.lournal of Management 1994, Vol. 20, No. 3, 581-604

The Effects of Environment and Technology on Managerial Roles Barrie Gibbs Pacific Strategies Two samples of managers are used to demonstrate that environmental and technological variables affect the frequency of managerial roles as defined by Mint&erg (1973). Environmental complexity increases the frequen~!y of informational roles while ~0mp~exit.v and d~~namismincrease the freq~eney of decisional roles. The interpersonal roles are predicted by an interaction between complexity and dynamism. Overall routineness decreases the frequency of all roles. The presence of rules increases the frequency of decisional and interpersonal roles. The findings suggest that environmental dimensions and technology need to be taken into account in future research and future theories of managerial work.

Why managers undertake different activities in different settings and the impact of managerial activity on organizations is central to the study of organizational behavior. Those who use skills (Fayol, 1930; Katz, 1974) or roles (Mintzberg, 1973, 1975) to study managerial behavior have used functional area and hierarchic~ level to explain differences in managerial behavior. Thus, the way in which tasks are divided internally has come to dominate our understanding of management activity regardless of the environment of the organization or the technology the organization may employ. The purpose of this paper is to show that managerial activities relate directly to environmental and technological characteristics of the manager’s subunit. Job tenure, age, span of control, and functional area are used as control variables in direct tests of hypotheses that examine the relationship between environmental complexity, environmental dynamism, technology and the frequency of managerial roles. The managerial roles examined are derived from Mintzberg (1973, 1975). Mintzberg argues that all managers have formal authority over their own organizational unit, and that the associated status leads managers to be involved in a series of interpersonal, informational, and decisional roles (1975, p. 55). These roles are leader, liaison, and figurehead in the interpersonal category; monitor, disseminator, and spokesperson in the informational category; and Direct all correspondence to: Barrie Gibbs, Pacific Strategies, Columbia, V6B XX Canada. Copyright

0 1994 by JAI Press Inc. 0149-2063 581

Suite 635,375

Water Street, Vancouver,

British

582

GIBBS

resource allocator, decisional category

entrepreneur, negotiator, (Mintzberg, 1973, 1975).

and disturbance

handler

in the

Environment The environment is here defined by two categories: complexity and dynamism. Environmental complexity describes the number of units with which interaction is required and the extent to which an organization or subunit must have a great deal of sophisticated knowledge about products, customers and so on (Aldrich, 1979, p. 74). Dynamism is the extent to which change occurs in the environment. A manager can predict future events because environmental situation(s) reoccur frequently through time. A manager cannot predict future events when changes are so drastic or so frequent that predictability of events is low. Complexity uses the number of elements as an explanatory variable while dynamism uses the rate of change of the elements as an explanatory variable. The concepts are related in that both deal with environmental elements but are viewed as distinct concepts by most authors (Thompson, 1967; Perrow, 1970; Jurkovich, 1974; Miles & Snow, 1974; Van De Ven & Delbecq, 1974; Mintzberg, 1979). Environments can thus be categorized by a simple two by two matrix with quadrants that are high and low in complexity and dynamism. The informational roles capture the manager’s activity in acquiring and disseminating information (Mintzberg, 1973) and will thus vary with the need for information. Since complexity describes both the number of units with which interaction is required and the amount of sophisticated knowledge the manager must secure, complexity will be the main driving force of the informational roles. The decisional roles vary with the need to make decisions and the extent to which decisions can be programmed. Dynamism has the greatest impact on the programmability of decisions (Thompson, 1967) and will be the main environmental predictor of decisional roles. Complexity and dynamism should have more than simple main effects since they are usually thought of as determining environmental uncertainty (Thompson, 1967). From this point of view the quadrants of the environment may be treated distinctly and not as pairs. As the environment moves from stable-simple to stable-complex to dynamic-simple to dynamic-complex, the absence of concrete information about the environment and the lack of knowledge about the effects of specific organizational actions increase. As a result the decision maker moves from certainty, through risk to uncertainty (Holloway, 1979; MacCrimmon &Taylor, 1976; Thompson, 1967, p. 134). Thus complexity increases the effects of dynamism and dynamism increases the effects of complexity. This view argues that the hypotheses may be stated as interactions. When combined with the relationships posited above, the following hypotheses can be stated: HI: The frequency of informational roles environmental complexity and are moderated JOURNAL

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dynamism such that the roles are morefrequent to stable environments.

in dynamic as opposed

H2: The frequency of decisional roles vary directly with environmental dynamism and are moderated by environmental complexity such that the roles are more frequent in complex as opposed to simple environments.

The relationship between the interpersonal roles and the environment is more complex but may also be stated in environmental terms. The liaison role is associated with developing a network of contacts to trade favors and build working relationships. It clearly increases the more often managers must change their plans because of unforeseen circumstances. The liaison role thus varies directly with dynamism and, since complexity should also affect both the need and the difficulty of building networks, the liaison role will be moderated by complexity such that the role increases in frequency the more complex the environment. The figurehead and leader roles are alike in that they also require interpersonal relationships that are related to the duties and responsibilities derived from the status of the position occupied. The environment affects several organizational processes that require such a relationship. These include the legitimation of the organization (Meyer & Rowan, 1977); the allocation of substantive and symbolic outcomes by external dependence and power (Pfeffer & Salancik, 1978); the need to secure normative commitment from the participants to the goals and activities of the organization (Etzioni, 1961; Pfeffer, 1977, 1981); and the need to compensate for organizational factors which are required to motivate subordinates, but which are not present in adequate amounts (Evans, 1970a; House, 1971). Almost all of these organizational processes require interpersonal relations, persuasive argument, and the manipulation of symbols by a manager. The figurehead role is viewed here as the use of symbolic and ritual activities and the use of normative argument required by legitimation, institutionalization, and power processes. In his review of Mintzberg’s (1973) work on managerial roles Weick (1974) convincingly argues that it is almost impossible to separate leadership from any of the other nine roles. The separation attempted here is between the managerial activity of motivating and controlling subordinates as opposed to securing normative commitment to and rationalization of goals as defined by strategy, institutionalization and power. Since both the figurehead and the leader role are part of the interpersonal role set, the separation provides clarity to the development of hypotheses. It does not argue that it is possible to separate such activity in managerial work. Institutionalization and legitimation can be viewed as two processes. In one, forms, structures, and technologies are imported into the organization to secure legitimacy from external participants (Parson, 1956, 1960; Perrow, 1970; Dowling & Pfeffer, 1975; Meyer & Rowan, 1977 ; Meyer, 1980; Meyer & Scott, 1983; DiMaggio & Powell, 1983). In the other these forms are enacted JOURNAL OF MANAGEMENT,

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ceremonially, loosely coupled from the operating core, and transmitted as social fact (Meyer & Rowan, 1977; Zucker, 1977, 1983). The point to be made is that one of the key actors in this process of adoption and transmission is the manager. The extent of adoption and the extent to which the forms are perceived as adopted by external interests may be viewed as partially defining the kind of climate or culture management would want the organization and its members to adopt (cf. Tichy & DeVanna, 1986). Meyer and Rowan (1977) argue that the number of institutional myths will vary with the number of organizations and their interdependence (p. 347). This is equivalent to the complexity dimension in the environmental typology being used here. Following Benson (1975) it is argued that the more stable the organizational field the more likely the institutional myths will be well known and easily transferrable. This also implies that organizational members are more likely to know of the rules and accept them as social fact (Zucker, 1977). In a dynamic environment, however, the rules are less likely to be well known and understood and are more likely to vary with time. Thus managers will have to play a more active role as the field shifts, and if the legitimacy arguments are to be believed, this role will be more crucial to organizational success (Ashforth & Gibbs, 1990). The arguments above lead to the assertion that the figurehead role varies directly with environmental dynamism and is moderated by environmental complexity. The leader as motivator may be understood within a path goal framework (Evans, 1970a, 1970b; House, 1971). Holding the contingency variables of this theory constant-subordinate characteristics, nature of task, authority system, work group composition, etc.-it is clear that the number of different goals and paths will vary with environmental dynamism and will increase with complexity. The leader role thus varies directly with environmental dynamism and is moderated by environmental complexity. The assertions made above may be summarized as one hypothesis for the interpersonal roles: H3: The frequency of the interpersonal roles varies directly with environmental dynamism and is moderated by environmental complexity such that the roles are more frequent in complex as opposed to simple environments. The previous discussion asserts that managerial roles may be predicted from environmental characteristics. It can thus be argued that when a manager’s role activities are examined, the complexity and dynamism of the environment of the manager’s subunit will allow us to predict a manager’s role activities independently of level, functional area, or other structural dimensions that are typically viewed as choices for management. Technology Technology has been treated as a major determinant of structure and managerial work in the past (Woodward, 1965; Thompson, 1967). The degree of mechanization, flexibility, operationality of sub-goals or the amount of JOURNALOF

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technical knowledge required by the job has been used to predict work group structure and other organizational variables (Udy, 1965; Pugh, Hickson, Hinings & Turner, 1968; Rosseau, 1979; Argote, 1982; Alexander & Randolph, 1985). Technology has also been studied as new ways develop to process materials or information to gain competitive advantage in the marketplace. The introduction of new technologies, especially computers, has led to a number of studies that examine the restructuring of the organization to make the new technology effective, the implications for personnel practices, or ways to retrain workers to increase productivity (e.g. Bracker & Pearson, 1986; Edwards, 1989; Heller, 1989; Liu, Denis, Kolodny & Stymme, 1990; Turnage, 1990) . While similarities exist between technology and the environment the concepts are distinct. As it is measured in this study, technology captures the management practices or organizational design decisions that can be considered to be internal characteristics or micro variables as opposed to the external or more macro variables of environmental complexity and dynamism. The current study uses Perrow’s (1967) theory of technology because it applies at the individual task level and not the system level found in Woodward or the studies conducted by the Aston group and because it can be more easily applied across different industries. The explicit dimensions in Perrow’s typology are: (1) the frequency of exceptional cases encountered in the work, which refers to the perceived nature of the raw materials; and (2) the search behaviors undertaken by individuals when exceptional cases appear, which refers to the actions of individuals in response to their perceptions of the nature of raw materials. Lynch’s (1974) scales of Perrow’s constructs measure overall routineness of work, the presence of rules to guide work, insufficient knowledge of work processes, task interdependence with other departments, and task interdependence within the same department. Lynch’s scales were chosen because they demonstrate both convergent and discriminant validity (Lynch, 1974). These variables have not been used before to examine managerial work. They are of interest in themselves since they examine the effects of some organization design decisions that are not necessarily captured in such constructs as level or functional area. A number of authors such as Alexander & Randolph (1985), Pugh et al. (1968) and Thompson (1967) argue that routine and rules decrease the need for handling information and making decisions. If one assumes that the manager’s technology scores are indicative of the work of subordinates as well, path goal leadership theory (Evans, 1970; House, 1971) would predict that routine work and the presence of rules would require fewer leadership behaviors from managers. Similarly Kerr & Jermier (1978) would argue that routine and rules are effective leadership substitutes. If rules and routine do decrease the need for information then the liaison role would also be reduced in work units that had a large number of rules and routine work. Institutional myths should also be well established in work units that can create a large number of rules or where work can be easily routinized. Thus the leader, liaison, and figurehead roles should decrease with rules and overall routineness. The arguments above lead to two hypotheses: JOURNAL OF MANAGEMENT,

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GIBBS H4: The greater the overall routineness of work the less frequent use of informational, decisional, and interpersonal roles. H5: The greater the number of rules the less frequent informational, decisional, and interpersonal roles.

the

the use of

Only one other study has examined variables that are similar to the remaining technology variables-insufficient knowledge of work processes, interdepartmental interdependence, and within department interdependence. David, Pearce and Randolph (1989) found that task predictability, problem analyzability, and interdependence were poor predictors of work group performance unless they were combined with other group variables such as horizontal differentiation, vertical differentiation, and connectedness. Unfortunately, horizontal and vertical differentiation and connectedness were not measured in the current study. However, clear hypotheses can be developed for interdepartmental interdependence. Almost all investigators and theorists who have examined interdependence have found an increased need for coordination between units that are interdependent (Thompson, 1967; Perrow, 1970; Randolph, 1978; Tushman, 1978, 1979a, 1979b; Argote, 1982). This implies an increase in the use of informational and decisional roles to ensure coordination between departments. H6: The greater the interdepartmental interdependence the use of the informational and decisional roles.

the greater

Control Variables To ensure a strong test of the hypotheses four control variables were used in the analysis-age of manager, span of control, tenure on the job, and functional area. Both age and job tenure reduce the possibility that experience or self confidence may account for increased or decreased use of roles. For instance, managers may seek and process information differently or may rely more on their intuition when making decisions when they are older or have more experience in their jobs. Similarly, a manager with a larger span of control may have to engage in more dissemination of information or make more decisions when compared to another manager with a smaller span of control even though each may operate in the same kind of environment and in the same functional area. Mintzberg (1973) provides three arguments for managers in different functional areas. Production managers manage a complex system in real time and will use decisional roles more than other roles. Staff managers spend most of their time providing advice and information to line managers and should use the informational roles more than other roles. And, sales or marketing managers are involved in establishing contacts and motivating others and should use the interpersonal roles more than other roles. JOURNALOF

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These ideas have been studied by Alexander (1979) McCall and Sergist (1980) and Pavett and Lau (1983). The empirical literature provides support for the relative emphasis on decisional roles by production managers , weaker support for the interpersonal orientation of sales managers, and partial support for the importance of informational roles for staff managers. These results are based on t-tests across managers in different functional areas for the importance and not the frequency of roles. The results are weak because the effects of other variables were not controlled. Alexander (1979), McCall and Sergist (1980), Paolillo (1987), and Pavett and Lau (1982,1983) have tested whether the importance (but not the frequency) of roles vary by hierarchical level. The picture that emerges from the results of these studies is one in which all roles except the leader, negotiator, and disturbance handler roles increase in perceived importance as one moves up the hierarchy. As groups of roles the evidence is strongest for the informational and the decisional roles. These findings are understandable. Most organizations are structured in such a way that information and decision are designed to be handled at higher managerial levels. Unfortunately the current study could not adequately operationalize level in both samples and level in hierarchy is not examined in the study. Research Methods

Sample The sixty five managers of the first sample were part of a larger study that involved over 100 managers in the validation of an assessment center, and a job analysis of engineering, marketing, and production managers. This sample includes managers from a large telecommunication business who agreed to be involved in the current study. Level and role information are available from a further forty managers in this sample since the role measure was included as part of the job analysis. The eighty-four managers in the second sample were part-time MBA students of a large Eastern university who worked in different firms and who completed the questionnaires themselves or had a manager complete them (multifirm sample). Each manager was asked to secure role information from one or more peers, one or more subordinates, and their superior in the telecommunication sample. Managers in the multifirm sample also provided information from their superior and one or more subordinates but not from their peers. Altogether, 106 superiors, 79 peers, and 265 subordinates provided role information. The peers, superiors, and subordinates were not asked questions that could identify them. The average age of managers in the entire sample was 40 (s.d. = 9.4 years) and ranged from 24 to 64. Job tenure was obtained from company files from the telecommunication sample and provided exact years and months. Job tenure was obtained from a single question for the multifirm sample and also provided years and months. The average job tenure for managers was 2.30 years (s.d. = 1.14 years) and ranged from less than a year to 14 years. Of the 138 managers in both samples who provided complete information, 48 were JOURNAL

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staff, 76 were line managers, and 14 could not be classified as distinctly line or staff. These latter included such positions as special coordinator or assistant to the president. In the telecommunication sample 20% were in marketing (sales, marketing, or customer service), 47% were in production or engineering departments and 33% were in staff departments (accounting, personnel, finance, research and development). In the multifirm sample 21a/, were in marketing, 27% were in production, and 52% were staff. Education and sex are available for the telecommunication sample only and were collected from company records. Of these managers 60% had a university degree and 19% were female. Not all of the part-time MBA students reported on their own job. Some asked their manager to complete the questionnaire. Over ninety percent of the multifirm sample had a university degree and two thirds were male. McCall and Sergist’s (1980) items were used for the scales of leader, liaison, monitor, spokesperson, entrepreneur, and resource allocator since all of these have good psychometric properties. The items for the roles of figurehead, disseminator, disturbance handler, and negotiator were drawn from all of the associated items of McCall and Sergist (1980) and Pavett and Lau (1983) as well as items prepared by the author to improve the reliability of these scales. The frequency of role use was determined by asking each respondent how often they engaged in the activities associated with each role. The eight point scale was anchored with ‘never, very seldom, seldom, sometimes, often, very often, almost always, always’. The scales varied from 0 to 7. The frequency of the 0 response was less than one percent. The general strategy for data collection was to use multiple respondents for role information. Each manager completed information for all ten roles and a superior, peer(s), and subordinate(s) provided information on the roles they were most likely to have valid information about. The superior rated the manager on the figurehead, liaison, spokesperson, monitor, resource allocator, entrepreneur, and negotiator roles. The peers rated the manager on the liaison, spokesperson, monitor, resource allocator, entrepreneur, and negotiator roles. The subordinate rated the manager on the leader, disseminator, monitor, and disturbance handler roles. Put another way, the superiors and peers were unlikely to have detailed knowledge about the leader, disseminator and disturbance handler roles, as well as these the peers were also unlikely to have detailed knowledge about the figurehead role; and the subordinates were unlikely to have detailed knowledge about the figurehead, liaison, and spokesperson roles or any of the decisional roles except for the role of disturbance handler. The number of items, means, and reliabilities of each scale for each rater, before further analysis, may be found in Table 1. To assure that the roles divide into the role sets as hypothes~ed by Mintzberg (1973, 1975) each respondent’s role scores were subjected to a confirmatory factor analyses using LISREL. The disseminator role was dropped from further analysis because its reliability was quite low (.36). Each JOURNAL OF MANAGEMENT,

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Table 1. Means, Standard Deviations, Reliability, and Number of Items for Role Measures Manager

Variable Leader Figurehead Liaison Spokesperson Disseminator Monitor Resource

All.

Entrepreneur Negotitor Disturbance

H.

mean(s.d.) alpha(items) mean(s.d.) alpha(items) mean(s.d.) alpha(items) mean(s.d.) alpha(items) mean(s.d.) alpha(items) mean(s.d.) alpha(items) mean(s.d.) alpha(items) mean(s.d.) alpha(items) mean(s.d.) alpha(items) mean(s.d.) alpha(items)

4.4(1.2) .84(8) 4.1(1.5) .66(4) 3.4(1.5) .64(4) 3.1(1.6) .52(2) 4.8(1.4) .36(2) 4.0(1.6) .75(4) 4.9( 1.4) .76(6) 4.5(1.4) .75(5) 3.3(1.7) .64(6) 5.1(1.4) .65(4)

Superior

Peer

Subordinate 5.4(.9) .78(7)

4.0(1.1) .39(3) 3.1(1.4) .58(3) 3.1(1.6) .52(2)

3.6(1.7) .75(4) 4.8(1.4) .74(5) 4.5(1.1) .76(7) 3.8(1.7) W4)

3.4( 1.3) .63(4) 3.4(1.5) .78(3)

3.6(1.7) .87(4) 3.8(1.4) .85(5) 4.8(1.3) .87(5) 4.4(1.3) .63(4)

5.2( 1.4) .27(2) 4.9(1.6) .77(5)

5.6(1.1) .67(4)

rater from each sample was then analyzed separately since a different factor structure is possible in each sample and since the peers are only available in the telecommunication sample. The number of managers in each sample-65 in the telecommunication sample and 84 in the multifirm sample-posed a further problem in that the number of items to respondents would be quite low if all of the roles were analyzed at the same time. To improve the ratio of items to respondents three confirmatory factor analyses, one for each role set, were conducted for each respondent in each sample. The confirmatory factor analysis for subordinates used a model to confirm the presence of three roles since subordinates report on one role from each of the role sets after the disseminator role was dropped. The results of the analyses for role sets are presented in Table 2 for all raters. All of the paths for the items have significant t values. Table 2 reports the significance of the models, the goodness of fit and adjusted goodness of fit indexes, as well as the number of items in each scale and their reliability using Cronbach’s Alpha. For example, when all of the items for the disturbance handler, entrepreneur, negotiator, and resource allocator roles are examined for the decisonal role set for managers in the mutifirm sample, only three of the roles are confirmed and a number of items are dropped from the remaining roles in a model that specifies the items of each role loading on their own factor. The Adjusted Goodness of Fit index for this model is .798 and p < .149. The manager items from Table 2 are summed into scales and are further analyzed in Table 3. Table 3 reports the results of a second confirmatory factor JOURNAL

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Subordinate

Superiors

All

31.53

0 28.3 1

4.36

Decisional

Informatonal Decisional

.91 14.04

Interpersonal Informtional

16.74

53.21

Decisional

Interpersonal

10.04

Informational

Peer

33.11

Interpersonal

Manager

Chi Sq.

Role Set

Confirmatory

Rater

Table 2. PC

Analysis

.992 ,907 ,986 .854 1.oo ,785

.999 .951 .999 .922

1.oo ,909 .958

,634 .368 ,824 .212 1.00 ,343 ,209

13 0 26 26

8

2 13

.901

.71 I

,889

.285

48

,262

,920

8

.951

AGFI

,765

,862

Sample

GFI

Leader Liaison F. Head Monitor Spokesperson Dist. Handler Entrepreneur Resource All. Negotiator Liaison Monitor Spokesperson Entrepreneur Negotiator F. Head Liaison Monitor Entrepreneur Resource All. Leader Monitor

Roles

of Role Sets for Two Samples

.918

43

Telecommunication

d.f

Factor

! 7 2

4 3 3 3 3 3 4 3 2 4 4 3 4 2 3 4 3

# items

.74 .59 .67 .79 .67 .66 .75 .66 .89 .63 .87 .78 .83 .89 .44 .53 .65 .79 .65 .79 .72

Alpha

0 E z

5

Subordinate

Superior

Manager

35.98

2.83 22.86

Informational Decisional

All

16.74

Interpersonal

41.43

5.04

Informational

Decisional

82.53

Interpersonal

24

2 19

13

33

8

74

.06

,242 ,244

.212

,149

,153

,753

Multifirm

Sample

.914

.913 ,907

.922

.911

,980

.874

.816

,967 ,802

.854

,798

.967

.774

Leader Liaison F. Head Monitor Spokesperson Dist. Handler Entrepreneur Negotiator F. Head Liaison Monitor Entrepreneur Negotiator Leader Monitor Disturbance H.

8 4 2 3 2 3 3 3 3 4 4 5 2 4 3 2

.84 .65 .63 .68 .52 .59 .63 .68 .35 .57 .I4 .69 .87 .57 .68 .68

GIBBS

592

Table 3.

Confirmatory Factor Analysis of Manager Scales for Two Samples and Correlations with Raters Used for Combining Scales Standard Coefficient

Roles

Telecommunication

Interpersonal Informational Decisional

Liaison Figurehead Monitor Spokesperson Disturbance H. Entrepreneur Resource All.

T- Value

Correlation Superiors

Correlation Peers

Sample

.791

5.55 6.86

477 .356

,872 .848 ,799 ,775 ,740

8.27 7.95 6.90 6.64 6.28

.390

657

Multifirm Sample

Interpersonal

Informational Decisional

Leader Liaison Figurehead Monitor Spokesperson Disturbance H. Entrepreneur Negotiator

.376 .696 .561 .652 .876 .664 .709 ,281

3.29 6.31 5.06 5.29 7.83 5.79 6.18 2.28

.339 .327 ,342 .494

analysis in which the scales are analyzed separately for each sample. Each model specified three factors that correspond to the informational, decisional, and interpersonal role categories. Each solution used an oblique rotation since many theorists argue that the role sets should have some correlation (Mintzberg, 1973; Weick, 1974; Shapira & Dunbar, 1980). The model for the telecommunication sample has a Goodness of Fit Index (GFI) of .943 and an Adjusted GFI of .906 (Chi square with 11 d.f. = 14.47, p < .208). The model for the multifirm sample has a GFI of .941 and an Adjusted GFI of .889 (Chi square with 17 d.f. = .889, p < .130). The table reports the T values for the paths and the standardized solution coefficients. For instance the path coefficients for the interpersonal role set in the telecommunication sample are .657 for liaison and .79 1 for figurehead. To assure construct validity, the scales used for the study are composed of the average of the manager’s scale from the second confirmatory factor analysis and the scales from the superior, subordinate, and peer from the previous analysis (Table 2) that were positively correlated with the manager’s scales. The rater scales that were positively correlated with the manager’s scales are reported in Table 3. As previously outlined overall routineness of work, presence of rules to guide work, and task interdependence with other departments were measured using Lynch’s (1974) scales (see Appendix A). Environmental complexity and dynamism were measured using Duncan’s (1972) measures as modified by Tung (1979). In Tung’s measure a number of factors are identified in different JOURNAL

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internal and external environmental components. There are three internal components and four external components. For instance, “competitors for suppliers” is a factor in the external competitor component. The complexity dimension in Tung’s measure is a function of three sets of variables: (1) the number of factors and components in the internal and external environment that must be taken into account in decision making, goal setting, and goal attainment; (2) the relative differentiation or variety of these factors and components; and (3) the degree of interdependency among the factors and components. The first two sets of variables refer to the heterogeneity or diversity of stimuli. The third component relates to the problem of manageability of the stimuli. This improves on Duncan’s (1972) measure since his measure simply measures heterogeneity. An open ended question asked managers whether the factors and components in the internal or external environment posed greater uncertainty in decision making. More than seventy percent indicated that the external environmental factors and components were less manageable. Following Tung (1979) factors in the internal environment received a weighting of one and factors in the external environment received a weighting of two. The number of components was squared as an indicator of similarity/ dissimilarity as was done in Duncan (1972) and Tung (1979). The formula for calculating the complexity score was as follows: [factors with weights assigned by location X components2]. Most organization theorists consider dynamism to be a lack of stability. This study therefore used a stability index to capture dynamism. The stability index also followed Tung’s procedures (1979, pp. 681-684) and involved adding and then averaging the respondent’s subscores on three subscales. These subscales measured the extent to which the focal unit had to contend with the same factors/components in the environment over time, the extent to which there was an adequate warning period preceding the onset of changes in the factors, and the extent to which the manager had concrete knowledge of what to expect if there were changes in the factors. Managers with a large number of factors that did not change, a long warning period for elements that did change, and a great deal of knowledge of what to expect received higher stability scores. Dynamism scores were obtained by reverse coding the stability scores. High dynamism scores are thus associated with new factors, factors for which the warning period is inadequate, and factors with little information about what to expect when they change. Functional area, span of control, age, and job tenure were collected from questionnaires for the multifirm sample and from company files for the telecommunication sample. The means, standard deviations, and intercorrelations of variables are presented in Table 4. Validation of measures While conducting the job analysis in the telecommunication sample three researchers also rated each respondent on the frequency of use of Mintzberg’s (1973) roles based on a one hour interview of their job activity. The average JOURNAL OF MANAGEMENT,

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Notes:

.20 .33

3.65 .38

4

5

6

.29** -.05 a.06 .16** .17* -.I0

-.I3 -.23

.09 .3tt* $15” -,63*

.40 .47

.Of .O9

-*IF .Ol

-.04 -.03 .a4 .02 -.27** -.24** -.27* -.I6 -.I5 .11 .17* .09

8

I.00 .55** 1.00

7

9

-.06 -.05

SW .11 -*II -.I2

.I4 -.I5

.03 -.17*

‘If -.14

I1

.07 -,04

12

13

-.03 .05

-.I?@ -.18

14

15

-04 1.00 .42** -.35** L.00

-.17* 1.00 -.14 -.34** 1.00

1.00 .34** 1.00

10

.09 -.01 -.Ol .19* .OO -.lS*

.07 .07 .28** -.06

-.Ol .06 -.37** .03 I.00 .23** .19* -.17* .18* .06 .09* .27** -0.5 -.I0 .O4

.56** .53** 100 -.03 .05 :30** 1.00 .OO .24** .16* .25** 1.00 .16 .I5 .15** .25** 1.00 .2P .I&+ .34** .I5 -.06 .08 -,09 -.w -.21** -.29*+ .02 -.I3

3

1.64 ‘72 .49

-216.02 428.87 2.93 .80 .80 1.20 .79 3.02

1.01 3.15 40.01 9.39 1.14 2.30 L1.78 14.03 -.I3 0.00 559.5 1 .71 0.00

.29+* I.00

1.03

2

3.31

I 1.00

Alpha

1.20

SD.

Correlations of Variables in the Study

2.81

Mean

* p < .05; ** p < .Ol; *** p < .OOI.

1. Informational Role Average 2. Decisianal Role Average 3. Interpersonal Role Average 4. Age af Manager 5. Tenure of Manager 6. Span of Control 7, Complexity (Centered) 8. Dynamism (Centered) 9. Complexity x Dynamism 10. Routiness 11. rules 12. Interdept. Interdependence 13. Dummy for Sample 14. Dummy for Sales/ Marketing 15. Dummy for Staff

Variable

Table 4.

ENVIRONMENT

AND TECHNOLOGY

595

intercorrelation among raters for the ten managers who were jointly interviewed was ‘79. The role estimates of the managers interviewed were averaged to produce ratings for each of the informational, decisional, and interpersonal role sets. The correlations of these interview ratings with manager’s scores on the revised scales (n = 34) were: informational (r = .46, p < .OS); decisional (r = -40, p < .05); interpersonal (Y= .32, p < .09). While these correlations are small they do provide evidence for convergent validity (Campbell & Fiske, 1959). The job analysis also allowed an examination of the number of meetings, span of control, number of in-house reporting relationships, number of areas of responsibility, and number of dotted line relationships. These measures were averaged to produce an interview measure of environmental complexity. Unfortunately the respondents could not be uniquely matched because the company personnel who assisted with the questionnaire did not keep track of respondents. The best that could be done was to separate managers by functional area and look at mean scores across departments. Both Tung’s (1979) measure and the job analysis measure ranked production/ engineering, marketing, and staff as first, second, and third when the means were used as indicators. Results Regression analysis was used to test hypotheses. Functional area was included in the regression equations through the use of a dummy variable for marketing and sales managers and a second dummy variable for staff managers. This procedure has the effect of treating production managers as an arbitrary point of comparison in examining differences across functions areas. The regression equations include a dummy variable to check for differences between samples, the three technological dimensions of overall routineness, rules, and interdepartmental interdependence, the control variables-age, span of control, job tenure, and two dummy variables for functional area. These variables as well as environmental complexity, environmenta dynamism, and the result of their multiplication are used to predict the average role score for each role set as outlined above. Interactions were tested by hierarchical regression (Arnold, 1982, 1984). To avoid problems with multicollinearity the dynamism and complexity scores were centered by subtracting the mean of each variable from the scores (Cronbach, 1987; Jaccard, Turrisi & Wan, 1990). These centered scores were multiplied together to form the interaction term. The correlation between the interaction term and the centered complexity score and centered dynamism score was -.36 (p < .OOO)and .03 (p < .34) respectively. Table 5 reports the results of adding five groups of variables as blocks in predicting the average of the three role types. The first block includes age, tenure, span of control, and the dummy to identify sample differences. The three technology variables are entered second. The dummy variables for functional area are added third. Environmental complexity and dynamism are added to the equation fourth and the interaction term is added in the last step. This JOURNAL

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3; P

P

5

“%

$

Lz! *+

2

a % &

2 2 :

8

,078 .064 .007

2,114

2,114

1,114

Dummy Variables for functional area

Complexity and Dynamism

Environmental Interaction

,042

.023

3,114

4,114

Change in R

.004 .009 ,292

4.82 1.12

,100

.467

PC

5.96

2.13

.900

F

Informational Role Average

.009 .309

1.04

,006

.781

.0002

.001

PC

4.80

,248

7.02

4.75

F

.058

.003

,128

.I16

Chatye in R

Decisional Role Average

Hierarchical Regression Analysis for Average Role Scores

Rules, Interdependent Interdependence, Routiness

Age, Tenure, Span, Dummy for Sample

d$

Table 5.

.017

.096

.005

.085

.099

Change in R

3.06

8.50

.462

5.01

4.40

F

.083

.0004

.631

.003

.002

PC

Interpersonal Role Average

ENVIRONMENT

Table 6.

AND TECHNOLOGY

Regression Equations for Predicting Role Averages Informational Role Average

Overall Routiness Interdepartmental

Intedependence

Rules Tenure

Age Span of Control Dummy for Staff Dummy for Sales/Marketing Dummy for Sample Complexity

(centered)

Dynamism

(centered)

Interaction

(centered)

d.f. F-Test Si nificance RF Adjusted R2

597

-.255* (.139) -.083 (.062) -.013 (.087) .026 (.lOS) ,018 I:;;;) (.006) .453* (.237) .932*** (.273) .021 (.292) .0006** (.0002) .049 (.172) .0002 (.0003) 12,114 3.16 .0006 .25 .17

Decisional Role Average -.515*** (.118) .019 (.053) .214** (.074) .189* (.089) ,001 (.Oll) .011* (.005) .058 (.201) -.106 (.231) ,373 (.247) .0006** (.0002) .290* (.146) .0002 (.0002) 12,114 4.19 .oooo .31 .23

Interpersonal Role Average -.402***

(.107) -.016 (.048) .138* (.067) .180* (.080) .014 (.OlO) .OOl (.005) .158 (.182) -.002 (.209) -.290 (.225) .0007*** (.0002) ,105 (.132) .0003* (.0002) 12,114 5.26 .oooo .26 .29

procedure allows an analysis of the interaction term and provides an assessment of the relative contribution of the different types of variables. The actual regressions for the test of each role set are found in Table 6. The entries are the simple B coefficients of the regression with the standard errors reported in parentheses below the coefficients. Hypothesis 1 specifies that the average informational role score varies directly with complexity and is moderated by dynamism. The interaction was not significant. As can be seen in Table 5, when dynamism and complexity are added to the variables already in the equation the change in R2 is .06 and this change is significant (F( 1,114) = 4.82, p < .009). When the interaction is added it is not significant (F(1,114) = 1.12, p < .292). As can be seen in Table 6 complexity is the only significant environmental variable to predict informational roles. Hypothesis 1 receives partial support in that environmental complexity increases the frequency of informational role use while the interaction was not significant. JOURNAL OF MANAGEMENT,

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GIBBS

Hypothesis 2 argues that decisional roles vary directly with dynamism and are moderated by complexity. The interaction is not significant. When complexity and dynamism are added the addition in R2 is .06 and is significant (F( I,1 14) = 4.80,~ < .009). The addition of the interaction term is not significant (F(1, 114) = 1.04, p < .309). Hypothesis 2 receives partial support in that, as can be seen in Table 6, environmental complexity and environmental dynamism increase the frequency of decisional role use while the interaction was not significant. In Hypothesis 3, dynamism is posed as the major determinant of interpersonal roles with complexity moderating the relationship. The interaction for interpersonal roles is significant. Adding complexity and dynamism to the other variables adds .09 in R2 and is significant (F( 1,114) = 8.50, p < .0004). The interaction term adds .02 to R2. Since the hypothesis predicts the sign of the interaction, the results of the F test may be halved for a one-tailed test. The test is significant (F(l,114) = 3.06, p < .OS)when using a one tailed test (p < .04). The interaction supports hypothesis 3 with the line for high values of dynamism having a steeper slope and higher values than a line for low values of dynamism. Hypothesis 4 specifies a direct negative relationship between routineness of work and the informational, decisional, and interpersonal roles. As can be seen in Table 6, overall routineness is a significant negative predictor of all of the roles. Hypothesis 5 specifies a direct negative relationship for rules on the frequency of the informational, decisional, and interpersonal roles. The presence of rules to guide work is a significant positive predictor of the decisional and interpersonal roles only. These results do not support the hypothesis. Hypothesis 6 asserts that interdepartmental interdependence decreases the frequency of informational and decisional roles. This hypothesis received no support. Two of the control variables were significant predictors of managerial roles. A manager with longer tenure is more likely to have an interpersonal relationship with subordinates, peers, and others thus explaining the significant positive association of tenure with interpersonal roles. Such a manager may also be better connected to the power structure and thus have a greater role in decision making. Managers with larger spans of control may use the decisional roles more frequently to oversee their subordinates or to make decisions about projects that their subordinates are involved in. Functional area has an effect on the frequency of informational roles only. Sales/ marketing managers use the informational roles more frequently than do production or staff managers when the frequency of these roles are examined by t-test. The marketing, staff, and production managers report means of 3.58, 2.89, and 2.58 respectively. Marketing/sales managers use the roles more frequently than do production managers (T(99) = 4.14, p < .OOO) or staff managers (T(78) = 2.70,~ < .OOS).Production and staff managers do not differ significantly from each other in their use of the informational roles (T(119) = -1.48, p < .142). JOURNAL OF MANAGEMENT,

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Summary and Discussion The current research has argued that the environment affects many of the processes that engage managers-motivation, legitimation, information seeking, decision making-and has shown that environmental complexity and dynamism do predict the frequency of roles used by managers. The fact that environmental dynamism and complexity added to the explained variance in roles after such variables as technology and functional area were included in a hierarchical regression indicates that the environmental dimensions provide additional new information in the prediction of managerial activity. The general picture that emerges in business organizations in the current study is a simple one. The informational roles vary with environmental complexity. the decisional roles vary with environmental dynamism and complexity. The interpersonal roles vary directly with dynamism and are moderated by complexity such that they are more frequent in complex as opposed to simple environments. Unlike such concepts as functional area, environmental characteristics are sufficiently general that they may be applied to managers of all types of organizations. In many organizations, such as nonprofits or government departments it is sometimes difficult to see how the classic hypotheses such as those for functional area can aid in the examination of managerial work. The hierarchical regressions of Table 2 show that the technology variables are stronger predictors of roles than the environmental dimensions of complexity and dynamism. Routine work decreases the use of all role sets. Rules increase the use of decisional and interpersonal roles. While one expects routines to decrease role use, the role of rules in decreasing decisional and interpersonal roles is unexpected and requires further elaboration. The significant increase in decisional roles associated with the presence of rules is counter-intuitive and contradicts Hypothesis 5. Perhaps the rules are ambiguous, or require clarification because the manager is faced with situations in which the rules are simply out of date. Or, perhaps, all rules involve managers in ensuring that the right rules are applied. In this latter case one can question the value of rules per se or one can invoke political hypotheses to explain the findings. For instance, many power theorists argue that rules are used to solidify the power structure (e.g. Pfeffer, 1981; Mintzberg, 1983). The presence of rules may thus involve the manager in checking the status of rules and deciding which among many rules to invoke. It would seem that the rules for the manager’s job do not act as leadership substitutes for subordinates and may simply involve the manager in enforcing rules that may not be central to the subordinates’ work. The role of rules as a positive predictor of interpersonal roles may be explained by examining the behavior required to operate in an environment with rules such as the verification and transmission of rules and checking to see that the rules have been observed. While the presence of rules may reduce or change the use of certain leadership styles it certainly does not seem to reduce managerial work. The introduction of new technologies which embed many of the rules of JOURNAL OF MANAGEMENT,

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GIBBS

production in numerically controlled procedures may provide interesting settings to test the relationships between rules and managerial work. The control variables are of interest in themselves. Span of control increases the use of decisional roles. Tenure in the job increases the use of both decisional and interpersonal roles. These factors have been ignored in previous research. Their inclusion in this study sheds light on their effects and should increase our confidence in the tests of other hypotheses. Mintzberg’s hypotheses for the effects of functional area are stated as within-subject and not between-subject hypotheses. That is, Mintzberg asserts that staff managers will use the informational roles more than decisional or interpersonal roles. Similarly marketing managers will use the interpersonal roles more and production managers will use the decisional roles more than other roles they may engage in. The current study finds support for the use of decisional roles among production managers in within-subject tests. When paired t-tests are used it is found that sales/marketing managers use the informational roles more than interpersonal roles (T(29) = 2.66, p < .013); production managers use the decisional roles more than informational roles (T(70) = 5.60,~ < .OOO)and the interpersonal roles more than the informational roles (T(70) = 7.72, p < .OOO);and the staff managers use the decisional roles more than the informational (T(49) = 3.23, p < .002) roles or the interpersonal roles (T(49) = 3.42,~ < .OOl). All other within-subject t-tests are not significant. The effects of functional area contradict the hypotheses put forward by Mintzberg (1973) and many of the process theorists (e.g. Katz, 1974; Fayol, 1930). While it is possible to argue that the external environment impinges on different levels and functional areas there is no consistent means to predict the frequency or types of issues or information that need to be dealt with. This is reflected in the effects of complexity on production and engineering managers in the telecommunication sample. These managers had to deal with complex technological problems directly for large customers as well as test and produce standard products. The findings for functional area in the current research reinforce the idea that hypotheses based on previous research may need to be tempered by a more thorough analysis of the manager’s job and hir or her subunit’s environment or by a re-assessment of how organizations structure themselves. The recent downturns and increased competition in the marketplace may account for the more frequent use of informational roles reported by marketing managers in t-tests and the hierarchical regression. The current research includes such variables as rules and routineness of work. Since these are included in the regressions it seems clear that these are not what functional area captures in explaining differences among functional managers. What there is in functional area that makes these differences arise requires more theoretical development. This study did not capture the rationale managers used for engaging in their work. It may be that functional managers acquire and disseminate information for different reasons or that they use different information media. This idea has been pursued in the literature that examines the information-carrying capacity of different media and its relative effectiveness in resolving ambiguity (Daft & Macintosh, 1981; Daft & Lengel, JOURNAL OF MANAGEMENT,

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1984; Daft, Lengel & Klebo Trevino, 1987). It may be, for instance, that different functional managers are trying to resolve different kinds of ambiguity with different types of information media and that this effect is captured in this study by relative differences in the frequency of use of the informational roles. In any event, it seems unlikely that the reasons put forward to date do justice to the concept of managerial work in a specific functional area. While it seems clear that structure may determine expectations and hence define the expected boundaries for roles of managers at different levels or in different functional areas, the current research suggests that the more general constructs of environmental complexity and dynamism are the more In the author’s view, the trends toward the parsimonious explanation. computerization of the technical core, the globalization of many businesses, and the increase in education of the work force implies a future inability for functional area to have the same consistent meaning throughout different organizational fields. This implies that the subunit’s environment and technology will be increasingly better predictors of managerial role activity than previous hypotheses of functional area, level in hierarchy or other internal structural dimensions. Given that complexity and dynamism are good predictors of job behavior then perhaps job design based on an information processing approach is better than one based on managerial rank or functional area (cf. Galbraith, 1973; Van de Ven, Delbecq & Koenig, 1976; Tushman, 1978, 1979, 1979a; Tushman & Nadler, 1978; Daft & Lengel, 1984). Selection devices based on the ability to handle complexity, change, or equivocality in the environment should then become even more important in matching individuals to positions than they are now. The research may be criticized for not including decentralization or other aspects of structure. Many alternative explanations can also be provided for why roles vary. These include the predilections of superiors, the expectations of subordinates and peers, the personality or need structure of the manager or the manager’s modeling of the past incumbent’s behavior (Salancik et al., 1975; Tsui, 1984). All of these require exploration to fully explain managerial role behavior. It is also possible that not all the processes or functions a manager is involved in have been identified. To the extent that such processes have not been explored our knowledge remains incomplete. The results and discussion above provide a useful guide for theory development. The potential of the newer perspectives needs to also be considered. Some potential directions include the role of the manager in developing organizational culture, the manager’s role in instituting new organizational forms, the manager’s role in dealing with the institutional environment and in legitimating institutional demands in the organization. Whether these roles affect the frequency of roles defined by the current typology, address the manner in which these roles are carried out, or define new roles requires theoretical statements from these theorists. JOURNAL OF MANAGEMENT,

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GIBBS

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