Steven H. Hanks, Utah State University
Gaylen N. Chandler, Utah State University
While it is well established that new organizations adopt more formal processes and systems as they increase in age and size, we know little about the nature and dynamics of this process. In this study we examine the pattern of formalization across four growth stage configurations identified in a sample of high technology firms. Three dimensions of formalization are explored: formalization of documentation and policies, formalization of structure and reporting relationships, and formalization of planning and control systems. Significant differences were found in the pattern of all three scales across the four growth stage configurations.
While it is well established that new organizations adopt more formal processes and systems as they increase in age and size, we know little about the nature and dynamics of this process. Much of the research to date has centered on formalization as a unitary construct, highly correlated with organization size (Walsh & Dewar, 1987). The objective of this study is to examine the nature of formalization across growth stage configurations in emerging business ventures. We do so by exploring the patterns of three dimensions of formalization across a four stage taxonomy of growth stages.
THEORETICAL FRAMEWORKp> The theoretical underpinnings of this study center around two constructs: formalization and the organization life cycle. In this section, we will introduce these constructs, discuss their relationship and present our hypotheses for the study.
Formalization refers to the degree to which communications and procedures in an organization are written (Daft, 1986; Pugh, et al., 1973). According to Pugh and associates, the formalization construct includes several aspects.
Formalization can include (1) statements of procedures, rules, roles (including contracts, agreements, and so on), and (2) operation of procedures, which deal with (a) decision seeking (applications for capital, employment, and so on), (b) conveying of decisions and instructions (plans, minutes, requisitions, and so on), and (c) conveying of information, including feedback. (Pugh, et al., 1973).
In their discussion of the historical development of the formalization construct, Walsh and Dewar (1987) noted that formalization in the organization context fills three roles. First, it serves as code, wherein it "reduces a complex set of activities to relatively less complex formulae, thus increasing the efficiency of the organization. Second, as a channel, formalization routes or directs human performance, thus yielding a predictable pattern of human performance. Third, formalization can improve efficiency through serving as a standard, guiding the allocation of organizational rewards and punishments. According to Walsh and Dewar, "the formalization process establishes the standards and measures against which action is compared and rewards or punishments are given out."
In filling these organizational roles, Walsh and Dewar noted that formalization can achieve two differing purposes. First, as noted above, formalization can serve the organization through increasing overall efficiency. Second, formalization has a darker side wherein it can be utilized to protect the vested interests of individuals, at the expense of the organization, yielding excessive bureaucracy and overemphasis on form as opposed to function or outcomes.
Formalization Across The Organization Life Cycle
In an effort to explain changes in organizations as they grow in size and complexity, organization theorists have adopted the biological analogy of the life cycle (Kimberly & Miles, 1980). Over the years, numerous theories and models have been proposed in an effort to explain the life cycle process (Adizes, 1989; Chandler, 1962; Churchill & Lewis, 1983; Dodge & Robbins, 1992; Galbraith, 1982; Greiner, 1972; Hanks, 1990; Hanks et al., 1993; Kazanjian, 1988; Miller & Friesen, 1984a; Quinn & Cameron, 1983; Scott, 1971; Scott & Bruce, 1987; Smith, Mitchell & Summer, 1985; Tyebjee, Bruno & McIntyre). Fairly extensive reviews of the organization life cycle literature can be found in Hanks (1990), Quinn and Cameron (1983), and Smith, Mitchell & Summer (1985).
While there are differences in the number of stages proposed and the dimensions used to describe specific stages, most models of the organization life cycle suggest a fairly common pattern of organization growth, comprising stages of start-up, growth and maturity. Studies centering on high technology firms have tended to divide start-up into two distinct stages: the first centering on R&D and prototype development activities, followed by an early commercialization stage. Thus, life cycle models centered on high-technology firms have suggested a four stage growth typology, comprising conception and development, commercialization, growth, and maturity (Galbraith, 1982; Hanks, et al., 1993; Kazanjian, 1988).
Life cycle proponents argue that as firms move through life cycle stages, differing problems must be addressed, resulting in the need for different management skills, priorities, and structural configurations (Chandler, 1962; Churchill & Lewis, 1983; Greiner, 1972; Hanks, et al., 1993; Kazanjian, 1988). Over the years, numerous models of the organization life cycle have been proposed, and virtually all of these models utilize formalization as a critical dimension in defining growth stages. Our review of eight of these models revealed several common themes in the theorized pattern of formalization across life cycle stages. These patterns are summarized in Table 1.
As can be observed in the table, we discuss characteristics of four theorized growth stages: start-up, commercialization, expansion and consolidation. In the start-up stage the focal business challenge is to identify a market niche and develop the basic product. As the organization is small, usually comprising the founder(s) and a few other individuals filling flexible roles, coordination and control can easily be handled on a personal, informal basis. Thus, organization structure and systems remain simple and informal.
During the commercialization stage, the focus of the organization changes from product development activities, to learning to produce and sell the organization's product or service in volume. Thus, more employees are hired, tasks and assignments begin to be delineated, and basic operating procedures begin to be developed. Systems and controls at this level, tend to remain quite informal, with little documentation.
As the firm's products begin to take hold in the market place, it is not uncommon for firms to experience a period of rapid growth. Focal organization tasks at this stage center around volume production and distribution, capacity expansion, garnering resources to support growth. Associated with this rapid pace of growth come a number of functional crises (i.e. cash flow problems, production quality problems, etc). In response to these problems, functional specialists are hired, and a gradual professionalization of the management team begins to occur (Hanks & Chandler, 1994; Kazanjian & Drazin, 1990; Walsh & Dewar, 1987). With this professionalization comes a greater need to delineate responsibilities, and to communicate this delineation to others in the organization to avoid unnecessary redundancy and conflict. Thus at this stage, organization structure becomes more formalized. Once on board, the professional functional specialists begin to establish policies and procedures in an effort to avoid crises, and improve the efficiency of daily operations. While formal systems begin to emerge in this stage their enforcement remains sporadic and inconsistent, as manager's attention tends to remain focused on putting out fires, and getting product out the door.
As growth begins to slow, the organization moves into a consolidation or maturity stage. The focal business task in this stage is to consolidate and rationalize operations, thus improving profitability. With slower growth, and professional management in place, attention now moves from the management of daily crises to a more systematic process of managing daily operations. In an effort to gain important efficiencies, clarification and enforcement of policies and procedures become issues of greater importance. Thus, formalized planning and control systems come into play with stricter enforcement. Performance toward specific objectives are monitored.
While significant conceptual work has been done relative to patterns of formalization in emerging ventures, there has been remarkable little empirical examination. As a matter of fact the life cycle has been plagued by a plethora of conceptual models with very little empirical examination (Hanks, et al., 1993; Kazanjian, 1988; Miller & Friesen, 1984). Empirical studies to date by Hanks, et al. (1993) and Smith, Mitchell & Summer (1985), have validated the fact that larger firms tend to have more formal structures and systems than smaller firms. We found no studies to date which explore the pattern of formalization across life cycle stages. Our purpose in the present study is to examine this pattern empirically. Therefore, based on the preceding discussion, we propose the following hypotheses regarding the pattern of formalization across four growth stage configurations.
H1. A pattern of increasing formalization will be exhibited across the sequence of stage configurations, start-up through maturity.
> H2. Formalization of documentation and policies will increase incrementally across growth stage configurations.
H3. The level of structural formalization will increase significantly between the commercialization and expansion stages, reflecting the transition from a simple to a functional structural form.
> H4. The formalization of planning systems will increase significantly between the growth and maturity stage configurations.
An exploratory field study was conducted to test our four hypotheses. Our purpose was to examine the pattern of formalization across organization life cycle stage configurations. The sampling frame for the study consisted of companies listed in Utah's High Tech Directory. Questionnaires were mailed to the presidents of all 275 companies listed in the directory. Usable responses were received from 121 firms, representing an effective response rate of 44 percent. The 121 firms represent 14 industry groups, have mean sales of approximately $4.5 million and employ a mean of 108 employees. Industries represented in the sample include computer software, electronic and communications equipment, chemicals, pharmaceuticals, aerospace equipment, lasers and optics, analytical and measuring devices. Firms more than 15 years old were removed from the analysis to insure the sample represented emerging businesses. Respondents were company chief executive officers or their designee.
Three sets of measures were utilized in the analyses. The first set consisted of measures utilized to operationalize organization life cycle stage configurations. We refer to these as "clustering variables." These variables include measures of organization age, size, growth rate, specialization, centralization, vertical differentiation, and structural form. Operationalization of these variables is summarized below.
Organization age (Age) was calculated by subtracting the year the firm was founded from the year data were collected. Organization size (Sizelog) was measured by the natural log of the organization's reported total employment. The natural log of this measure minimizes the effect of skewness in these distributions (Blau & Schoenherr, 1971).
The growth rate measure (Employee Growth2) reflects organization growth for the firm's most recent year of performance. It was calculated using self reported employment data, based on the following formula:
Employee Growth2 = (Total Employment - Employment Previous Year) Total Employment
This measure of growth, though somewhat unorthodox, was used because it allowed us to retain new firms in our analysis. For example if a firm had 10 employees at the time of data collection and 0 employees the previous year, it is impossible to calculate a growth rate using traditional growth measures [(Yr2-Yr1)/Yr1] because any number divided by 0 is undefined. By calculating employee growth as a proportion of the present year's employment we were able to keep new firms in the data set . A more detailed discussion of this measure may be found in Hanks, et al. 1993.
Employment-based measures of organization size and growth, as opposed to sales based measures were utilized in formation of the clusters for two reasons: first it was believed that organization structural response would be more closely related to number of employees than sales; and second, the disclosure rate of respondents was higher for employment figures than sales figures, thus allowing us to retain more firms in the analysis.
Vertical differentiation (Levels) consists of the total number of organization levels (Dewar & Hage, 1978). Respondents were asked to count the number of levels in the longest line between direct workers and the organization chief executive, including both of these levels (Pugh & Hinkson, 1976). Structural form was self reported by respondents based on brief descriptions. The structure variable was coded as follows: simple structure, 1; by function, 2; by division, 3; and other, 4.
The specialization scale is adapted from Pugh et al. (1968). Respondents were given a list of 20 functional areas and were asked to check those in which the had at least one full-time employee. The item is scored by counting the number of functions checked.
Centralization was measured through an adapted, abbreviated version of the Aston Studies Scale number 54.10 (Pugh et al., 1968). Respondents were given a list of five decision issues. They were then asked to indicate the level of management that must approve the decision before legitimate actions may be taken. The scale is scored by adding up the total of all five responses. A high score on this scale indicates a high level of centralization in the firm.
The second group of variables used in the analysis consisted variables which, though not used in the formation of the growth stage clusters, are useful to aid in interpretation of the clusters. We refer to these variables as descriptive variables These include Total Sales, Total Employment and Sales Growth.
The final set of variables used in the analysis include measures designed to capture the pattern of formalization across life cycle stages. Formalization was measured using 8 items. Items were developed based on a review of formalization scales developed previously in the literature (Inkson, Pugh & Hinkson, 1970; Hage & Aiken, 1967; House & Rizzo, 1972). Scale items were measured on 7 point Likert-type scales ranging from strongly agree to strongly disagree. Principal components analysis, with an orthoginal rotation was employed to identify the underlying dimensions of the formalization items. Based upon these results, three unit-weighted factor scales were developed, representing three dimensions of formalization:
Scale 1: Documentation & Policies (alpha = .77) 1. Formal policies and procedures guide most decisions. 2. Important communications between departments are documented by memo.
>Scale 2: Structural and Reporting Relationships (alpha = .74) 1. The top management team is comprised of specialists from each functional area. 2. Reporting relationships are formally defined. 3. Lines of authority are specified in a formal organization chart.
Scale 3: Planning and Control Systems (alpha = .73) 1. Capital expenditures are planned well in advance. 2. Plans tend to be formal and written. 3. Formal operating budgets guide day to day decisions.
Data analysis occurred in two phases. The first phase involved derivation and interpretation of growth stage configurations. The second phase of the analysis involved examination of the patterns of the three formalization scales across the growth stage configurations.
Phase I - Derivation of Growth Stage Configurations. Exploratory cluster analysis was utilized to derive a taxonomy of growth stage configurations based on common patterns in the data. The rationale underlying this approach is explained in detail in Hanks, et al. (1994). Cluster analysis is an exploratory technique which groups observations in a manner that maximizes between group variance and minimizes within group variance. An agglomerative hierarchical method that used Ward's (1963) criterion was employed in the analysis. Ward's method was selected because studies of multiple algorithm's found this method to be one of the more reliable (Milligan, 1980). Data were standardized prior to the analysis and outliers (5%) were removed to avoid clusters of one (Milligan, 1980).
To verify that the centroids of the derived clusters were indeed different, multivariate analysis of variance was conducted. Independent variables in the analysis were the derived life cycle stage clusters, dependent variables included the seven clustering variables. An F-test was performed to verify that group centroids were significantly different. This was followed by a series of univariate analyses of variance with the life cycle stage cluster as the independent variable and the individual contextual and structural variables as dependent variables.
To aid in interpreting the clusters, three variables, not utilized in forming the clusters were then profiled across the clusters (Gnanadesikan, Kettenring, & Landwehr, 1977). Profiled variables included total sales, number of employees, and employee growth rate. These variables were used to gain greater insight into the type of firms in each cluster.
Phase II - Examination of formalization patterns across the growth stage taxonomy. Multivariate analysis of variance (MANOVA) was conducted to test our first hypothesis, that the pattern of formalization differs across the four growth stage configurations. An F-test was performed to verify that group centroids were significantly different. This was followed by a series of univariate analyses of variance to test for differences in mean values for the individual formalization scales across the growth stage configurations. Tukey's multiple pairwise comparison procedure was then used to determine the statistical significance of differences between cluster mean values for each formalization scale.
Cluster analysis using Wards (1963) minimum variance method was employed to develop a taxonomy of organization life cycle stage configurations (Hanks et al. 1994). The four cluster solution was selected. Multivariate analysis of variance of the five groups and seven variables resulted in a multivariate F(21,299)= 16.76(p<.0001), indicating that the clusters present appreciably different configurations of the clustering variables. One-way analysis of variance was conducted to test for differences in cluster means for each of the eight individual variables. The resulting F statistics indicate that significant differences exist in mean values for each of the variables. Mean values for each variable, by cluster, and corresponding F statistics are reported in Table 2
As reported in the table, mean values for each of the clustering variables are significantly different across the four clusters.
To aid in interpretation of the clusters, mean values by cluster, of the three descriptive variables are also reported in the table. A review of cluster characteristics reveals a sequence of four clusters, , labeled stages 1 through 4 in Table 2. The pattern of means for the clustering and descriptive variables across the four clusters suggests an increasing pattern of size and organizational complexity. Organization size, specialization and number of levels increase incrementally across the four stage clusters, while centralization decreases incrementally. Growth rates peak at the third stage. In all the pattern of means across the stages appear to reflect stages of start-up, commercialization, expansion and consolidation, respectively.
Multivariate analysis of variance was conducted to examine differences in centroids of the three formalization scales across the four stage clusters. This analysis resulted in a multivariate F (9,239) of 4.03 (P < .0001), indicating that the stage clusters present appreciably different patterns of formalization, supporting our first hypothesis. Analysis of variance was conducted for each of the formalization scales. The results of these analyses are reported in Table 2. As can be viewed in the table significant differences were found in the pattern of means across the cluster stages for each of the formalization scales. To examine the specific nature of these differences, Tukey's multiple pairwise comparison procedure was conducted. Results of this analysis are reported in Table 2.
> Differences in the pattern of mean values for the three formalization scales across the four stage clusters are illustrated in Figure 1. As can be viewed in the figure, formalization of documentation and policies are increases incrementally across the stages, rising from a mean importance rating of 3.04 in Stage 1 to 5.0 in Stage 4. Significant differences are found between the first and third, and first and fourth stages (See Tukey's results in Table 2). Structural formalization increases from 3.91 to 6.04 across the stages, with a significant increase occurring between the first and second stages as hypothesized in H3. Finally, formalization of planning and control systems increases across the stages from 4.7 to 5.19 across the stages, with significant differences occuring between the second and fourth stages. Thus our fourth hypothesis was partially supported.
Our purpose in this study has been to examine the pattern of formalization across the organization life cycle in emerging business ventures. Our first hypothesis stated that a pattern of increasing formalization would be exhibited across the four stage configurations. This hypothesis was supported in the analysis. This finding is supportive of previous analyses by Hanks, et al. (1993) and Smith, Mitchell & Summer (1985) who had similar findings.
Our study then began to look more specifically at pattern changes in specific sub-dimensions of formalization. We hypothesized that the pattern of formalized documentation and policies would increase incrementally across growth stages. This pattern was supported in our analysis, with significant change occurring between the first and third stages. This is supportive of assertions by Smith, Mitchell & Summer (1985) that communications would begin to formalize during the growth stage.
In our third hypothesis, we proposed that formalization of organization structure would occur between the commercialization and expansion stages. Our findings revealed that significant change occurred earlier in the life cycle than we proposed. The most significant change in structure and reporting relationships occurred between the first and second stages. This transition appears to coincide with the transition between a simple and functional structure (See mean values for the Structural form variable in Table 2) which also occurs between the first and second stages.
Finally, in our fourth hypothesis, we proposed that significant changes in formal planning and budgeting systems would occur between the expansion (Stage 3) and consolidation (Stage 4) stages. The results indicate that this transition appears to occur between the commercialization and consolidation stages.
What than can we conclude from this analysis. First, it has long been argued that as firms increase in size and complexity, formalization increases as well. This has been demonstrated in numerous previous studies, and our findings support this, as we expected it would. Second, it is our contention in this study, that formalization is a multidimensional construct and that different aspects of formalization become significant at different stages of the life cycle, reflecting the organizational challenges faced by the firm as it develops. Our findings indicate that this is the case. Formal systems communications, structures and systems are typically very unimportant in the start-up stage. As the organization begins to increase in size and specialization, it becomes necessary to increase the clarity of reporting relationships through the formalization of organization structure. As the organization undergoes rapid growth, formalized policies and procedures emerge coinciding with the arrival of professional managers and specialists. Finally, as the organization moves into the consolidation stage, formalized planning systems and budgets appear to become more important.
Recognition that formalization is not a unitary construct, and that the pattern of increase varies across growth stage configurations is a useful contribution to our knowledge about how firms change in response to organization growth. While the general pattern of our hypotheses were supported, we were not exact in our projections. This may be a function of either a weakness in our theory, or perhaps a weakness in our measures of life cycle stage. Additional studies may help us answer these questions.
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