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A NEW VENTURE’S COGNITIVE LEGITIMACY: AN ASSESSMENT BY CUSTOMERS
Dean A. Shepherda and Andrew Zacharakisb a
University of Colorado, USA and bBabson College, USA.
ABSTRACT Many legitimacy problems associated with a new venture appear to stem from a lack of customers’ knowledge and understanding of the new venture. Of particular concern to entrepreneurs is cognitive legitimacy. The findings of this article suggest that customers appear to have a preference for greater rather than lesser information about a new venture’s product, organization and management (holding the content of that information constant) stressing the importance of new venture’s cognitive legitimacy. Furthermore, customers appear to use a contingent decision policy. However, the nature of some of the interactions differs across customers and the importance of these interactions in the customers’ decision policy, relative to the main effects, is low. INTRODUCTION The number of new ventures launched in the United States has grown rapidly from about 600,000 per year in the seventies to approximately 3.5 million per year in 1996 [55]. A more recent report estimates that there are over 7.3 million start-up efforts underway at any given time [63]. These new, small and expanding firms generate virtually all new jobs and also generate 50% of all innovations and 95% of all radical innovations [59]. However, new ventures fail at an alarmingly high rate. Twenty four percent of new ventures fail within their first two years and 63 percent within six years [55]. While there is dispute over the rate of failure it is generally acknowledged that new ventures fail at a greater rate than do established firms [21, 47, 52]. In light of the economic benefit derived from new ventures, it is important to understand why new ventures fail. What is unique about new ventures (relative to established organizations) that result in this higher mortality risk? Business failure has been investigated by scholars using an IO strategy perspective (e.g., [2, 17, 40] and also a sociological perspective [47, 52]. The sociological perspective is particularly interesting as it specifically addresses the uniqueness of a venture’s early stage of life and how this increases an organization’s mortality risk (the probability of death due to failure). Some sociology scholars have suggested that a new venture’s higher risk of failure is derived from the liability of newness which includes: the costs of learning new tasks [47, 52]; the characteristics of the new product; the strength of conflicts regarding new organizational roles [47, 52]; the
presence or absence of informal organizational structures [52]; the stability of links with key stakeholders [47, 52]; and the degree of organizational stability/inertia [21]. Sociologists argue that obtaining legitimacy is central to the process of survival of new organizations [21]. More generally, the higher mortality risk faced by newer ventures is due, in part, to a lack of external legitimacy [47]. External legitimacy is conferred to a new venture when its actions are endorsed by powerful external collective actors [51] and strong relationships are developed with external constituencies [47]. Many legitimacy problems associated with a new venture appear to stem from a lack of knowledge and understanding [1, 20]. By gaining legitimacy among its stakeholders, a venture finds it easier to obtain access to resources and to respond to competitive threats [5] and attract customers [62]. For example, the process by which customers learn about a new venture and come to perceive it as established will effect its risks [1, 47], i.e., customers purchase from those firms that appear to them to be more legitimate. Therefore, one of the keys to understanding a new venture’s survival is an investigation of customers’ perceptions of a new venture’s legitimacy. In this article, we build upon the legitimacy model proposed by Aldrich and Fiol [1]. Specifically, we develop a finer-grained view of the cognitive legitimacy construct as it applies to the most important constituent for most new ventures – the customer. This article’s approach represents a departure from previous work in a number of important ways. First, most research into the legitimacy of new ventures has been conducted at the industry-level of analysis [1]. For example, industry size is an indication of legitimacy [20, 44]. Our level of analysis is at the firm level (i.e., the cognitive legitimacy of a new venture from the perspective of customers is based on the characteristics of that venture). Second, legitimacy has been used by scholars from a variety of disciplines in a coarse-grained manner [54]. The current model utilizes research from sociology (supplemented with research from marketing) to theoretically expand the concept of cognitive legitimacy. By gaining a greater understanding of customers’ probability of purchase, there is likely to be some prescriptive benefit to entrepreneurs. Understanding what aspects of the new venture are the primary causes of a lower level of legitimacy, in the minds of customers, provides entrepreneurs an opportunity to undertake actions that will actively increase legitimacy and therefore the probability of selling their products. The paper proceeds as follows: First, a model of cognitive legitimacy is presented and hypotheses are generated. Second, the research method to test these hypotheses is detailed and the results are presented.
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Finally, the findings are discussed, limitations of the study detailed and implications for scholars and practitioners offered. A MODEL OF COGNITIVE LEGITMACY Aldrich and Fiol [1] propose that liability of newness is a function of two types of legitimacy: sociopolitical legitimacy and cognitive legitimacy. In order to survive, a new venture needs to acquire and retain both types of legitimacy. Sociopolitical legitimacy is the extent to which macro-level stakeholders (i.e., government, general public, etc.) accept the new venture as appropriate given the norms of society and ‘laws of the land’. This is typically not a concern for new ventures as people accept “profit-seeking activities [as] valid unless otherwise specified” [15: 247]. Therefore we focus on cognitive legitimacy. Cognitive legitimization refers to the spread of knowledge about a new venture [1]. From the cognitive perspective of legitimacy, organizations are legitimate when they are understandable rather than consider when they are desirable. Knowledge of an organization provides cognitive legitimacy regardless of whether that knowledge is positive, negative or neutral [1, 27, 54]. From a customer perspective, why does an increase in knowledge of a new venture increase the probability of purchase even when the content of that information is held constant? Sociology scholars suggest that as ventures become so familiar and well known they are taken for granted by the stakeholders. This allows the customers to conserve resources associated with organizing and with searching for alternative sources. Scholars from behavioral economics find supporting evidence of the importance of knowledge to decision-makers regardless of the content of the information gained and offer the explanation that people are uncertainty and ambiguity averse [28, 29]. Aldrich and Fiol [1] argue that from a consumer’s point of view, cognitive legitimization means that people are knowledgeable users of the product or service. This has considerable support from the marketing literature (e.g., [11]). We extend this meaning of cognitive legitimacy to include knowledge of the organization and of the management. Where customer knowledge is incomplete on any or all facets of the venture, cognitive legitimacy is lowered and therefore customers are less likely to purchase the new venture's market offerings. We also argue that the degree to which customer uncertainty affects the probability of purchase will be moderated by the importance of the product purchase to the customer [4, 39]. These relationships are now discussed. Customers’ Knowledge of Product/Service Part of what makes new ventures so risky is uncertainty about the rate at which customers will substitute old technology for new [31]. In other words, to what extent and how quickly customers will switch from a
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product with which they are familiar to an unknown product? This uncertainty may simply stem from a customer's lack of knowledge regarding both the performance and benefits of the new product [7, 43, 48] - potential customers often lack a frame of reference for understanding new product concepts and the benefits of a venture’s offerings [48]. For example, customers may not even know the product can meet their needs. They may also lack the necessary knowledge to be able to appreciate how the product works and what its true benefits are relative to existing alternatives [3]. The classic example of this concerned the introduction of microwave ovens where households were unaware of its true benefits and how it could be used in conjunction with traditional recipes. The lack of a frame of reference for a new product/service is a consistent problem for pioneers of products with a high technological content [58], such as computers and software. Even once customers understand a pioneer's product, they may still perceive a risk in substituting into the new market. The risks may arise out of the uncertainty of dealing with something new (i.e., customers perceive the industry's offerings as untried and untested). They may also stem from reasonable tradeoffs between product performance and the convenience associated with established products [58]. For example, in its early marketing of the personal computer, IBM benefited from its reputation, even though its product did not offer superior performance. This made the IBM PC a product that you could not get fired for buying. Customers’ level of knowledge of the product also impacts the ease of adoption. For example, users of new software programs may be concerned about the time necessary to achieve proficiency, adopters of new textbooks may worry about the ease with which the book can be adopted into existing course plans, and purchasers of new food products may worry about how the item will fit into overall mealtime needs [45]. The above discussion leads to hypothesis 1, where customer knowledge of product refers to the customers’ experience with, or knowledge of, how the products being offered will benefit them. Hypothesis 1: All else equal, the higher customers’ knowledge of product the more likely they will be to purchase the new venture’s market offerings. Customer Knowledge of Organizational Identity A new venture can be seen as either an entirely new identity by customers or as an established corporate identity venturing into a new market with a new product. The ability of a corporate identity to provide an umbrella for new product development and new venturing has long been recognized [6, 11, 16, 30, 60, 61] and is a source of competitive advantage. Given the apparent importance of customer knowledge of the organization
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on the purchase decision it is surprising that there has been little systematic empirical research on the nature of this relationship [11]. The research that has been conducted to date has investigated the nature of the relationship between the content of the information (good corporate image or reputation) to the customers’ purchase decision. We extend the concept of cognitive legitimacy to knowledge of the organization (holding the content of that knowledge constant). To the authors’ knowledge there has not been an investigation of the relationship between customers’ level of knowledge of the organization and their purchase decision controlling for information content. Such an approach represents a possible explanation for Dacin and Brown’s [11] ‘surprising’ finding that companies that are perceived by customers as negative were not necessarily destined to have negative customer responses. Cognitive legitimacy may have more than compensated for the customer’s low evaluation of the organization. For example, suppose that IBM launched a new venture into the automobile industry. While IBM would be a new player in the automobile industry, the firm is a well-known producer of computer systems. While there may be few obvious relationships between computers and automobiles, customers' knowledge and/or experience with IBM may provide some cognitive legitimacy. Therefore, a corporate venture into an unrelated industry, while still considered a new venture can be viewed as more familiar to customers than a venture in which the customers are completely ignorant about the organization launching the venture.1 Start-ups, on the other hand, do not have a previous organizational affiliation to leverage. As such, independent start-ups attempt to build a perceived organizational identity by leveraging relationships that they have acquired (e.g., funded by reputable venture capitalist, or supplied by well-known firm). Organizational knowledge by affiliation is helpful, but unlikely to be as strong as knowledge of the organization itself. The above discussion leads to hypothesis two where customer knowledge of organization represents knowledge of, or experience with, the company (organization) that makes this new product or any entity directly associated with this company. Hypothesis 2: All else equal, the higher customers’ knowledge of organization the more likely they will be to purchase the new venture’s market offerings.
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Of course, if the linkage between corporate identity and the venture is not established, then the corporate identity may be a liability for the venture as it confuses rather than educates consumers - - customers knowledge decreases.
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Customer Knowledge of Management While business transactions that are critical for new venture success frequently occur between organizations, individual relationships with suppliers, buyers, and customers will also be important for a venture. The uncertainty that individuals have regarding a venture and its products may be mitigated if customers know and trust the individuals running a venture [54]. New ventures with a known and well-reputed management team will likely have greater legitimacy than ventures in which customers have no knowledge or experience with the management team [54]. For especially novel and innovative ventures, having a well known management team may be essential, since representations about the characteristics of the venture’s products and services will need managerial credibility to be accepted. Even in circumstances where that knowledge and experience may not be positive, the old saying of "better the devil you know than the unknown" increases the probability of purchase for the venture with a "known" management team. This occurs when the personal backgrounds of the managers will communicate useful information to customers about general levels of training and experience independently of the personal histories of the managers. For example, having a manager or trader with Wall Street experience may be crucial for a financial service venture [45]. The above discussion leads to hypothesis 3, where customer knowledge of management refers to the amount of knowledge and experience customers have with the members of the management team. Hypothesis 3: All else equal, the higher customers’ knowledge of management the more likely they will be to purchase the new venture’s market offerings Product Importance Customer attitudes and purchasing behavior may differ between products based on the importance of the product to the customer [8, 39] - - product importance can moderate the relationship between customer uncertainty and the purchase decision [26, 37]. We argue that if the purchase price represents a significant amount of the buyer’s income and/or represents an emotional decision, then the importance placed on the product is high. If the product represents only a small fraction of the buyer’s income and/or the life of the product is relatively small (i.e., the risk of a bad decision is low), then the decision is less important. The above discussion leads to hypotheses four and five. Hypothesis 4: The degree of product importance moderates the relationship between the probability of purchase and customer knowledge of (a) product, (b) organization, and (c) management.
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Hypothesis 5: As customer knowledge of (a) product, (b) organization, or (c) management increases, the probability of purchase increases at a greater rate for high importance products than for low importance products. In sum, we build on the Aldrich and Fiol [1] model and argue that cognitive legitimacy is a function of product knowledge, organization knowledge and reputation of the top management team. Furthermore, we propose that product importance moderates the relationship between these factors and customers’ assessment of cognitive legitimacy. As an initial attempt to assess whether this finer grained perspective of cognitive legitimacy is valid, we conducted a conjoint exercise with 53 consumers. The next section explains the test and its results. RESEARCH METHOD Sampling Plan, Survey Method and Sample A conjoint exercise was administered to a random sample of 51 consumers in an affluent suburb west of Boston. A random number generator selected 250 names out of the community’s electronic phone book. The town has a median family income of $90,000 per year and the median home price is $365,000. The community is along Boston’s famed Route 128 (“The Technology Highway”). Based upon the broad exposure to high technology, new ventures, and the relative affluence of the sample, this is a conservative test of our legitimacy model. Affluent people with broad technology exposure are more apt to try new products from new companies without prior knowledge of the management team. Two hundred and fifty conjoint experiments were sent out, twenty six were returned due to invalid address (the person had moved since the last electronic phone book was published), 53 exercises were returned for a response rate of 24%. Two were removed due to incomplete responses. The sample was approximately 70% male, the average education was at the masters level of college, the average age was 55, and the average income was over $80,000. Attributes, Levels and Dependent Variable For this conjoint experiment, customers evaluated a series of hypothetical conjoint profiles which describe new ventures in terms of four attributes, each with two levels: (1) Customer knowledge of product: Low - you are completely unfamiliar with the product or its benefits to you. High - you are highly familiar with the product and its benefits to you. (2) Customer knowledge of organization: Low - you have no knowledge or experience with the company who makes this new product or any entity directly associated with this company. High - you have considerable knowledge and experience with the company who makes this new product. (2) Customer
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knowledge of management: Low - you have no knowledge or experience with any of the managers of the company. High - you have considerable knowledge and experience with three of the managers of the company whether that knowledge was gained when they were with this company, another company or socially. (4) Importance of the product to the customer: Low - the purchase price of the product represents a small amount of your income and is not visible to others. High - the purchase price of the product represents a substantial amount of your income and is highly visible to others. Attribute levels were chosen to represent variation that typically occurs in the decision environment of customers, thereby maintaining believability and response validity. A pre-test with customers and academics confirmed the face validity for both the attributes and their levels. The customers are asked to consider each scenario (based on combinations of the levels above) and make an assessment of the probability that they will purchase the product. Probability of Purchase - the probability that you would purchase this new product and is represented by an eleven-point scale with end anchors describing “very low probability of purchase” and “very high probability of purchase.” Research Instrument and Experimental Design The research instrument contained a cover letter, task instructions, the conjoint experiment and a postexperiment questionnaire that asked customers to answer questions regarding characteristics of themselves and their household. Relevant term definitions were also included on a detachable sheet that could be referred to while completing the survey. Once instructions were read, customers considered each conjoint venture description and provided a rating on an 11 point scale for the dependent measure - - probability of purchase. For the conjoint experiment, an orthogonal fractional factorial design was used to reduce the number of attribute combinations and thus make the decision making task more manageable [18]. Each of the four attributes was varied at two levels in a fractional factorial design consisting of eight profiles [19]. The original profiles were replicated to permit estimates of individual subject error for use in subsequent statistical analysis and allow a test retest measure of reliability. The original eight profiles and the replicated profiles were randomly assigned to avoid order effects. A practice case was also used to familiarize respondents with the task. Therefore, the experiment presented customers with 17 profiles to evaluate. Analysis This study uses conjoint analysis to determine if customers’ level of knowledge affects their likely purchase decision. Conjoint analysis is a general term referring to a technique that requires respondents to make a series of judgments based on a set of attributes (cues) from which the underlying structure of their cognitive system can
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be investigated [46]. From this series of judgments the respondent’s decisions can be decomposed, thus providing the researcher an opportunity to investigate the underlying structure of the decisions. Conjoint analysis and policy capturing have been used in hundreds of studies of judgment and decision-making [46, 50]. These studies vary from research into consumer purchase decisions [32], manager’s strategic decisions [25, 42] and expert judgment [12]. Importantly, this technique avoids the use of retrospective and self reported data by collecting information about a decision as that decision is made [46]. Regression and analysis of variance (ANOVA) are the statistical techniques used to decompose the decision (e.g., regression decomposes an assessment into its underlying structure as represented by the independent variables and their corresponding regression coefficients). To identify attributes statistically significant at the aggregate level, the regression coefficient (B) for each attribute (derived from the individual-subject level of analysis) are averaged across individuals with the sign of the regression coefficient indicating the nature of the relationship [57]. The mean regression coefficients represent a model of the sample's decision making. A Zstatistic aggregates the t-statistics derived from the individual-subject analysis for that attribute in order to identify whether a particular attribute is significantly used by the sample [13]. A Z statistic is used to first test if the sample significantly uses a contingent decision policy and second if the interaction is significantly used in the same way (labeled ‘moderation’ and ‘direction’ in table 1 respectively). The Z score ‘moderation’ aggregates the magnitude of the t-statistics whereas the Z score ‘direction’ includes the signs of the t-statistics in the calculation. Although two or more attributes may significantly affect the decision process, it is unlikely that those attributes will be of equal importance. Therefore, the significance at the aggregate level of analysis is supplemented with a measure of relative importance - - Hays' [22] omega squared (ω2). Omega Squared is a measure of explained variance, and is used to assess the relative importance of the attributes. The mean omega squared values corresponding to all main effects and hypothesized interactions were calculated. RESULTS Fifty percent of the individual models of customers’ assessments of the probability of purchase explained a significant proportion of the variance in their decision making with a mean R2 of 0.63. Reliability (i.e., test retest correlation on the 16 replicated profiles) was significant for 90% of the sample with a mean test-retest correlation of 0.83 providing assurances the new venture decision making task was performed consistently by the participants.
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Table 1 displays: (1) the aggregated regression coefficients for each decision criterion, (2) its corresponding Z score ‘moderation’ (which represents whether the interaction was significantly used regardless of the nature of the interaction), (3) its corresponding Z score ‘direction’ (which represents whether the interaction was used and used in the same manner by those in the sample) and (4) its corresponding omega squared value. At the aggregate level of analysis, the Z score ‘direction’ demonstrates that each main effect relationship is significant and positive. This indicates that increases in customers’ knowledge of the new venture’s product, increases in the customers’ knowledge of the new venture’s organization and increases in the customers’ knowledge of the new venture’s management are all associated with a higher probability of purchase (providing support for hypotheses one, two and three). The Z-score ‘moderation’ also indicates that the knowledge of product x product importance interaction, knowledge of organization x product importance interaction, and knowledge of management x product importance interaction were all significantly used by the sample providing support for proposition 4(a), 4(b) and 4(c). However, an analysis was also conducted to investigate whether customers significantly used these contingent relationships in the same way - - the Z score ‘direction’ takes into consideration the nature of the relationship in determining the significance of the interaction. While the findings from this analysis indicate that the knowledge of organization x product importance interaction was significantly used in the same way by the sample (providing support for hypothesis 5b), the other two interactions were not significantly used in the same way (no support for hypotheses 5a and 5c). Combining the two analyses of interactions indicates that knowledge of product x product importance interaction and knowledge of management x product importance interaction are significantly used but are used differently by different customers. Whereas, for the sample, as customers’ knowledge of the organization increased the probability of purchase increased at a greater rate for high importance products than for low importance products. This will be explored further in the discussion section below. TABLE 1. Significance and Importance of Decision Criteria Decision Criteria B Coefficient Z Score (Moderation) Knowledge of Product 2.355 9.280*** Knowledge of Organization 1.265 4.517*** Knowledge of Management 1.385 5.271*** Product Importance 1.270 4.174*** Product x Importance -0.275 1.911** Organization x Importance 0.765 5.526*** Management x Importance 0.035 3.810*** Constant 2.18 *** p
