Pre-startup planning and the survival of new small businesses: theoretical linkages
Gary J. Castrogiovanni
Conventional wisdom is that new businesses be planned prior to their startup. Nevertheless, research on the value of planning in general, and on pre-startup planning in particular, has yielded mixed results (King, 1983; Pearce, Freeman & Robinson, 1987; Shrader, Taylor & Dalton, 1984). In support of this “conventional wisdom,” several studies have shown positive linkages between planning and business performance (Ackelsberg & Arlow, 1985; Ansoff, Avner, Brandenburg, Portner & Radosevich, 1970; Bracker, Keats & Pearson, 1988; Bracker & Pearson, 1986; Herold, 1972; Karger & Malik, 1975; Orpen, 1985; Robinson, 1979; Schwenk & Shrader, 1993; Wood & LaForge, 1979). In other studies, however, relationships either were not found or they differed across industry sectors (Fulmer & Rue, 1974; Hogarth & Makridas, 1981; Kudla, 1980; Leontiades & Tezel, 1980; Lindsay, Boulton, Franklin & Rue, 1982; Robinson & Pearce, 1983; Shrader, Mulford & Blackburn, 1989; Thune & House, 1970). To account for these inconsistent results, various researchers have raised methodological concerns (Bracker & Pearson, 1986; Pearce et al., 1987; Ramanujam & Venkatraman, 1987; Shuman & Seeger, 1986; Wood & LaForge, 1979).
From their review of the planning literature, Pearce et al. (1987, p. 671) concluded that “The principal methodological concern is the lack of attention to contextual influences.” In what is perhaps the most comprehensive review of planning to date, Mintzberg (1994) noted ways in which planning differs across the contexts elaborated in his organization typology (Mintzberg, 1979). He argued, for example, that planning is most appropriate for machine organizations, and that it should be minimal within entrepreneurial organizations. Although some may disagree with Mintzberg’s conclusions about entrepreneurial organizations, students of entrepreneurship have long argued that planning processes and their effects differ (and should differ) between small and large businesses (Gilmore, 1971; Robinson, 1982; Steiner, 1967; Trow, 1961).
Still, most entrepreneurship texts suggest that formal planning should take place prior to a new business’s startup (Burstiner, 1989; Goldstein, 1990; Hisrich & Peters, 1989; Sexton & Bowman-Upton, 1991; Timmons, Smollen & Dingee, 1985). Nevertheless, it is easy to find notable cases where new businesses proved very successful despite the fact that pre-startup planning was limited. Consider the following:
* Apple Computer began as a mail-order business operated out of a garage. Business plans were not developed until after initial mail order successes encouraged the founders to expand operations.
* Fred Smith of Federal Express spent years developing and refining a business plan. But then implementation proved impossible because the Federal Reserve – i.e., the primary customer on which the plan hinged – decided not to go along with it. By then, Smith had already spent millions on aircraft and other plant and equipment. He had to scramble to find alternative ways of pursuing his vision of an overnight delivery service.
* Microsoft burst into prominence, not because of any planned actions, but because its founder, Bill Gates, seized an unexpected opportunity to develop the IBM PC’s operating system.
In fact, a survey of 220 “INC 500” businesses (i.e., relatively small, but among the fastest growing businesses nationwide) revealed that 51% did not have formal business plans when they started (Shuman & Seeger, 1986; Shuman, Shaw & Sussman, 1985). Of the 49% that did have plans, an overwhelming majority (70%) generated them, for the most part, simply to get external financing. Furthermore, those businesses without formal plans tended to be more profitable.
Thus, most would agree that planning can be beneficial, but some suggest that benefits may differ across contexts. This article focuses on where, why, and how benefits are derived from planning within what Mintzberg (1989) calls the “entrepreneurial context,” in particular, planning for small business startups. It will be argued that within this focus there are still many contextual differences that can be observed.
In a meta-analysis of 14 small-business planning studies, for example, Schwenk and Shrader (1993) found a significant-but-small relationship between planning and performance. They concluded that, while planning does appear to benefit small businesses, contextual factors such as industry structure, uncertainty, and organizational life cycle stage may have considerable moderating effects. By delineating such differences within the entrepreneurial context instead of lumping all new small businesses into one category, this article therefore extends the entrepreneurship views of Mintzberg (1994), Robinson (1982; Robinson & Pearce, 1983) and others (Schwenk & Shrader, 1993; Steiner, 1967).
In this article, pre-startup planning is considered beneficial to the extent that it facilitates business survival. This “survival focus” was chosen for three reasons. First, survival may be the primary concern of new businesses since most fail within five years (Dun and Bradstreet, 1988; Scott & Bruce, 1987). Second, survival is a necessary precondition of most other desirable outcomes such as profitability or growth. In their review, Robinson and Pearce (1984, p. 129) concluded that most small business studies examining impacts of planning “infer that the presence (or absence) of planning influenced the eventual survival of small firms.” Third, focus on a single outcome enhances conceptual clarity since planning may have differential impacts on alternative outcome variables (Bracker & Pearson, 1986).
Pre-startup planning is “the process by which the entrepreneur, in exploiting an opportunity, creates a vision of the future and develops the necessary objectives, resources, and procedures to achieve that vision” (Sexton & Bowman-Upton, 1991, p. 118). This process includes collecting and analyzing data prior to the new business startup, and then using knowledge thus gained to develop a business plan itself (Grinyer, Al-Bazzaz & Yasai-Ardekani, 1986; Ramanujam, Venkatraman & Camillus, 1986; Shuman & Seeger, 1986; Smeltzer, Farm & Nikolaisen, 1988). Pre-startup planning can range from essentially no planning to the development of very comprehensive and detailed, long-term plans (Lindsay & Rue, 1980). Bracket and Pearson (1986), for example, employed a four-level classification: (1) unstructured plans, (2) intuitive plans, (3) structured operational plans, and (4) structured strategic plans. Similarly, Shrader et al. (1989) used a three-level classification, ranging from no plans to comprehensive plans coupled with considerable analysis and control procedures, where each higher level of planning encompassed the lower ones.
Little research has examined the impacts of pre-startup planning, or on planning-survival linkages. Regarding the latter, an exception is a study by Sexton and Van Auken (1985) in which 20% of non-planners failed within three years, whereas the rate was only 8% for businesses engaged in planning. Such research, however, is inherently problematic since there is no theoretical rationale for studying direct planning-performance linkages. Business survival, profitability or other performance outcomes do not result directly from pre-startup planning. Instead, planning’s impact on business outcomes is transitive: for example, certain direct benefits of planning in turn may enhance the business’s ability to act in a manner conducive to survival, profit maximization, etc. (Ramanujam & Venkatraman, 1987; Ramanujam et al., 1986).
Research is thus needed to examine the direct effects of planning, and how these in turn affect business performance (Bresser & Bishop, 1983). Table 1 illustrates how various authors typically have defined the direct benefits of pre-startup planning, and it shows that these direct benefits may fall into three categories: (1) symbolism, (2) learning, and (3) efficiency. Figure 1 uses these benefit categories to suggest direct (e.g., planning-symbolism) and indirect (e.g., symbolism-survival) effects of pre-startup planning.
Pre-startup planning is likely to generate a symbolism benefit in that (1) it legitimizes the new venture proposal and (2) improves communication with various external stakeholders, particularly potential investors or other financiers. From a social legitimacy perspective, pre-startup planning is desirable simply because many believe it should be done. Perrow (1961) suggested that organizations sometimes imitate the practices of others who are considered successful, in order to gain prestige and an aura of success for themselves. DiMaggio and Powell (1983) contended that this is particularly true when considerable uncertainty is present. Galaskiewicz and Wasserman (1989) showed that such mimetic behavior is prevalent when the imitating and imitated organizations have certain network ties.
To some extent, therefore, pre-startup planning tends to occur because, despite the mixed evidence previously noted, successful businesses are perceived as doing it, and consequently potential financiers expect it to be done. Furthermore, Mintzberg (1989) and others (Hisrich & Peters, 1989; Sexton & Bowman-Upton, 1991) have pointed out that financiers are unlikely to provide funding simply because someone discusses a “hot idea” in general terms. Financiers want to see specific details, and they want to be able to study those details in order to decide whether they feel the proposed venture has a good chance of success. Consequently, they want to see a business plan. Thus, through its symbolism benefit, planning helps business founders obtain sufficient financing to both create their business and ride out the startup phase where cash flows are likely to be negative (Scott & Bruce, 1987). Greater access to financing is a direct effect of pre-startup planning, attributed in Table 1 and Figure 1 to “symbolism.” This symbolism benefit, in turn, generates an indirect effect of planning on survival – to the extent that greater financing facilitates new business survival.
P1a: Degree of pre-startup planning positively impacts the amount of external financing that a founder can raise for a proposed new business venture.
P1b: The amount of external financing obtained for a new venture positively impacts the survival prospects of that venture.
[TABULAR DATA FOR TABLE 1 OMITTED]
Controversy over whether planning is beneficial led to a recent debate on the merits of planning versus organization learning (Ansoff, 1991; Goold, 1992; Mintzberg, 1990, 1991). As shown in Figure 1, however, planning and learning are not mutually exclusive. Planning is, in some respects, a method of learning. Further, because learning in turn may impact symbolism and efficiency ([ILLUSTRATION FOR FIGURE 1 OMITTED] and the discussion below), learning may in fact be the most important benefit of the pre-startup planning process.
Planning’s Impact on Learning. Miles and Randolph (1980) noted that alternative learning styles range along a continuum with enactive and proactive styles at the extremes. Enactive learning occurs when managers create knowledge out of their experiences. They act first and later attempt to discern causal relationships between actions and outcomes. Examples include test marketing and logical incrementalism (Quinn, 1980). Proactive learning occurs when managers absorb knowledge from the world around them. They attempt to discern causal relationships prior to acting. Examples include environmental scanning, business feasibility studies, and computer simulations. Since formal planning approaches generally incorporate some proactive learning techniques (Armstong, 1982; Ramanujam & Venkatraman, 1987; Ramanujam et al., 1986; Smeltzer et al., 1988), planning is likely to contribute to proactive learning. Since plans prescribe action, however, they precede it by definition. Therefore, planning cannot lead to enactive learning where action comes first.(1)
Many (Armstrong, 1982; Grinyer et al., 1986; Shrader et al., 1989; Smeltzer et al., 1988) have argued that, through planning, managerial uncertainties are reduced. Armstrong (1982, p. 202), for example, noted:
As uncertainties increase, the organization can benefit by planning to deal with these uncertainties . . . My hypothesis is that over a practical range for uncertainty, high uncertainty would require more planning.
In support of this view, Shrader et al. (1989) found correlations between perceived uncertainty and the amount of strategic and operational planning undertaken by small businesses. If planning indeed reduces managerial uncertainty, then this is its direct learning benefit. Leblebici and Salancik (1981) suggested that uncertainty is the absence of relevant knowledge about cause-effect relationships in a decision context. Since learning is the acquisition of knowledge, then the uncertainty-reduction benefit of planning posited by various scholars can be described as a learning effect of planning.
P2a: Degree of pre-startup planning is positively associated with proactive learning in that planning enhances a founder’s knowledge about the proposed business.
Indirect Impacts Through Learning. Figure 1 shows a link between learning and symbolism which is, in a sense, an indirect impact of pre-startup planning. As previously noted, financiers want the opportunity to scrutinize details of a business proposal in order to evaluate the founder’s chance of success. To the extent that planning results in learning, the details presented to financiers are likely to be more appropriate for the business being proposed, and consequently, financiers will be more likely to provide funding. Suppose that a location analysis reveals that what was perceived as a desirable location actually is undesirable. Heavy traffic patterns, for example, may have resulted in the initial perception of desirability, but further analysis might have suggested that targeted customer groups were not well-represented within that traffic flow. Based on this analysis, the prospective business founder would likely find a more suitable location before submitting a business plan to potential financiers, and statistics indicating the greater suitability of that location would help persuade the financiers to provide funding. Thus,
P2b: Proactive learning, gained by a founder out of the planning process, is positively related to the ease at which capital can be raised from prospective financiers.
In a similar manner, learning that occurs through pre-startup planning also may enhance the efficiency of a new business once operations begin. By studying the practices of established competitors, for example, a prospective business founder can identify efficient practices, which can be incorporated into the pre-startup plan and then implemented when the business gets underway. Additionally, costly mistakes may be avoided (Ramanujam et al., 1986). For example, a market analysis may reveal that certain products should not be part of the product mix.
P2c: Proactive learning, gained by a founder out of the planning process, is positively associated with the efficiency levels obtained by the new business following its startup.
It has been argued, thus far, that planning is directly associated with business learning, and through this learning, planning has indirect influences on symbolism and efficiency. As Figure 1 indicates, learning also affects business survival. This learning-survival linkage is another indirect effect of planning.
As suggested by preceding arguments regarding the learning-symbolism relationship, planning processes can help business founders learn ways of improving on the business concept as initially envisioned. These improvements give financiers greater confidence in the business’s chances of success. If this confidence is justifiable, then chances of business success – not just financiers’ assessment of those chances – have increased as a result of learning during the pre-startup planning process, and the business is more likely to survive.
Additionally, learning can provide insights on how the founder might adapt if conditions change. As noted previously, Fred Smith found it impossible to implement his plan for Federal Express when the Federal Reserve decided not to go along with it. Through his planning processes, however, it is likely that Smith learned of alternative customer groups that could be targeted. Consequently, he was able to react quickly. Thus, the learning that results from pre-startup planning can increase business adaptability. Informally, founder insight and intuition are enhanced. Through more formal means, the process may include assessment of alternative scenarios and development of contingency plans prior to the startup (Porter, 1980).
P2d.1: Proactive learning prior to the startup is positively related to business survival.
P2d.2: Proactive learning prior to the startup is negatively related to the response time needed to address changing conditions.
Pre-startup planning can directly enhance post-startup efficiency in at least two ways. First, communication of plans to members of the new business organization can result in cost savings through improved coordination (Grinyer et al., 1986). Second, less time is spent after the startup working out details that could have been addressed before overhead and other costs increased with the business startup (Shuman et al., 1985; Timmons et al., 1985). For an extreme example, imagine a retailer beginning to pay cashier wages and rent before learning where to get merchandise and supplies. This working out of details (i.e., the planning-efficiency link in [ILLUSTRATION FOR FIGURE 1 OMITTED]) should not be confused with the discovery of details (i.e., the learning-efficiency link). To clarify the distinction, consider the generation of a grocery shopping list as a planning process. Using the list, one can shop more efficiently, spending less time in the supermarket as a result. No learning occurs, however, in that one does not learn how to shop by simply generating a list of things to buy.
P3a: Degree of pre-startup planning is positively related to operational efficiency during the startup and early growth phases of a new business.
As Figure 1 shows, efficiency, in turn, influences business survival. In the previous discussion of the symbolism-survival linkage, it was noted that external financing provides businesses with the resources needed to survive the startup phase when cash flows are likely to be negative. Efficiency ensures that those resources are not wasted, thus further enhancing the prospects of business survival.
P3b: Operating efficiencies facilitate business survival; thus, efficient businesses are more likely to survive than inefficient ones.
To date, little research has examined the linkages described in the preceding arguments and depicted in Figure 1. Most studies (Robinson & Pearce, 1983; Watts & Ormsby, 1990) have focused on post-startup planning and performance dimensions other than survival, and most have tested for direct planning-performance linkages rather than the indirect ones shown in Figure 1. Although some researchers (Camillus, 1975; Shuman et al., 1985) have endeavored to identify direct benefits of planning – such as the symbolism, learning, and efficiency benefits shown in Figure 1 – there has been little attempt to show how these in turn affect business performance.
To a large extent, these limitations of prior studies may be due to biases toward cross-sectional research in the entrepreneurship and planning literatures. To test for direct and indirect effects of planning, longitudinal designs are desirable because there is a temporal ordering among the effects. Consider, for example, the symbolism relationships posited in Propositions 1a and 1b. First, planning influences financing prior to the startup (1a), and then financing influences business survival after the startup (1b).
Small Business Development Centers, Small Business Institutes, and similar organizations affiliated with many universities provide avenues for longitudinal designs of pre-startup planning research that are generally unavailable for post-startup planning studies. Since many of these organizations assist businesses in their pre-startup planning efforts (see Robinson, 1982), they are well-positioned to assess both the direct and indirect effects of those efforts.
For example, the planning-learning linkage (P2a) might be examined by assessing the perceived uncertainty of business founders both before and after pre-startup planning is undertaken. Decreases in perceived uncertainty would then be evidence that learning had indeed occurred during the planning process. Simultaneously, the learning-efficiency link (P2c) might be tested by first asking prospective business founders to suggest operating practices at the start of the planning effort, and then comparing these to the practices specified in the final business plan. In this comparison, changes toward more efficient practices would be evidence that learning during the planning process had served to enhance efficiency. Alternatively, researchers might track businesses after their startup to compare pre-startup uncertainty reduction (as evidence of learning) with post-startup efficiency. Then, to assess the indirect impact of planning (through learning) on survival (2d.1), researchers could test for correlation between uncertainty reduction and business survival over, say, the first three years following the startup.
Although these suggestions are useful for testing the relationships indicated in Figure 1, researchers should keep in mind that the particular purposes of their studies may warrant additional considerations. Since Figure 1 (and this article in general) focuses on the effects of pre-startup planning, comprehensive discussion of non-planning influences on symbolism, learning, and efficiency has not been provided here. Regarding symbolism, for example, planning may ease access to financing (P1a), but other factors such as the amount of funds requested will also influence the amount obtained from financiers.
As previously noted, scholars (Pearce et al., 1987; Schwenk & Shrader, 1993) have argued that considerable attention should be paid to contextual influences in studies of planning-performance relationships. Figure 2 thus extends Figure 1 to account for contextual factors, broadly defined as Environmental Conditions and Founding Conditions. In the following paragraphs, potential influences of three environmental-conditions (uncertainty, munificence, and industry maturity) and two founding conditions (knowledge and capital) are described. It is possible that other contextual factors (such as organization life cycle stage and strategy content: see Schwenk & Shrader, 1993) might affect planning-performance linkages, but the ones discussed below seem most pertinent, given the pre-startup planning-survival focus of this article.
In his review of literature on environmental analysis, Castrogiovanni (1991) presented a framework in which organization environments are viewed as being both multilevel and multidimensional. Regarding environmental levels, he argued that “Higher level environmental forces . . . are expected to have transitive influences on organizations through their impacts on lower level forces” (Castrogiovanni, 1991, p. 547). Calling for multilevel assessment of environments, Castrogiovanni explained that, while research obviously should focus on the level most pertinent to the issues at hand, examination of other, usually adjacent, levels adds richness to the findings. Regarding environmental dimensions, uncertainty and munificence have been discussed often in the literature on organizations (Aldrich & Mindlin, 1978; Lawrence & Dyer, 1983). Furthermore, from their review of the literature, Shaftman and Dean (1991) concluded that these dimensions seem strongly related to organization survival.(2) Thus, the following discussion of environmental conditions first focuses on uncertainty and munificence at the task environment level (e.g., local market). Then, industry maturity is considered, in order to add richness by addressing a more encompassing level of abstraction.
Environmental Uncertainty. As previously noted, most would agree that the learning derived from pre-startup planning is desirable to reduce uncertainty so that managers will make better decisions and consequently take more effective actions (Armstrong, 1982; Grinyer et al., 1986; Shrader et al., 1989; Smeltzer et al., 1988). Thus, uncertainty may stimulate planning. Few acknowledge, however, that environmental uncertainty may also impose an upper limit on the extent to which such proactive learning is possible. When Apple Computer was started as a home-based mail order business, for example, there was no way to accurately ascertain current and potential demand for personal computers since markets had not yet been developed. Under such uncertain conditions, pre-startup planning is impeded. These points – that uncertainty stimulates planning but can impede learning – are clarified in the following paragraphs.
Long ago, Shannon (1948) defined uncertainty as “data,” the number of signals transmitted over a communication system. As the number of signals increases, it becomes more difficult for a receiver to comprehend the total message being sent. Shannon noted that there are two types of uncertainty. One is the number of signals sent over a communication channel during a given time interval, while the other is the number of alternative communication channels (i.e., the number of signals being transmitted at a given point in time). These two types of uncertainty – signals over time and signals at a given point in time – have been examined often in the literature on organizations, under such labels as environmental “dynamism” and “complexity,” respectively (Duncan, 1972). Shannon (1948) further noted that redundancy reduces uncertainty. If, for example, a message is repeated, the receiver of the communication is more certain of the intended message content. In the organization literature, researchers have attempted to control for redundancy by factoring discernable trends or patterns out of their environmental dynamism and complexity measures. To measure environmental dynamism, for example, Dess and Beard (1984) de-trended longitudinal data by measuring dynamism as dispersal around regression trend lines.
In Shannon’s terms, planning can be said to reduce uncertainty as various analytic methods used in the planning process uncover subtle redundancies. (As noted, this is the learning benefit previously discussed.) For example, when a market analysis reveals that X should not be included in a product mix, the implication is that the claim “I do not want X” is redundant across many customers. As uncertainty increases, the costs of reducing it through planning increase as well (Ramanujam et al., 1986). These costs include, for example, managerial time commitment, the hiring or contracting of planning specialists, computer resources for various analytic techniques, etc. (Camillus, 1975; Grinyer et al., 1986; Ramanujam et al., 1986). Simon (1947) suggested that these “search costs” become prohibitive at some point. Additionally, businesses may encounter some “unknowables,” in which case, further information search would be fruitless. When that happens, proactive learning (through planning) ceases, and organizations act (i.e., “satisfice”). Then, since action takes place under uncertain conditions, successful outcomes are not assured. Thus, environmental uncertainty has a negative impact on a business’s chances of survival.
In sum, these arguments suggest that environmental uncertainty stimulates planning because more learning is desired under uncertain conditions than under certain ones. At the same time, however, uncertainty impedes the learning that may result from planning by making it more difficult and costly. In order to disentangle these dual impacts of environmental uncertainty, Figure 2 depicts them as two separate relationships (environment-planning and an environment-planning-learning interaction). Additionally, Figure 2 shows an environment-survival linkage.
P4a.1: Environmental uncertainty is positively related to the degree of pre-startup planning undertaken by prospective business founders.
P4b.1: Environmental uncertainty moderates the relationship between pre-startup planning and proactive learning gained by prospective business founders.
P4c.1: There is a negative relationship between environmental uncertainty and the survival of new small businesses.
Environmental Munificence. Environmental munificence is the abundance of demand and other necessary resources available to the new business. In other words, it is the magnitude of the opportunity that the business is seeking to exploit (Castrogiovanni, 1991). Thus, munificence may be described as the extent to which an environment can support a new business and enable it to grow and prosper (Child & Kieser, 1981; Randolph & Dess, 1984; Starbuck, 1976). Accordingly, it would be almost tautological to state that munificence facilitates survival, except for the fact that a (munificent) condition conducive to business survival is conceptually distinct from the survival concept itself. Given that favorable supply-demand tradeoffs exist under munificent conditions (Castrogiovanni, 1991), it is easier to turn a substantial profit when munificence is high than when it is low (Beard & Dess, 1981; Kudla, 1980). Poorly managed businesses may be able to generate profits despite their own ineptitude. Thus, there is less need for planning’s symbolism, learning, and efficiency benefits. Consequently, munificence reduces business founder incentives to engage in pre-startup planning. Further, munificence may create disincentives when coupled with founder concerns that a vast window of opportunity might suddenly close. For these reasons, environmental munificence is likely to have a negative impact on degree of planning, while having a positive one on business survival.
P4a.2: Environmental munificence is negatively related to the degree of pre-startup planning undertaken by prospective business founders.
P4c.2: Environmental munificence is positively related to the survival of new small businesses.
Industry Maturity. Although small businesses can be started in most industries, they tend to be found in emerging or fragmented ones (Mintzberg, Quinn & Voyer, 1995; Porter, 1980). In emerging industries, small businesses are common because demand, distribution channels, and other resources have not yet configured in quantities sufficient to sustain larger enterprises. In more mature, fragmented industries, small businesses are common for reasons such as a need to generate products/services locally, or the absence of scale economies (Porter, 1980).
These two generic industry types tend to have opposite levels of environmental uncertainty and munificence.(3) In many mature, fragmented industries, uncertainty is low because past industry trends, successful operating practices, customer preferences, etc., are generally known throughout the industry. If they are unknown to a potential business founder, they can be learned through the pre-startup planning process. Additionally, munificence tends to be low because demand growth often slows with maturity (Abell & Hammond, 1979), and there are many competitors seeking to direct demand and other resources away from a focal business (Porter, 1980). In contrast, emerging industries often have high levels of environmental uncertainty, in part, because they have no past, and markets are ill-defined and undeveloped (see Teplensky, Kimberly, Hillman & Schwartz, 1993). Characteristic of this high level of uncertainty, munificence is unknown in many particular cases, although hindsight may subsequently establish that munificence was high, moderate, or low in any given case. On average, however, munificence is likely to be higher in emerging industries than in mature, fragmented ones, in part, because there is little competition, and consequently, firms can, more-or-less, monopolize whatever demand and other resources are available (Porter, 1980).
These contrasts suggest that industry maturity (i.e., emerging industries versus more mature ones) has mixed effects on degree of pre-startup planning and survival. The lower uncertainty of mature industries reduces incentive for planning and increases survival prospects (see arguments surrounding P4a.1 and 4c.1, above). However, the lower munificence increases incentive for planning and reduces survival prospects (see arguments surrounding 4a.2 and 4c.2, above).
The key influence of industry maturity, therefore, may center on the environment-planning-learning interaction ([ILLUSTRATION FOR FIGURE 2 OMITTED], relationship 4b): Because of low uncertainty, new businesses entering mature industries can learn by analyzing the experiences of established competitors. Furthermore, proactive learning may be more important since these businesses need to minimize competitive disadvantages associated with their inexperience.
P4b.2: Industry maturity moderates the relationship between pre-startup planning and proactive learning gained by prospective business founders.
Two founding conditions, knowledge and capital, influence the extent to which pre-startup planning is likely to benefit a business. The influences of these conditions are elaborated below.
Founder Knowledge. A prospective business founder’s knowledge, often the result of enactive learning in various endeavors prior to the start of the particular new venture, reduces the need for symbolism effects of planning since prospective financiers tend to give considerable weight to the background and experiences of the founder (i.e., to factors indicating founder knowledge; see Stuart & Abetti, 1990). Thus, it is easier to obtain financing when there is evidence of considerable founder knowledge, even when the business plan and the process used to generate it are relatively weak. In essence, founder knowledge generates a symbolism effect of its own which may reduce the need for planning’s symbolism effect.
Knowledge also reduces the need for the learning and efficiency effects of planning. With regards to learning, much of what might be learned through planning is already known. Regarding efficiency, the prospective business founder may already have operating details worked out – informally, within his mind. Suppose, for example, that an individual decides to open a tavern after managing one for the past twenty years. Suppose also that the new tavern is to be very similar to the one he has been managing. In this case, it is likely that the prospective business founder has such a deep knowledge of the business that he can act, more-or-less, without thinking, to create an effective business concept and develop efficient operating practices (Mintzberg, 1989). Then, if conditions change, he can readily adapt to ensure business survival because he has seen it all before and already knows what to do (Mintzberg, 1995).
In sum, a founder’s preexisting knowledge of the business creates disincentives for planning since it reduces the need for planning’s symbolism, learning, and efficiency benefits. Founder knowledge also moderates the planning-learning relationship since there is less to learn. Finally, founder knowledge enhances the likelihood of business survival since it facilitates proper alignment of the business concept with customer preferences and other market conditions.
P5a.1: There is a negative relationship between preexisting founder knowledge and the degree of pre-startup planning undertaken.
P5b.1: Preexisting founder knowledge moderates the relationship between degree of planning and founder learning.
P5c.1: Preexisting founder knowledge is positively related to business survival.
Founder’s Capital Investment. Startup capital serves three purposes: (1) to purchase the assets needed to operate a business; (2) to sustain a business during its early period when cash flows are likely to be negative (Scott & Bruce, 1987); and (3) to buffer against management mistakes, environmental uncertainties, and other unforeseen difficulties (Bourgeois, 1981). All other things (e.g., capital intensity, scale of operations) remaining equal, the need for external financing decreases as the internal capital invested by the founder increases. Since planning’s symbolism effect pertains largely to financing, capital available from the founder reduces the need for symbolism. Further, an abundance of capital means that the business can afford the luxury of enactive learning, from its various mistakes and inefficiencies during the startup period. Thus, planning’s efficiency benefit is less important since abundant capital enables the business to tolerate inefficiency. For these reasons, capital reduces incentives to plan and, consequently, is likely to have a negative impact on the degree of planning undertaken.
P5a.2: The amount of capital invested by the founder (as a proportion of the minimum startup capital needed for the venture) is negatively related to the degree of pre-startup planning undertaken.
An abundance of capital also ensures that business has sufficient unabsorbed slack (i.e., “uncommitted, liquid resources,” see Singh, 1986, p. 567) to survive changing conditions. Since strategic change is not cost-free (Castrogiovanni, Baliga & Kidwell, 1992; Smart & Vertinsky, 1984), businesses need cash and other resources to make changes in response to changing conditions. In a study of bankrupt firms matched with survivors from the same industries, Hambrick and D’Aveni (1988) noted that the eventual bankrupts had less unabsorbed slack than the survivors as far back as ten years before bankruptcy occurred. By enhancing the level of unabsorbed slack, a founder’s capital investment thus improves business survival prospects since this increases the business’s ability to cope with changing conditions.
P5c.2: The amount of capital invested by founders (as a proportion of the minimum startup capital needed for their ventures) is positively related to new small business survival.
Building on the methodological points raised previously, contextual conditions should be examined in studies of pre-startup planning. This would ensure the testing of better-specified models, and would yield a more complete understanding of the phenomena being investigated. Again, longitudinal research designs are encouraged. In such designs, contextual conditions might be assessed immediately prior to a pre-startup planning effort, in order to facilitate conclusions about causality.
However, measurement of contextual conditions is very problematic because the relevant constructs are aggregate abstractions of attributes pertaining to specific events, tendencies, and other phenomena. Although such abstraction is necessary for purposes of theoretical parsimony, information is lost when measures employ a level of aggregation corresponding to that of the abstract constructs. Castrogiovanni (1991) noted that munificence, assessed at the task environment level of abstraction, is an aggregate composite of conditions at lower levels – i.e., the subenvironment and resource pool levels in his framework. For example, raw material supplies might be plentiful (high munificence) while demand for the products/services of a business might be scarce (low munificence). If these conditions are averaged in an assessment of task environment munificence, the resulting “moderate munificence” estimate could not clarify the differential impacts of the supplier and customer “subenvironments” on planning, survival, etc.
Similarly, levels of environmental uncertainty and founder knowledge may be unequal across information domains (Gerloff, Muir & Bodensteiner, 1991; Milliken, 1987). Demand levels, for example, might be relatively certain while customer preferences are uncertain. Regarding knowledge, consider the previously drawn example of a tavern manager desiring to open his own establishment. While his operating knowledge may be considerable as a result of his managerial experience, his administrative knowledge (e.g., on financing issues) might be limited. Since these factors influence the need for planning, they are likely to have differential impacts on various facets of the planning process and its outcomes. If, for example, some sufficient demand level is certain, there may be little reason to undertake a business feasibility study as part of the pre-startup planning process. However, if at the same time customer preferences are uncertain, this could stimulate other forms of research needed to determine the optimal product mix. Thus, while aggregated measures of contextual factors may be useful for discerning relationships with aggregate characteristics of planning processes and outcomes, the use of disaggregated measures to test propositions at lower levels of abstraction provides additional insights (Castrogiovanni, 1991). In fact, studies focused at lower levels of abstraction may yield stronger results since the loss of information through aggregation may limit an (aggregated) independent variable’s ability to account for variance in a dependent variable.
Toward a Research Agenda
This article extends the extant literature on small business planning by distinguishing direct effects (e.g., learning) of pre-startup planning from indirect ones (e.g., survival), and by considering very explicitly the role of contextual factors. Consequently, the model presented in Figure 2 offers insight into such questions as: (1) Why do some founders plan more than others? (2) When is pre-startup planning likely to be most beneficial? and (3) What factors can be manipulated to increase the likelihood of new business survival?
The view taken here is relatively parsimonious and coherent. Starting from the perspective that social systems can be characterized as combinations of resource pools and information flows (Aldrich & Mindlin, 1978), pre-startup planning is considered a process for changing the alignment of resources and information between a founder and the business environment. Thus, uncertainty and munificence are attributes of the information flows and resource pools, respectively, in the environment, and knowledge and capital are corresponding attributes of the founding conditions. Planning benefits define the manner in which pre-startup planning can affect this alignment: Symbolism is associated with resource access; learning pertains to information access; and efficiency results from the use of information to allocate resources among business activities. Business creation is an attempt to change this alignment by redirecting resources away from the environment toward the founder, and business survival is evidence that this attempt has thus far been successful. The parsimony and coherence of this view, as just described, facilitates extension beyond the focus of this article. For example, organizational resource and information attributes can be assessed (instead of founding conditions) to gain understanding of ongoing planning processes during post-startup stages of business development.
Given practical limitations, it is unlikely that the relationships shown in Figure 2 could be examined in a single study. It was suggested, for example, that Small Business Development Centers (SBDCs) can provide avenues for studying the various relationships involving learning and efficiency. Since SBDCs generally encourage and facilitate planning, however, findings of such studies may not generalize to non-planners (that is, the range of the planning variable would be restricted.) Further, if through an SBDC’s efforts its clients tended to end up with uniform business plans (i.e., in terms of comprehensiveness, detail, etc.), it might be impossible to assess the planning-symbolism linkage. Conversely, while alternative research designs, not involving SBDCs, may be preferable for examining certain relationships such as that between planning and symbolism, they may be limited in their capacity for examining other relationships.
Thus, Figure 2 is a starting point for studying a broad range of planning issues. First, a series of studies is needed to test the relationships suggested by that model. Next, other studies measuring constructs at lower levels of theoretical abstraction would offer additional insights, particularly regarding the generalizability from more abstract to more specific levels of analysis. Then, with minor modifications, the model might be extended to other planning domains including:
* Pre-startup planning for new ventures of large, established firms;
* Post-startup planning processes in established small businesses; and
* Influences of planning on performance outcomes other than survival.
Finally, since this article focuses on the process through which pre-startup planning influences business survival, attributes of good plans were not addressed. Instead, it was assumed that if degree of planning is sufficient, given founder knowledge, environmental uncertainty, etc., then good plans will result (through the learning benefit of planning). Future research could extend Figure 2 in order to test this assumption or obviate the need for it. For example, the variable, “degree of planning,” might be decomposed into several variables associated with various methods and procedures for ensuring that good plans result.
Aside from clarifying the value of planning in particular contexts, this line of research might also identify the potential substitutability of planning and other “success” factors. In addition to inadequate planning, for example, poor “management” (i.e., application of knowledge), and insufficient working capital are often cited as reasons for small business failure (cf. Castrogiovanni, Justis & Julian, 1993; Dun and Bradstreet, 1988; Haswell & Holmes, 1989). Figure 2, however, suggests possible tradeoffs – i.e., the possibility that an abundance of knowledge or capital might compensate for inadequate planning. Converse arguments would suggest that planning might compensate for limited knowledge or capital, or at least that the importance of planning is heightened when these other factors are limited. Suppose that a narrow window of opportunity requires fast action – too fast for comprehensive pre-startup planning to take place. Entrepreneurs might be advised to increase capital investment so that there is sufficient slack to compensate for costly mistakes that may result from inadequate planning.
Finally, this line of research has implications for the manner in which new small businesses are assisted by government and other organizations. Currently, many universities – for example, through affiliated Small Business Institutes (SBIs) – send teams of students to develop plans for new local businesses. Presumably, everyone benefits: universities get monetary compensation from the Small Business Administration (SBA), students learn, and businesses get plans. From a public policy perspective, however, Figure 2 implies that this may not be the best way to assist small businesses, and consequently, not the best possible use of taxpayer funds by the SBA (cf. Robinson, 1982). If the plans thus generated are good enough, they may yield symbolism benefits by helping businesses obtain financing. Since they generate the plans, however, students rather than businesses gain the most knowledge from the planning process. Thus, the learning benefits derived by the businesses are limited (Timmons et al., 1985). Since learning, in turn, affects efficiency, the use of students also may limit the extent to which businesses gain efficiency benefits from the planning process. It may be better to encourage businesses to generate their own plans and limit the roles of SBIs, students, and other advisors to planning process consultation (Robinson, 1982; Van de Ven, 1980).
Most new small businesses fail within five years – and most entrepreneurship researchers are aware of this fact. Yet surprisingly little research has directly examined factors influencing survival. Biases toward survey research contribute to this deficiency because non-survivors tend to get excluded from survey research samples. For example, businesses that failed prior to the onset of a study may not appear on listings used to guide sample selection, or they subsequently get treated as nonrespondents to a survey instrument. By offering a framework for examining relationships between pre-startup planning and survival, and by suggesting ways for dealing with the related methodological concerns, this article will, hopefully, stimulate empirical research on this important topic.
Acknowledgment: Thanks are extended to James J. Chrisman and Charles B. Shrader for helpful comments on earlier versions of this manuscript.
1. Some might contend that enactive learning is possible when the formal planning process is iterative, with various cycles and feedback loops. Although this argument may have merit, it is irrelevant given this article’s focus on pre-startup planning.
2. Sharfman and Dean did not examine uncertainty per se, but rather its two forms, dynamism and complexity. The view that these are indeed two forms of uncertainty is elaborated below.
3. Although the framing of this discussion of industry maturity in terms of environmental uncertainty and munificence may seem redundant with the earlier discussions of those environmental dimensions, it is useful for three reasons. First, parallels between these discussions provide descriptive parsimony since new terms need not be presented and explained in order to make the central points of this section. Second, to some extent, assessment of industry maturity would likely capture residual uncertainty and munificence effects not assessed through direct measures of those dimensions because of inherent measure limitations. Third, industry maturity may have greater practical relevance for prospective business founders if it is easier to make intuitive distinctions between emerging and more mature industries than between levels of environmental uncertainty and munificence.
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