Environmental Dimensions In Measuring Uncertainties
The second dimension of Dess and Beard’s measure was “complexity.” Complexity referred to “the level of complex knowledge that understanding the environment requires” (Sharfman and Dean, 1991, p. 683). This dimension was concerned with the overall number of factors that a firm needed to analyze in its external environment. Thompson’s heterogeneity/homogeneity dimension and Duncan’s simple-complex component were both very similar to complexity. As the number of environmental factors that must be considered by a firm increased, so did the level of uncertainty present in the environment.
The final component of Dess and Beard’s (1984) operationalization was “munificence,” also known as hostility. This dimension was not part of the earlier constructs developed by Thompson and Duncan, and was referred to as “illiberality” by Child. Munificence described “the level of resources available to firms from various sources of the environment” (Tan, 1996, p. 33). Covin and Slevin (1989) argued, “hostile environments are characterized by precarious industry settings, intense competition, harsh, overwhelming business climates, and the relative lack of exploitable opportunities” (Covin and Slevin, 1989, p. 75).
According to these authors, the concepts of dynamism, hostility, and complexity could be utilized in order to measure the level of uncertainty present in a given environment (Table I). High levels of dynamism, hostility, and complexity all acted to create high levels of uncertainty. Low levels acted to reduce the overall amount of environmental uncertainty. By analyzing the levels of dynamism, hostility, and complexity present in an environment, firms were able to formulate and implement strategies to match these environments.
State, effect and response uncertainty
Extending the multidimensional conceptualization of environmental uncertainty, Milliken (1987) built on the work of Lawrence and Lorsch (1967) to develop a measure that distinguished between three types of uncertainty that existed in a firm’s external environment. Milliken’s typology included “state uncertainty,” “effect uncertainty,” and “response uncertainty.” “State uncertainty” referred to the general unpredictability of the environment and its various components. “Effect uncertainty” was the inability of firms to predict the effect of future environmental changes on their business operations. “Response uncertainty” captured the difficulty firms had in predicting the response of their competitors to a particular strategy that the firm implemented. According to Milliken, these three concepts acted together to determine the overall level of uncertainty present in a firm’s external environment.
Analyzing key elements of the uncertainty construct: a new categorization scheme
An examination of the evolution of the conceptualization and operationalization of the environmental uncertainty construct reveals that the seminal works on uncertainty can be categorized according to two predominant factors:
1. the primary source of uncertainty theorized by the author (i.e. information uncertainty or resource dependence); and
2. the complexity of the measure employed to operationalize this uncertainty (i.e. simple versus complex).
Figure 1 summarizes the major works on environmental uncertainty according to these two factors.
It is important to note that while operationalizations of environmental uncertainty have become more complex with time, simple measures can still significantly contribute to organizational research. Indeed, depending on the research questions under consideration the operationalizations of environmental uncertainty in each sector of this figure have the potential to address critical issues. Issues that delineate the appropriateness of each measure include whether uncertainty is a primary or secondary variable of interest and the characteristics of the population under consideration, including firm and industry level factors.
Simple measures are useful when uncertainty is a secondary variable of interest and only broad analyses are necessary. These measures of uncertainty tend to be less precise than complex measures, but are generally easier to calculate. Multidimensional operationalizations are useful when uncertainty is the primary variable of interest. These measurements tend to be more comprehensive than those attained through simpler methods and provide a more complete set of information for the researcher.
Characteristics of the population under consideration provide a useful indication of whether a researcher should employ measures from the information uncertainty or resource dependence schools. Information uncertainty measures are useful in studying firms that are dependent on information for their economic prosperity, such as those in technology-based industries (i.e. Internet firms, the electronics industry, etc.). These firms tend to be agile and flexible, and usually operate in highly competitive industries. Resource dependence theory provides an effective tool for measuring the uncertainty faced by firms in resource-intensive industries (i.e. mining, manufacturing, etc.). These firms tend to have larger, more traditional organizational structures and are less dependent on technology for their survival.
Figure 2 summarizes the primary research situations in which measures from the four different quadrants should be utilized. It also lists examples of recent articles that have productively employed each particular operationalization.
The first quadrant (information uncertainty/simple measure) of this matrix contains measures useful when studying firms competing in information-based industries, where only a general measure of uncertainty is needed. For example, Bergh and Lawless (1998) employed a very simple measure of uncertainty in an article related to firm diversification. The authors calculated uncertainty as the change in net sales over a given period of time. Although this did not provide a very precise measure of environmental uncertainty, it was sufficient to support their findings that uncertainty affects the relationship between diversification strategy and portfolio restructuring (Bergh and Lawless, 1998, p. 98).
The measures in the second quadrant (information uncertainty/complex measure) allow for a much more precise measurement of uncertainty. Boyd and Fulk (1996) employed a very sophisticated measurement of information uncertainty in their study. They developed four perceptual measures to gauge the amount of uncertainty present in the environment: the adequacy of information available about the environment, and the overall analyzability, predictability, and variability of the environment. Given their particular research situation, their findings supported modeling uncertainty “with multiple indicators” (Boyd and Fulk, 1996, p. 14).
The third quadrant (resource dependence/simple measure) contains operationalizations that can be effectively utilized while performing research on traditional firms in resource-intensive industries. Finkelstein (1997) examined resource dependence theory by utilizing a basic construct developed by Pfeffer (1972). Similar to Pfeffer’s seminal work, Finkelstein measured inter-industry mergers in the context of resource dependence theory. Although their findings were not identical, Finkelstein concluded, “the basic resource dependence hypothesis on the relationship between interindustry transactions and mergers was supported” (Finkelstein, 1997, p. 808).
The fourth quadrant (resource dependence/complex measure) contains measures that should be employed when uncertainty is the primary variable of interest and resource availability is a major factor being considered. Lawless and Finch (1989) utilized a very complex construct in order to measure resource dependence theory. The authors used the values calculated by Dess and Beard (1984) to determine the validity of Hrebiniak and Joyce’s (1985) model of organization-environment relations. They measured munificence, complexity, and dynamism for all four environmental types proposed in the model. Their findings suggest, “relationships between returns and particular strategy types vary by environment” (Lawless and Finch, 1989, p. 360).
Although this is by no means an exhaustive list of the articles that have recently employed measures of environmental uncertainty, it is clear that each quadrant in this classification scheme has value in answering specific research questions. Simple measures are effective when uncertainty is a secondary variable of interest, while complex measures allow for precise measurements when uncertainty is the primary variable being studied. Operationalizations from the information uncertainty and resource dependence schools can also be effectively utilized when performing organizational research, depending primarily on the characteristics of the firm and industry being studied.
A decision tree for studying the environmental uncertainty construct
The categorization scheme developed in this paper provides a decision tree that can be utilized when studying the environmental uncertainty construct. First, the researcher must determine whether environmental uncertainty is the primary or secondary variable being studied. If uncertainty is the primary variable of interest, then the researcher should employ a complex measure in order to ensure more precision and comprehensiveness while measuring the construct. If uncertainty is only a secondary variable of interest, then researchers need only employ simple measures that are easier to calculate and provide more generalized information regarding the amount of uncertainty present in the external environment.
Second, the attributes of the firms and industry in the study must be closely examined. The primary focus of the researcher during this stage should be in determining whether a information uncertainty or resource dependence perspective more closely aligns with their specific research questions and sample characteristics. If the industry being studied tends to experience rapid change and the firms in this industry are dependent on information from the environment, then measures based on the information uncertainty perspective should be employed. If the change rate in the industry is slow and firms tend to be more dependent on acquiring environmental resources than information, then researchers should utilize measures developed from resource dependence theory.
After performing these two analyses, the organizational researcher can determine the measure of uncertainty that would be most appropriate in their study. Figure 3 provides a decision tree that can aid researchers in determining which of the four types of environmental uncertainty measures delineated in this article would be most appropriate for their purposes. For example, a researcher who is studying uncertainty as a primary variable of interest (thus needing a precise measure of uncertainty) and whose sample consists of firms in a rapidly changing, information-dependent industry (such as e-commerce), could choose from the measures of environmental uncertainty developed by Thompson (1967), Duncan (1972), or Milliken (1987). Thus, through the two-step process of determining whether uncertainty is a primary or secondary variable of interest, and analyzing the characteristics of the firms and industry being studied, organizational researchers can utilize the decision tree presented in Figure 3 to choose and employ measures of uncertainty that provide the richest information in their particular research situation.
Multiple operationalizations have developed over the last 60 years to measure the amount of uncertainty present in the external environment. Each of these measures can be effectively utilized in performing organizational research depending upon the specific research questions being addressed. This article has presented a systematic method for determining which measure should be utilized in a given research situation. While none of the four categories of measures discussed in this paper is perfect in every research situation, each can be effectively employed in specific situations to perform research on the topic of environmental uncertainty.
The most significant problem raised by this analysis is the threat of concept stretching in regard to the environmental uncertainty construct. As conceptualizations of uncertainty have continued to evolve and diverge from one another over the last 60 years, integrating research streams and ensuring the generalizability of results on this topic has become increasingly difficult. The categorization scheme and decision tree developed in this paper provide a starting point to reverse this unsettling trend.
Research on environmental uncertainty has several practical implications. In order to sustain organizational growth and survival, firms must be able to successfully interact with their external environment. One of the key factors in so doing is a firm’s ability to effectively handle the problems created by environmental uncertainty. By studying the topic of uncertainty, researchers are better able to understand the relationship that exists between an organization and its external environment. The categorization scheme presented in this paper provides a valuable tool for future investigation of the uncertainty construct. By determining the theoretical foundation of the question under consideration and the role of environmental uncertainty in the research model, investigators can employ this categorization scheme to choose the appropriate measure of environmental uncertainty.