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Hospital information systems in Arab Gulf countries
Information & Management North-Holland
20 (1991) 3233332
323
Research
Hospital information systems in Arab Gulf countries * Characteristics of adopters Haifa M. Nabali American
1. Introduction
University of Beirut, Beirut, Lebanon
138 managers from a representative sample consisting of 24 hospitals in five countries of the Arab Gulf were asked to report on the presence and adoption of Computer-Based Information Systems (CBIS) in their hospitals. Two groups of variables were studied to assess their ability to discriminate between users and non-users of CBIS: manager characteristics and hospital organizational (contextual and structural) characteristics. The findings reveal that: hospitals owned by Ministries of Health are lower adopters of CBIS; that managers of departments that use CBIS have more favorable attitudes towards user involvement; that departments in smaller hospitals are more likely to use CBIS; and that managers of user departments tend to be older. Keywords: Adoption of IS, International issues in IS, IS in Arab Gulf, Organizational factors and IS, Individual factors and IS, Hospital IS.
Haifa M. Nabali is assistant professor of health services administration at the American University of Beirut, Lebanon. She received her B.S. in Environmental Health in 1978 and her MPH in 1979 from the American University of Beirut. In 1985 she received a M.S. and in 1988 a Ph.D. in Management (MIS) from Case Western Reserve University in Cleveland, Ohio. Her research interests center around issues in the effectiveness of MIS, impact of managerial styles particularly from the cultural perspective, and design and implementation issues for health information systems. She has previously worked with the Ministries of Health in both the State of Bahrain and the United Arab Emirates and conducted computer and MIS workshops. * Based on part of the findings from a Ph.D. Dissertation, “Factors Affecting the Adoption of Computer-Based Hospital Information Systems in the Arab Gulf”. 037%7206/91/$03.50
0 1991 - Elsevier Science Publishers
Organizations worldwide are increasingly turning towards the use of computer-based information systems (CBIS) for support in some or all phases of their operations. As a result, computers have become a standard aspect of many organizations, offering opportunities for significantly improving organizational life. However, not all organizations acquire CBIS. The distinction between organizations that do and those that do not has been attributed to several factors in the “Innovation Adoption” and “Management Information Systems (MIS)” literatures. It is argued that there is an identifiable cluster of characteristics that “predispose” organizations to adopt CBIS, in specific, or new technologies, in general [6,12]. Two of the factors that have been linked to the predisposition to adopt new technologies are: (1) characteristics of the organization, and (2) characteristics of the individual [2,21]. The role of a subset of variables chosen from the above mentioned factors are explored in this research with respect to their ability to discriminate between adopters and non-adopters of CBIS.
2. The adoption of CBIS The adoption of CBIS is viewed as a special case of innovation adoption. An adoption is the acceptance and actual use of a practice common elsewhere, while an innovation is the application of a technology in a new way or use [9]. Although in the present study CBIS technology is not necessarily being applied in a new way, the innovation
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adoption literature provides a framework through which to understand why, for example, one hospital or hospital department is more likely and/or more prepared to adopt a CBIS as compared to another. Research in the area of innovation adoption takes one of three approaches [l]: 1. The inputs approach; identifying variables that predispose organizations to innovate. 2. The outputs approach; determining the number and kind of innovations adopted. 3. The process approach; investigating the sequence of events from inputs to outputs.
2.1. The inputs approach Several classifications for inputs have been proposed. For example, Kimberly and Evanisko consider organizational, individual and contextual factors. Glaser, Abelson & Garrison use the terms personal and social, organizational, and political influences, while Rogers refers to the socioeconomic and personality characteristics as well as to the communication behaviors of adopters. Other studies of inputs in the MIS literature consider characteristics of the innovation (or, in this case, of the information system being adopted), characteristics of the tasks being performed or supported, as well as other characteristics related to organizational policies, beliefs, and interactions among organizational members.
2.2. The outputs approach The output or the dependent variable in innovation adoption studies has usually been the number of innovations adopted [3,15]. Damanpour and Evan [4] also use a relative measure of innovation adoption in which the number of innovations adopted form a percentage of all possible such adoptions within a given time period. The “outputs” approach was used in a related study [16] whereby managers were given a list of functions performed in their departments and asked to indicate whether or not computers were used to support that function. A percentage score, “extent of computerization” was then computed for each department.
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2.3. The process approach The process of “innovation adoption” has been described as consisting of several stages: stimulus, conception, proposal, and adoption. If, however, one understands adoption to include actual use, then two more stages are added: implementation and evaluation. The research on “innovation adoption” has attempted to show that distinct stages do exist and that these may or may not occur in a certain order, depending on the complexity and originality (developed in-house, adapted, or borrowed) of the innovation [17]. Other researchers in the MIS field, have studied how the activities that occur within one stage of the process may impact the implementation stage, and, consequently, affect system success [8,24].
3. Current research As this was a first time study into computerization in Arab Gulf hospitals, the “inputs” approach was used to provide baseline data on the adoption of CBIS. Moreover, because of the exploratory nature of this study, only a subset of all possible factors was studied. Other factors were excluded, because a more detailed investigation of how they present themselves in the organizations under study is required before a data collection instrument can be developed. Individual or personal factors include such variables as age, education, occupation, and personality characteristics which have been considered in several studies. For example, Kjerulff et al. [13] claim that some of these variables could be used to predict the adaptability of hospital employees to information systems and Shepard [20] argues that an organization staffed with strong self-actualizing personalities will generate more ideas and will be more innovative. Organizational factors have probably been studied the most to date. These include centralization, formalization, functional differentiation, and complexity. Moth among others [e.g., 231, demonstrated how size of the organization both directly and indirectly affected the number of innovations adopted by hospitals. Sapolsky [19] argues that diversity of task and incentive structure stimulates proposals for innovation whereas homogeneity in structure enhances adoption.
H. M. Nahali / Hospital Information Systems
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3. I. Research background Twenty four hospitals in five Arab Gulf countries - Bahrain, Kuwait, Qatar, Saudi Arabia and the United Arab Emirates - were included in the study sample. Although these five countries are all developing nations, they are also distinguishable in that they exhibit similar social, political, economic, historic, and cultural characteristics. They are members of the Organization of Petroleum Exporting Countries (OPEC), and even within OPEC they cluster together as a group. The Arab Gulf countries are more like developed nations in terms of their capital resources and ambitious health, education, and other public service programs. However, they differ from developed nations in the absence of long standing traditions and experience with regards to such programs. In addition, they suffer from skilled manpower shortages in the areas of management and technology; this, combined with the relatively low density of the native population in these countries, has led to heavy reliance on expatriate labor. Fortunately, enabling resources (capital) have been available to facilitate and speed the acquisition of much needed skills. The health industry is but one of many areas that benefited from oil wealth in the Arabian Gulf. Health centers and hospitals were built. Sanitation, such as safe drinking water and sewage disposal, were much improved and major efforts were undertaken to control infectious diseases. Today, with the construction phase almost complete and with health manpower being trained in the various skill requirements, attention is being focused on managing the health resource more efficiently and effectively. As stated in various health plans and annual reports throughout the region, the development of management skills and IS to support management of health care are considered vital to such an undertaking. As a result, these countries have recently begun to witness increased computerization in the delivery of health care; a trend which government health officials indicate will continue. 3.2. Dependent
and independent
variables
The dependent variable in this research is a dichotomous measure indicating the use or non-use of CBIS by hospital departments. The indepen-
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dent variables are drawn from individual and organizational factors, as mentioned earlier. A description of these variables appears in the appendix along with summary univariate statistics. 3.3. Sampling
methodology
The population frame consists of all non-military hospitals in the Arab Gulf Region. Moreover, since hospital size has been associated with the degree of computerization in the literature, and since hospitals in the region have only recently begun to computerize, it was decided to sample only hospitals of over 200 beds. Doing so would also ensure the availability of a range of computerization within the sample. Table I represents hospital beds in the Region with 63.4% of beds located in Saudi Arabia. Consequently, it was decided to select half the sample from Saudi Arabian hospitals and the other half, from “all other Gulf” hospitals. Moreover, due to financial constraints and time limitations, 25 to 30 hospitals could feasibly be included in the overall sample. This led to the selection of 14 ‘other gulf’, and 14 Saudi Arabian, hospitals. For similar reasons the Saudi Arabian sample was further restricted to the Eastern Region (the Riyadh and Dammam areas). The number of hospitals sampled from each of the “other gulf” (Bahrain, Kuwait, Oman, Qatar and U.A.E.) hospitals was based on the proportionate number of beds in each country to total beds in all “other gulf” countries (Table 2). On this basis, one hospital was selected from each of Bahrain and Qatar. In actuality, these two countries each have only one non military hospital meeting the over 200 bed size requirement and
Table 1 Number of hospital
beds in arab countries
CountIy
of the Gulf 1985.
# of hospitals
# of hospital beds
State of Bahrain State of Kuwait Sultanate of Oman State of Qatar Kingdom of Saudi Arabia United Arab Emirates
9 25 18 9 176 32
1726 6317 2960 900 30959 6000
3.5 13.0 6.0 1.8 63.4 12.3
Total
269
48862
100.0
% of total beds
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Table 2 Number, proportion of beds in other gulf hospitals ber of hospitals sampled from each country. Country
No. of beds
Bahrain Kuwait Oman Qatar U.A.E.
and num-
Proportion
Times 14
No. selected
1126 6317 2960 900 6000
0.096 0.353 0.165 0.050 0.335
x14 x14 x14 x14 x14
1.349 4.939 2.314 0.703 4.69
17903
0.999
= I = 5 = 2 = 1 = 5 14
would therefore have been unconditionally entered into the sample. For Kuwait, the U.A.E., Saudi Arabia and Oman, simple random sampling from within each country’s list of hospitals was used to determine which hospitals would be included. Again for logistic reasons the Omani hospitals were later dropped from the study population. 3.4. Data collection In the Summer of 1987, questionnaires were sent to ten department managers in all hospitals in the study sample. Eleven departments appear in Table 3, but “MR/PA Joint” does not refer to a department but to two departments, medical records and patient admissions, that are managed by one person. The questionnaires were filled out by the managers and collected by the researcher a few days after the manager had been given the
Table 3 Response
rates by department.
Department
Administration Patient admissions Medical records MR/PA joint Personnel Central supplies Laboratory Pharmacy Radiology Medicine Nursing Total
Distributed
Received
Response rate
24 9 10 12 13 13 24 24 24 24 24
15 4 8 9 8 7 18 18 17 17 17
62.5 44.4 80.0 75.0 61.5 53.8 75.0 75.0 70.8 70.8 70.8
206
138
66.9
questionnaire. At the time of collecting the questionnaire, the manager was interviewed for purposes of gaining additional understanding of the adoption process. Response rates were high (66.9%) due largely to on-site distribution and collection of the questionnaire. Non-response is, in part, accounted for by the fact that the questionnaire was administered in English and therefore, non-english speaking managers were excluded from the study. It must be noted that managers of clinical departments were more likely to have a working knowledge of the English language as compared to managers of non-clinical departments, with the exception of the hospital director. Finally, ministry of health officials were also interviewed in each of the countries to surmise national policies for the adoption of CBIS by hospitals owned by ministries of health. 3.5. Data analysis and findings SPSS and SPSSPC were used for data analysis: descriptive findings appear in the appendix. Seventy seven percent of the respondents reported that decision making occurred either outside the hospital or jointly between the hospital administration and an external body. This is a reflection of the large number of hospitals owned and managed by ministries of health and in which an external body, such as the MOH or a royally appointed Board of Trustees, plays a major role in organizational decision making. Inadequate organizational communication apparently was experienced in most organizations, a finding that may have more significance when investigating factors that affect the success of CBIS adoptions. As expected, educational levels among hospital managers are high, with 89% of the managers holding a university degree. Moreover, male managers far outnumber female managers and expatriate managers account for 62% of the study population. Average number of years with the hospital was 6, a relatively low figure due, most likely, to the large expatriate labor force rather than to frequent changing of jobs. Manager personality characteristics on the measures of “orientation to change” and “cognitive structure” reveal that, on the average, managers in this study have moderate orientation to change but relatively high cognitive structures. The latter means that
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Systems
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managers do not like ambiguity in their lives and that they desire to make decisions based on definite knowledge. Exposure to computers in the study sample is low, but managers have positive attitudes toward computers and the role of CBIS in their hospitals. Seventy three percent of the managers in the sample had never been involved in a CBIS adoption project. Nevertheless, attitudes to user involvement were high, with managers favoring their involvement and that of their subordinates in such projects. In the second stage of the analysis, factors playing a role in discriminating between CBISusing departments and CBIS non-using departments were identified by using multiple discriminant analyses.
dent variables to discriminate between adopters and non-adopters of CBIS. Three discriminant analyses were run; one for individual factors, one for organizational factors, and the last for both factors considered simultaneously. The Wilks method was used. This utilizes a stepwise technique for entering variables into the discriminant function, was used. The p value for entering the discriminant function was .05. The individual variables of “attitudes towards user involvement”, “exposure to computers”, “orientation to change”, age sex, and nationality (Western) enter the discriminant function for the
4. Adopters and non-adopters of CBIS
Factor
Standardized coefficients
Unstandardized coefficients
User involvement Exp. to computers Orientation to change Age Sex Nationality (West)
+ 0.428 + 0.791 - 0.214 -0.251 + 0.285 + 0.413
+ + + +
Computer use in hospital departments is relatively new in this part of the world. The earliest record of CBIS support for departmental functions in this study was 12 years ago and the most recent acquisition was made six months prior to the administration of the questionnaire. On average, the 53 hospital departments who were users of CBIS - at least one computer-supported function - had been using these systems for 3.65 years with a standard deviation of 2.38 years (Table 4). In addition, a multiple discriminant analysis was run to investigate the capacity of the indepen-
Table 4 Computer
use in the 138 departments
Department
Administration Patient adm. Medical records Personnel Central supplies Laboratory Pharmacy Radiology Clinical (medicine) Clinical (nursing) Total
# of departments
studied # Using computers
& Users
15 13 17 8 7 18 18 17 17 17
8 11 3 3 4 8 2 5 2
46.61 61.54 64.70 37.50 42.86 22.22 44.44 11.76 29.41 11.76
147 *
53
36.05
* This figure exceeds 138 respondents because 9 managers were responsible for both medical records and patient admissions.
Table 5 Canonical criminators
Canonical N = 90.
discriminant of adoption
correlation
functions: Individual factors vs. non-adoption of CBIS.
as dis-
0.102 0.191 0.078 0.031 0.691 0.944
= 0.701
Table 6 Canonical discriminant functions: Organizational factors discriminators of adoption vs. non-adoption of CBIS. Factor
Standardized coefficients
Unstandardized coefficients
Hospital size Ownership (MOW)
- 0.333 + 0.987
- 0.002 + 0.245
Canonical N=138.
correlation
= 0.613.
Table I Canonical discriminant functions: Individual and organizational factors as discriminators of adoption vs. non-adoption of CBIS Factor
Standardized coefficients
Unstandardized coefficients
User involvement Exp. to computers Age Hospital size Ownership
+ +
- 0.072 -0.158 + 0.034 - 0.001 +0.171
Canonical N = 100.
correlation
0.301 0.655 0.277 0.268 0.688
= 0.761.
as
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first analysis (Table 5). When the multiple discriminant analysis is run for organizational factors, only hospital ownership and hospital size, the two organizational contextual variables, enter the discriminant function (Table 6). All the previous variables with the exception of “orientation to nationality, and sex, discriminate bechange”, tween users and non-users of CBIS when organizational and individual variables are considered in the multiple discriminant analysis (Tuble 7).
5. Discussion The findings reveal that
from
the
discriminant
analysis
(1) users of CBIS have more favorable
attitudes towards user involvement than non-users; (21 users of CBIS have had more exposure to computers (includes computer-related education) than non-users; departments use CBIS significantly (3) non-MOH more than MOH hospital departments; in smaller hospitals are more (4) departments likely to have CBIS; that have CBIS are (5) managers of departments more likely to be older than managers of nonuser departments. It was evident from this research that users of CBIS were in favor of user involvement, while non-users afforded this variable little importance. The role of user involvement in CBIS adoption has been repeatedly demonstrated in the literature [14]. The purpose of encouraging user involvement is seen as a means for enabling organizational members to “buy into” the change. Not doing so may, at best, lead to under-utilization and, at the worst, to sabotage that could have detrimental impacts on patient welfare [5]. Moreover, it seems that experience with CBIS adoption leads to an appreciation of the need for user involvement. The second finding (that managers in CBISusing departments are exposed to computers) comes as no surprise, particularly since this variable was measured to include present exposure to computers. There is considerable support for the hypothesis that departments in non-MOH hospitals tend to use CBIS more than departments in MOHowned hospitals. This fact was also evident from
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the visits made to the study sites. It may, in part, be due to the scarcity of expert manpower and shrinking financial resources. MOH-owned hospitals have been affected more than others by the economic recession witnessed in the Region throughout most of the eighties. For example, in the case of ‘Other Public’ hospitals, fund allocation for CBIS acquisitions is often made during the planning and commissioning of the hospital. Moreover, these hospitals are less subject to budgetary constraints, able to attract better qualified manpower, and usually better equipped. Private hospitals, meanwhile, are more vulnerable to changing economic conditions, because they lack ‘public’ support in terms of funds and long term planning and commitment to projects. Consequently, investments in CBIS are made only to the extent to which CBIS are perceived as costsaving and as a marketing tool for attracting patients and organizational health care contracts. Hospital size and age of manager have the least discriminating power and do not appear in any of the bivariate tests that were run. Hospital size was limited in this study to no less than 200 beds with the majority of the hospitals having less than 700 beds. The “other public” hospitals were mostly in the 200 to 500 bed size range and were also the higher adopters, while the larger hospitals were MOH-owned. It may be that hospital ownership accounts in some way for the significance associated with number of beds, however, bivariate analyses revealed no significant association between number of beds and ownership. Individual differences between managers of MOH-owned hospitals and other managers were studied using bivariate statistical analyses to shed additional light on the finding regarding hospital ownership. It was found that non-MOH managers had more computer expertise (p = O.OOOO), and were more inclined toward user involvement in adoption projects (p = 0.01). Moreover, there were significantly more Gulf Arab managers in MOHowned hospitals than in other hospitals (p = 0.04) and more Arab managers (Gulf and other) in MOH-owned hospitals (p = 0.0012). Finally it appears that MOH managers are younger and have higher cognitive structures than do other managers (p = 0.07 and p = 0.08 respectively). We have no explanation for the finding regarding manager age; we relegate verification of this to future research. It would seem, therefore, that an organizational
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contextual variable, in specific hospital ownership, accounts partly for the differences between hospitals with regards to use of CBIS. It most likely does so in two ways: the first through organizational structural characteristics (such as centralization, location of decision making, and participation in decision making) that tend to vary from one hospital to the other, but are usually associated with hospital ownership; the second through individual characteristics of managers: meaning that MOH-owned hospitals attract a different calibre of personnel than do non-MOH hospitals.
6. Conclusion For policy makers, as well as for other interested parties, the findings and on-site visits reveal differences among MOH, ‘Other Public’, and Private hospitals in how and why they adopt CBIS. They also demonstrate differences in managerial and technical manpower skills among the hospital classes. National policies for quickening and facilitating computerization are already in place. However, there are no apparent communication channels between the different sectors of the economy that can facilitate the diffusion of these new technologies, particularly to the hospital industry. It has been demonstrated that ‘Other Public’ hospitals have acquired systems to a larger degree than have MOH hospitals. Consequently, more efforts need to focus on learning from organizations that have already been through the adoption process. This relates to hardware and software acquisitions, and also to organizational impacts of the adoption process. Second, the central role of awareness and education in the effective transfer and diffusion of CBIS technology cannot be over-emphasized. The
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research and on-site visits revealed that managers and users were often unaware of the term CBIS and how it differed from computers used in diagnoses or in the area of clinical medicine, in general. Moreover, only a small number of hospital personnel have had training in computers during their years of formal education. Consequently, although the term ‘CBIS’ was defined in the questionnaire, the concept is likely to have remained vague to many respondents. This means that hospitals seeking computerization should expend considerable efforts in the area of training. Support for the above two conclusions is found in a study on computer usage in Kuwait [lo]: “Most obstacles reported related to user interface: lack of complete awareness by users and management at different levels; problems with quality of original data; and organizational resistance to change. Nevertheless, respondents noted that it was not excessively difficult to integrate computers into their organizations’ operations . . Respondents noted the need for: enhanced training (for staff and users at all levels); more local interaction and international communication; and more evaluation and follow-up on both successful and unsuccessful computing experience.” Finally, a variance approach was adopted in this research to identify variables that may discriminate users of CBIS from non-users. Many of these variables cannot be controlled by either system designers or users and therefore cannot lead to prescriptive action. Consequently, future research should not only identify controllable variables but should probably employ 1 different method of inquiry such as the process ‘..j 1 the case study approach, as appropriate.
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Appendix Variable measurement
and univariate
statistics.
1. Organizational contextual variables Organization size Number of beds
Ownership
200-299 300-399 400-499 500-599 500-699 > 700
Number of hospitals 5 4 1 6 5 3 24
1. Ministry of health 2. Other public 3. Private
15 7 2 24
2. Organuational
structural variables
Centralization a. Location of decision making
b. Participation
c. Participation
1. Between hospital & external body 2. Within hospital 3. External to hospital
in decision
in decision
54.0% 33.3 12.7
making (managers) (adapted from Taylor & Bowers) 1. Decisions are announced with no chance to raise questions and give comments 2. Decisions are announced and explained and chance is given to ask questions 3. Decisions are made but discussed with us and sometimes changed before implementation 4. Decision making in this hospital lies in the hands of clinical and administrative managers, 5. Decision making in this hospital is shared between clinical and administrative managers and their subordinates.
a. Quickness
adapted
4 = a lot, 5 = extensively from Taylor & Bowers
25.2
37.8
x = 3.24
units x = 2.86
to use new work techniques 1 = not at all, 2 = a little, 3 = moderately, 4 = a lot, 5 = extensively
b. Adequacy,
22.0
(users) 1 = not at all, 2 = a little, 3 = moderately,
Orgamrational communication Adequacy of information received from organizational and extra-organizational 1 = not at all, 2 = a little, 3 = moderately, readiness,
6.3
making
4 = a lot, 5 = extensively
Technical
8.7%
efficiency
x = 3.61
and maintenance of resources 1 = not at all, 2 = a little, 3 = moderately, 4 = a lot, 5 = extensively
X = 3.49
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(continued)
Appendix
3. Individual characteristics
Age
Years
fz = 39.58
Education
1= Primary 2 = Secondary 3 = Secondary 4 = Associate 5 = University 6 = University
0.0% 3.1 1.5 5.9 21.3 61.6
vocational degree first degree grad. degree
Sex
1 = Male 2 = Female
77% 23
Nationality
Gulf Arab Other Arab Asian Western
38.0% 23.4 14.6 24.1
Years with organization
Number
X = 6.0
Personality
characteristics
a. Orientation
b. Cognitive
(Jackson
Personality
Scale [ll])
Score ranging from 0 (low) to 16 (high), computed from 16 true/false statements
to change
Score ranging
structure
computed Exposure
of years
from 0 (low) to 16 (high),
from 16 true/false
Score ranging
to computers
X = 9.57
statements
x = 12.54
from 0 to 22 derived
from 4 questions
on computer
use
X = 4.4
4. Attitudes and beliefs regarding computers General
attitudes
to computers
(Kjerulff
et al.) Score ranging from 20 (unfavorable) to 140 (favorable), computed from a 20 bipolar
Attitude
to Contribution
of CBIS, Adapted
semantic
differential
scale
from Kjerulff et al. Score derived from a 16 item, 5 point Likert scale, ranging from 16 (low) x = 67.28
to 80 (high) Attitude
x = 110.36
to User Involvement Score derived from a 7 item, 5 point Likert scale, ranging from 7 (low) to 35 (high)
References [l] Becker, SW., and T.L. Whisler. 1967. The innovative organization: A selective view of current theory and research. Journal of Business 40 October: 462-469. [2] Chandler, J.S. and H.P. Holzer. 1985. Pre-conditions for the introduction of computer-based accounting systems in less developed countries. Management International Review 25 No. 2: 53-66. [3] Cohn, S.F., and R.M. Turyn. 1984. Organizational structure: Decision-making procedures, and the adoption of
X = 15.51
innovations. IEEE Transactions on Engineering Management 31 November: 154-161. [4] Damanpour, F., and W.M. Evan. 1984. Organizational innovation and performance: The problem of “organizational lag”. Admmistrative Science Quarterly 29 September: 392-409. [5] Dowling, A.F. 1980. Do hospital staff interfere with computer system implementation? Health Care Management Review 5 Fall: 23-32. [6] Ein-Dor, P., and E. Segev. 1978. Organizational context and the success of management information systems. Management Suence 24 October: 1067-1077.
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[7] Ein-Dor. P., and E. Segev. 1982. Organizational context and MIS structure: Some empirical evidence. MIS Quarter!v 6 September: 55-68. [8] Ginrberg, M.J. 1975. Implementation as a Process of Change: A Framework and Empirical Study, Unpublished manuscript, Massachusetts Institute of Technology, Sloan School of Technology, Cambridge, Mass. [9] Glaser, E.M., H.H. Abelson, K.N. Garrison. 1983. Puttrng technologv to use: Faclhtatrng the diffusion of knowledge und the implementation of planned change. Washington: Jossey-Bass Publishers. [lo] Ibrahim, R. 1985. Computer usage in developing studies: Case study Kuw-ait. Information and Mnnugement 8 February: 103-112. [ll] Jackson, D.N. 1984. Personality Research Form. Michigan: Research Psychologist Press. [12] Kimberly J.R.. and M.J. Evanisko. 1981. Organizational innovation: The influence of individual and contextual factors on hospital adoption of technological and administrative innovations. Academy of Manugement Journal 24 December: 689-713. [13] Kjerulff. K.H., M.A. Counte, J.C. Salloway, and B.C. Campbell. 1982. Predicting employee adaptation to the implementation of a medical information system. In the si.xth annucrl symposium on computer opplrcations m medical core held rn Silver Springs Md October 1982, 392- 396. Silver Springs Md: IEEE Computer Society. [14] Lucas. H.C. 1986. Information system concepts for management (2nd ed.). New York: McGraw,-Hill. 1151 Moth, M.K. 1976. Structure and organizational resource
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[17]
[18] [19] [20]
[21]
[22]
[23]
[24]
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allocation. Administrotioe Science Qunrter(y 21 December: 661-673. Nabali, H.M. 1988. Factors affecting the adoption of computer-based hospital information systems in the Arab Gulf. Ann Arbor: University Microfilms International. Pelr, D.C. 1983. Quantitiative case histories of urban innovations: Are there innovating stages? IEEE Transactions on Engineering Management EM-30 May: 60-67. Rogers. E.M. 1983. Dlffu.rlon of Innooatmns. New York: The Free Press. Sapolsky, H. 1967. Organizational structure and innovation. Journal of Busmess 40 October: 97-510. Shepard, H.A. 1967. Innovation-resisting and innovationproducing organizations. Journu/ of Business 40 October: 470-477. Shultz. R.L., M.I. Ginzberg, H.C. Lucas. 1984. A structural model of implementation. Management Science Implementutrons, (Supplement 1 to Applications of Management Sciences). ed. R.L. Shultz and M.I. Ginzberg, 55587. Greenwich, Connecticut: JAI Press. Inc. Taylor, J.C. and D.G. Bowers. 1972. Survey of Orgamzations; o Machine Scored Standardized Questronnaire Instrument. Ann Arbor: Center for research on utilization of scientific knowledge, University of Michigan. Turner, J.A. 1982. Firm size, performance, and computer use. In Proceedmg.r of the Third International Conference on Information Systems, 102-120. Ann Arbor, Michigan. Zand, D.E., and R.E. Sorenson. 1975. Theory of change and the effective use of management science. Admimstrotiue Scrence Quarterb 20 December: 532-545.
20 (1991) 3233332
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Hospital information systems in Arab Gulf countries * Characteristics of adopters Haifa M. Nabali American
1. Introduction
University of Beirut, Beirut, Lebanon
138 managers from a representative sample consisting of 24 hospitals in five countries of the Arab Gulf were asked to report on the presence and adoption of Computer-Based Information Systems (CBIS) in their hospitals. Two groups of variables were studied to assess their ability to discriminate between users and non-users of CBIS: manager characteristics and hospital organizational (contextual and structural) characteristics. The findings reveal that: hospitals owned by Ministries of Health are lower adopters of CBIS; that managers of departments that use CBIS have more favorable attitudes towards user involvement; that departments in smaller hospitals are more likely to use CBIS; and that managers of user departments tend to be older. Keywords: Adoption of IS, International issues in IS, IS in Arab Gulf, Organizational factors and IS, Individual factors and IS, Hospital IS.
Haifa M. Nabali is assistant professor of health services administration at the American University of Beirut, Lebanon. She received her B.S. in Environmental Health in 1978 and her MPH in 1979 from the American University of Beirut. In 1985 she received a M.S. and in 1988 a Ph.D. in Management (MIS) from Case Western Reserve University in Cleveland, Ohio. Her research interests center around issues in the effectiveness of MIS, impact of managerial styles particularly from the cultural perspective, and design and implementation issues for health information systems. She has previously worked with the Ministries of Health in both the State of Bahrain and the United Arab Emirates and conducted computer and MIS workshops. * Based on part of the findings from a Ph.D. Dissertation, “Factors Affecting the Adoption of Computer-Based Hospital Information Systems in the Arab Gulf”. 037%7206/91/$03.50
0 1991 - Elsevier Science Publishers
Organizations worldwide are increasingly turning towards the use of computer-based information systems (CBIS) for support in some or all phases of their operations. As a result, computers have become a standard aspect of many organizations, offering opportunities for significantly improving organizational life. However, not all organizations acquire CBIS. The distinction between organizations that do and those that do not has been attributed to several factors in the “Innovation Adoption” and “Management Information Systems (MIS)” literatures. It is argued that there is an identifiable cluster of characteristics that “predispose” organizations to adopt CBIS, in specific, or new technologies, in general [6,12]. Two of the factors that have been linked to the predisposition to adopt new technologies are: (1) characteristics of the organization, and (2) characteristics of the individual [2,21]. The role of a subset of variables chosen from the above mentioned factors are explored in this research with respect to their ability to discriminate between adopters and non-adopters of CBIS.
2. The adoption of CBIS The adoption of CBIS is viewed as a special case of innovation adoption. An adoption is the acceptance and actual use of a practice common elsewhere, while an innovation is the application of a technology in a new way or use [9]. Although in the present study CBIS technology is not necessarily being applied in a new way, the innovation
B.V. (North-Holland)
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adoption literature provides a framework through which to understand why, for example, one hospital or hospital department is more likely and/or more prepared to adopt a CBIS as compared to another. Research in the area of innovation adoption takes one of three approaches [l]: 1. The inputs approach; identifying variables that predispose organizations to innovate. 2. The outputs approach; determining the number and kind of innovations adopted. 3. The process approach; investigating the sequence of events from inputs to outputs.
2.1. The inputs approach Several classifications for inputs have been proposed. For example, Kimberly and Evanisko consider organizational, individual and contextual factors. Glaser, Abelson & Garrison use the terms personal and social, organizational, and political influences, while Rogers refers to the socioeconomic and personality characteristics as well as to the communication behaviors of adopters. Other studies of inputs in the MIS literature consider characteristics of the innovation (or, in this case, of the information system being adopted), characteristics of the tasks being performed or supported, as well as other characteristics related to organizational policies, beliefs, and interactions among organizational members.
2.2. The outputs approach The output or the dependent variable in innovation adoption studies has usually been the number of innovations adopted [3,15]. Damanpour and Evan [4] also use a relative measure of innovation adoption in which the number of innovations adopted form a percentage of all possible such adoptions within a given time period. The “outputs” approach was used in a related study [16] whereby managers were given a list of functions performed in their departments and asked to indicate whether or not computers were used to support that function. A percentage score, “extent of computerization” was then computed for each department.
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2.3. The process approach The process of “innovation adoption” has been described as consisting of several stages: stimulus, conception, proposal, and adoption. If, however, one understands adoption to include actual use, then two more stages are added: implementation and evaluation. The research on “innovation adoption” has attempted to show that distinct stages do exist and that these may or may not occur in a certain order, depending on the complexity and originality (developed in-house, adapted, or borrowed) of the innovation [17]. Other researchers in the MIS field, have studied how the activities that occur within one stage of the process may impact the implementation stage, and, consequently, affect system success [8,24].
3. Current research As this was a first time study into computerization in Arab Gulf hospitals, the “inputs” approach was used to provide baseline data on the adoption of CBIS. Moreover, because of the exploratory nature of this study, only a subset of all possible factors was studied. Other factors were excluded, because a more detailed investigation of how they present themselves in the organizations under study is required before a data collection instrument can be developed. Individual or personal factors include such variables as age, education, occupation, and personality characteristics which have been considered in several studies. For example, Kjerulff et al. [13] claim that some of these variables could be used to predict the adaptability of hospital employees to information systems and Shepard [20] argues that an organization staffed with strong self-actualizing personalities will generate more ideas and will be more innovative. Organizational factors have probably been studied the most to date. These include centralization, formalization, functional differentiation, and complexity. Moth among others [e.g., 231, demonstrated how size of the organization both directly and indirectly affected the number of innovations adopted by hospitals. Sapolsky [19] argues that diversity of task and incentive structure stimulates proposals for innovation whereas homogeneity in structure enhances adoption.
H. M. Nahali / Hospital Information Systems
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3. I. Research background Twenty four hospitals in five Arab Gulf countries - Bahrain, Kuwait, Qatar, Saudi Arabia and the United Arab Emirates - were included in the study sample. Although these five countries are all developing nations, they are also distinguishable in that they exhibit similar social, political, economic, historic, and cultural characteristics. They are members of the Organization of Petroleum Exporting Countries (OPEC), and even within OPEC they cluster together as a group. The Arab Gulf countries are more like developed nations in terms of their capital resources and ambitious health, education, and other public service programs. However, they differ from developed nations in the absence of long standing traditions and experience with regards to such programs. In addition, they suffer from skilled manpower shortages in the areas of management and technology; this, combined with the relatively low density of the native population in these countries, has led to heavy reliance on expatriate labor. Fortunately, enabling resources (capital) have been available to facilitate and speed the acquisition of much needed skills. The health industry is but one of many areas that benefited from oil wealth in the Arabian Gulf. Health centers and hospitals were built. Sanitation, such as safe drinking water and sewage disposal, were much improved and major efforts were undertaken to control infectious diseases. Today, with the construction phase almost complete and with health manpower being trained in the various skill requirements, attention is being focused on managing the health resource more efficiently and effectively. As stated in various health plans and annual reports throughout the region, the development of management skills and IS to support management of health care are considered vital to such an undertaking. As a result, these countries have recently begun to witness increased computerization in the delivery of health care; a trend which government health officials indicate will continue. 3.2. Dependent
and independent
variables
The dependent variable in this research is a dichotomous measure indicating the use or non-use of CBIS by hospital departments. The indepen-
325
dent variables are drawn from individual and organizational factors, as mentioned earlier. A description of these variables appears in the appendix along with summary univariate statistics. 3.3. Sampling
methodology
The population frame consists of all non-military hospitals in the Arab Gulf Region. Moreover, since hospital size has been associated with the degree of computerization in the literature, and since hospitals in the region have only recently begun to computerize, it was decided to sample only hospitals of over 200 beds. Doing so would also ensure the availability of a range of computerization within the sample. Table I represents hospital beds in the Region with 63.4% of beds located in Saudi Arabia. Consequently, it was decided to select half the sample from Saudi Arabian hospitals and the other half, from “all other Gulf” hospitals. Moreover, due to financial constraints and time limitations, 25 to 30 hospitals could feasibly be included in the overall sample. This led to the selection of 14 ‘other gulf’, and 14 Saudi Arabian, hospitals. For similar reasons the Saudi Arabian sample was further restricted to the Eastern Region (the Riyadh and Dammam areas). The number of hospitals sampled from each of the “other gulf” (Bahrain, Kuwait, Oman, Qatar and U.A.E.) hospitals was based on the proportionate number of beds in each country to total beds in all “other gulf” countries (Table 2). On this basis, one hospital was selected from each of Bahrain and Qatar. In actuality, these two countries each have only one non military hospital meeting the over 200 bed size requirement and
Table 1 Number of hospital
beds in arab countries
CountIy
of the Gulf 1985.
# of hospitals
# of hospital beds
State of Bahrain State of Kuwait Sultanate of Oman State of Qatar Kingdom of Saudi Arabia United Arab Emirates
9 25 18 9 176 32
1726 6317 2960 900 30959 6000
3.5 13.0 6.0 1.8 63.4 12.3
Total
269
48862
100.0
% of total beds
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Table 2 Number, proportion of beds in other gulf hospitals ber of hospitals sampled from each country. Country
No. of beds
Bahrain Kuwait Oman Qatar U.A.E.
and num-
Proportion
Times 14
No. selected
1126 6317 2960 900 6000
0.096 0.353 0.165 0.050 0.335
x14 x14 x14 x14 x14
1.349 4.939 2.314 0.703 4.69
17903
0.999
= I = 5 = 2 = 1 = 5 14
would therefore have been unconditionally entered into the sample. For Kuwait, the U.A.E., Saudi Arabia and Oman, simple random sampling from within each country’s list of hospitals was used to determine which hospitals would be included. Again for logistic reasons the Omani hospitals were later dropped from the study population. 3.4. Data collection In the Summer of 1987, questionnaires were sent to ten department managers in all hospitals in the study sample. Eleven departments appear in Table 3, but “MR/PA Joint” does not refer to a department but to two departments, medical records and patient admissions, that are managed by one person. The questionnaires were filled out by the managers and collected by the researcher a few days after the manager had been given the
Table 3 Response
rates by department.
Department
Administration Patient admissions Medical records MR/PA joint Personnel Central supplies Laboratory Pharmacy Radiology Medicine Nursing Total
Distributed
Received
Response rate
24 9 10 12 13 13 24 24 24 24 24
15 4 8 9 8 7 18 18 17 17 17
62.5 44.4 80.0 75.0 61.5 53.8 75.0 75.0 70.8 70.8 70.8
206
138
66.9
questionnaire. At the time of collecting the questionnaire, the manager was interviewed for purposes of gaining additional understanding of the adoption process. Response rates were high (66.9%) due largely to on-site distribution and collection of the questionnaire. Non-response is, in part, accounted for by the fact that the questionnaire was administered in English and therefore, non-english speaking managers were excluded from the study. It must be noted that managers of clinical departments were more likely to have a working knowledge of the English language as compared to managers of non-clinical departments, with the exception of the hospital director. Finally, ministry of health officials were also interviewed in each of the countries to surmise national policies for the adoption of CBIS by hospitals owned by ministries of health. 3.5. Data analysis and findings SPSS and SPSSPC were used for data analysis: descriptive findings appear in the appendix. Seventy seven percent of the respondents reported that decision making occurred either outside the hospital or jointly between the hospital administration and an external body. This is a reflection of the large number of hospitals owned and managed by ministries of health and in which an external body, such as the MOH or a royally appointed Board of Trustees, plays a major role in organizational decision making. Inadequate organizational communication apparently was experienced in most organizations, a finding that may have more significance when investigating factors that affect the success of CBIS adoptions. As expected, educational levels among hospital managers are high, with 89% of the managers holding a university degree. Moreover, male managers far outnumber female managers and expatriate managers account for 62% of the study population. Average number of years with the hospital was 6, a relatively low figure due, most likely, to the large expatriate labor force rather than to frequent changing of jobs. Manager personality characteristics on the measures of “orientation to change” and “cognitive structure” reveal that, on the average, managers in this study have moderate orientation to change but relatively high cognitive structures. The latter means that
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Systems
327
managers do not like ambiguity in their lives and that they desire to make decisions based on definite knowledge. Exposure to computers in the study sample is low, but managers have positive attitudes toward computers and the role of CBIS in their hospitals. Seventy three percent of the managers in the sample had never been involved in a CBIS adoption project. Nevertheless, attitudes to user involvement were high, with managers favoring their involvement and that of their subordinates in such projects. In the second stage of the analysis, factors playing a role in discriminating between CBISusing departments and CBIS non-using departments were identified by using multiple discriminant analyses.
dent variables to discriminate between adopters and non-adopters of CBIS. Three discriminant analyses were run; one for individual factors, one for organizational factors, and the last for both factors considered simultaneously. The Wilks method was used. This utilizes a stepwise technique for entering variables into the discriminant function, was used. The p value for entering the discriminant function was .05. The individual variables of “attitudes towards user involvement”, “exposure to computers”, “orientation to change”, age sex, and nationality (Western) enter the discriminant function for the
4. Adopters and non-adopters of CBIS
Factor
Standardized coefficients
Unstandardized coefficients
User involvement Exp. to computers Orientation to change Age Sex Nationality (West)
+ 0.428 + 0.791 - 0.214 -0.251 + 0.285 + 0.413
+ + + +
Computer use in hospital departments is relatively new in this part of the world. The earliest record of CBIS support for departmental functions in this study was 12 years ago and the most recent acquisition was made six months prior to the administration of the questionnaire. On average, the 53 hospital departments who were users of CBIS - at least one computer-supported function - had been using these systems for 3.65 years with a standard deviation of 2.38 years (Table 4). In addition, a multiple discriminant analysis was run to investigate the capacity of the indepen-
Table 4 Computer
use in the 138 departments
Department
Administration Patient adm. Medical records Personnel Central supplies Laboratory Pharmacy Radiology Clinical (medicine) Clinical (nursing) Total
# of departments
studied # Using computers
& Users
15 13 17 8 7 18 18 17 17 17
8 11 3 3 4 8 2 5 2
46.61 61.54 64.70 37.50 42.86 22.22 44.44 11.76 29.41 11.76
147 *
53
36.05
* This figure exceeds 138 respondents because 9 managers were responsible for both medical records and patient admissions.
Table 5 Canonical criminators
Canonical N = 90.
discriminant of adoption
correlation
functions: Individual factors vs. non-adoption of CBIS.
as dis-
0.102 0.191 0.078 0.031 0.691 0.944
= 0.701
Table 6 Canonical discriminant functions: Organizational factors discriminators of adoption vs. non-adoption of CBIS. Factor
Standardized coefficients
Unstandardized coefficients
Hospital size Ownership (MOW)
- 0.333 + 0.987
- 0.002 + 0.245
Canonical N=138.
correlation
= 0.613.
Table I Canonical discriminant functions: Individual and organizational factors as discriminators of adoption vs. non-adoption of CBIS Factor
Standardized coefficients
Unstandardized coefficients
User involvement Exp. to computers Age Hospital size Ownership
+ +
- 0.072 -0.158 + 0.034 - 0.001 +0.171
Canonical N = 100.
correlation
0.301 0.655 0.277 0.268 0.688
= 0.761.
as
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first analysis (Table 5). When the multiple discriminant analysis is run for organizational factors, only hospital ownership and hospital size, the two organizational contextual variables, enter the discriminant function (Table 6). All the previous variables with the exception of “orientation to nationality, and sex, discriminate bechange”, tween users and non-users of CBIS when organizational and individual variables are considered in the multiple discriminant analysis (Tuble 7).
5. Discussion The findings reveal that
from
the
discriminant
analysis
(1) users of CBIS have more favorable
attitudes towards user involvement than non-users; (21 users of CBIS have had more exposure to computers (includes computer-related education) than non-users; departments use CBIS significantly (3) non-MOH more than MOH hospital departments; in smaller hospitals are more (4) departments likely to have CBIS; that have CBIS are (5) managers of departments more likely to be older than managers of nonuser departments. It was evident from this research that users of CBIS were in favor of user involvement, while non-users afforded this variable little importance. The role of user involvement in CBIS adoption has been repeatedly demonstrated in the literature [14]. The purpose of encouraging user involvement is seen as a means for enabling organizational members to “buy into” the change. Not doing so may, at best, lead to under-utilization and, at the worst, to sabotage that could have detrimental impacts on patient welfare [5]. Moreover, it seems that experience with CBIS adoption leads to an appreciation of the need for user involvement. The second finding (that managers in CBISusing departments are exposed to computers) comes as no surprise, particularly since this variable was measured to include present exposure to computers. There is considerable support for the hypothesis that departments in non-MOH hospitals tend to use CBIS more than departments in MOHowned hospitals. This fact was also evident from
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the visits made to the study sites. It may, in part, be due to the scarcity of expert manpower and shrinking financial resources. MOH-owned hospitals have been affected more than others by the economic recession witnessed in the Region throughout most of the eighties. For example, in the case of ‘Other Public’ hospitals, fund allocation for CBIS acquisitions is often made during the planning and commissioning of the hospital. Moreover, these hospitals are less subject to budgetary constraints, able to attract better qualified manpower, and usually better equipped. Private hospitals, meanwhile, are more vulnerable to changing economic conditions, because they lack ‘public’ support in terms of funds and long term planning and commitment to projects. Consequently, investments in CBIS are made only to the extent to which CBIS are perceived as costsaving and as a marketing tool for attracting patients and organizational health care contracts. Hospital size and age of manager have the least discriminating power and do not appear in any of the bivariate tests that were run. Hospital size was limited in this study to no less than 200 beds with the majority of the hospitals having less than 700 beds. The “other public” hospitals were mostly in the 200 to 500 bed size range and were also the higher adopters, while the larger hospitals were MOH-owned. It may be that hospital ownership accounts in some way for the significance associated with number of beds, however, bivariate analyses revealed no significant association between number of beds and ownership. Individual differences between managers of MOH-owned hospitals and other managers were studied using bivariate statistical analyses to shed additional light on the finding regarding hospital ownership. It was found that non-MOH managers had more computer expertise (p = O.OOOO), and were more inclined toward user involvement in adoption projects (p = 0.01). Moreover, there were significantly more Gulf Arab managers in MOHowned hospitals than in other hospitals (p = 0.04) and more Arab managers (Gulf and other) in MOH-owned hospitals (p = 0.0012). Finally it appears that MOH managers are younger and have higher cognitive structures than do other managers (p = 0.07 and p = 0.08 respectively). We have no explanation for the finding regarding manager age; we relegate verification of this to future research. It would seem, therefore, that an organizational
Informarion & Managemem
contextual variable, in specific hospital ownership, accounts partly for the differences between hospitals with regards to use of CBIS. It most likely does so in two ways: the first through organizational structural characteristics (such as centralization, location of decision making, and participation in decision making) that tend to vary from one hospital to the other, but are usually associated with hospital ownership; the second through individual characteristics of managers: meaning that MOH-owned hospitals attract a different calibre of personnel than do non-MOH hospitals.
6. Conclusion For policy makers, as well as for other interested parties, the findings and on-site visits reveal differences among MOH, ‘Other Public’, and Private hospitals in how and why they adopt CBIS. They also demonstrate differences in managerial and technical manpower skills among the hospital classes. National policies for quickening and facilitating computerization are already in place. However, there are no apparent communication channels between the different sectors of the economy that can facilitate the diffusion of these new technologies, particularly to the hospital industry. It has been demonstrated that ‘Other Public’ hospitals have acquired systems to a larger degree than have MOH hospitals. Consequently, more efforts need to focus on learning from organizations that have already been through the adoption process. This relates to hardware and software acquisitions, and also to organizational impacts of the adoption process. Second, the central role of awareness and education in the effective transfer and diffusion of CBIS technology cannot be over-emphasized. The
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329
research and on-site visits revealed that managers and users were often unaware of the term CBIS and how it differed from computers used in diagnoses or in the area of clinical medicine, in general. Moreover, only a small number of hospital personnel have had training in computers during their years of formal education. Consequently, although the term ‘CBIS’ was defined in the questionnaire, the concept is likely to have remained vague to many respondents. This means that hospitals seeking computerization should expend considerable efforts in the area of training. Support for the above two conclusions is found in a study on computer usage in Kuwait [lo]: “Most obstacles reported related to user interface: lack of complete awareness by users and management at different levels; problems with quality of original data; and organizational resistance to change. Nevertheless, respondents noted that it was not excessively difficult to integrate computers into their organizations’ operations . . Respondents noted the need for: enhanced training (for staff and users at all levels); more local interaction and international communication; and more evaluation and follow-up on both successful and unsuccessful computing experience.” Finally, a variance approach was adopted in this research to identify variables that may discriminate users of CBIS from non-users. Many of these variables cannot be controlled by either system designers or users and therefore cannot lead to prescriptive action. Consequently, future research should not only identify controllable variables but should probably employ 1 different method of inquiry such as the process ‘..j 1 the case study approach, as appropriate.
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Appendix Variable measurement
and univariate
statistics.
1. Organizational contextual variables Organization size Number of beds
Ownership
200-299 300-399 400-499 500-599 500-699 > 700
Number of hospitals 5 4 1 6 5 3 24
1. Ministry of health 2. Other public 3. Private
15 7 2 24
2. Organuational
structural variables
Centralization a. Location of decision making
b. Participation
c. Participation
1. Between hospital & external body 2. Within hospital 3. External to hospital
in decision
in decision
54.0% 33.3 12.7
making (managers) (adapted from Taylor & Bowers) 1. Decisions are announced with no chance to raise questions and give comments 2. Decisions are announced and explained and chance is given to ask questions 3. Decisions are made but discussed with us and sometimes changed before implementation 4. Decision making in this hospital lies in the hands of clinical and administrative managers, 5. Decision making in this hospital is shared between clinical and administrative managers and their subordinates.
a. Quickness
adapted
4 = a lot, 5 = extensively from Taylor & Bowers
25.2
37.8
x = 3.24
units x = 2.86
to use new work techniques 1 = not at all, 2 = a little, 3 = moderately, 4 = a lot, 5 = extensively
b. Adequacy,
22.0
(users) 1 = not at all, 2 = a little, 3 = moderately,
Orgamrational communication Adequacy of information received from organizational and extra-organizational 1 = not at all, 2 = a little, 3 = moderately, readiness,
6.3
making
4 = a lot, 5 = extensively
Technical
8.7%
efficiency
x = 3.61
and maintenance of resources 1 = not at all, 2 = a little, 3 = moderately, 4 = a lot, 5 = extensively
X = 3.49
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331
(continued)
Appendix
3. Individual characteristics
Age
Years
fz = 39.58
Education
1= Primary 2 = Secondary 3 = Secondary 4 = Associate 5 = University 6 = University
0.0% 3.1 1.5 5.9 21.3 61.6
vocational degree first degree grad. degree
Sex
1 = Male 2 = Female
77% 23
Nationality
Gulf Arab Other Arab Asian Western
38.0% 23.4 14.6 24.1
Years with organization
Number
X = 6.0
Personality
characteristics
a. Orientation
b. Cognitive
(Jackson
Personality
Scale [ll])
Score ranging from 0 (low) to 16 (high), computed from 16 true/false statements
to change
Score ranging
structure
computed Exposure
of years
from 0 (low) to 16 (high),
from 16 true/false
Score ranging
to computers
X = 9.57
statements
x = 12.54
from 0 to 22 derived
from 4 questions
on computer
use
X = 4.4
4. Attitudes and beliefs regarding computers General
attitudes
to computers
(Kjerulff
et al.) Score ranging from 20 (unfavorable) to 140 (favorable), computed from a 20 bipolar
Attitude
to Contribution
of CBIS, Adapted
semantic
differential
scale
from Kjerulff et al. Score derived from a 16 item, 5 point Likert scale, ranging from 16 (low) x = 67.28
to 80 (high) Attitude
x = 110.36
to User Involvement Score derived from a 7 item, 5 point Likert scale, ranging from 7 (low) to 35 (high)
References [l] Becker, SW., and T.L. Whisler. 1967. The innovative organization: A selective view of current theory and research. Journal of Business 40 October: 462-469. [2] Chandler, J.S. and H.P. Holzer. 1985. Pre-conditions for the introduction of computer-based accounting systems in less developed countries. Management International Review 25 No. 2: 53-66. [3] Cohn, S.F., and R.M. Turyn. 1984. Organizational structure: Decision-making procedures, and the adoption of
X = 15.51
innovations. IEEE Transactions on Engineering Management 31 November: 154-161. [4] Damanpour, F., and W.M. Evan. 1984. Organizational innovation and performance: The problem of “organizational lag”. Admmistrative Science Quarterly 29 September: 392-409. [5] Dowling, A.F. 1980. Do hospital staff interfere with computer system implementation? Health Care Management Review 5 Fall: 23-32. [6] Ein-Dor, P., and E. Segev. 1978. Organizational context and the success of management information systems. Management Suence 24 October: 1067-1077.
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