- Email: [email protected]
Task characteristics, decentralization, and the success of hospital information systems
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Task characteristics, decentralization, and the success of hospital information systems K. Kyu Kim inha
University,
1. Introduction
Inchou, Koreu
Considerable attention has been given to the importance of structural and contextual variables in the successful implementation of management information systems (MIS). Empirical studies, however, are inconclusive on the effect of the organizational context and MIS structure on MIS success. This study examines the relationship between the decentralization of hospital information systems (HIS) management and HIS development task characteristics, and the impact of that relationship on the functioning of HIS development groups. The results indicate that decentralization interacts with task predictability to influence user information satisfaction. When HIS development tasks are unpredictable, a decentralized hierarchy of authority is more effective in achieving high user satisfaction. Also, extensive HIS employee participation in decision making is desirable when HIS development personnel encounter many exceptions. Keywords: MIS Structure, tion, Hospital Information mation Satisfaction.
Task Characteristics, DecentraliiaSystems, MIS Success, User Infor-
Kyung Kyu Kim is an Assistant Professor of Management Information Systems at Inha University. He received his Ph.D. in Business Administration from the University of Utah in 1986. His recent articles have appeared in
The Accounting Review, MIS Quarterly
and Journal of Information Systems. His research interests include strategic use of information systems and MIS implementation issues.
North-Holland Information & Management
19 (1990) 83-93
0378-7206/90/$03.50 0 1990 - Elsetier
Science Publishers
The issue of centralization versus decentralization of MIS activities has generated much debate and subsequent research over the years (e.g., see Ein-Dor and Segev [lo], McFarlan and Mckenney [25], Olson [28]). The process of determining precisely which MIS organizational structure may be appropriate under what conditions, however, has received inadequate attention. This study addresses the question: Should the decision making authority in MIS development groups be centralized or decentralized in order to be effective in meeting the users’ information needs? The HIS development function includes the analysis, design, and programming of new applications, and the maintenance of existing applications. The management literature usually explains organizational structure by resorting to structural contingency theory. This contends that those organizations with structures closely matching the requirements of the organization’s context are more effective than those that do not. Examples of organizutional context thought to be critical for the functioning of organizations include environmental uncertainty [8,22], organizational size [5,433, and task characteristics [41,45]. In small departments, task characteristics among other contextual factors has been found to have a significant effect on organizational structure because their personnel are located close to the technical activities of that unit [16,21]. Pfeffer [33] provides the theoretical underpinnings of the task characteristics-structure relationship: [Task characteristics] “affects the skills and discretion of the work force and, thus, the control that must be employed; different structural arrangements imply different types of control structures and procedures; and,
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therefore, is linked to structure through its requirements for procedures to control work The purpose of this study is to explore whether those MIS development groups with structures matching the requirements of their task characteristics are more effective than those that do not in a field study of hospital information systems (HIS) development groups.
2. A Review of Prior Research There have been few empirical investigations of the relationship between MIS context and MIS organizational structure; moreover, these investigations have generally had mixed results. In their extensive review of the investigations on the effect of the MIS context and MIS structure on system effectiveness, Cerveny and Sanders [3] concluded that empirical studies were inconclusive. Through interviews with corporate executives and information processing managers in 43 business organizations, Olson and Chervany [30] attempted to identify those organizational characteristics that are associated with the structure of the information services function. Few organizational characteristics, however, were found to influence the structure of information services consistently across all organizations. Ein-Dor and Segev [ll], using a sample of 53 organizations in a large U.S. metropolitan area, tested the proposition that organizational structure is related to the dimensions of MIS structure, such as degree of centralization and integration of MIS. The major findings were that the degree of centralization is significantly correlated with the degree of centralization of organizational decision making authority which, in turn, is closely associated with organizational size. As a possible explanation on the mixed findings, the concept of “fit” between MIS context and MIS structure was proposed [e.g., 91. The basic premise of this view is that work groups are open systems which must deal with work-related uncertainty, defined as the difference between information possessed and information required to complete a task [13]. There are several sources of uncertainty to which work groups must respond. The major sources of work-related uncertainty include work group environment, task characteris-
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tics, and work group size. Given the various sources of uncertainty, a basic function of the work group structure is to create the most appropriate configuration of work groups to facilitate the effective collection, processing and distribution of information [42]. Different MIS organizational structures have different capacities for effective information processing. For example, research indicates that organic structures are able to deal with greater amounts of work-related uncertainty than mechanistic structures. As the amount of uncertainty that a work unit faces increases, so does the need for increased information processing capacity. Therefore, to be effective, MIS organizational structure should match MIS context. In addition to the mixed findings, previous studies that have investigated the relationship between work group context and MIS organizational structure share a common weakness in the selection of dependent variables. The major argument of contingency theory is that those work groups with structures closely matching the requirements of the work group’s context perform better than those that do not. Thus, the phenomenon to be explained by a contingency theory is MIS performance. Unfortunately, this generally has not been investigated as a dependent variable in previous studies. This study attempts to shed new light on conflicting results of previous research: the relationship between MIS context and MIS organizational structure. Further, system development group performance is selected as the dependent variable to mitigate some previous limitations.
3. Decentralization, Success
Task Characteristics,
and MIS
3.1. Decentralization Centralization versus decentralization, as used here, concerns the distribution of decision making authority within the organization [12]. Centralization then implies the concentration of decision making power in a single person or small group at the top level; decentralization implies that decisions can be made at low levels in the organizational hierarchy. Hage and Aiken [14] subdivided centralization of decisions into two subconstructs. One is the concentration of decisions referring to
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resource distribution or policy formulation, with the indicator being the participation in decision making. The other is the concentration of decisions referring to accomplishment of tasks, with the indicator being the hierarchy of authority. 3.2. Task Characteristics In defining task characteristics, various authors have posited a variety of typologies, but a number of studies appear to be related to the ideas described by Perrow [31]. Several studies operationalized one or more of the dimensions he proposed, and several other studies have the potential to fit within the categories he described [44]. These dimensions are: task predictability and problem analyzability. Task predictability denotes the number of exceptions in the work, or the frequency of unexpected and novel events that occur in the conversion process [32]. Workgroups with few exceptions experience considerable certainty about the occurrence of task related activities; many exceptions mean that participants typically cannot predict problems in advance. Problem analyzability denotes the degree to which one can analyze an unexpected event once it is encountered. Objective or computational procedures are usually followed to resolve analyzable exceptions. For unanalyzable exceptions, there is no set of techniques or procedures to advise a person exactly what to do. Employees rely on accumulated experience, intuition, and judgment. 3.3. MIS
Success
MIS success can be defined as the extent to which the MIS supports the organization in achieving its goals. However, measuring MIS success is a difficult task, because of the difficulty of tracing and measuring the effects of MIS through a tangle of intermediate impacts upon organizational effectiveness. Thus, MIS researchers have developed surrogate measures for MIS effectiveness, such as user information satisfaction, system usage, and user acceptance; these are assumed to relate to the organizational effectiveness. User information satisfaction (UK) is generally recognized by many MIS researchers as one of the more important indicators of success in designing and implementing MIS [37,46]. Ives et al. [17]
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defined UIS as “the extent to which users believe the information system available to them meets their information requirements.” UIS is considered as a meaningful surrogate for MIS effectiveness in that it measures satisfaction of organization members who actually use the MIS output to meet their organizational responsibilities. Thus, UIS is used here as a surrogate for MIS success.
4. Hypotheses Building upon an understanding of the task characteristics and decentralization concepts, one can proceed to explore the “best matches” between the two concepts. In structural contingency theory, the term interaction between task characteristics and structure requires that they predict a third variable (in our case MIS success) [1,36]. Thus, a series of interaction hypotheses are developed between individual task characteristics and decentralization dimensions. Task predictability is expected to interact with decentralization to influence MIS success [e.g., 351. When system development tasks are repetitious and system developers experience few exceptions, decision making authority can be kept at the management level because of the high certainty of tasks. MIS development personnel need little discretion and power in the unit; they simply perform the well-defined problem solutions assigned to them. Participants will be more committed if their time is not wasted by involvement in decisions with obvious solutions [38]; however, as the number of exceptions increases, centralization becomes less effective, because information processing requirements may overburden the hierarchy by the sheer number of exceptional cases. System development groups structured to refer all exceptional cases upward to a centralized decision point are likely to suffer significant delays in implementing decisions because of the lengthy referral process. Thus, decentralization of decisions would appear applicable when information must be processed by MIS personnel directly in the development process. These personnel should participate in the decisions that affect how they perform their tasks. Accordingly, the following hypotheses are developed: Hl: Task predictability will interact with hierarchy of authority to influence UIS.
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Hla:
Hlb:
H2
When task predictability is low, decentralized hierarchy of authority will be positively associated with UIS. When task predictability is high, centralized hierarchy of authority will be positively associated with UK
Task predictability will interact with MIS employee participation in decision making to influence UIS.
H2a: When task predictability is low, increases in MIS employee participation in decision making will be positively associated with UIS. H2b: When task predictability is high, decreases in MIS employee participation in decision making will be positively associated with UIS. Also problem analyzability is expected to interact with decentralization to influence performance. When problems are analyzable, centralized decision making can be effectively utilized to establish contingency plans for dealing with problems that occur. A system development group dealing with mostly analyzable problems could identify a priori potential problems and could specify appropriate responses. However, when problems encountered are more difficult to analyze, it becomes increasingly difficult to develop contingency plans in advance. Instead, extensive search behavior by system development personnel, which involves on-the-spot sharing of information among them, is required to discover solutions to these problems. Thus, to be effective, system development personnel should actively participate in the gathering of salient data and the implementation of decisions. Hence the following hypotheses: H3: Problem analyzability will interact with hierarchy of authority to influence UIS. H3a: When problem analyzability is low, decentralized hierarchy of authority will be positively associated with UIS. H3b: When problem analyzability is high, centralized hierarchy of authority will be positively associated with UIS. H4:
Problem analyzability will interact with MIS employee participation in decision making to influence UIS.
H4a: When problem analyzability is low, increases in MIS employee participation in decision making will be positively associated with UIS. H4b: When problem analyzability is high, decreases in MIS employee participation in decision making will be positively associated with UIS.
5. Methods Using questionnaires, a field study was conducted in hospital information systems (MIS) development groups. 5.1. Sample Questionnaires were sent to 91 hospitals that were members of a private national association of HIS. The research objectives required that, in each hospital, the information come from both the HIS group and HIS users. Regarding primary users of HIS, business offices were chosen to receive the user questionnaire based on the survey of Ball and Boyle [2]. In their survey of HIS, they concluded that the HIS emphasized business-oriented applications, such as diagnostic related groups (DRGs), general ledger, admissions and discharges, patient billing, and materials management. Typically, these tasks were performed by hospital business offices. Thus, these offices were selected as an appropriate user group for HIS success evaluation. HIS directors were asked to select randomly five system development personnel and to distribute an HIS employee questionnaire to each of them. To ensure a fair evaluation, the author directly contacted the business office managers and supervisors independently. HIS development personnel were asked to complete the questionnaire regarding both their task characteristics and the degree of decentralization within the HIS department. The completed questionnaires were mailed back directly to the primary researcher. Of the 91 hospitals originally contacted, responses arrived from both user groups and HIS departments in 29 hospitals for a response rate of 31 percent. See Table I. Thus, those 29 hospitals comprised the final research sample.
In/ormation
K. Kyu Kim / Hospital Information
& Management
Table 1 The Response Rate.
Table 3 Measurement of Task Characteristics.
Business Office
HIS Department Response
Nonresponse
Total
Response Nonresponse Total
29 * (32%) 12 (13%) 41 (45%)
32 (35%) 18 (20%) 50 (55%)
61 (67%) 30 (33%) 91 (100%)
* Number of hospitals.
In order to check for nonresponse bias, one-way ANOVAs were computed for statistically significant differences in UIS between the hospitals in which the data were collected from both groups and the hospitals in which only the business office responded. No significant differences appeared. The size of the hospitals, measured by the number of patient beds, ranged from medium to large: 11 hospitals between 150 and 400 beds, 11 between 500 and 850 beds, and 7 over 1000 beds. Since the size range of hospitals was wide, one-way ANOVA was performed to check for statistically significant differences in UIS among these groups. No significant differences appeared. 5.2. Measures As already discussed, user information satisfaction (UIS) is utilized in this research as a surrogate measure for MIS success. Among many UIS measures, the Jenkins and Ricketts [18] measure was adapted to the unique need for this research to arrive at users’ satisfaction with information quality. The instrument is presented in Table 2.
Table 2 Measurement of User Information 1. 2. 3.
Satisfaction (UIS). a
The contents of outputs from the MIS are very accurate. The outputs from the MIS are very easy to understand. The outputs from the MIS are very useful for identifying and defining problems. 4. The outputs provided by the MIS are very well formatted. 5. The outputs from the MIS are very useful in resolving problems. 6. b The outputs delivered by the MIS contain too much information. 7. The outputs from the MIS are very useful for selecting among alternative courses of action. a Seven-point Lickert scale. b Scores are reversed.
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a,b
Predictability (PRED) 1. How many of these tasks are the same from day-to-day? 2. To what extent would you say your work is routine? 3. People in this unit do about the same job in the same way most of the time. 4. Basically, unit members perform repetitive activities in doing their jobs. 5. How repetitious are your duties? Analyzability (ANAL) 6. To what extent is there a clearly known way to do the major types of work you normally encounter? 7. To what extent is there a clearly defined body of knowledge or subject matter that can guide you in doing your work? 8. To what extent is there an understandable sequence of steps that can be followed in doing your work? 9. To do your work, to what extent can you actually rely on established procedures and practices? 10. To what extent is there an understandable sequence of steps that can be followed in carrying out your work? a Adapted from Withey, Daft, and Cooper [44]. b Seven-point scale.
Seven information attributes attempting to measure information quality were accuracy, amount of information, format, understandability, usefulness for identifying and resolving problems, and usefulness for selecting among alternative courses of action. Users were asked to circle a number between one and seven that indicated their degree of agreement with each item. Users’ responses to the questions were averaged to achieve an overall HIS success score. The reliability of the UIS instrument was 0.87, using Cronbach’s alpha coefficient. The independent variables were the level of decentralization and task characteristics of the HIS development group. Task characteristics and decentralization of the HIS development group were measured on semantic differential scales that indicated their employees’ degree of agreement on a description of the work done in their unit and of the work group structure, respectively. The measures for the task characteristics and decentralization dimensions are presented in Table 3 and 4 respectively. In order to check the validity of the measurement of the task characteristics, principal component analysis was applied to those scales presented in Table 3. Two factors were extracted. These are
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Table 4 Measurement
Information
of Decentralization
Participation (PAR) We frequently participate new software. We frequently participate new HIS policies. We frequently participate We frequently participate the staff.
Dimensions.
Table 6 Factor Analysis
a,b
Factor in decisions
on the adoption
of
in decisions
on the adoption
of
in decisions in decisions
to hire new HIS staff. on promotions within
Hierarchy of Authority ’ (AUTH) 5. Little action is taken here until a supervisor approves a decision. 6. A person who wants to make his own decisions here is quickly discouraged. a Adapted from Hage and Aiken [14]. ’ Seven-point scale. ’ Scores are reversed.
orthogonal, that is, mutually independent. The loadings of the variables on the factors are given in Table 5. Factor 1 is most highly correlated with task predictability. Factor 2 is most highly correlated with problem analyzability. Only one item in problem analyzability, item 6, was not highly loaded on either factor and this item was dropped from subsequent analysis. Thus the validity of this instrument was confirmed. The Cronbach’s alpha coefficient was 0.88 for the task predictability
Table 5 Factor Analysis
Results
on Task Characteristics. Factor
1. Task Predictability item 1 item 2 item 3 item 4 item 5
1
Factor
* * * * *
-0.04 0.10 0.01 0.21 0.08
2
1. Participatron item 1 item 2 item 3 item 4
Factor
2
(PAR) 0.77 0.79 0.87 0.85
2. Hierarchy of Authority item 5 item 6 Variance explained Cronbach’s Alpha * Highest
1
Dimensions.
(So,)
(A UTH) 0.20 0.11 52.7 0.86
* * * *
0.25 0.30 0.13 0.02
0.81 * 0.85 * 18.9 0.62
row loadings.
scales and 0.87 for the problem analyzability scales. In order to check the validity of the measurement of the decentralization dimensions, principal component analysis was applied to those scales presented in Table 4. Two factors were extracted. The loadings of the variables on the factors are given in Table 6. Factor 1 is most highly correlated with participation. Factor 2 is most highly correlated with hierarchy of authority. Thus the validity of this instrument was confirmed. The reliability of the instrument was also assessed using Cronbach’s alpha coefficient. The alpha coefficient was 0.86 for the participation scales and 0.62 for the hierarchy of authority scales. HIS employee responses were averaged to produce work unit scores for each measure of task characteristics and decentralization, giving equal weight to each individual.
(PRED) 0.87 0.88 0.81 0.80 0.94
2. Problem Analyzability item 6 item I item 8 item 9 item 10 Variance explained Cronbach’s Alpha
Results on Decentralization
& Management
(‘%)
* Highest row loadings cal analysis.
6. The Results
(ANAL) 0.52 - 0.05 0.08 0.17 0.02
0.59 0.74 0.92 0.90 0.89
45.77 0.88
28.53 0.87
and selected for the subsequent
* * * *
statisti-
In the past, contingency studies have been criticized for lack of variation in the data, especially in the contingent variables. To ensure that the data in this study showed adequate variation to test the task characteristics-structure contingency relationships, median splits were performed on all variables, and the resultant mean differences were compared using T-tests. Means for all variables were significantly different at the p < 0.001 level. Means and standard deviations for all variables are shown in Table 7.
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Table I Zero-order Correlation between Variables (N = 29).
x
Strd. Dev.
5.03
0.91
Task Characteristics 3.22 2. PRED 4.06 3. ANAL
0.77 0.79
0.31 0.30
0.42 *
0.82 1.22
0.30 0.25
-0.41 * - 0.29
1
2
3
4
5
-0.15 0.12
0.27 - 0.29
0.57 ***
MIS Success 1. UIS
Decentralization 4. AUTH
5. PAR
3.06 3.46
* p < 0.05 * * p < 0.01 *** p i 0.001
6.1.
Zero-order
Correlations
between Variables
Table 7 presents the zero-order correlation matrix between UIS and the independent variables. As Table 7 shows, task predictability (PRED) correlated highly with hierarchy of authority (AUTH) (r = - 0.41; p < 0.05): the negative correlation means that the lower the task predictability, the more decentralized the hierarchy of authority. Problem analyzability (ANAL) correlated highly with task predictability (r = 0.42; p < 0.05). Workgroups reporting higher incidences of exceptional cases required greater levels of search behavior. A positive relationship between participation (PAR) and hierarchy of authority (AUTH) was found (r = 0.57; p < 0.001). The more decentralized the hierarchy of authority, the more opportunities there are for HIS employee participation in decision making. 6.2. Hypotheses
Testing
The most common approach to the interaction test of contingency relationship consists of a series of two-way analyses of variance with organizational context, unit structure, and interactions of organizational context with unit structure, as the independent variables, with unit performance (e.g., user information satisfaction) as the dependent variable [7]. With a standard multiple regression model using multiplicative interaction terms that combines elements of independent variables in the model, one faces the possibility of multicollinearity. In this case, regression coefficients are very
unreliable and a depressed regression coefficient could lead to a rejection of the interaction model [26]. This was so here. For example, the correlation between ANAL and AUTH * ANAL was 0.883 and the correlation between PRED and PRED * PAR was 0.834. Thus, testing the statistical significance of the regression coefficient of the interaction term is inappropriate for testing the interaction hypotheses. To conduct two way ANOVAs, the two task characteristics and the two decentralization dimensions were dichotomized at the median into low and high levels. In order to test the interaction effects between task predictability (PRED) and hierarchy of authority (AUTH), Hl, and between PRED and participation in decision making Table 8 Analysis of Variance of Task Predictablilty (PRED), Hierarchy of Authority (AUTH), and Interaction Effects on UIS. Mean Square
F
2 1 1
2.030 3.053 2.851
3.14 4.73 * 4.41 *
3.138
1
3.138
4.86 *
Explained
7.199
3
2.400
3.72 *
Residual
16.148
25
0.646
Total
23.348
28
0.834
Source of Variation
Sumof Squares
DF
Main Effects AUTH PRED
4.061 3.053 2.851
2-way Interactions PRED AUTH
* p < 0.05 * * p < 0.01 *** p i 0.001
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(PAR), H2, two separate two-way ANOVAs were conducted using UIS as the dependent variable. Table 8 and Table 9 show the results of the ANOVA tests for UIS. An examination shows two significant interaction effects that explained user information satisfaction. The interaction between task predictability (PRED) and hierarchy of authority (AUTH), Hl, was significant (F= 4.86; p < O.OS), as expected. Thus, Hl was supported. The statistical power of this test was 0.801 using Cohen’s [6] table, given the specification of alpha = 0.05, N = 29, and the effect size = 0.35. Also a significant interaction between task predictability (PRED) and participation (PAR), H2, was observed (F = 8.93; p < 0.01). Thus, H2 was supported. The statistical power of this test was 0.900, given the specification of alpha = 0.05, N = 29, and the effect size = 0.56. To test the subhypotheses (Hla, Hlb, H2a, and H2b) describing the nature of the interactions, subgroup analysis was utilized. In the subgroup analysis, task predictability and decentralization dimensions were dichotomized at the median. T-tests were performed to see if the mean UIS of the predictability-decentralization combinations suggested by the theory, e.g., low predictability-high participation, was significantly higher than the mean UIS of the other possible combinations, e.g., low predictability-low participation. The results are presented in Table IO. These results indicate that the differences are significant only when task predictability is low. Thus,
Table 9 Analysis of Variance of Task Predictability (PRED), Participation (PAR), and Interaction Effects on UK Mean square
F
2 1 1
1.531 2.053 1.542
2.56 3.44 2.58
5.339
1
5.339
8.93 * * *
Explained
8.400
3
2.800
4.68 * *
Residual
14.947
25
0.598
Total
23.348
28
0.834
Source of Variation
Sum of Squares
DF
Main Effects PAR PRED
3.062 2.053 1.542
2-way Interactions PAR PRED
* p < 0.05 * *p < 0.01 *** p < 0.001
Table 10 T-test Results for Hla, Hlb, H2a, and H2b. Criterion
Group
Size
Mean a
t-value
Prob
Low PRED
Low AUTH High AUTH
10 3
5.20 3.57
2.32
0.05
Low AUTH High AUTH
5 11
5.29 5.16
0.44
n.s.
Low PAR High PAR
5 8
3.89 5.41
2.62
0.05
Low PAR High PAR
9 7
5.30 5.07
0.92
n.s.
(Hla) High PRED (Hlb) Low PRED (H2a) High PRED (H2b)
a Mean UIS for each group
Hla and H2a were supported, but Hlb and H2b were not supported. In order to test the interaction effects between ANAL and the two decentralization dimensions (AUTH and PAR), H3 and H4, two separate two-way ANOVAs were conducted using UIS as the dependent performance variable. Contrary to expectations, no significant interaction effects were observed. Thus, H3 and H4 were not supported. The statistical power of the H3 test was 0.314 using Cohen’s [6] table, given the specification of alpha = 0.05, N = 29, and the effect size = 0.138. And the statistical power of the H4 test was 0.263, given the specification of alpha = 0.05, N = 29, and the effect size = 0.114. Further analysis to test the subhypotheses (H3a through H4b) was not performed because they could not be supported given the rejection of the interaction hypotheses. As a supplementary test to check the overall explanatory power of the group of the interaction PAR * ANAL, variables (i.e., PAR * PRED, AUTH * PRED, AUTH * ANAL), moderated regression analysis was performed. In the restricted model, task characteristics (PRED and ANAL) and decentralization dimensions (PAR and AUTH) were entered as predictors of UIS. In the unrestricted model, the interaction terms of task and decentralization (e.g., characteristics PAR * PRED) were entered in addition to the main effects. A test [19] was performed to see whether the addition of the interaction terms resulted in a significant increment in the percent of variance explained in UIS over that already explained by the main effect terms. The incremental R2 was 0.20 (F = 2.49), which was significant at an alpha of 0.10. The results confirm that system
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development group performance, defined as UIS, appears to be very much influenced by the match between task characteristics and decentralization dimensions.
7. Conclusion and Implications The relationship between MIS task characteristics and decentralization, and the impact of that relationship on the effective functioning of MIS development groups have been examined. The results suggest that decentralization interacts with task predictability to influence user information satisfaction. When MIS development tasks are unpredictable, a decentralized hierarchy of authority is more effective in achieving high user satisfaction. Also, extensive MIS employee participation in decision making is desirable when system analysts/ programmers encounter many exceptions. Theoretical implications of this finding can be found in Pfeffer’s [33] criticism: “Structural contingency theory specifies an overall perspective of managerial adaptation to [contextual] constraints, but the specific structural dimensions so adapted, as well as the specific elements of context that affect structural choices, are left unspecified.” Thus, what seems to be needed is research looking into which of the various elements of organizational context is important for understanding which elements of structure, under what conditions. The findings of this study suggest that task predictability, in a departmental context, is an important contingency for decentralization, especially when task predictability is low. Contrary to our expectations, when task predictability is high, the interaction between task predictability and decentralization was not significantly related to UIS. One plausible interpretation of this finding can be drawn from the literature dealing with the sociology of professions, such as HIS development personnel. The literature suggests that control is largely not imposed from without, but rather resides within the individual. On this point, Hall [15] stated, for example, that: “As the level of professionalization of employees increases, the level of formalization
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decreases. The presence of professionals appears to cause a diminished need for formalized rules and procedures. Since professionals have internalized norms and standards, the imposition of organizational requirements is not only unnecessary, it is likely to lead to professional-organizational conflict.” In other words, even when tasks are relatively predictable, decentralization of decision making authority may be desirable. Centralization, by using the combination of rules for repetitive situations and upward referral in the hierarchy for exceptional situations, may lead to professionalorganizational conflict in MIS development group and thus make them less effective. One should view the results of this research in the light of two limitations. First, since the study participants were not randomly selected from the population of HIS development groups, drawing the inferences to that population must be done with caution. Keeping in mind the similarity of the system development tasks within the hospital industry, however, one would not expect the research results to be significantly different if all HIS development groups had participated in this research. Second, this research shares a weakness common to other research at the work group level, i.e., small sample size; this does, however, provide a certain advantage here, since it is more difficult to find statistical significance with such a sample. Thus the results appear to be more robust than would be found in a larger setting. The replication of this research using a different sample might contribute additional insight into the appropriate HIS department structure. For example, some HIS are trying to integrate clinical information systems in order to address problems such as a dependence in medical care on the physician’s memory and a lack of meaningful feedback about the appropriateness of care. Task characteristics of clinical care may be different from administrative work and may, therefore, require a different organizational structure. Further research is needed on the relationship between other contextual factors (e.g., competitive strategy, organizational control structure) and MIS structure, and the impact of that relationship on MIS success. Also research focusing on the relative impact of those MIS contextual factors on
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MIS success will have useful managerial implications. With respect to user evaluations of MIS success, user responses may be gathered from various management levels to get an overall evaluation of MIS. The attributes of required information may vary depending on the levels of managerial activity and the relative degree of structure in the decisions being made. Thus, hospital administrators may perceive the HIS success differently from managers in business offices. Also individual differences of users, such as cognitive style, locus of control, risk-taking propensity, intelligence, tolerance of ambiguity, etc., can influence user perceptions of HIS success [24]. These propositions are empirical questions to be tested in future. Finally, this study used UIS as a surrogate measure for MIS performance. However, previous work has found that the two are not always positively associated with each other. Based on this, researchers must be cautious about using surrogate measures and specify clearly the exact nature of the dependent variables. One should, therefore, view the results of this research in the limited context of UIS: the replication of this study using different measures of MIS performance might contribute additional insight into the appropriate MIS department structure.
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111Arnold, H.J., Moderator variables: A clarification of con-
ceptual, analytic, and psychometric issues. Organizatronnl Behavior and Human Performance, 29 (1982), 143-174. PI Ball, M.J. and Boyle, T.M., Jr., Hospital information systems: Past, present, and future. Hospital Financial Management (February 1980), 12-24. I31 Cerveny, R. and Sanders, L., Implementation and structural variables. Information & Management, 11 (1986). 191-98. [41 Child, J., Predicting and understanding organizational structure. Administrative Science Quarterly, 18 (1973), 168-185. [51 Child, J., Managerial and organizational factors associated with company performance: Part II. A contingency analysis. The Journal of Management Studies, 12 (1975), 12-27. 161 Cohen, J., Statistical power analysis for the behavioral sciences. New York, NY: Academic Press, 1977. I71 Drazin, R. and Van de Ven, A., Alternative forms of fit in contingency theory. Administrative Science Quarterly, 30 (1985), 514-539.
Information & Management PI Duncan, R., Characteristics of organizational environments and perceived environmental uncertainty. Adminwtratiue Science Quarterly, 17 (1972), 313-327. [91 Ein-Dor, P. and Segev, E., Organizational context and the success of management information systems. Management Science, 24 (1978) 1067-1077. UOI Ein-Dor, P. and Segev, E., Centralization, decentralization and management information systems. Information & Management, 3 (August 1978), 1699172. [111 Ein-Dor, P. and Segev, E., Organizational context and MIS structure: Some empirical evidence. MIS Quarterly, 6 (1982), 55-67. [=I Fry, L.W. and Slocum, J., Jr. Technology, structure, and work group effectiveness: A test of contingency model. Academy of Management Journal, 27 (1984), 221-246. Galbraith, J., Organizational design, Reading, MA: Addison-Wesley, 1977. Hage, J., and Aiken, M., Social change in complex organizations. New York: Random House, 1970. Hall, R.H., Organizations: Structures and processes. Englewood Cliffs, NJ: Prentice-Hall, 1972. Hrebiniak, L., Job technology, supervision and work group An abbreviated replication. Administratioe Scrence Quarter/y, 19 (1974), 395-410. [I71 Ives, B., Olson, M., and Baroudi, J., The measurement of user information satisfaction. Communications of the ACM, 26 (1983). 785-793. 181 Jenkins, A.M., and Ricketts, J.A., Development of an instrument to measure user satisfaction with management information systems. Graduate School of Business, Indiana University, 1979. 191 Kerlinger, F. and Pedhazur, E., Multiple regression in behavior research. New York, NY: Holt, Rinehart and Winston, 1973. [201 Khandwalla, P., Mass orientation of operations technology and organizational structure. Administrative Science Quarterly, 19 (1974), 74-97. WI Kmetz, J., A critique of the Aston studies and results with a new measure of technology. Organization and Administrative Science, 8 (1977/1978), 1233144. [221Lawrence, P., and Lorsch, J., Organization and enuironment: Managing differentiation and integration. Homewood, IL: Irwin, 1969. v31 Lucas, H., Jr., User reactions and the management of information services. Management Informatics, 2 (1973), 165-172. 1241 Lucas, H., Jr., Performance and the use of an information stem. Management Science, 21 (1975) 908-919. ~251 McFarlan, F.W. and McKenney, J.L., Corporate rnformation systems management: The issues facing senior executioes. Homewood, IL: Richard D. Irwin, Inc., 1983. WI Neter, J. and Wasserman, W., Applied linear statistical models. Homewood, IL: Irwin, 1974. ~271 Nolan, R. and Seward, H., Measuring user satisfaction to evaluate information systems. in R.L. Nolan (ed.), Managing the data resource function, Los Angeles, CA: West Publishing Co., 1974, 253-275. WI Olson, M.H., An investigation of organizational contingencies associated with structure of the information servites functron. Ph.D. dissertation, University of Minnesota, 1978.
Information & Management [29] Olson, M.H., User involvement and decentralization of the development function: A comparison of two case studies. Systems, Objectives, Solutions, 1 (April 19X1), 59-69. [30] Olson, M.H. and Chervany, N.L., The relationship between organizational characteristics and the structure of the information service function. MIS Quarter&, 4 (19X0), 57-68. [31] Perrow, C., A framework for the comparative analysis of organizations. Amerrcan Sociological Review, 32 (1967), 194&20X. [32] Perrow, C., Organizational analysis: A sociological view. Belmont, CA: Wadsworth Publishing Co., 1970. 1331 Pfeffer, J., Organizations and organization theory. Boston, MA: Pitman Publishing Inc., 1982. [34] Porat, A. and Haas, J., Information effects on decision making. Behavioral Science, 14 (1969), 9X-104. 1351 Randolph, A., Matching technology and the design of organization units. California Management Review, 23 (1981). 39-4X. [36] Schoonhoven, C., Problems with contingency theory: Testing assumptions hidden within the language of contingency. Administrative Science Quarterly, 26 (19X1), 349-77. [37] Srinivasan, A., Alternative measures of system effectiveness: Associations and implications. MIS Quarter/y, 9 (19X5), 243-53.
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[38] Strauss, G., Workers participation in management: An international perspective. In B. Staw & L. Cummings (eds.), Research in organrrational behavior (Vol. 4), Greenwich, Conn.: JAI Press, 1982, 173-266. [39] Streufert, S., Effects of information relevance on decision making in complex environments. Memory and Cognition, 1 (1973), 224-228. [40] Swanson, E., Management information systems: Appreciation and involvement. Management Science, 21 (1974). 17X-198. [41] Thompson, J.D., Organizations m action. New York: McGraw-Hill, 1967. [42] Tushman, M. and Nadler, D., Information processing as an integrating concept in organizational design. Academy of Management Review, 3 (197X), 613-624. [43] Van de Ven, A.H., Delbecq, A., and Koenig, Jr., Determinants of coordination modes within organizations. American Socrological Review, 41 (1976), 322-33X. [44] Withey, M., Daft, R., and Cooper, W., Measures of Perrow’s work unit technology: An empirical assessment and a new scale. Academy of Management Journal, 26 (19X3), 45-63. [45] Woodward, J., Industrial organization: Theory and practice. London: Oxford University Press, 1965. [46] Zmud, R., Individual difference and MIS success: A review of the empirical literature. Management Scrence, 25 (1979). 966-979.
Research
Task characteristics, decentralization, and the success of hospital information systems K. Kyu Kim inha
University,
1. Introduction
Inchou, Koreu
Considerable attention has been given to the importance of structural and contextual variables in the successful implementation of management information systems (MIS). Empirical studies, however, are inconclusive on the effect of the organizational context and MIS structure on MIS success. This study examines the relationship between the decentralization of hospital information systems (HIS) management and HIS development task characteristics, and the impact of that relationship on the functioning of HIS development groups. The results indicate that decentralization interacts with task predictability to influence user information satisfaction. When HIS development tasks are unpredictable, a decentralized hierarchy of authority is more effective in achieving high user satisfaction. Also, extensive HIS employee participation in decision making is desirable when HIS development personnel encounter many exceptions. Keywords: MIS Structure, tion, Hospital Information mation Satisfaction.
Task Characteristics, DecentraliiaSystems, MIS Success, User Infor-
Kyung Kyu Kim is an Assistant Professor of Management Information Systems at Inha University. He received his Ph.D. in Business Administration from the University of Utah in 1986. His recent articles have appeared in
The Accounting Review, MIS Quarterly
and Journal of Information Systems. His research interests include strategic use of information systems and MIS implementation issues.
North-Holland Information & Management
19 (1990) 83-93
0378-7206/90/$03.50 0 1990 - Elsetier
Science Publishers
The issue of centralization versus decentralization of MIS activities has generated much debate and subsequent research over the years (e.g., see Ein-Dor and Segev [lo], McFarlan and Mckenney [25], Olson [28]). The process of determining precisely which MIS organizational structure may be appropriate under what conditions, however, has received inadequate attention. This study addresses the question: Should the decision making authority in MIS development groups be centralized or decentralized in order to be effective in meeting the users’ information needs? The HIS development function includes the analysis, design, and programming of new applications, and the maintenance of existing applications. The management literature usually explains organizational structure by resorting to structural contingency theory. This contends that those organizations with structures closely matching the requirements of the organization’s context are more effective than those that do not. Examples of organizutional context thought to be critical for the functioning of organizations include environmental uncertainty [8,22], organizational size [5,433, and task characteristics [41,45]. In small departments, task characteristics among other contextual factors has been found to have a significant effect on organizational structure because their personnel are located close to the technical activities of that unit [16,21]. Pfeffer [33] provides the theoretical underpinnings of the task characteristics-structure relationship: [Task characteristics] “affects the skills and discretion of the work force and, thus, the control that must be employed; different structural arrangements imply different types of control structures and procedures; and,
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Research
therefore, is linked to structure through its requirements for procedures to control work The purpose of this study is to explore whether those MIS development groups with structures matching the requirements of their task characteristics are more effective than those that do not in a field study of hospital information systems (HIS) development groups.
2. A Review of Prior Research There have been few empirical investigations of the relationship between MIS context and MIS organizational structure; moreover, these investigations have generally had mixed results. In their extensive review of the investigations on the effect of the MIS context and MIS structure on system effectiveness, Cerveny and Sanders [3] concluded that empirical studies were inconclusive. Through interviews with corporate executives and information processing managers in 43 business organizations, Olson and Chervany [30] attempted to identify those organizational characteristics that are associated with the structure of the information services function. Few organizational characteristics, however, were found to influence the structure of information services consistently across all organizations. Ein-Dor and Segev [ll], using a sample of 53 organizations in a large U.S. metropolitan area, tested the proposition that organizational structure is related to the dimensions of MIS structure, such as degree of centralization and integration of MIS. The major findings were that the degree of centralization is significantly correlated with the degree of centralization of organizational decision making authority which, in turn, is closely associated with organizational size. As a possible explanation on the mixed findings, the concept of “fit” between MIS context and MIS structure was proposed [e.g., 91. The basic premise of this view is that work groups are open systems which must deal with work-related uncertainty, defined as the difference between information possessed and information required to complete a task [13]. There are several sources of uncertainty to which work groups must respond. The major sources of work-related uncertainty include work group environment, task characteris-
Information
& Management
tics, and work group size. Given the various sources of uncertainty, a basic function of the work group structure is to create the most appropriate configuration of work groups to facilitate the effective collection, processing and distribution of information [42]. Different MIS organizational structures have different capacities for effective information processing. For example, research indicates that organic structures are able to deal with greater amounts of work-related uncertainty than mechanistic structures. As the amount of uncertainty that a work unit faces increases, so does the need for increased information processing capacity. Therefore, to be effective, MIS organizational structure should match MIS context. In addition to the mixed findings, previous studies that have investigated the relationship between work group context and MIS organizational structure share a common weakness in the selection of dependent variables. The major argument of contingency theory is that those work groups with structures closely matching the requirements of the work group’s context perform better than those that do not. Thus, the phenomenon to be explained by a contingency theory is MIS performance. Unfortunately, this generally has not been investigated as a dependent variable in previous studies. This study attempts to shed new light on conflicting results of previous research: the relationship between MIS context and MIS organizational structure. Further, system development group performance is selected as the dependent variable to mitigate some previous limitations.
3. Decentralization, Success
Task Characteristics,
and MIS
3.1. Decentralization Centralization versus decentralization, as used here, concerns the distribution of decision making authority within the organization [12]. Centralization then implies the concentration of decision making power in a single person or small group at the top level; decentralization implies that decisions can be made at low levels in the organizational hierarchy. Hage and Aiken [14] subdivided centralization of decisions into two subconstructs. One is the concentration of decisions referring to
Information
& Management
resource distribution or policy formulation, with the indicator being the participation in decision making. The other is the concentration of decisions referring to accomplishment of tasks, with the indicator being the hierarchy of authority. 3.2. Task Characteristics In defining task characteristics, various authors have posited a variety of typologies, but a number of studies appear to be related to the ideas described by Perrow [31]. Several studies operationalized one or more of the dimensions he proposed, and several other studies have the potential to fit within the categories he described [44]. These dimensions are: task predictability and problem analyzability. Task predictability denotes the number of exceptions in the work, or the frequency of unexpected and novel events that occur in the conversion process [32]. Workgroups with few exceptions experience considerable certainty about the occurrence of task related activities; many exceptions mean that participants typically cannot predict problems in advance. Problem analyzability denotes the degree to which one can analyze an unexpected event once it is encountered. Objective or computational procedures are usually followed to resolve analyzable exceptions. For unanalyzable exceptions, there is no set of techniques or procedures to advise a person exactly what to do. Employees rely on accumulated experience, intuition, and judgment. 3.3. MIS
Success
MIS success can be defined as the extent to which the MIS supports the organization in achieving its goals. However, measuring MIS success is a difficult task, because of the difficulty of tracing and measuring the effects of MIS through a tangle of intermediate impacts upon organizational effectiveness. Thus, MIS researchers have developed surrogate measures for MIS effectiveness, such as user information satisfaction, system usage, and user acceptance; these are assumed to relate to the organizational effectiveness. User information satisfaction (UK) is generally recognized by many MIS researchers as one of the more important indicators of success in designing and implementing MIS [37,46]. Ives et al. [17]
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85
defined UIS as “the extent to which users believe the information system available to them meets their information requirements.” UIS is considered as a meaningful surrogate for MIS effectiveness in that it measures satisfaction of organization members who actually use the MIS output to meet their organizational responsibilities. Thus, UIS is used here as a surrogate for MIS success.
4. Hypotheses Building upon an understanding of the task characteristics and decentralization concepts, one can proceed to explore the “best matches” between the two concepts. In structural contingency theory, the term interaction between task characteristics and structure requires that they predict a third variable (in our case MIS success) [1,36]. Thus, a series of interaction hypotheses are developed between individual task characteristics and decentralization dimensions. Task predictability is expected to interact with decentralization to influence MIS success [e.g., 351. When system development tasks are repetitious and system developers experience few exceptions, decision making authority can be kept at the management level because of the high certainty of tasks. MIS development personnel need little discretion and power in the unit; they simply perform the well-defined problem solutions assigned to them. Participants will be more committed if their time is not wasted by involvement in decisions with obvious solutions [38]; however, as the number of exceptions increases, centralization becomes less effective, because information processing requirements may overburden the hierarchy by the sheer number of exceptional cases. System development groups structured to refer all exceptional cases upward to a centralized decision point are likely to suffer significant delays in implementing decisions because of the lengthy referral process. Thus, decentralization of decisions would appear applicable when information must be processed by MIS personnel directly in the development process. These personnel should participate in the decisions that affect how they perform their tasks. Accordingly, the following hypotheses are developed: Hl: Task predictability will interact with hierarchy of authority to influence UIS.
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Research
Hla:
Hlb:
H2
When task predictability is low, decentralized hierarchy of authority will be positively associated with UIS. When task predictability is high, centralized hierarchy of authority will be positively associated with UK
Task predictability will interact with MIS employee participation in decision making to influence UIS.
H2a: When task predictability is low, increases in MIS employee participation in decision making will be positively associated with UIS. H2b: When task predictability is high, decreases in MIS employee participation in decision making will be positively associated with UIS. Also problem analyzability is expected to interact with decentralization to influence performance. When problems are analyzable, centralized decision making can be effectively utilized to establish contingency plans for dealing with problems that occur. A system development group dealing with mostly analyzable problems could identify a priori potential problems and could specify appropriate responses. However, when problems encountered are more difficult to analyze, it becomes increasingly difficult to develop contingency plans in advance. Instead, extensive search behavior by system development personnel, which involves on-the-spot sharing of information among them, is required to discover solutions to these problems. Thus, to be effective, system development personnel should actively participate in the gathering of salient data and the implementation of decisions. Hence the following hypotheses: H3: Problem analyzability will interact with hierarchy of authority to influence UIS. H3a: When problem analyzability is low, decentralized hierarchy of authority will be positively associated with UIS. H3b: When problem analyzability is high, centralized hierarchy of authority will be positively associated with UIS. H4:
Problem analyzability will interact with MIS employee participation in decision making to influence UIS.
H4a: When problem analyzability is low, increases in MIS employee participation in decision making will be positively associated with UIS. H4b: When problem analyzability is high, decreases in MIS employee participation in decision making will be positively associated with UIS.
5. Methods Using questionnaires, a field study was conducted in hospital information systems (MIS) development groups. 5.1. Sample Questionnaires were sent to 91 hospitals that were members of a private national association of HIS. The research objectives required that, in each hospital, the information come from both the HIS group and HIS users. Regarding primary users of HIS, business offices were chosen to receive the user questionnaire based on the survey of Ball and Boyle [2]. In their survey of HIS, they concluded that the HIS emphasized business-oriented applications, such as diagnostic related groups (DRGs), general ledger, admissions and discharges, patient billing, and materials management. Typically, these tasks were performed by hospital business offices. Thus, these offices were selected as an appropriate user group for HIS success evaluation. HIS directors were asked to select randomly five system development personnel and to distribute an HIS employee questionnaire to each of them. To ensure a fair evaluation, the author directly contacted the business office managers and supervisors independently. HIS development personnel were asked to complete the questionnaire regarding both their task characteristics and the degree of decentralization within the HIS department. The completed questionnaires were mailed back directly to the primary researcher. Of the 91 hospitals originally contacted, responses arrived from both user groups and HIS departments in 29 hospitals for a response rate of 31 percent. See Table I. Thus, those 29 hospitals comprised the final research sample.
In/ormation
K. Kyu Kim / Hospital Information
& Management
Table 1 The Response Rate.
Table 3 Measurement of Task Characteristics.
Business Office
HIS Department Response
Nonresponse
Total
Response Nonresponse Total
29 * (32%) 12 (13%) 41 (45%)
32 (35%) 18 (20%) 50 (55%)
61 (67%) 30 (33%) 91 (100%)
* Number of hospitals.
In order to check for nonresponse bias, one-way ANOVAs were computed for statistically significant differences in UIS between the hospitals in which the data were collected from both groups and the hospitals in which only the business office responded. No significant differences appeared. The size of the hospitals, measured by the number of patient beds, ranged from medium to large: 11 hospitals between 150 and 400 beds, 11 between 500 and 850 beds, and 7 over 1000 beds. Since the size range of hospitals was wide, one-way ANOVA was performed to check for statistically significant differences in UIS among these groups. No significant differences appeared. 5.2. Measures As already discussed, user information satisfaction (UIS) is utilized in this research as a surrogate measure for MIS success. Among many UIS measures, the Jenkins and Ricketts [18] measure was adapted to the unique need for this research to arrive at users’ satisfaction with information quality. The instrument is presented in Table 2.
Table 2 Measurement of User Information 1. 2. 3.
Satisfaction (UIS). a
The contents of outputs from the MIS are very accurate. The outputs from the MIS are very easy to understand. The outputs from the MIS are very useful for identifying and defining problems. 4. The outputs provided by the MIS are very well formatted. 5. The outputs from the MIS are very useful in resolving problems. 6. b The outputs delivered by the MIS contain too much information. 7. The outputs from the MIS are very useful for selecting among alternative courses of action. a Seven-point Lickert scale. b Scores are reversed.
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87
a,b
Predictability (PRED) 1. How many of these tasks are the same from day-to-day? 2. To what extent would you say your work is routine? 3. People in this unit do about the same job in the same way most of the time. 4. Basically, unit members perform repetitive activities in doing their jobs. 5. How repetitious are your duties? Analyzability (ANAL) 6. To what extent is there a clearly known way to do the major types of work you normally encounter? 7. To what extent is there a clearly defined body of knowledge or subject matter that can guide you in doing your work? 8. To what extent is there an understandable sequence of steps that can be followed in doing your work? 9. To do your work, to what extent can you actually rely on established procedures and practices? 10. To what extent is there an understandable sequence of steps that can be followed in carrying out your work? a Adapted from Withey, Daft, and Cooper [44]. b Seven-point scale.
Seven information attributes attempting to measure information quality were accuracy, amount of information, format, understandability, usefulness for identifying and resolving problems, and usefulness for selecting among alternative courses of action. Users were asked to circle a number between one and seven that indicated their degree of agreement with each item. Users’ responses to the questions were averaged to achieve an overall HIS success score. The reliability of the UIS instrument was 0.87, using Cronbach’s alpha coefficient. The independent variables were the level of decentralization and task characteristics of the HIS development group. Task characteristics and decentralization of the HIS development group were measured on semantic differential scales that indicated their employees’ degree of agreement on a description of the work done in their unit and of the work group structure, respectively. The measures for the task characteristics and decentralization dimensions are presented in Table 3 and 4 respectively. In order to check the validity of the measurement of the task characteristics, principal component analysis was applied to those scales presented in Table 3. Two factors were extracted. These are
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Research
Table 4 Measurement
Information
of Decentralization
Participation (PAR) We frequently participate new software. We frequently participate new HIS policies. We frequently participate We frequently participate the staff.
Dimensions.
Table 6 Factor Analysis
a,b
Factor in decisions
on the adoption
of
in decisions
on the adoption
of
in decisions in decisions
to hire new HIS staff. on promotions within
Hierarchy of Authority ’ (AUTH) 5. Little action is taken here until a supervisor approves a decision. 6. A person who wants to make his own decisions here is quickly discouraged. a Adapted from Hage and Aiken [14]. ’ Seven-point scale. ’ Scores are reversed.
orthogonal, that is, mutually independent. The loadings of the variables on the factors are given in Table 5. Factor 1 is most highly correlated with task predictability. Factor 2 is most highly correlated with problem analyzability. Only one item in problem analyzability, item 6, was not highly loaded on either factor and this item was dropped from subsequent analysis. Thus the validity of this instrument was confirmed. The Cronbach’s alpha coefficient was 0.88 for the task predictability
Table 5 Factor Analysis
Results
on Task Characteristics. Factor
1. Task Predictability item 1 item 2 item 3 item 4 item 5
1
Factor
* * * * *
-0.04 0.10 0.01 0.21 0.08
2
1. Participatron item 1 item 2 item 3 item 4
Factor
2
(PAR) 0.77 0.79 0.87 0.85
2. Hierarchy of Authority item 5 item 6 Variance explained Cronbach’s Alpha * Highest
1
Dimensions.
(So,)
(A UTH) 0.20 0.11 52.7 0.86
* * * *
0.25 0.30 0.13 0.02
0.81 * 0.85 * 18.9 0.62
row loadings.
scales and 0.87 for the problem analyzability scales. In order to check the validity of the measurement of the decentralization dimensions, principal component analysis was applied to those scales presented in Table 4. Two factors were extracted. The loadings of the variables on the factors are given in Table 6. Factor 1 is most highly correlated with participation. Factor 2 is most highly correlated with hierarchy of authority. Thus the validity of this instrument was confirmed. The reliability of the instrument was also assessed using Cronbach’s alpha coefficient. The alpha coefficient was 0.86 for the participation scales and 0.62 for the hierarchy of authority scales. HIS employee responses were averaged to produce work unit scores for each measure of task characteristics and decentralization, giving equal weight to each individual.
(PRED) 0.87 0.88 0.81 0.80 0.94
2. Problem Analyzability item 6 item I item 8 item 9 item 10 Variance explained Cronbach’s Alpha
Results on Decentralization
& Management
(‘%)
* Highest row loadings cal analysis.
6. The Results
(ANAL) 0.52 - 0.05 0.08 0.17 0.02
0.59 0.74 0.92 0.90 0.89
45.77 0.88
28.53 0.87
and selected for the subsequent
* * * *
statisti-
In the past, contingency studies have been criticized for lack of variation in the data, especially in the contingent variables. To ensure that the data in this study showed adequate variation to test the task characteristics-structure contingency relationships, median splits were performed on all variables, and the resultant mean differences were compared using T-tests. Means for all variables were significantly different at the p < 0.001 level. Means and standard deviations for all variables are shown in Table 7.
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89
Table I Zero-order Correlation between Variables (N = 29).
x
Strd. Dev.
5.03
0.91
Task Characteristics 3.22 2. PRED 4.06 3. ANAL
0.77 0.79
0.31 0.30
0.42 *
0.82 1.22
0.30 0.25
-0.41 * - 0.29
1
2
3
4
5
-0.15 0.12
0.27 - 0.29
0.57 ***
MIS Success 1. UIS
Decentralization 4. AUTH
5. PAR
3.06 3.46
* p < 0.05 * * p < 0.01 *** p i 0.001
6.1.
Zero-order
Correlations
between Variables
Table 7 presents the zero-order correlation matrix between UIS and the independent variables. As Table 7 shows, task predictability (PRED) correlated highly with hierarchy of authority (AUTH) (r = - 0.41; p < 0.05): the negative correlation means that the lower the task predictability, the more decentralized the hierarchy of authority. Problem analyzability (ANAL) correlated highly with task predictability (r = 0.42; p < 0.05). Workgroups reporting higher incidences of exceptional cases required greater levels of search behavior. A positive relationship between participation (PAR) and hierarchy of authority (AUTH) was found (r = 0.57; p < 0.001). The more decentralized the hierarchy of authority, the more opportunities there are for HIS employee participation in decision making. 6.2. Hypotheses
Testing
The most common approach to the interaction test of contingency relationship consists of a series of two-way analyses of variance with organizational context, unit structure, and interactions of organizational context with unit structure, as the independent variables, with unit performance (e.g., user information satisfaction) as the dependent variable [7]. With a standard multiple regression model using multiplicative interaction terms that combines elements of independent variables in the model, one faces the possibility of multicollinearity. In this case, regression coefficients are very
unreliable and a depressed regression coefficient could lead to a rejection of the interaction model [26]. This was so here. For example, the correlation between ANAL and AUTH * ANAL was 0.883 and the correlation between PRED and PRED * PAR was 0.834. Thus, testing the statistical significance of the regression coefficient of the interaction term is inappropriate for testing the interaction hypotheses. To conduct two way ANOVAs, the two task characteristics and the two decentralization dimensions were dichotomized at the median into low and high levels. In order to test the interaction effects between task predictability (PRED) and hierarchy of authority (AUTH), Hl, and between PRED and participation in decision making Table 8 Analysis of Variance of Task Predictablilty (PRED), Hierarchy of Authority (AUTH), and Interaction Effects on UIS. Mean Square
F
2 1 1
2.030 3.053 2.851
3.14 4.73 * 4.41 *
3.138
1
3.138
4.86 *
Explained
7.199
3
2.400
3.72 *
Residual
16.148
25
0.646
Total
23.348
28
0.834
Source of Variation
Sumof Squares
DF
Main Effects AUTH PRED
4.061 3.053 2.851
2-way Interactions PRED AUTH
* p < 0.05 * * p < 0.01 *** p i 0.001
90
Information& Management
Research
(PAR), H2, two separate two-way ANOVAs were conducted using UIS as the dependent variable. Table 8 and Table 9 show the results of the ANOVA tests for UIS. An examination shows two significant interaction effects that explained user information satisfaction. The interaction between task predictability (PRED) and hierarchy of authority (AUTH), Hl, was significant (F= 4.86; p < O.OS), as expected. Thus, Hl was supported. The statistical power of this test was 0.801 using Cohen’s [6] table, given the specification of alpha = 0.05, N = 29, and the effect size = 0.35. Also a significant interaction between task predictability (PRED) and participation (PAR), H2, was observed (F = 8.93; p < 0.01). Thus, H2 was supported. The statistical power of this test was 0.900, given the specification of alpha = 0.05, N = 29, and the effect size = 0.56. To test the subhypotheses (Hla, Hlb, H2a, and H2b) describing the nature of the interactions, subgroup analysis was utilized. In the subgroup analysis, task predictability and decentralization dimensions were dichotomized at the median. T-tests were performed to see if the mean UIS of the predictability-decentralization combinations suggested by the theory, e.g., low predictability-high participation, was significantly higher than the mean UIS of the other possible combinations, e.g., low predictability-low participation. The results are presented in Table IO. These results indicate that the differences are significant only when task predictability is low. Thus,
Table 9 Analysis of Variance of Task Predictability (PRED), Participation (PAR), and Interaction Effects on UK Mean square
F
2 1 1
1.531 2.053 1.542
2.56 3.44 2.58
5.339
1
5.339
8.93 * * *
Explained
8.400
3
2.800
4.68 * *
Residual
14.947
25
0.598
Total
23.348
28
0.834
Source of Variation
Sum of Squares
DF
Main Effects PAR PRED
3.062 2.053 1.542
2-way Interactions PAR PRED
* p < 0.05 * *p < 0.01 *** p < 0.001
Table 10 T-test Results for Hla, Hlb, H2a, and H2b. Criterion
Group
Size
Mean a
t-value
Prob
Low PRED
Low AUTH High AUTH
10 3
5.20 3.57
2.32
0.05
Low AUTH High AUTH
5 11
5.29 5.16
0.44
n.s.
Low PAR High PAR
5 8
3.89 5.41
2.62
0.05
Low PAR High PAR
9 7
5.30 5.07
0.92
n.s.
(Hla) High PRED (Hlb) Low PRED (H2a) High PRED (H2b)
a Mean UIS for each group
Hla and H2a were supported, but Hlb and H2b were not supported. In order to test the interaction effects between ANAL and the two decentralization dimensions (AUTH and PAR), H3 and H4, two separate two-way ANOVAs were conducted using UIS as the dependent performance variable. Contrary to expectations, no significant interaction effects were observed. Thus, H3 and H4 were not supported. The statistical power of the H3 test was 0.314 using Cohen’s [6] table, given the specification of alpha = 0.05, N = 29, and the effect size = 0.138. And the statistical power of the H4 test was 0.263, given the specification of alpha = 0.05, N = 29, and the effect size = 0.114. Further analysis to test the subhypotheses (H3a through H4b) was not performed because they could not be supported given the rejection of the interaction hypotheses. As a supplementary test to check the overall explanatory power of the group of the interaction PAR * ANAL, variables (i.e., PAR * PRED, AUTH * PRED, AUTH * ANAL), moderated regression analysis was performed. In the restricted model, task characteristics (PRED and ANAL) and decentralization dimensions (PAR and AUTH) were entered as predictors of UIS. In the unrestricted model, the interaction terms of task and decentralization (e.g., characteristics PAR * PRED) were entered in addition to the main effects. A test [19] was performed to see whether the addition of the interaction terms resulted in a significant increment in the percent of variance explained in UIS over that already explained by the main effect terms. The incremental R2 was 0.20 (F = 2.49), which was significant at an alpha of 0.10. The results confirm that system
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development group performance, defined as UIS, appears to be very much influenced by the match between task characteristics and decentralization dimensions.
7. Conclusion and Implications The relationship between MIS task characteristics and decentralization, and the impact of that relationship on the effective functioning of MIS development groups have been examined. The results suggest that decentralization interacts with task predictability to influence user information satisfaction. When MIS development tasks are unpredictable, a decentralized hierarchy of authority is more effective in achieving high user satisfaction. Also, extensive MIS employee participation in decision making is desirable when system analysts/ programmers encounter many exceptions. Theoretical implications of this finding can be found in Pfeffer’s [33] criticism: “Structural contingency theory specifies an overall perspective of managerial adaptation to [contextual] constraints, but the specific structural dimensions so adapted, as well as the specific elements of context that affect structural choices, are left unspecified.” Thus, what seems to be needed is research looking into which of the various elements of organizational context is important for understanding which elements of structure, under what conditions. The findings of this study suggest that task predictability, in a departmental context, is an important contingency for decentralization, especially when task predictability is low. Contrary to our expectations, when task predictability is high, the interaction between task predictability and decentralization was not significantly related to UIS. One plausible interpretation of this finding can be drawn from the literature dealing with the sociology of professions, such as HIS development personnel. The literature suggests that control is largely not imposed from without, but rather resides within the individual. On this point, Hall [15] stated, for example, that: “As the level of professionalization of employees increases, the level of formalization
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decreases. The presence of professionals appears to cause a diminished need for formalized rules and procedures. Since professionals have internalized norms and standards, the imposition of organizational requirements is not only unnecessary, it is likely to lead to professional-organizational conflict.” In other words, even when tasks are relatively predictable, decentralization of decision making authority may be desirable. Centralization, by using the combination of rules for repetitive situations and upward referral in the hierarchy for exceptional situations, may lead to professionalorganizational conflict in MIS development group and thus make them less effective. One should view the results of this research in the light of two limitations. First, since the study participants were not randomly selected from the population of HIS development groups, drawing the inferences to that population must be done with caution. Keeping in mind the similarity of the system development tasks within the hospital industry, however, one would not expect the research results to be significantly different if all HIS development groups had participated in this research. Second, this research shares a weakness common to other research at the work group level, i.e., small sample size; this does, however, provide a certain advantage here, since it is more difficult to find statistical significance with such a sample. Thus the results appear to be more robust than would be found in a larger setting. The replication of this research using a different sample might contribute additional insight into the appropriate HIS department structure. For example, some HIS are trying to integrate clinical information systems in order to address problems such as a dependence in medical care on the physician’s memory and a lack of meaningful feedback about the appropriateness of care. Task characteristics of clinical care may be different from administrative work and may, therefore, require a different organizational structure. Further research is needed on the relationship between other contextual factors (e.g., competitive strategy, organizational control structure) and MIS structure, and the impact of that relationship on MIS success. Also research focusing on the relative impact of those MIS contextual factors on
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MIS success will have useful managerial implications. With respect to user evaluations of MIS success, user responses may be gathered from various management levels to get an overall evaluation of MIS. The attributes of required information may vary depending on the levels of managerial activity and the relative degree of structure in the decisions being made. Thus, hospital administrators may perceive the HIS success differently from managers in business offices. Also individual differences of users, such as cognitive style, locus of control, risk-taking propensity, intelligence, tolerance of ambiguity, etc., can influence user perceptions of HIS success [24]. These propositions are empirical questions to be tested in future. Finally, this study used UIS as a surrogate measure for MIS performance. However, previous work has found that the two are not always positively associated with each other. Based on this, researchers must be cautious about using surrogate measures and specify clearly the exact nature of the dependent variables. One should, therefore, view the results of this research in the limited context of UIS: the replication of this study using different measures of MIS performance might contribute additional insight into the appropriate MIS department structure.
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