00
Over 200 Large 26 17.33
100.00
Industry classification
1. Manufacturing 40.6
2. Construction 4.2
3. Agriculture 0.7
4. Services 25.2
5. Oil and energy 18.9
6. Trade, retailing, leasing 10.4
100.0
Table 2
Profile of respondent individuals
Age No. % No. %
20±25 years 10 6.7
26±30 years 29 19.3
31±40 years 73 48.7
41±50 years 33 22.0
51 and above 5 3.3
Total 150 100.0
Access to a PC Ownership of a PC
No access 0 0.0 Yes 79 52.7
Low 12 7.9 No 71 47.3
Average 65 43.1 Total 150 100.0
High 74 49.0
Total 151 100.0
Experience in using the PC Educational background
None 2 0.13 Less than High school 1 0.06
Low 31 20.67 High school 10 6.67
Moderate 88 58.67 Some years in college 15 10.00
High 29 19.33 Bachelor's degree 85 56.67
Total 150 100.00 Graduate degree 39 26.00
Total 150 100.00
consistent with the underlying theoretical constructs of the survey instrument. In further statistical analysis of the job performance facilitation items, JPF1 was not included. For example, the Cronbach'sreported for
the job performance facilitation does not include JPF1. The internal consistency of the eight scales was assessed using Cronbach's alpha (). We report two inter-rater correlation coef®cients (ICCs) that are estimates of reliability and validity. ICC(1) is an estimate of the degree to which responses on each item are similar, is reported in Table 4, and ICC(2) is an estimate of the reliability of the mean scores [22], is reported in the last column of Table 8. ICC(1) and ICC(2) address two different issues [33]. If two respondents were randomly sampled from the same group and their two sets of scores correlate, the
resulting correlations would approximately equal ICC(2) which is scaled as zero (0) when the observable level of agreement is minimal and one (1) when there is perfect agreement. For intermediate values, Landis and Koch [28] suggest the following interpretations: below 0.0poor agreement; 0.00±0.20slight agreement; 0.21±0.40fair agreement; 0.41± 0.60moderate agreement; 0.61±0.80substantial agreement; and 0.81±1.00almost perfect agree-ment. ICC(2) ratings of 0.60 and above suggest that one can reject the hypothesis that respondents were scoring an item randomly and that one can conclude that acceptable levels of mean score reliability exist [32]. ICC(1) indicates the extent to which respondents within the same group assign the same psychological meaning to, or agree in their perceptions of, the Table 3
Rotated factor matrix of attitude items
Attitude items 1 2 3 4 5 6 7 8
LT1 0.648 0.148 0.027 ÿ0.055 0.054 0.018 ÿ0.064 0.121
LT2 0.784 0.041 ÿ0.004 0.143 ÿ0.100 0.108 0.089 0.145
LT3 0.758 0.233 0.055 ÿ0.030 0.015 ÿ0.145 0.098 0.026
LT4 0.690 0.122 ÿ0.032 0.235 ÿ0.183 0.212 ÿ0.074 0.116
LT5 0.655 0.111 0.185 0.131 ÿ0.077 0.079 ÿ0.041 0.279
LT6 0.675 0.154 0.066 0.124 0.079 0.195 0.053 ÿ0.145
JPF1 0.125 0.172 0.170 0.375 ÿ0.193 ÿ0.203 ÿ0.027 0.504
JPF2 ÿ0.041 0.431 ÿ0.049 0.038 0.323 0.105 ÿ0.142 0.095
JPF3 0.158 0.818 ÿ0.046 0.087 ÿ0.066 0.100 0.895 ÿ0.000
JPF4 0.191 0.787 0.113 0.092 ÿ0.056 0.047 0.176 0.017
JPF5 0.204 0.664 0.075 0.030 ÿ0.043 ÿ0.166 0.100 0.052
JPF6 0.243 0.753 0.037 0.056 ÿ0.131 0.007 0.062 0.223
FC1 0.215 0.026 0.646 0.168 ÿ0.010 0.240 0.006 ÿ0.070
FC2 ÿ0.015 0.039 0.761 0.064 ÿ0.141 0.214 0.176 0.034
FC3 0.052 0.092 0.744 0.140 0.046 0.100 0.175 ÿ0.060
FC4 ÿ0.020 ÿ0.040 0.785 0.132 ÿ0.102 ÿ0.000 0.005 0.175
SF1 ÿ0.122 0.091 ÿ0.014 0.521 ÿ0.099 0.024 0.459 ÿ0.006
SF2 0.239 0.218 0.292 0.695 0.033 0.022 0.139 ÿ0.051
SF3 0.076 ÿ0.028 0.093 0.787 ÿ0.045 0.069 0.146 0.177
SF4 0.230 0.075 0.381 0.677 ÿ0.113 ÿ0.056 ÿ0.109 0.123
COM1 0.118 ÿ0.232 ÿ0.118 0.068 0.565 ÿ0.179 0.136 ÿ0.466
COM2 ÿ0.078 ÿ0.030 0.035 ÿ0.133 0.720 0.075 0.013 ÿ0.008
COM3 ÿ0.127 ÿ0.021 ÿ0.093 ÿ0.067 0.775 ÿ0.158 0.050 0.026
COM4 ÿ0.069 ÿ0.070 ÿ0.279 0.138 0.537 0.276 ÿ0.408 ÿ0.155
TR1 0.158 0.046 0.248 0.010 0.021 0.849 0.007 ÿ0.064
TR2 0.190 0.024 0.270 0.018 ÿ0.042 0.838 0.000 ÿ0.014
UT1 0.012 0.203 0.141 0.190 0.033 ÿ0.108 0.737 0.111
UT2 ÿ0.004 0.053 0.097 0.041 ÿ0.005 0.036 0.681 ÿ0.081
UT3 0.109 0.014 0.088 0.020 0.089 0.183 0.494 0.425
AF1 0.225 0.160 ÿ0.014 0.364 0.182 0.046 ÿ0.015 0.570
AF2 0.336 0.198 0.228 0.029 0.174 ÿ0.286 ÿ0.221 0.374
Table 4
Intercorrelations, means and mean rankings of the importance of attitude items in Saudi Arabia compared with Canadian rankings from the [14] study (See Table 5 for description of variables)
Alpha Mean Standard
deviation
Mean rank Canada
Intercorrelations of social factors items Standard
SF1 SF2 SF3 SF4 Mean Deviation Rank
SF1 1.000 0.776 3.68 1.22 21st 1.88 0.86 28th
SF2 0.286b 1.000 0.599 4.01 1.19 13th 3.04 1.21 25th
SF3 0.292b 0.586b 1.000 0.610 4.47 0.81 4th 3.81 1.30 17th
SF4 0.2131a 0.5293b 0.491b 1.000 0.656 4.40 0.84 5th 4.24 1.05 4th
Intercorrelations of affect factors
AF1 AF2 AF3
AF1 1.000 0.285 4.58 0.62 2nd 4.11 0.78 10th
AF2 0.342b 1.000 0.406 3.81 1.12 18th 4.22 0.78 5th
AF3 0.2550a 0.166a 1.000 0.510 3.76 1.21 20th n/a n/a n/a
Intercorrelations of near-term consequences: complexity
CO1 CO2 CO3 CO4
CO1 1.000 0.628 2.46 1.35 28th 4.39 0.94 2nd
CO2 0.286b 1.000 0.605 1.70 0.95 31st 4.47 0.89 1st
CO3 0.389b 0.420b 1.000 0.549 2.23 1.67 29th 3.49 1.14 19th
CO4 0.273b 0.309b 0.351b 1.000 0.633 2.15 1.23 30th 4.30 1.07 3rd
Intercorrelations of near-term consequences: job fit (usefulness)
UF1 UF2 UF3 UF4 UF5 UF6
JPF1 1.000 0.780 4.18 1.23 8th 4.11 1.17 9th
JPF2 0.021 1.000 0.795 3.80 1.36 19th 3.92 1.27 14th
JPF3 0.099 0.160 1.000 0.669 4.65 0.70 1st 4.16 1.02 7th
JPF4 0.196 0.140 0.705b 1.000 0.656 4.51 0.83 3rd 4.19 0.97 6th
JPF5 0.196 0.143 0.483b 0.530b 1.000 0.692 4.23 1.05 7th 4.13 1.00 8th
JPF6 0.319b 0.151 0.655b 0.655b 0.532b 1.000 0.646 4.40 0.83 5th 4.07 0.87 11th
194
M.A.
Al-Khaldi,
R.S.O.
W
allace
/
Information
&
Management
36
(1999)
LT1 LT2 LT3 LT4 LT5 LT6
LT1 1.000 0.835 3.88 1.06 15th 3.39 1.17 20th
LT2 0.465b 1.000 0.797 4.12 0.81 10th 3.20 1.25 23rd
LT3 0.488b 0.537b 1.000 0.823 4.03 0.96 12th 2.74 1.32 27th
LT4 0.409b 0.557b 0.391b 1.000 0.807 4.16 0.84 9th 3.23 1.23 22nd
LT5 0.398b 0.550b 0.440b 0.605b 1.000 0.813 4.11 0.85 11th 3.01 1.19 26th
LT6 0.2774b 0.5690b 0.4033b 0.5567b 0.4279b 1.0000 0.824 3.87 1.01 16th 3.22 1.262 4th
Intercorrelations of facilitating conditions
FC1 FC2 FC3 FC4
FC1 1.000 0.786 3.56 1.16 23rd 3.59 1.30 17th
FC2 0.421b 1.000 0.715 3.86 1.02 17th 3.88 1.25 15th
FC3 0.430b 0.576b 1.000 0.747 3.68 1.16 21st 3.57 1.25 18th
FC4 0.462b 0.605b 0.474b 1.000 0.731 3.91 1.11 14th 4.00 1.12 12th
Intercorrelations of training and experience factors
TR1 TR2 EX1 EX2
TR1 1.000 0.056 3.32 1.43 25th n/a n/a n/a
TR2 0.844b 1.000 0.050 3.41 1.45 24th n/a n/a n/a
EX1 ÿ0.046 ÿ0.034 1.000 0.603 2.96 0.67 26th 3.99 1.39 13th
EX2 0.079 0.084 ÿ0.016 1.000 0.506 0.506 7.29 4.87 n/a 3.22 1.08 24th
Intercorrelations of utilization
UT1 UT2 UT3
UT1 1.000 0.350 4.20 1.04 3.56 1.38
UT2 0.456b 1.000 0.449 4.77 0.54 4.33 1.03
UT3 0.289b 0.212a 1.000 0.627 3.63 1.31 3.57 1.27
ap0.05. bp0.01.
M.A.
Al-Khaldi,
R.S.O.
W
allace
/
Information
&
Management
36
(1999)
185±204
relevance of research results on a computer utilization factor. ICC(1) compares the between sample sum of squares to the total sum of squares from the results of a one-way analysis of variance. In past research, ICC(1) values have ranged from 0 to 0.5, with a median of 0.12 [26]. There are no de®nite guidelines on accep-table ICC(1) values.
The responses on each factor were averaged (aggre-gated) and these were used to determine the relative importance of each factor. This aggregation of responses suggests that respondents were agreed on mean scores though the reported standard deviations challenge the assumption of convergence. In fact, the mean scores do not reveal variability across situations, such as age of respondents or size of the ®rm in which a respondent is employed; nor do they permit predic-tion of speci®c behaviors in given situapredic-tions. To unveil the fac,ade of convergence from the pooled data, we analyzed the responses in two different ways. First, we used the responses showing agreement with the sug-gestion that a factor is important and those not agree-ing with the suggestion of importance to derive an index of agreement that was introduced by Herbert and Wallace [22] in their study of the perceptions of U.K. Chief Finance Of®cers on corporate ®nance research topics. An index of agreement is the ratio of total agreeing responses (i.e., 5s and 4s for positive questions or 1s and 2s for negative questions) to total disagreeing response (i.e., 1s and 2s for positive questions and 5s and 4s for negative questions). The index excludes equivocal (i.e., `not sure'3) responses which vary from item to item. To make an index of agreement comparable in this study, each index was adjusted to re¯ect the ratio of unequivocal responses to all responses by multiplying that ratio with the index of agreement.
Second, we disaggregated the responses on the basis of organization size into large, medium, and small ®rms. Although the pooled data suggests that many of the factors warranted more research, signi®cant dif-ferences may exist among ®rms of different sizes.
7. Results
The ®rst question is the comparative attitudes of Canadian and Saudi respondents toward the utilization of the PC. Since the questionnaire for Saudi
respon-dents was similar to that for the Canadian responrespon-dents, we compared the mean responses from each. The settings, situations and contexts are our referential scopes because it is dif®cult to delve into the minds of our subjects and to substantiate what seems to be within the realm of mere conjecture. The process of our reference must, therefore, rely heavily on per-ceived behavior and we have been concerned exclu-sively with organizationally constructed modes of behavior. The results of the comparison of the mean responses from the Saudi and Canadian respondents are reported in Table 4.
While the three highest levels of Saudi respondents' agreement with our suggestible questions were, respectively, in respect of (1) the ®tness of the PC to respondents' job (Question JPF3 with mean response of 4.65, 0.67), (2) affect (AF1, mean response of 4.58,0.29) and (3) job ®tness (JPF4, mean response of 4.51,0.66), those of the Cana-dian respondents were mainly with the suggestible questions on complexity: (1) Question CO2 with mean response of 4.47, (2) Question CO1, mean response of 4.39, and (3) CO4, mean response of 4.30. It is surprising that Saudis perceived the PC as relatively less dif®cult to understand than Canadians. If this observation is true, we would expect to ®nd that the level of PC utilization in the two studies would show that the Canadians would reveal a lower PC utilization than the Saudis. The results con®rm this expectation because on each of the PC utilization constructs, the mean responses from the Saudis were greater than those from the Canadians.
The results from the use of a different procedure for aggregating the responses from the Saudi respondents are similar to those from the use of mean responses, except that affect item AF1 and job performance facilitation item JPF3 swapped positions, with job performance facilitation item JPF6 tying with job performance facilitation item JPF4 as the items with the third highest level of respondent agreement with the suggestible questions.
responses with a few exceptions. The ®rst three items in order of perceived level of agreement are AF1, JPF3, and JPF4 and JPF6 (both taking the third posi-tion), while the ®rst three attitude items according to mean responses are JPF3, AF1, and JPF4.
The above analyses are based on pooled responses. The level of respondents' agreement may however
diverge when responses are partitioned on the basis of organization size. Table 6 reports the results of such analysis. The rankings of the attitude items differ from one organization size to another. While respondents from small companies agreed that AF1, EX1, and JPF3 are the ®rst three in the order of importance, those from medium companies preferred AF1, LT2, Table 5
Pooled sample indexes of agreement on the level of importance accorded attitude items (n151)
Attitude items
Index of agreement
Rank Adjusted index of agreement
Rank
Social
SF1 The proportion of departmental co-workers who use a PC 2.94 20th 2.39 20th
SF2 The senior management of this business unit has been helpful in introducing PCs 5.90 9th 5.43 9th
SF3 My boss is very supportive of PC use for my job 22.67 5th 21.32 5th
SF4 In general, the organization has supported the introduction of PCs 16.50 6th 15.30 6th
Affect
AF1 PCs make work more interesting 142.00 1st 135.37 1st
AF2 Working with a PC is a fun 4.82 14th 4.08 14th
AF3 PCs are okay for some tasks but not the kind of task I want (score reversed) 3.38 16th 2.88 17th
Near-term consequences: complexity
CO1 Using a PC takes too much time from my normal work duties 0.54 24th 0.47 24th
CO2 Working with PCs is so complicated, it is difficult to understand what is going on 0.09 27th 0.08 27th CO3 Using a PC involves too much time doing mechanical operations
(e.g., data input)
0.30 26th 0.25 26th
CO4 It takes too long to learn how to use a PC to make it worth the effort 0.30 25th 0.28 25th JPF1 Use of a PC will have no effect on my work performance (score reversed) 5.00 13th 4.80 12th JPF2 Use of a PC can decrease the time needed for my important job responsibilities 3.35 17th 3.02 16th JPF3 Use of a PC can significantly increase the quality of output of my job 48.33 2nd 47.69 2nd JPF4 Use of a PC can increase the effectiveness of performing work tasks (e.g., analysis) 23.17 3rd 22.39 3rd JPF5 A PC can increase the quantity of output for same amount of effort 9.62 7th 8.91 7th JPF6 Considering all tasks, use of PCs could assist on work to a great extent 23.17 3rd 22.25 4th
Long-term consequences
LT1 Use of a PC will increase the level of challenge on my job 5.71 10th 4.61 13th
LT2 Use of a PC will increase the opportunity for preferred future job assignments 8.39 8th 7.49 8th
LT3 Use of a PC will increase the amount of variety on my job 5.63 11th 5.22 10th
LT4 Use of a PC will increase the opportunity for more meaningful work 3.04 19th 2.63 18th
LT5 Use of a PC will increase the flexibility of changing jobs 3.07 18th 2.57 19th
LT6 Use of a PC will increase the opportunity to gain job security 0.77 23rd 0.52 23rd
Facilitating conditions
FC1 Guidance is available to me in the selection of hardware and software 4.26 15th 3.16 15th FC2 A specific person (or group) is available for assistance with software difficulties 1.83 22nd 1.41 22nd FC3 Specialized instruction concerning the popular software is available to me 2.62 21st 2.02 21st FC4 A specific person (or group) is available for assistance with hardware difficulties 5.55 12th 4.85 11th
Utilization
UT1 The intensity of job-related use (minutes per day, at work) 7.13 6.13
UT2 The frequency of PC use 144.00 138.27
LT3 and EX1. Respondents from large companies suggest JPF3, and JPF4 as the most important item followed by Af1 and JPF6.
7.1. Influence of respondents' profiles on perceptions of PC utilization factors
To investigate the link between respondents' pro-®les and their perceptions of PC utilization motivators, we employed two different model speci®cations
(MANOVA and F-statistic approximations). MAN-OVA was used to evaluate the causes of the differing responses from the respondents on the level of impor-tance of each PC utilization factor.F-statistic approx-imation was used to test the hypothesis that each of the respondents' pro®les has no overall effect on differ-ences in their perceptions of a set of combined (dyad) PC utilization factors. The results of the MANOVA and Wilks'tests are shown in Table 7. Age seems to
have no in¯uence on all (but affect and facilitating Table 6
Cross-size comparisons of means and indexes of agreements on importance of attitude items
Attitude items Small companiesn100 Medium companiesn24 Large companiesn26
means adjusted index
of agreement
means adjusted index
of agreement
means adjusted index
of agreement
SF1 3.87 3.77 3.29 1.78 3.31 1.29
SF2 4.03 5.99 3.71 2.71 4.23 10.15
SF3 4.47 20.94 4.33 19.25 4.57 25.00
SF4 4.49 28.84 4.08 4.55 4.37 17.77
AF1 4.57 89.30 4.58 1(96%)a 4.65 1(96%)
AF2 3.74 3.64 3.65 2.63 4.27 17.77
AF3 3.82 3.49 3.46 1.75 3.80 2.40
CO1 2.42 0.39 2.65 0.54 2.50 0.78
CO2 1.60 0.04 1.96 0.20 1.81 0.13
CO3 2.21 0.25 2.58 0.37 2.00 0.18
CO4 2.18 0.31 2.29 0.27 1.88 0.18
JPF1 4.23 5.62 3.71 2.00 4.42 10.15
JPF2 3.87 3.25 3.50 2.43 3.81 3.04
JPF3 4.64 48.02 4.50 21.08 4.85 1(100%)
JPF4 4.48 17.47 4.33 21.08 4.77 1(100%)
JPF5 4.25 9.66 4.17 9.17 4.24 6.72
JPF6 4.43 22.80 4.13 9.17 4.58 1(96%)
LT1 4.36 4.71 3.79 5.25 4.07 9.28
LT2 4.13 21.59 4.08 1(79%) 4.11 1(65%)
LT3 3.95 6.89 4.08 1(79%) 4.31 19.47
LT4 4.16 16.23 4.00 9.17 4.31 21.23
LT5 4.12 11.35 4.00 7.50 4.15 1(85%)
LT6 3.90 8.63 3.75 3.75 3.88 6.21
FC1 3.57 2.34 3.29 1.42 3.92 8.46
FC2 3.88 5.12 3.75 3.72 3.88 7.67
FC3 3.65 2.75 3.71 4.22 3.79 3.81
FC4 3.96 5.57 3.58 2.44 4.00 7.67
TR 3.28 1.73 3.08 1.25 3.69 3.18
EX1 4.55 43.73 4.29 21.08 4.46 23.08
EX2 3.49 6.31 3.04 0.63 3.46 7.00
UT1 4.22 6.10 4.04 3.33 4.27 17.77
UT2 4.79 94.12 4.63 1(92%) 4.85 1(96%)
UT3 3.63 1.59 3.79 2.89 3.50 1.57
Ave UT 4.00 10.67 4.38 23.00 3.69 4.85
aPercentage of unequivocal responses to total responses.
conditions) of the PC utilization factors and each set of the combined interaction factors. A respondent's level of education seems to have no effect on any of the factors suggested to in¯uence PC utilization or any of the possible interaction factors.
PC utilization is in¯uenced strongly by degree of PC Access (thus supporting H8), and experience (H9), but not respondents' age (H10), education (H11) and PC training (H7) and ownership of a PC. The non-signi®cance of PC ownership is surprising but understandable. Some respondents may own a PC but may not use them. The PC may be acquired for the use of their children. Some respondents who do not own a PC may have one at home for of®cial use. As a result, ownership of a PC is not a predictor
of PC utilization and is different from having access to the PC.
7.2. The influence of attitude on PC utilization
Table 8 reports the intercorrelations among sum-marized group of attitude items, their mean values and alphas. The results suggest that social factors, facil-itating conditions, and Job Fit are signi®cantly corre-lated with PC utilization, while Affect is mildly correlated with PC utilization.
Table 9 suggests that Social Factors, Affect, Job Performance Facilitation and Facilitating Conditions are signi®cant determinants of PC utilization. Com-plexity and Long-Term Consequences are not signi®-Table 7
Influence of respondents profile on importance perceptions of PC utilization: ANOVA andFapproximations results
Motivating factors Access Age Education Experience Training
Panel A: (F-statistics)
Social 8.86c 0.09 0.18 9.65c 3.32b
Affect 3.03b 1.23 1.40 3.01b 1.84
Complexity 3.83c 0.91 0.34 3.39b 2.75b
Job performance facilitation 2.59b 0.86 1.10 2.74b 1.27
Long-term consequences 2.88b 1.69 1.59 1.07 3.69c
Facilitating conditions 5.81c 2.83b 0.56 3.16b 6.40c
Utilization 3.60c 0.30 0.47 10.41c 1.12
Panel B: Comparison of PC utilization motivators (Wilks') Interacting motivators:
Social/affect 5.41c 0.76 0.83 5.84c 2.48b
Complexity/affect 3.10c 1.12 0.79 2.97b 2.17b
Job performance facilitation/affect 2.26b 0.97 1.05 2.76b 1.65
Long-term consequences/affect 2.28b 1.51 1.40 2.32b 3.02c
Facilitating conditions/affect 4.23c 2.13b 1.06 2.96b 4.13c
Job performance facilitation/long-term consequences 1.98a 1.64 1.10 1.58 2.14b
Complexity/long-term consequences 3.01c 1.25 0.88 2.01a 3.01c
Facilitating conditions/long-term consequences 3.85c 2.26b 1.17 1.97a 4.48c
Social/long-term consequences 4.99c 1.01 0.86 4.82c 3.01c
Complexity/social 5.76c 0.51 0.25 5.79c 2.71c
Job performance facilitation/social 4.81c 0.55 0.63 5.77c 1.92a
Facilitating conditions/social 5.38c 1.63 0.36 5.01c 3.94c
Complexity/job performance facilitation 3.00c 0.93 0.67 2.90c 1.87
Facilitating conditions/job performance facilitation 3.90c 1.97a 0.89 2.92c 3.52c
Facilitating conditions/compexity 4.26c 1.61 0.50 2.81b 4.18c
ap|z|0.10. bp|z|0.05. cp|z|0.01.
cant predictors of PC utilization. These results suggest that H1, H2, H4, and H6 are supported while H3 and H5 are not.
The signi®cant positive relationship between social factors and PC utilization is consistent with the theory of reasoned action and is similar to the ®nding in Canada. Our ®nding that the use of PC evokes mild positive emotions (Affect) among knowledge workers is inconsistent with Thompson et al. who found no signi®cant relationship between Affect and PC utilization. It is, however, similar to the ®nding of Davis et al.
In respect of cognitive components of attitudes as measured by Job Performance Facilitation, Complex-ity, and Long-Term Consequences, the ®rst is the only signi®cant predictor of PC utilization. Although the relationship between complexity and PC utilization is not signi®cant as was the case in Canada, the direction (negative) is consistent with previous studies. Unlike the Canadian study, the relationship between facilitat-ing conditions and PC utilization is positive and signi®cant. The relationship between each of Social Factors, Job Performance Facilitation, and Facilitating Conditions and PC Utilization is stronger than the
relationship between Affect and PC utilization. These results are consistent with those reported for the Canadian study though Affect was not signi®cant in that study.
8. Conclusion
This paper has reported the results of a question-naire study of perceived importance of suggested attitude items in promoting PC utilization by knowl-edge workers in Saudi Arabia. We were interested in two set of questions:
1. What are the essential differences between the perceptions of respondents in Saudi Arabia and of Canadian respondents?
2. Do different attitude constructs have differing effects on PC utilization in Saudi Arabia?
Like Canadian respondents, the Saudis perceived Social Factors, Affect, Job Performance Facilitation, and Facilitating Conditions as signi®cant contributors to PC utilization, but unlike the Canadians, they suggested the Complexity and Long-Term conse-Table 8
Intercorrelations, means and alphas of summarized factors of computer utilization
Factors 1 2 3 4 5 6 7 Mean Standarddeviation
1. Social 1.000 4.17 0.62 0.70
2. Affect 0.232a 1.000 4.05 0.70 0.44
3. Job performance facilitation 0.262a 0.323a 1.000 4.32 0.67 0.68
4. Complexity ÿ0.154 ÿ0.159 ÿ0.099 1.000 2.13 0.83 0.68
5. Facilitating conditions 0.391a 0.105 0.117 ÿ0.210a 1.0000 3.76 0.88 0.79
6. Long-term consequences 0.234a 0.477a 0.385aÿ0.145 0.1780 1.0000 4.03 0.68 0.84 7. Utilization 0.763a 0.168a 0.218aÿ0.116 0.2533a 0.1113 1.0000 4.20 0.72 0.51
ap-Value1%.
Table 9
Regression model of attitudes and PC utilization
Attitudes Estimates t-Value p-Value Standard error
Social factors 0.337 4.09 0.000 0.08
Affect 0.179 2.01 0.046 0.09
Complexity 0.106 1.71 0.089 0.06
Job performance facilitation 0.322 3.37 0.001 0.09
Long-term consequences ÿ0.060 ÿ0.84 0.398 0.07
Facilitating conditions 0.181 2.61 0.010 0.07
Note:F-value2.73,p-value0.01, MSE1.32,R20.101.
quences are not signi®cant predictors of PC utiliza-tion. Based on mean scores, the Saudis perceived the PC as relatively less dif®cult than the Canadians and revealed a higher PC utilization than them. The impli-cation is that there are differing perceptions of PC appreciation and utilization: multinational corpora-tions operating in different countries need to adopt several different motivation policies for PC utilization.
major differences between and within the samples from each country. In addition, it does not appear that the variables under investigation are homogeneous, because they may mean one thing to all respondents in a national sample but seem different between respon-dents in different nations. May be the measurement scales of Canadian respondents are different from those of Saudis. Scale homogeneity is generally not suf®cient for cross-national comparison; it is also important that the topics being analyzed should be homogeneous in itscontent.
Another possible explanation of the differing levels of perception of PC utilization is the differing stages in the PC utilization learning curve. As the PC is intro-duced, and individuals within a country try to use it, they begin to see some bene®ts and feel happy about using it. This could result in an over-estimation of the value of the PC. As a result, the revealed perceptions (mean values) of PC utilization and its potential bene®ts could vary between the two stages of the learning curve. These differences between the mean perceptions of the Saudis and the Canadians is encap-sulated in Fig. 3 as a maturation dilemma. There is a need to investigate the existence of differences in levels of importance which end-users accord to PC utilization factors across time and space. More cross-national comparison is needed, just as more within-country studies at different time intervals are needed. While the rankings accorded each PC motivation factor by respondents from companies belonging to one organization size group differed from those accorded by respondents from companies belonging to another organization size group in Saudi Arabia, organization size does not appear to be a signi®cant discriminator of respondents' attitudes toward PC uti-lization. PC training, PC experience, and PC access have a strong in¯uence on PC utilization and its determinants. Age, education, and PC ownership reveal no signi®cant in¯uence on PC utilization and its determinants.
The low reliability coef®cients of affect and utiliza-tion items may have arisen from the fact that these constructs capture only two and three items, respec-tively. Future research seeking to capture these con-structs would probably need to extend the list of items that can be studied. TheR2from the estimation of the regression model reported in Table 9 is low (0.101). This probably suggests that there are other important variables excluded from the model.
Our ®ndings are based on quantitative analysis of Likert-scales responses. We advise caution here. Our questionnaire elicits information on whether each of the items was `very important'. It is possible for the respondents to answer that all items are very impor-tant, regardless of whether that information would affect PC utilization. So, our questionnaire could be eliciting less information than if the respondents were asked to rank the items. Another limitation is the general inability of persons to describe their informa-tion usageÐrespondents may be using less PC than they say they do. Such behavior could, however, be culture-dependent.
Acknowledgements
The authors acknowledge the ®nancial support of King Fahd University of Petroleum and Minerals in the conduct of this research. The helpful comments and suggestions of Dr. Eid Sendi Shammari, Dr. Stephen Owusu-Ansah, Rosemary Wallace, the Editor (Dr. Edgar H. Sibley) and two anonymous reviewers are appreciated. The authors also thank the respon-dents for their willingness to participate in the study. Any remaining errors are the authors' responsibility.
References
[1] A.H. Abdul-Gader, End-user computing success factors: further evidence from a developing nation, Information Resources Management Journal 3(1), 1990, pp. 1±13. [2] A.H. Abdul-Gader, K.A. Kozar, The impact of computer
alienation of information technology investment decisions: an exploratory cross-national analysis, MIS Quarterly 19(4), 1995, pp. 535±559.
[3] I.M. Al-Jabri, Gender differences in computer attitudes among secondary school students in Saudi Arabia, Journal of Computer Information Systems 37(1), 1996, pp. 70±75. [4] I.M. Al-Jabri, A.H. Abdul-Gader, Software copyright
infringements: an exploratory study of the effects of individual and peer beliefs, Omega, International Journal of Management Science 25(3), 1997, pp. 335±344.
[5] I.M. Al-Jabri, M. Al-Khaldi, Effects of user characteristics on computer attitudes among undergraduate business stu-dents, Journal of End-User Computing 9(2), 1997, pp. 16±22. [6] I. Ajzen, From intentions to actions: a theory of planned behavior, in: J. Kuhl, J. Beckmann (Eds.), Action-control: From Cognition to Behavior. Springer, Heidelberg, 1985, pp. 11±39.
[7] I. Ajzen, Attitudes, Personality, and Behavior. Dorsey Press, Chicago, 1988.
[8] I. Ajzen, The theory of planned behavior, Organizational Behavior and Human Decision Processes 50, 1991, pp. 179± 211.
[9] M.A. Al-Khaldi, I.M. Al-Jabri, The relationship of attitudes to computer utilization: new evidence from a developing nation, Computers in Human Behavior 14(1), 1998, pp. 23±42. [10] M.A. Al-Khaldi, K. Ben-Bakr, The conceptual utilization of
personal computing in a developing country: the case of Saudi Arabia, in Proceedings of the 1993 Arab Management Conference, University of Bradford, Bradford, UK, 1993, pp. 115±132.
[11] U. Basaddiq, Computer Seen Playing Vital Role in Saudi Life. Arab News, 19 October 1986, p. 19.
[12] B. Bjerke, A. Al-Meer, Culture's consequences: management in Saudi Arabia, Leadership and Organization Development Journal 14, 1993, pp. 30±35.
[13] O. Culpan, Attitudes of end-users towards information technology in manufacturing and service industries, Informa-tion and Management 28, 1995, pp. 167±176.
[14] F.D. Davis, R.P. Bagozzi, P.R. Warsaw, User acceptance of computer technology: a comparison of two theoretical models, Management Science 35(8), 1989, pp. 983±1003. [15] W.H. DeLone, Small size and the characteristics of computer
use, MIS Quarterly 4(4), 1981, pp. 65±77.
[16] G. DeSanctis, Expectancy theory as an explanation of voluntary use of a decision-support system, Psychological Reports 52, 1983, pp. 247±260.
[17] P. Ein-Dor, E. Segev, Organizational context and the success of management information systems, Management Science 24(6), 1978, pp. 1067±1077.
[18] M. Fishbein, I. Ajzen, Belief, Attitude, Intentions and Behaviors: An Introduction To Theory and Research. Addison±Wesley, Boston, MA, 1975.
[19] D. Goodhue, IS Attitudes: Toward Theoretical and De®nition Clarity. Data Base, 19(3/4), Fall/Winter 1988, pp. 6±15. [20] C.P. Gressard, B.H. Loyd, Validation scales of a new
computer attitude scale, Association for Educational Data Systems Journal 18(4), 1986, pp. 295±301.
[21] C.P. Gressard, B.H. Loyd, Age and staff development experience with computers and factors affecting teacher attitudes toward computers, School Science and Mathematics 85(19), 1985, pp. 203±209.
[22] W.E. Herbert, R.S.O. Wallace, A corporate view of research needs in corporate ®nance, Accounting and Business Research 26(2), 1996, pp. 107±123.
[23] G. Hofstede, Culture's Consequences. London, Sage, 1984. [24] M. Igbaria, An examination of micro-computer usage in
Taiwan, Information and Management 22, 1992, pp. 19±28. [25] M. Igbaria, N. Zinatelli, P. Cragg, A.L.M. Cavaye, Personal computing acceptance in small ®rms: a structural equation model, MIS Quarterly 21, 1997, pp. 279±305.
[26] L.R. James, Aggregation bias in estimates of perceptual agree-ment, Journal of Applied Psychology 67, 1982, pp. 215±231. [27] P.G.W. Keen, MIS research: reference disciplines and a
cumulative tradition, Proceedings of the First International
Conference on Information Systems, Philadelphia, PA, December 1980, pp. 9±18.
[28] J.R. Landis, G.G. Koch, The measurement of observer agree-ment for categorical data, Biometrics 33, 1977, pp. 159±174. [29] B.H. Loyd, C.P. Gressard, The Effects of sex, age and
computer experience on computer attitudes. Association for Educational Data Systems Journal. 17(2) (1984a) 67±77. [30] B.H. Loyd, C.P. Gressard, Reliability and factorial validity of
computer attitude scales. Educational and Psychological Measurement, 44 (1984b) 501±505.
[31] B.H. Loyd, C.P. Gressard, Gender and amount of computer experience of teachers in staff development programs: effects on computer attitudes and perceptions of the usefulness of computers, Association for Educational Data Systems Journal 18(4), 1986, pp. 302±311.
[32] J. Nunnally, Psychometry Theory. second edn., McGraw-Hill, New York, NY., 1978.
[33] C. Ostroff, N. Schmitt, Con®gurations of organizational effectiveness and ef®ciency, Academy of Management Journal 36, 1997, pp. 1351±1361.
[34] M. Rahman, A. Abdul-Gader, Knowledge workers' use of support software in Saudi Arabia, Information and Manage-ment 25, 1993, pp. 303±311.
[35] L. Raymond, The impact of computer training on the attitudes and usage behavior of small business managers, Journal of Small Business Management 26(3), 1988, pp. 8±13. [36] L. Raymond, Organizational context and IS success. Journal
of Management Information Systems 6(4) (1990a) 5±20. [37] L. Raymond, End-user computing in the small business
context: foundations and directions for research. Database. 20(4) (1990b) 20±26.
[38] D. Robey, User attitudes and management information system use, Academy of Management Journal 22(3), 1979, pp. 466±474.
[39] J.F. Rockart, L.S. Flannery, The management of end user computing, Communications of the ACM 26(10), 1983, pp. 776±784.
[40] R.L. Schultz, D.P. Slevin, Implementation and Organiza-tional Validity: An Empirical Investigation. In: Schultz, R.L., Slevin, D.P. (Eds.), Implementing Operation and Research/ Management Science. American Elsevier Publishing Co., New York, NY., 1975, pp. 153±182.
[41] R.L. Thompson, C.A. Higgins, J.M. Howell, Personal computing: toward a conceptual model of utilization, MIS Quarterly 15(1), 1991, pp. 125±143.
[42] R.L. Thompson, C.A. Higgins, J.M. Howell, In¯uence of experience on personal computer utilization: testing a conceptual model, Journal of Management Information Systems 11(1), 1994, pp. 167±187.
[43] H.C. Triandis, Attitude and Attitude Change. John Wiley and Sons, Inc., New York, NY., 1971.
[44] H.C. Triandis, Values, Attitudes, and Interpersonal Behavior. Nebraska Symposium on Motivation. 1979. In Beliefs, Attitudes, and Values, University of Nebraska Press, Lincoln, NE., 1980, pp. 195±259.
[46] R.S.O. Wallace, Disclosure of Accounting Information in Developing Countries: A Case Study of Nigeria. A doctoral thesis, University of Exeter, Devon. 1987.
[47] G. Yaverbaum, Critical factors in the user environment: an experimental study of users. Organizations and tasks, MIS Quarterly 12(1), 1988, pp. 75±88.
Dr. Muhammad A. Al-Khaldiis the Chairman of the Department of Account-ing and Management Information Sys-tems at King Fahd University of Petroleum and Mineral (KFUPM), Dhah-ran, Saudi Arabia. His degrees include B.S. and MBA from KFUPM and PhD from Oklahoma State University. He has contributed articles to professional and academic journals and conferences at both national and international levels. His articles have appeared inComputer in Human Behaviorand
Journal of End-User Computing. His research interests include end-user computing, decision support systems, and applications of meta analysis.
R.S. Olusegum Wallaceis a Professor of Accounting and MIS in the Depart-ment of Accounting and MIS at KFUPM, Dhahran, Saudi Arabia. He received his PhD from the University of Exeter. His research interests include cross-national comparison of financial reporting practices and regulation, en-vironmental auditing issues, methodol-ogy in accounting research and the end-user computing in business. His articles have appeared in a variety of journals includingAccounting and Business Research,British Accounting Review,Accounting Education,Accounting,Business and Financial History,Journal of Business Finance and Account-ing,Research in Third World Accounting,Research in Accounting in Emerging Economies, Journal of Accounting Literature,
Accounting Horizons, Journal of Accounting and Public Policy, andInternational Journal of Accounting. Professor Wallace is the Managing Editor of Research in Accounting in Emerging Economies, Co-editor ofThe International Journal of Accounting and Associate Editor of British Accounting Review.
