RECOGNIZING HIGH-TECH OPPORTUNITIES: A LEARNING AND COGNITIVE APPROACH
Andrew C. Corbett, Rensselaer Polytechnic Institute
The primary objective of this study is to examine the relationship between an individual’s learning mode, cognitive style, human capital and his or her ability to recognize and develop opportunities in a high technology environment. This study contributes to the current research by providing empirical evidence that suggests that the manner in which one assimilates and processes information has a significant impact on one’s ability to recognize opportunities. This work brings a new concept into the current conversation from educational psychological—learning mode—and details its importance in the opportunity recognition process. The study also substantiates Baron’s (1998) proposition regarding the importance of cognitive mechanisms, and extends Shane’s (2000) research regarding prior knowledge.
Exploring the process of opportunity recognition is important to gaining a greater understanding of the entrepreneurship phenomenon (Venkataraman 1997) and it has been suggested that the key to understanding the opportunity recognition process lies in an investigation of an individual’s prior knowledge and his ability to cognitively process information (Shane & Venkataraman 2000).
Despite its importance to the field, research is still lacking. An exhaustive review of the opportunity literature (Gaglio 1997) summarized that there was a dearth of empirical work. While there has been an increase in interest the intervening years since Gaglio’s review, Busenitz et al (forthcoming) show that only one study investigating the nexus of the individual and the opportunity has been published in the top management journals in the years from 1985–2000. Busenitz et al. state that four distinct themes are emerging in the conceptual domain of entrepreneurship (individuals & teams, opportunities, modes of organizing, and the environment) and that researchers should examine these concepts at their intersections. These study heads the call of Busenitz and his colleagues by investigating the concept of opportunity and cognition.
The broad goal of this research was to examine how learning informs the process of opportunity recognition. The specific research question to be investigated is: What role do differences in information acquisition, information processing, and knowledge play in the recognition of high technology opportunities?
A review of the opportunity recognition literature shows that researchers have examined this phenomenon from many perspectives, including alertness (Gaglio & Katz; Kirzner 1999), knowledge (Shane 2000), psychology (Shaver & Scott 1991), motivation (Herron & Sapienza (1992) and social networks (Singh, Lumpkin, and Hills 1999). However, the majority of work done in opportunity recognition has been theoretical (Gaglio 1997). While more empirical work has been done since 1997, there is a paucity of work that examines the concept in regard to cognition, learning or knowledge. The few studies that run counter to this statement, however, have focused exclusively on knowledge (Shepherd & DeTienne 2001; Shane 2000).
Shepherd and DeTienne (2001) focused on the importance of knowledge and the interaction of financial reward in relation to opportunity recognition and exploitation. Shane’s (2000) work makes a significant contribution to entrepreneurship by showing the importance of prior knowledge in the opportunity recognition process. Specifically, Shane showed that an individual’s prior knowledge was a contributing factor in one’s ability to recognize opportunities.
Shane and Venkatarman (2000) state that unlocking the puzzle of opportunity recognition lies in examining an individual’s prior knowledge and his ability to cognitively process information. However, the extant opportunity recognition research has focused on knowledge with no direct empirical investigation of cognition or learning. Busenitz and Barney (1996) researched the differences in the cognition of entrepreneurs and managers, but focused on the concept of problem solving, not opportunity recognition. Baron (1998) details many propositions that examine the relationship between cognitive mechanisms and opportunity recognition, but these relationships have yet to be tested empirically.
In sum, while there have been some empirical investigations examining opportunity recognition and knowledge, there is much work to be done. While Shane (2000) shows how individuals use knowledge to find opportunities, his work focuses only on a few specific types of knowledge and does not focus on how this knowledge is acquired. Additionally, the theoretical contributions of Baron (1998) regarding the role of cognition have yet to be tested. The current study fills this gap by using psychology and learning theories to investigate how high technology professionals acquire and process new knowledge and how they then use it together with their prior knowledge in the pursuit of opportunities.
KNOWLEDGE, COGNITION, & LEARNING
In this section, I review the concept of absorptive capacity and the theories of social learning and experiential learning in order to provide the theoretical grounding for my empirical study. Absorptive capacity is examined to show the need for investigating different types of knowledge within the process of opportunity recognition. Social learning theory explores the importance of cognition in this process and experiential learning details how learning mode may be a factor in opportunity recognition.
Absorptive Capacity and Human Capital. Cohen and Levinthal’s (1990) described the concept of absorptive capacity in the following manner:
We argue that the ability to evaluate and utilize outside knowledge is largely a function of the level of prior related knowledge. At the most elemental level, this prior knowledge includes basic skills or even a shared language but may also include knowledge of the most recent scientific or technological developments in a given field. Thus, prior related knowledge confers an ability to recognize the value of new information assimilate it, and apply to it commercial ends. These abilities collectively constitute what we call a firm’s absorptive capacity. (1990: 128)
Cohen and Levinthal provide a link to Shane’s (2000) work on the importance of prior knowledge when recognizing opportunities, and they also suggest the importance of both basic skills and specific scientific or technical knowledge in the process of recognizing new opportunities. In the past, entrepreneurship scholars (Cooper et al 1994; Gatewood et al 1995; and Greene and Brown 1997) have examined the influence of these variables in the process of entrepreneurship, referring to them as “human capital.” Becker (1964) developed the concept by extending microeconomic analysis to a wide range of human behavior, and he popularized the idea that education and training are investments, just like a company’s purchase of a new plant or equipment. He argued that human capital is comprised of accumulated attributes which can be linked to increased productivity
In the current study, I build upon the work of both Cooper et al (1994) and Davidsson and Honig (in press) by examining the effects of human capital in opportunity recognition. While Davidsson and Honig stressed the importance of experience and knowledge in the opportunity recognition process, Cooper et al used more specific dimensions of human capital to show their various effects on the failure, survival, and eventual high growth of new ventures. Following Cooper et al (1994), I will parse out the human capital dimension and focus on two constructs, general human capital (basic skills) and specific human capital (direct industry and technical skills). Following Davidsson and Honig, I will examine these constructs in the realm of opportunity recognition.
Social Learning and Cognitive Style. Social learning (Bandura 1977) explains human behavior by examining the interaction between cognitive elements, behavioral actions, and events in the environment. Bandura theorizes that thought and behavior are influenced when one learns through observations and direct experience. He also emphasizes that this is a self-regulatory process of learning in which individuals have some measure of control over the environment and what influences them, which suggests that prior knowledge and experience may play a role in how they view different situations. “People are not simply reactors to external influences. They select, organize, and transform the stimuli that impinge upon them” (1977: vii).
Essentially, people take cues from their interaction with other people and from events in the environment, and then cognitively process them. Social learning theory emphasizes that individuals will organize information in their own unique manner and that we all have a particular cognitive style (Allinson & Hayes 1996). Both Busenitz and Barney (1996) and Baron (1998) have demonstrated this in their work on the cognition of entrepreneurs. Both studies show that entrepreneurs tend to use different simplifying strategies when making decisions. From this I propose that this theory could also shed light on the opportunity recognition process. The same environmental and cognitive elements that affect the decision making process may help explain the differences in opportunity recognition capabilities.
Prior cognition research shows that each of the hemispheres of the brain tends to specialize in the functions it carries out and that individuals tend to rely on one of these for their primary information processing (Nickerson 1985; Doktor 1978, Ornstein 1977). This work provides two distinct types of active information processing (Allinson & Hayes 1996). Allinson and Hayes show that “right brain” thinkers tend to be intuitive and “left brain” thinkers tend to be analytical. Individuals that rely on their right hemisphere emphasize the immediate synthesis of many inputs at once, whereas those who rely of the left side will process individual pieces of information sequentially. Additionally, Allinson and Hayes report that these patterns of processing are “ not merely transient; people seem to have a rather permanent stylistic orientation to the use of one hemisphere.” (1996: 122)
Learning Mode and Information Acquisition. Absorptive capacity suggest the importance that different types of human capital might play in the opportunity recognition process. The theory of social learning recognizes the importance of cognition and suggests that each individual’s unique cognitive processing style may play a role. However, neither of these concepts explains how individuals initially acquire information. Kolb’s work in experiential learning provides this missing piece. Kolb (1984) defines experiential learning as a process by which knowledge is created through the transformation of experience. According to the theory, prior to the processing of information, individuals first go through a process of information acquisition.
Kolb (1984) explains that experiential learning occurs through a process of acquisition and transformation. For this study, I focus only on his theory of information acquisition, which he terms prehension. The concept of prehension refers to two different ways in which an individual can acquire information in the world—either through direct experience or through a recreation of experiences. Apprehension is a reliance on the tangible, felt qualities of immediate experience. Comprehension refers to a reliance on conceptual interpretation and symbolic representation.
Furthermore, it can be seen that there are three primary opposing forces that make up these distinct learning modes. When acquiring new information by apprehension an individual is focusing on the present situation in an acritical manner which allows him to develop unique personal knowledge. Conversely, when acquiring information through comprehension, an individual relies on the past, and then critically reinterprets this information, which develops social knowledge.
Jung (1977) states that we all tend to have a preference for different learning mechanisms and that the complex interactions of our minds and the chaos of the environment in which we live help explain why there is great variability in the learning process.
Outer circumstances and inner disposition frequently favor the one mechanism, and restrict or hinder the other; whereby a predominance of one mechanism naturally arises. (1977: 12 )
Realizing this, we can see why different individuals may recognize different opportunities: Individuals rely on different combinations of information acquisition (prehension—the learning mode), information processing (cognitive style), and previous knowledge and experiences (human capital). These theories and concepts are appropriate for the study of opportunity recognition because they provide theoretical insights into the fundamental question of why some individuals recognize opportunities and others do not.
Theorizing about their concept of absorptive capacity, Cohen and Levinthal (1990) state that when an individual is primed with new technical information, his ability to make sense of and use this information to find new opportunities is a function of his prior knowledge and experience. Since prior research (Becker 1964) terms an individual’s ability to make productive use of his previous knowledge and experience in a market as human capital, that is the terminology that will be used here.
Previous entrepreneurship research has delineated the differences between general human capital and industry specific human capital (Bruderl et al. 1992;Cooper et al. 1994). Following these works, I examine the concept at two different levels: basic skills and experience and specific skills and experience (Cohen and Levinthal 1990). First, I examine general human capital and opportunity recognition. In this study, the general human capital construct is comprised of age (Cressy & Storey 1995) and experience (Cooper 1981, 1985; Westhead 1995). The general human capital construct is an index of each subject’s age, job level, industry experience, years at their current firm, and years in their current position. Hypothesis one is formally stated below.
Hypothesis 1: There will be a positive relationship between an individual’s level of general human capital and the number of opportunities he can identify.
While an individual’s basic skills and prior experience may shed some light on his ability to recognize opportunity, Dawes (1988) states that experience is not always a good teacher. Dawes details eight specific biases and problems that may result from a reliance on experiences and suggests that specific expertise may be a better tool to recognize opportunity. Cooper et al. (1994) affirm Dawes’ ruminations and show the importance of specialized technical knowledge as a predictor of new venture performance. Shane (2000) showed that prior knowledge about markets and expertise with customers in a specific industry had a bearing on opportunity recognition. Borrowing from Cooper et al. (1994), but in the context of Shane’s study, I propose that detailed technical know-how will have a direct effect on opportunity recognition. The formal statement of hypothesis two is below.
Hypothesis 2: There will be a positive relationship between an individual’s level of specific human capital and the number of opportunities he can identify.
Hypothesis three is developed from Bandura’s social learning theory and the fact that cognitive differences play such an important role in this theory. As shown in the earlier section, individuals tend to organize and process information in either a more “right brain” or “left brain” manner. As Allinson and Hayes (1996) explain, those who focus on the right brain process information through immediate judgment based on feeling and the adoption of a global perspective. These individuals are seen as being more intuitive than their “left brain” counterparts. Those that focus on left-brain processing are called analysts and rely on judgment based on mental reasoning and a focus on detail. Essentially, the cognitive processes of the analyst work in a more straightforward, logical pattern, whereas the intuitivist takes a broader perspective and incorporates many different inputs at once. Based on the broader and more varied cognitive perspective of the intuitivist, I offer the following hypotheses:
Hypothesis 3: Intuitivists will identify more opportunities than analysts.
While an individual’s cognitive style examines his preference for processing information when carrying out a learning activity (Valley 1997), an individual’s learning mode preference describes how each individual acquires information. Kolb (1984) submits that through one’s various forms of socialization people tend to build a reliance on one of the two basic forms of grasping information: apprehension and comprehension. When individuals acquire information through comprehension they are relying on their ability to think through abstract concepts and reinterpret prior information. When individuals acquire information through apprehension they rely on their feelings to digest the direct, concrete occurrence that they are currently experiencing. Table 1 summarizes the differences in learning mode with regard to each modes’ orientation toward time, objectivity, and the type of knowledge that results.
I argue that based on these contrasting modes, a difference in the ability to recognize opportunity results partially from the fact that individuals tend to rely on one of these modes. While the process of apprehension develops unique personal knowledge, this learning mode is restrictive, relative to the mode of comprehension. Individuals who rely on this mode would be less discerning than their comprehension counterparts. Those who rely on comprehension have a forward-looking schema that uses past knowledge to help make sense of newly acquired information. Their more critical eye also leads me to propose that they would have an edge over the “apprehensions” in their ability to recognize opportunities. Therefore, the fourth hypothesis is as follows:
Hypothesis 4: Individuals with a learning mode of comprehension will identify more opportunities than individuals with a learning mode of apprehension.
In addition to the four main effect hypotheses stated above, this study also posits two interaction effects. First, an interaction between an individual’s learning mode and level of specific human capital is offered. Following the idea of entrepreneurial fit (Brigham 2001), I propose that a combination of detailed specific technical knowledge that is examined with a critical eye will result in many opportunities being recognized. It seems logical that someone with detailed specific technical knowledge could identify more opportunities than someone who lacks this insight. What is examined here is the additional effect that an individual’s learning mode will have on this single variable. I theorize that an individual with a high level of technical insight will be additionally aided in his recognition abilities by a learning mode of comprehension. When this individual’s critical schema is applied to his high level of technical knowledge, the result should be an ability to recognize many more opportunities. As such, hypothesis five is stated below.
Hypothesis 5: Learning mode will moderate the relationship between an individual’s specific human capital and the number of opportunities he can identify.
Hypothesis three examines the relationship between an individual’s cognitive style and his ability to recognize opportunities. Prior entrepreneurship research using Allinson and Hayes’ (1996) cognitive style index shows that fit between an individual’s cognitive style and other environmental elements and behavior are important (Brigham 2001). Following this stream, I propose that fit between learning mode and cognitive style is important in recognizing opportunities. Specifically, I posit that when the open-minded, global perspective of an intuitivist is combined with the interpretive behavior of the comprehension learning mode, the most ideal recognition fit is achieved. The intuitivist uses many different methods of exploration (as opposed to the analyst) and those who rely on a comprehension learning mode are good at reinterpreting past experiences in a critical manner. Together, this combination of behaviors seems to be most ideally suited for opportunity identification, when compared to the other three combinations that could be made from the two cognitive styles and two learning modes. Based upon this, I hypothesize the following.
Hypothesis 6: Learning mode will moderate the relationship between an individual’s cognitive style and the number of opportunities he can identify.
Multiple methods were used in the collection of data for this study. Prior to developing the questionnaire and quasi-experiment, sixteen semi-structured interviews of high technology entrepreneurs and professionals were conducted to gain an understanding of what factors may contribute to the process of opportunity recognition. The individuals interviewed represented various levels of their organizations and included founders, owners, top management team members, engineers, and researchers. Based on these interviews, a survey and experiment were developed that examined opportunity recognition capabilities.
In accordance with the Total Design Method (TDM) described by Dillman (2000), a four-step process including an initial introductory letter, the survey itself, a follow-up reminder, and a second survey were mailed. A random sample of 1,592 founders, owners, top management team members, engineers, and researchers of Colorado technology-based firms was used in this study. The sample came from the 2002 Rocky Mountain High Technology Directory. This four-step process yielded 380 completed instruments and 83 returns of incorrect addresses. This response rate of 25.1% is consistent with Stimpert’s (1992) report that typical response rates range from 14% to 34%.
Dependent Variable. The dependent variable, number of recognized opportunities, was captured from respondents’ answers in the quasi-experiment. In the experiment, each subject was asked to list as many new business opportunities as they could, based upon a detailed description of a new technological protocol (the Bluetooth wireless protocol). The actual wording of the quasi-experiment is detailed in Figure 1. The 380 respondents listed 1,454 ideas. These statements were then judged for their viability as a true opportunity by three independent raters. The judgment of the three raters had an initial inter-rater reliability of 89.1%. Subsequently, the raters further discussed the items that were in disagreement until they reach 100% agreement.
Independent Variables. Four independent variables are measured in relation to the dependent variable in this study: general human capital, specific human capital, cognitive style and learning mode. In accordance with Becker (1964), general human capital was captured by using an index of each subject’s age and job experience. For specific human capital I measured (by Likert scale) each individual’s level of knowledge and familiarity with the Bluetooth protocol that was to be used in the experiment. Cognitive style was measured using Allinson and Hayes’ (1996) Cognitive Style Index (CSI). Using 38 questions, the CSI measures the generic intuition-analysis dimension of an individual’s cognition and is seen as one of the few instruments that reliably captures an individual’s information processing approach Sadler-Smith (1998). Learning mode was captured by using a normative version (Geiger et al 1993) of Kolb’s Learning Style Inventory (1985). The 24 independent statements regarding one’s preferred learning mode was scored a 7-point Likert scale.
DATA ANALYSIS AND RESULTS OF RESEARCH HYPOTHESES
Correlation analysis and results from the regression analysis are reported in Table 2 and Table 3. Table 3 shows the results for the base model (with the four control variables listed), the main effect model and the interaction effect model.
The results of the statistical analysis show that only one of the main effect hypotheses were found to be significant (learning mode), while there was some partial support for another (specific human capital). Most importantly, however, was the fact that both interaction effects were significant; the discussion section will focus on these interaction results.
This study was designed to investigate what role learning plays in the opportunity recognition process. Specifically, I investigated the effects of information acquisition (learning mode), information processing (cognitive style) and knowledge (general and specific human capital).
The distinct contribution of this work is the evidence that suggests that learning mode is an important variable in the opportunity recognition process. While the direct main effect of learning mode was significant, it is the results of the two learning mode interactions that are most interesting.
Whether or not an individual had a high or low level of specific human capital did not significantly affect his recognition abilities. However, when combined with learning mode, we see a significant change (see Figure 2a). For individuals who tend to acquire information primarily from their direct experiences (apprehension) the interaction of learning mode again has no impact. However, if you have a high level of specific human capital and also tend to rely on critically reinterpreting your own prior information and information from others, the results show that you will recognize an even greater number of opportunities. What this suggests is that the issue of fit between behaviors is important. For instance, these results suggest that individuals who have a great amount of industry specific, technical knowledge will recognize more opportunities by employing a learning mode that focuses on reinterpreting past and current knowledge.
Similar to the first interaction, while the main effect cognitive style hypothesis was not significant, its interaction with learning mode was found to be significant. With this hypothesis I expected a fit between the learning mode of comprehension and the intuitive cognitive style. This did not occur (see Figure 2b). In fact, the opposite occurred; the learning mode of apprehension (direct personal experience) combined with the intuitive cognition style to make a big difference. What this seems to suggest is that the more bureaucratic style of the analyst negatively combines with the learning mode of apprehension to make one less aware of opportunities. In this case, the individual relies too heavily on direct experiences and a methodical behavior, while eschewing other experiences and their interpretations. While the space limitations of this format constrain further analysis, it is important to summarize by noting the important contributions of this paper.
First, the results from the study suggest that learning mode plays a significant role in determining whether or not individuals recognize technical opportunities and that the most important results come from interactions with other constructs. Second, the study also confirms and extends the work of Shane (2000) regarding knowledge, by investigating the impact of various human capital variables. Lastly, the study substantiates Baron’s (1998) propositions regarding cognition, by showing that the manner in which individuals process information does make a difference.
CONTACT: Andrew C. Corbett, Lally School of Management & Technology, Rensselaer Polytechnic Institute, 110 Eighth Street, Troy, NY 12180-3590; corbea@rpi.edu
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