Analysis Paralysis: Using BizCafe Journal Entries to Predict
Entrepreneurial Performance
Laura Erskine
Much attention has been paid to the individual differences
that may play a role in distinguishing successful
entrepreneurs. We use qualitative data gathered in real time
to assess the degree to which two different modes of
thinking (analytical and experiential) may influence
performance. We find that being disposed toward cognitive
activity detracts from performance. This finding is consistent
with the “action bias” view of entrepreneurship. To the
degree that one's use of language reflects underlying
thought patterns, too much thinking is an undesirable trait for
entrepreneurs. This paper allows for a closer examination of
cognitive processes by using computer-aided text analysis to
evaluate the kinds of words used in an entrepreneurial
simulation.
Keywords: Entrepreneurship, decision-making, cognitive processes, action
orientation, analysis orientation, computer-aided text analysis (CATA), BizCafe
Introduction
Given the dynamic environment in which entrepreneurs operate, rapid decision
making under conditions of ambiguity and uncertainty is a key skill (Batstone &
Pheby, 1996; Douglas, 2005). Entrepreneurial decision making has sometimes been
described as an enactment process in which acting precedes thinking (Bakker,
Curseu, & Vermeulen, 2007). Opportunities come and go rapidly, especially in
volatile markets. Entrepreneurs do not have the time to examine every possible
outcome before acting, and inaction can be fatal. Tending toward action rather than
prolonged analysis can mean the difference between success and a missed
opportunity.
Recent advances in cognition theory prove promising in explaining such cognitive
differences between successful and unsuccessful entrepreneurs (Sadler-Smith,
2004; Trevelyan, 2008). These include counterfactual thinking, the influence of
emotions on decision-making, attributional style, and self-justification (Baron, 1998).
Entrepreneurs may also be subject to overconfidence (Forbes, 2005).
In the spirit of Pina E Cunha (2007, p. 3), we view entrepreneurship as
decision making – ―on the basis of their information, knowledge, and experience,
entrepreneurs decide to explore an opportunity they have identified.‖ Given that
entrepreneurs often operate in situations with incomplete, ambiguous information
and pressure to act quickly, prompting the question of how they process this jumble
of data and then act on their conclusions (Batstone & Pheby, 1996), the decision
___________________________
Laura Erskine, Assistant Professor, Illinois State University, Normal, IL 61761, USA, e-mail:
lerskine@ilstu.edu
making process and strategies used by these individuals can be a source of insight
for practitioners and scholars alike.
The Individual in Entrepreneurship Research
For a period of time, research focus on the individual entrepreneurial actor fell out of
favor. Personality studies had been numerous and largely fruitless. Shaver and
Scott's (1991) landmark article ―Person, Process, Choice,‖ laid the foundation for a
return to the entrepreneur as a focal point of research, albeit from a more
sophisticated and well-grounded theoretical perspective. At the core of a new
venture is always some person or small set of people making decisions in an
environment to which they bring their own unique views of the world and personal
inclinations and habits. In the early stages of a new venture, it is no exaggeration to
say that the founding entrepreneur is the business.
The characteristics and personal attributes of entrepreneurs can be a key source of
competitive advantage for the new venture (Trevelyan, 2008, p. 986). While team
entrepreneurship exists, specialization of entrepreneurial resources is more likely to
emerge and individuals may play a specific role based of their unique resources
(Mosakowski, 1998). Recent research has re-established the centrality of the
entrepreneur in explaining the survival and development of start-ups and new
ventures are the direct result of individual intentions and actions (Shook, Priem, &
McGee, 2003). ―Individual differences (e.g., attitudes, predispositions, traits, skills
and abilities, and cognitive differences) influence the development of entrepreneurial
intentions, opportunity search and discovery, decision processes and subsequent
action (Shook et al., 2003, p. 383).
Situational and behavioral factors work together in providing the impetus for
entrepreneurial activity. Situational factors such as a precipitating event (e.g., being
fired or released from a job), family support, financial support, and a supportive
environment increase the likelihood that a new venture will be initiated (Greenberger
& Sexton, 1988). While we are not trying to argue that the environment is not an
important factor in the creation of entrepreneurial activity, our focus is on the
individual’s role in the formation of intent and the desire to exploit an opportunity.
Analytical/Experiential Orientation and Entrepreneurial Success
Much attention has been paid to the individual differences that may play a role
in distinguishing successful entrepreneurs and recent advances in social cognitive
concepts proves promising (Sadler-Smith, 2004; Trevelyan, 2008). Two different
modes of thinking may impact entrepreneurial success. Analytical thinking is
rigorous, deductive, and critical; this can be contrasted with experiential thinking – a
style that is expansive, active, and creative (Barbosa, Kickul, & Smith, 2008; Pina E
Cunha, 2007; Sadler-Smith, 2004; Slovic, Finucane, Peters, & MacGregor, 2004).
The analytical and experiential cognitive styles may be thought of as tendencies
toward thinking and/or doing. While both modes of thinking can be used by people, it
is likely that an individual will have preferences for one style over the other and this
will impact knowledge gathering, information processing, and decision making
(Barbosa et al., 2008). Thus,
H1: Within individuals, an analytical orientation and an experiential orientation will
have a negative relationship.
Analytical/Thinking Orientation
In the entrepreneurial context, analytical thinking is used to discover new
opportunities as people act upon their environments (Pina E Cunha, 2007). While
deep analysis may increase the probability of success, it can also reduce the
opportunities to act. Significant planning can lead to missed opportunities if the
window to act closes before the analysis is complete (Barbosa et al., 2008; Busenitz,
1999). This is especially important for entrepreneurs who work with incomplete
information; deep analysis may not be an achievable goal.
Some people may also develop a bias toward analysis when faced with the
risk of failure (Barbosa et al., 2008). While individuals do not need to be highly
intuitive in order to make successful decisions, being systematic and rational tends
to inhibit innovation (Sadler-Smith, 2004). Individuals with an analytical orientation
may fail to take advantage of opportunities, instead cycling through analysis
endlessly (Barbosa et al., 2008; Busenitz, 1999). To coin a stereotype, an ―idea
person‖ may not be the best entrepreneur. Thus,
H2: An analytical orientation will be negatively associated with entrepreneurial
performance.
Experiential/Doing Orientation
An experiential orientation has been demonstrated to influence an
entrepreneur’s business performance positively (Astebro, Jeffrey, & Adomdza, 2007)
and is a key component of resourcefulness – the ability to cope with ambiguity and
lack of structure (Kanungo & Menon, 2005). Individuals with an experiential
orientation avoid inertia and take concrete action toward goal accomplishment rather
than dwelling in an abstract state of ―what-ifs‖ and experiential thinking leads to the
exploitation of those opportunities as people act upon their environments (Batstone
& Pheby, 1996; Pina E Cunha, 2007).
Experiential thinking has been linked to improved decision making (Slovic et
al., 2004). Individuals who exhibit an inclination toward action or experiential thinking
are more likely to perceive entrepreneurial undertakings as more feasible and more
desirable than those with highly analytical thinking (Barbosa et al., 2008; Batstone &
Pheby, 1996; Busenitz, 1999; Douglas, 2005; Sharifi & Zhang, 2009).
Entrepreneurial decision making has sometimes been deemed an enactment
process, meaning that acting precedes thinking (Bakker et al., 2007). The logic
underlying this assumption derives from the increased complexity and uncertainty
entrepreneurs are exposed to. Unlike managers in established companies, these
decision makers do not have access to historical trends or specific market
information. In his own study of British entrepreneurs, Sadler-Smith (2004) found that
active styles of decision making were positively related to performance. Those
individuals that make use of an experiential decision making style gravitate towards
and function better in situations of uncertainty (Busenitz, 1999). One reason may be
that experiential thinking cuts analysis short, favoring action over an excess of
thought. Thus,
H3: An experiential orientation will be positively associated with entrepreneurial
performance.
Measuring Entrepreneurial Cognition
Examining the thoughts of entrepreneurs is difficult (Batstone & Pheby, 1996)
and most research approaches to date use survey instruments to measure the
variables of interest. There are two fundamental problems with this approach:
priming and demand characteristics (Orne, 1962). Priming occurs when one's
response is altered by a recently occurring event. For example, asking someone to
report on his/her cognitions immediately after receiving news of an impending birth
may result in a more positive response. Likewise, asking someone to report after
having been splashed by a passing car may result in a more negative one. Demand
characteristics are prompted by the measures themselves. Subjects often respond to
questions with what they think the researcher wants to hear.
One solution to this problem is to capture cognitive data ―in the moment,‖ free of at
least some of the biases normally present in traditional research. This may be done
by analyzing data that has been generated ―free form‖ at or near the time of task
completion. Until recently, such research was nearly impossible because of the
sheer magnitude of the data analysis. Data gathered in numerical form, such as with
a Likert scale, is easy to manage but often lacks richness and is subject to the
biases described above. Qualitative data is rich, but difficult to analyze and interpret.
Computer-aided text analysis uses the best of both approaches, converting large
chunks of text into meaningful variables based on the frequency of occurrence of
certain words or word categories.
To our knowledge, no study has used qualitative data gathered in real time to
assess the degree to which cognitive variables such as analytical orientation and
experiential orientation influence performance. The present study seeks to fill this
gap in research by conducting an exploratory study of journal entries justifying
decisions made during an entrepreneurial simulation.
Computer-Aided Text Analysis
Computer-Aided Text Analysis (CATA) is based on the premise that what is
communicated explicitly and intentionally may mask or contradict important content
embedded in the choice of particular words or word patterns. Like its predecessor,
content analysis, CATA seeks to uncover attributions, cognitions, and other themes
in communication that may not be stated explicitly (Short & Palmer, 2008).
CATA is widely used in public communication, journalism, and mass-media research
(Frey, Botan, & Kreps, 1999) and allows for the processing of hundreds of
documents quickly with extremely high reliabilities (Short & Palmer, 2008). Morris
(1994) identified five advantages of computerized content analysis over humancoded content analysis: (a) perfect stability of the coding scheme; (b) explicit coding
rules yielding formally comparable results; (c) perfect coder reliability; (d) easy-to-
create word-frequency counts, keyword-in-context listings, and concordances; and
(e) the ability to process large volumes of qualitative data at low cost.
The extraction of meaning from text may involve developing categories directly from
the text—the conventional approach. The directed approach uses a theoretical
foundation to direct coding efforts, while the summative approach goes one step
further and assigns an interpretation to word frequencies and combinations on the
basis of context (Hsieh & Shannon, 2005).
The theoretical value of the variables generated by CATA or any other computational
form of content analysis depends upon reliability and validity. Both show promise.
For example, DICTION has established variable reliability in the fields of public
communication and journalism (Hart, 2001) and entrepreneurship (Short, Broberg,
Cogliser, & Brigham, 2010). External validity has been demonstrated with personality
variables using the Linguistic Inquiry and Word Count (LIWC) program (Pennebaker,
Francis, & Booth, 2001; Pennebaker & King, 1999; Yarkoni, 2010).
Methods
Content analysis is a systematic procedure that involves (a) selecting texts, (b)
developing content categories, and (c) coding and analyzing the data (Frey et al.,
1999).
Text Selection
We analyzed student journal entries made in conjunction with an entrepreneurship
simulation called BizCafe (James, 2009), a simulation used widely in teaching
entrepreneurship skills at the university level. The use of BizCafe allows for a
laboratory-type control over the situational factors that may lead individuals to start
entrepreneurial ventures. The decisions required in BizCafe are similar to decisions
a real coffee shop owner would make. In BizCafe, students received $25,000 from a
local entrepreneur to start a coffee shop near a university. Decisions on staffing,
marketing, and purchasing were made for each of 13 simulated weeks. The main
dependent variable of interest is Net Income—the bottom line of BizCafe. Challenges
included keeping customers and employees happy, ordering the correct amount of
coffee and cups to meet demand, and attracting new customers, all with profit as the
overarching goal.
Students were recruited from an introductory entrepreneurship class at a mid-size
university in the Midwest. Each student was offered extra credit equivalent to 2% of
the grade for the course. Gender makeup was 40% female, 60% male. A wide
variety of strategies can be successful in BizCafe, though performance is enhanced
when decisions form a cohesive strategy. For example, the number of servers hired
for the coffee shop should be enough to cover the floor, but not unnecessarily
increase payroll costs. Subjects learned to run the simulation by reading a case
study and a manual and then practicing two decision sets. Afterward, the simulation
was reset and the full simulation (13 decision sets) was run. Decision sets were due
three times per week, making the total time a little over four weeks. Each decision
set took from five to ten minutes.
In addition to the quantitative data captured by the program, open-ended responses
were gathered at the end of each decision period in response to the prompt, ―What
thoughts do you have about your performance on this simulation so far?‖ Written
communication samples were 13 journal entries for each of the 31 students
participating in the simulation. The total number of words in each text ranged from
181 to 2,235 with a mean of 831.
DICTION
DICTION, a popular CATA software program, was used to explore the possibility of
using word patterns in student journals to predict performance on an entrepreneurial
computer simulation. DICTION is a dictionary-based package that examines a text
for its verbal tone by analyzing five semantic features (Activity, Optimism, Certainty,
Realism and Commonality) as well as thirty-five sub features. DICTION categorizes
more than 10,000 words and none of the search terms is duplicated across the thirtyfive sub features, which allows the user to get a rich understanding of a sample text
(Digitext, Inc., 2000). Based in linguistic theory (Bligh, Kohles, & Meindl, 2004) the
dictionaries were developed from a number of different types of narrative texts
including annual reports, mission statements, and CEO speeches. DICTION has
established variable reliability in the fields of public communication and journalism
(Hart, 2001) and entrepreneurship (Short et al., 2010).
DICTION makes a modest statistical accommodation for homographs, words spelled
the same but having different meanings (for example, the word ―lead‖ – a quality of
command or a metal found in nature). Benign homographs (such as ―bass‖ – a fish
or the lower note in music) are ignored, whereas confounding homographs are
weighted differentially. This statistical accommodation for homographs strengthens
the content validity of the analysis (Krippendorff, 2003). DICTION reproduces the
text being analyzed alongside its statistical results so that the user can analyze
language behavior both quantitatively and qualitatively, increasing reliability and
validity (Frey et al., 1999).
Coding and Analyzing the Data
After processing the input files, DICTION produced report files with summaries of
high-frequency words, percentages, and standardized scores for each content
category, as well as comparative statistics reported as ±1 SD from the mean.
Content categories were assessed by the presence of certain words. We were
interested in two sub-components available in DICTION: Cognition and
Accomplishment.
Cognition. The sub-component of cognition includes words referring to cerebral
processes, both functional and imaginative. This includes are modes of discovery
(learn, deliberate, consider, compare), mental challenges (question, forget, reexamine, paradoxes), as well as three forms of intellection: intuitional (invent,
perceive, speculate, interpret), rationalistic (estimate, examine, reasonable,
strategies), and calculative (diagnose, analyze, software, fact-finding). We used
cognition as a proxy for analytical cognitive styles, reasoning that repeated use of
these words signifies an emphasis on thinking over acting.
Accomplishment. The sub-component of accomplishment includes words expressing
task-completion (establish, finish, influence, proceed), capitalistic terms (buy,
produce, employees, sell), modes of expansion (grow, increase, generate,
construction), and programmatic language: (agenda, enacted, working, leadership).
We used this measure as a proxy for experiential orientation, reasoning that
concreteness in language choice reflects a preference for taking material action.
Performance. Performance was measured using the financial performance of the
simulated coffee shop at the end of the 13 periods. Performance ranged from a
coffee shop that lost $62,650.91 to one that earned $34,023.25 with mean
performance of $11,978.92.
Results
Correlations were conducted between cognition, accomplishment, and
performance (Table 1). Hypothesis 1 was not supported as cognition and
accomplishment were not negatively related to each other. In support of Hypothesis
2, cognition was negatively (and significantly) related to performance. Finally,
Hypothesis 3 was not supported as accomplishment was not significantly related to
performance.
Accomplishment
Performance
Table 1: Correlation Results
Cognition
Accomplishment
Pearson Correlation -.295
Significance (2-tailed) .107
Pearson Correlation -.483**
.032
Significance (2-tailed) .008
.871
† Correlation is significant at the 0.10 level.
* Correlation is significant at the 0.05 level.
** Correlation is significant at the 0.01 level.
Regression analysis was also conducted using both cognition and
accomplishment to predict performance. The model combining both cognition and
accomplishment predicted 26% of the variation in performance and was significant at
the .01 level (Table 2). However, it appears that the strength of this finding was
driven by the negative impact of cognition. Our findings suggest that CATA may add
value as an analytical tool in entrepreneurship research.
Table 2: Regression Results for Individual Performance
Model 1
Model 2
Model 3
Cognition
-.483**
-.547*
Accomplishment
.032
-.172
N
31
29
29
F
8.222**
0.027
4.542**
2
R
.233
.001
.259
† Significant at the 0.1 level.
* Significant at the 0.05 level.
** Significant at the 0.01 level.
Discussion
People with an analytical orientation are likely to have two shortcomings as
entrepreneurs. First, they may tend to get stuck in a type of cognition that bears little
on action to be taken. Rumination, a characteristic of analysis, denotes a slow,
leisurely reflection or a persistent turning over in the mind of certain thoughts, not the
development of a crisp, decisive action plan. This may be explained by resource
allocation theory, which has established that a person's cognitive resources are
limited (Kahneman, 1973). For example, when faced with a complex task, individuals
must allocate time between actually doing the task and developing strategies to do it
(Kanfer & Ackerman, 1989).
Second, even if their cognition does result in good strategies, people with an
analytical orientation are likely to continue generating alternatives and mapping out
possible outcomes rather than acting. Specific entrepreneurial strategies can rarely
be developed for long time horizons. They are enacted with the understanding that
changing course suddenly is the norm. Knowing when to change course can only
come after action is initiated, precisely the thing a thinking-oriented person avoids.
Thus a disposition toward acting should prove superior to a disposition toward
thinking when it comes to entrepreneurial tasks.
The present research suggests that being disposed toward cognitive activity
detracts from performance. This finding is consistent with the ―action bias‖ view of
entrepreneurship. To the degree that one's use of language reflects underlying
thought patterns, too much thinking is an undesirable trait for entrepreneurs.
Interestingly, this research raises a knotty question concerning the best way to go
about complex tasks such as opening a business. Previous findings in the goal
setting literature suggest that a focus on outcomes can be deleterious to
performance and that a focus on learning (specifically, strategy generation) is
preferred. At first blush, this seems to contradict our findings about the lower
success associated with cognition. One possibility is that some people see cognition
and action as complementary while others see them as incompatible. These
tendencies may be reflected in goal orientation (mastery versus performance).
Limitations
Since there were no experimental manipulations in the present study, there is
a question of what separates the ―thinkers‖ from the ―doers.‖ Individual
characteristics such as proactive personality may help explain dispositions toward
one or the other. Likewise, situational variables such as previous performance and
mood may play a role. There remains the question of how stable these dispositions
are. Since the journal entries analyzed occurred in the context of a specific task, it is
possible that other situations or situational variables such as previous performance
and mood may prompt a different use of language. Finally, while we have a small
number of subjects, the number of words produced by each of the subject allows for
a robust analysis of the textual data.
Future Research
Our findings may contribute to the discipline in more consequential ways than
finding support for the hypothesis that analytical thinking can undermine
performance. By establishing that CATA is a viable research methodology for
entrepreneurship, we can safely suggest extending research efforts toward richer
and more sophisticated data sources.
For example, a set of real entrepreneurs could be asked at random times to
record their thoughts in a journal or in a voice mail to the researchers. These
recordings could be converted to text with voice-recognition software and analyzed
with CATA. Such rich insight into the thought processes of entrepreneurs would
extend the boundaries of our understanding of entrepreneurial cognition and help us
learn what kinds of thought processes to encourage in aspiring entrepreneurs.
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