Cognitive differences in the exploration and exploitation of
opportunities
Entrepreneurship and innovation scholars offered extolling descriptions of ambidextrous decision-makers, those who
can balance exploration and exploitation, and sought contextual and personal factors that distinguish them. Yet, in
decades of research, few empirically investigated the cognitive processes that underlie differences in ambidexterity. So
here we combine laboratory experiments, protocol analysis, and text analysis to uncover how decision-makers behave
and think in exploration-–exploitation situations. We observe a variety of behaviors, group them into five categories,
and describe the cognitive mechanisms that underlie different exploration-exploitation paths. We find that
ambidextrous behavior is multi-hued: It may be generated by various cognitive processes, and similar revealed
behaviors and performance could stem from differing cognitive processes. This realization holds implications for
research on entrepreneurship, cognition as well as management practice and organizational design.
Keywords: Ambidexterity; Cognition; Experiment; Protocol analysis; Text analysis
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In 1975, David Tran, born in Vietnam, began making chili sauces. After moving to the United States in 1980, he
founded Huy Fong Foods, a company named after the ship that first brought him to the country (Chandler, 2014).
Tran started personally delivered his sauces to local restaurants and quickly became known for one sauce in particular:
Sriracha Hot Sauce. In the following years, Tran outgrew his production facilities multiple times, as the Sriracha sauce
became more popular. The sauce is now produced in a 650,000-square-foot facility and sold worldwide (Huy Fong
Foods, 2017). While some entrepreneurs become successful by exploiting initial or related business opportunities,
others keep exploring new paths and may become serial entrepreneurs (e.g., Hyytinen and Ilmakunnas, 2007). Take
Eli Broad for example: In 1957, he started his first business, building and selling homes. In 1971, Broad acquired a
second business, an insurance company that he transformed into a retirement savings company. Both businesses
came to dominate their respective industries, construction and finance. Their success made Broad the only
entrepreneur who started two Fortune 500 companies in different industries (The Bridgespan Group, 2013). Over the
years, Broad sold both companies, created The Broad Foundations, and is now dedicated to supporting education,
science, and the arts (Broad Institute, 2017).
Entrepreneurs pursue opportunities, whether starting a business, developing a product, or entering an
industry (Alvarez et al., 2013). In their pursuit, they may be faced with a tension: persist with existing
opportunities that yield predictable outcomes or explore other, perhaps more promising, ones. While
exploration is more likely to lead to breakthrough innovations, such departure from the current way of doing
things also involves a higher risk of failure (Groysberg and Lee, 2009). In the past decades, entrepreneurship,
innovation, and strategy scholars aimed to grasp this tension between the promise of the new and the safety
of the old, seminally described by March (1991) as the exploration-exploitation trade-off. Yet, much remains
unknown. Here we focus on ambidexterity: whether and how decision-makers can balance exploration with
exploitation (Mom et al., 2009; Rogan and Mors, 2014).
Why do decision-makers, like David Tran and Eli Broad, pursue different paths? What cognitive
processes do they use to interpret, evaluate, and pursue opportunities? Do individual characteristics such as
risk preferences play a role? Answering these questions is not a straightforward task: It requires us to
simultaneously capture behavioral patterns, cognitive mechanisms, and individual characteristics. To do so,
we deploy a rigorous combination of experiments, protocol analysis, text analysis, and self-reports. We
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examine how decision-makers consider, reason, and behave when they face exploration–exploitation
continuously, with feedback, in an experimental task. To capture reports of thought processes side-by-side
with behaviour, we use protocol analysis, a method developed by Ericsson and Simon (1984). We
complement qualitative analysis of these reports with quantitative analysis through Linguistic Inquiry and
Word Count (LIWC; Pennebaker, Booth, Boyd and Francis, 2015), a coding software that compares texts on
several pre-defined and validated categories (e.g., analytical thinking). Through this mix of methods, we add
to previous research in multiple ways.
We study actual behavior in real time and uncover differences between decision-makers. Many studies
acknowledge that exploration and exploitation is rooted in individual decisions (e.g., Gibson and Birkinshaw, 2004;
Tripsas and Gavetti, 2000), but few have studied ambidexterity among decision-makers (Raisch et al., 2009, p. 687).
As a result, we know little about how decision-makers actually behave in situations of exploration and exploitation.
And, although studies have developed useful self-reported measures of exploration–exploitation activities (Mom et al.,
2009), they lack in behavioral measures: Do decision makers recognize the exploration–exploitation tension and, if so,
attempt to balance the two over time? A seeming presumption is that decision-makers identify and address the
tension in the same way; that they differ only in their ability to balance exploration with exploitation. Researchers then
attempt to identify the psychological or context factors that support this ability (e.g., Jasmand et al., 2012). Yet they
rarely examine the underlying decision-making processes that give rise to ambidexterity. Therefore, we do not know
of the cognitive differences among decision-makers, which may help some deal better with exploration–exploitation.
By complementing behavior in the experimental task with protocol analysis, we can capture the underlying
decision-making mechanisms of exploration-exploitation, replacing assumptions with data. Perhaps due to data
availability, most ambidexterity research relied on secondary sources (e.g., Stettner and Lavie, 2014), simulation
models (e.g., Rhee and Kim, 2014), and questionnaires (e.g., Jansen et al., 2009). And these were typically gathered
from teams or asked retrospectively about organizations, keeping the decision-making process cloaked. To identify
cognitive mechanisms, we complement manual coding of verbal protocols with the LIWC software (Pennebaker et
al., 2015), which has been used elsewhere in management research (e.g., Nadkarni and Chen, 2014).
We also track exploration–exploitation decisions over time and study how behavioral patterns emerge.
Ambidexterity researchers argue that individuals can only balance exploration and exploitation over time (Mom et al.,
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2009), but studying decisions over time is not easy. Many examinations of individual decision-makers rely on surveys,
but these portray only snapshots of behavior, not a stream of decisions. They may be useful in identifying contextual
and personal factors, such as social networks and inclinations towards self-efficacy (Kauppila and Tempelaar, 2016;
Rogan and Mors, 2014), but not how behavior changes over time, as experience accumulates.
Finally, the controlled environment of the experimental laboratory allows us to observe differences in behavior
and investigate their resources with fewer risks of endogeneity, spurious correlations, and the complexity introduced
by interaction of multiple factors. We also answer the call of Mom et al. (2009) and present instruments to
behaviorally measure ambidexterity, thereby reducing the risk of recall and social desirability biases, which lurk in selfreported measures. This is particularly timely — Bonesso et al. (2014) expose inconsistencies between managers’
reported exploration and their actual behavior.
Our findings reveal differences in how decision-makers decide whether to pursue different opportunities over
time. Based on participants’ repeated decisions between exploiting existing opportunities or exploring new ones, we
identify five categories. None is pure type, but they can be placed on a continuum ranging from the most explorative
to the most exploitative: High Wanderers, Low Wanderers, Late Bloomers, Focusers, and Staying Local. We show
how these types differ in their underlying cognition and describe the decision-making mechanisms that give rise to
different paths of exploration–exploitation: experimenting, reducing risk, forward looking, backward looking, building
slack (i.e., excess resources that create a buffer). These findings imply that various cognitive processes underlie a range
of ambidextrous behaviors, so decision-makers differ in how they perceive and deal with the exploration-exploitation
tension. Contrary to what one might expect, we find that an optimal pathway to ambidexterity does not exist. This
implies, for instance, that in some situations, decision-makers who mostly exploit may be as effective as those who are
more balanced. We also find that similar levels of ambidexterity can stem from different underlying reasoning. Thus,
managers who aspire to design ambidextrous organizations or policy makers who attempt to encourage
entrepreneurship should consider cognitive processes.
With these findings we add to current debates in entrepreneurship literature (Alvarez et al., 2013) and offer
insights for ambidexterity research (Raisch et al., 2009). We build on research that uses brain imaging techniques to
study the cognition of exploration–exploitation Laureiro-Martínez et al., 2015). And since “ambidextrous
organizations need ambidextrous managers” (Mom et al., 2015, p. S134), we contribute to understanding the
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microfoundations of strategy (Teece, 2007; Winter, 2013), the practice of management, and the design of
organizations (Gulati and Puranam, 2009).
INDIVIDUAL AMBIDEXTERITY: DESCRIPTIONS AND DRIVERS
Years of exploration–exploitation research reveals that the tension between exploration and exploitation is
omnipresent in organizational decision-making and beyond (for reviews see Hills et al., 2015; Mehlhorn et al., 2015).
Researchers agree that managing this tension is difficult (Andriopoulos and Lewis, 2009; Gupta et al., 2006) and that
overemphasizing one activity over the other is likely to lead to suboptimal solutions (Billinger et al., 2014; MironSpektor et al., 2011). However, the question of how decision-makers can balance exploration and exploitation remains
unresolved, partially because few have studied individual ambidexterity and findings to date are fragmented (Table 1
for an overview).
Insert Table 1 Here
What Do Ambidextrous Decision-Makers Do?
Some researchers offered descriptions of ambidextrous decision-makers, mostly conceptual descriptions based
on studies of teams and firms, not individuals (e.g., Mom et al., 2009; Smith and Tushman, 2005). As an exception,
Volery et al. (2015) observed six entrepreneurs for four days and described six behavioral patterns that assisted these
entrepreneurs in balancing exploration with exploitation. For example, the authors found that ambidextrous
entrepreneurs create platforms to discuss exploration and exploitation activities with their peers.
What Makes a Decision-Maker Ambidextrous?
Context features. Researchers have looked at some features of the environment, with few overlaps. Gibson and
Birkinshaw (2004, p. 209) spoke of “contextual ambidexterity” and collected survey data to show how features of the
organizational context (e.g., trust, support) help managers in balancing conflicting tensions. Smith and Tushman
(2005) described organizational conditions that can assist ambidexterity in teams, such as leader coaching, frequent
interactions, and rewards. Ederer and Manso (2013) and Lee and Meyer-Doyle (2017) pointed to the role of incentive
schemes and provided empirical evidence that decision-makers favor exploitation over exploration when they are paid
for performance. Rogan and Mors (2014) used surveys to study managers’ networks and found that features such as
managers’ heterogeneity of contacts explain differences in managers’ ability to engage in both exploration and
exploitation. In a similar vein, Mom and associates (2009) collected survey data on a manager’s connectedness to
other organizational members and found that it positively affects ambidexterity as well as the extent to which
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managers take up cross-functional roles. The authors also offered evidence that managers who have higher decisionmaking authority are better able to balance exploration with exploitation.
Personal features. Others, inspired by psychology research, attempted to identify personal characteristics of
ambidextrous decision-makers. Kauppila and Tempelaar (2016) drew on social cognitive theory and identified
managers’ self-efficacy as a driver of ambidexterity, using survey data. Jasmand, Blazevic, and de Ruyter (2012)
collected survey data and found ambidexterity is facilitated by a preference for quickly “getting things done” rather
than spending time evaluating the best possible action, drawing on the work of Kruglanski et al. (2000). In an
experimental task, Good and Michel (2013) suggested that individual ambidexterity may be linked to: divergent
thinking (Guildford, 1950); focused attention (Treisman and Gelade, 1980), and cognitive flexibility (Canas et al.,
2003). Finally, based on archival data, observations, and interviews with managers in ambidextrous organizations,
Andriopoulos and Lewis (2009) argued that discipline and passion play a role.
Cognitive features. But none of these studies observed how decision-makers address choices along the
continuum of exploration–exploitation, nor do they consider the reasoning for these choices, especially when made
repeatedly, over time, with feedback. With few exceptions (e.g., Ederer and Manso, 2013; Volery et al., 2015), most
empirical studies relied on surveys or interviews, which provided snapshot measures, and often self-reported ones.
These measures may be helpful in some respects, but cannot examine the mental processes through which
ambidexterity emerges. To our knowledge, the brain imaging study conducted by Laureiro-Martínez, Brusoni,
Canessa, and Zollo (2015) studying the brain regions that are activated when people choose to explore or exploit, she
and her collaborators identified cognitive processes related to exploration (e.g., attentional control) and exploitation
(e.g., reward seeking). We build on this study and examine not only why people choose to explore or exploit, but also
how they attempt to balance the two over time. By combining experiments with protocol and text analysis, we
capture the cognitive underpinnings of ambidexterity, and importantly, how they may be different for different
decision-makers.
METHOD
How experiments allow us to study entrepreneurial behavior
Entrepreneurship literature has long debated about the origin opportunities. Do opportunities simply exist in the
world, waiting to be discovered by an entrepreneur (Eckhardt and Shane, 2003; Kirzner, 1997; Shane, 2000)? Or do
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opportunities not come into existence until an entrepreneur creates them (Alvarez and Barney, 2007)? Recently,
scholars attempted to reconcile these views. Ramoglou and Tsang (2016) suggest a realist perspective of
entrepreneurship: Opportunities are out there in the world independent of the entrepreneur, but differences arise
because entrepreneurs perceive these opportunities in a subjective way. Following this view, opportunities are not
created nor discovered, but rather actualized when entrepreneurs interpret, evaluate, and choose to pursue them.
Although this view has intuitive appeal, it is hard to offer systematic evidence of how decision-makers would
actualize opportunities in a world full of noisy data. This is where experimental methods offer unique advantages to
understand entrepreneurial behavior. Experiments are considered the gold standard in science, because they allow for
the collection of precise, objective data in a controlled environment. Colquitt (2008, p. 616) acknowledges that
“inferring causality is one of the most difficult aspects of scientific research”, so he suggests studying behavior in a
controlled setting. There, the isolated effects of distinct mechanisms on outcomes can be directly tested and causality
can be inferred while controlling for alternative explanations (Cook et al., 1979; Mill, 1884). Moreover, in experiments,
individual characteristics can be measured separately from performance, removing common method bias (Podsakoff
et al., 2003) and recall bias. When behavior is measured repeatedly, experiments give insights into dynamic behavioral
patterns, for example of decision-making over time (Håkonsson et al., 2016). Finally, experiments allow for easy
replication, a practice that helps counter growing concerns about the validity of scientific findings (Bettis et al., 2016;
Camerer et al., 2016; Desai, 2013).
When combined with protocol analysis, experiments can also be a powerful tool to capture decision-making
processes side-by-side with behavior. During protocol analysis, participants verbalize their thoughts as they emerge
while performing a task. Ericsson and Simon (1984) formalized the method and offer guidelines on how to elicit
verbal reports that accurately reflect participants’ cognitive processes. Participants are asked to “think out loud” and
report their thoughts as they come to mind. It is up to the researcher, not the participants, to explain and examine
these thoughts.
To analyze these decision-making mechanisms, we complement manual coding with coding by a text mining
software: Linguistic Inquiry and Word Count (LIWC). The software, developed by Pennebaker, Booth, Boyd, and
Francis (2015), analyzes texts on emotional, structural, and cognitive elements. It contains an internal dictionary of
multiple categories, each consisting of a number of words and word stems. The software analyzes text documents
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word-by-word and compares the words in the document with the words in the dictionary to classify them into one of
the pre-defined, validated categories (Pennebaker et al., 2015).
Instruments and Measures
We developed two experimental instruments and complemented them with questionnaires. This design allowed
us to separate measurements of characteristics, behavior, and performance.
A measure of exploration–exploitation. We created an experimental task in which decision-makers repeatedly
choose on a continuum from exploiting existing opportunities to exploring completely novel ones (Figure 1). Our
main instrument assesses behavior in a canonical setting: a rugged landscape consisting of “peaks” and “valleys”
(Kauffman and Levin, 1987). We adapted Wildcat Wells, a behavioral game used to study communication in
networks (Mason and Watts, 2012). The game features a lot of sand that is said to contain hidden oil fields. We told
participants, without deception, that the oil was spread in fixed patterns, but did not tell them what the patterns are.
Each participant sought to collect as much oil as possible, by deciding whether to keep drilling in a known location
with unchanged performance; drill nearby, where performance varied a bit; or jump far in hope of higher
performance but with much uncertainty. We designed the task to reflect the key features of entrepreneurship as per
the (evolutionary) realist views of Alvarez, Barney, and Young (2010) and Ramoglou and Tsang (2016) (Table 1).
Insert Table 2 Here
To measure exploration behavior, we first calculated the Euclidian search distance between consecutive drilling
spots. Participants chose 20 drilling spots, but the first spot provided no distance from previous, so we could calculate
only 19 search distances per participant. We then plotted these search distances for each participant over time
(Appendix A) and calculated average measures of their search distances as a percentage of the maximum search
distance on the landscape. High values indicate long jumps or exploration, whereas low values indicate search in the
local neighborhood or exploitation. Since decision-makers could not simultaneously explore and exploit, we study
ambidexterity as the ability to balance the two over time. After the task, we also surveyed measures of confidence and
action bias during the experiment (Appendix B).
Insert Figure 1 Here
Measures of risk and ambiguity preferences. A second instrument measured a participant’s ambiguity and
risk aversion. We adapted it from an influential study of brain responses to degrees of uncertainty (Hsu et al., 2005),
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one of the earliest to include measures of ambiguity, and converted it into a web-based version. Risk (and ambiguity)
preferences may explain behavior in uncertain environments, for example whether entrepreneurs proactively face
changes in highly volatile situations (e.g., Mullane et al., 2002). Since exploration–exploitation decisions are also made
under uncertainty, we take these preferences into account. In the task, participants chose repeatedly, for 48 times,
between a low certain pay and one that was higher but risky or ambiguous. The instrument reflected the fundamental
difference between risky decisions, in which outcomes are unknown but probabilities are (as in gambling), and
ambiguous (or uncertain) decisions, where probabilities are unknowable (as in the chance your manuscript will be
published). So, in the risk treatment (Figure 2a) participants could calculate the probability of the higher payoff
whereas in the ambiguity treatment (Figure 2b) no probabilities were available. We assessed a participant’s risk and
ambiguity preferences by the number of times she preferred the lower but certain win.
Insert Figure 2 Here
Design Considerations
Participants chose on a continuum of exploration–exploitation repeatedly, over time. As Lavie, Stettner, and
Tushman (2010) proposed, we represented exploration–exploitation as two ends, with myriad choices on the
continuum between them. This better reflects reality, where few activities are strictly one or the other. By locating
both activities on the same scale, we can directly assess the trade-off between them, as postulated by March (1991).
Even if one can attempt to reconcile exploration and exploitation, for example by separating the activities over time,
the inherent trade-off remains because resources are limited.
Measures to increase validity. The landscape was static, so information was a valid form of feedback, avoiding
the risk of dysfunctional exploration in dynamic environments (March, 1991). To avoid a bias towards exploration, we
set oil reservoirs so that they cannot be depleted, since decision-makers are likely to move away when availabilities
decrease (Charnov, 1976). Participants received the same number of barrels however many times they drilled in the
same spot. This is both realistic (wells do not deplete overnight) and theoretically important: It allowed repeated
drilling in the same location, a perfectly exploitative behavior. As also common in real life decisions, there was no
information about the possible range, minimum and maximum amounts of recoverable oil. Participants learned only
through experience, so foregone yet unknown earnings could not influence behavior (Yechiam et al., 2015). Finally,
the landscape contained rewards that varied in size. The highest peak was about ten times higher than the lowest
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valley. Some areas were barren but others were rich to prevent participants from giving up due to lack of rewards
(Teodorescu and Erev, 2014). We suspected that changes in landscape ruggedness would have affected the results,
and generating multiple landscapes of the same ruggedness could have introduced random effects that may not cancel
out in an experimental sample. So, all participants faced the same landscape, and their performance is directly
comparable.
In both studies, we combined self-reporting with an induced-value approach (Smith, 1976). Per this approach,
decisions must carry economic value. When choosing where to drill or which cards to pick, participants made choices
that affected the ultimate cash payment each received. That is, the choices participants made had real and known
effects on their compensation. The experiments were reviewed and approved by the Institutional Review Board.
Sample Size and Participants
For what was billed as a study in decision making in which one can earn money, we recruited 57 (mostly
graduate) students. They ranged 20–31 years old, 30% female, and earned $21 per hour, on average, about double the
rate of alternative employment. To the uninitiated, participants’ age and the financial stakes may limit generalizability
to managers and companies. But even in complicated tasks where experience can reasonably play a role, such as
inventory planning (Bolton et al., 2012), they differ insubstantially (For reviews, Fréchette, 2015, 2016).
Protocol and Text Analysis
Each experimental session was conducted in a conference room. It involved one participant and at least one
experimenter, as to facilitate the thinking aloud required for protocol analysis. With their consent, we recorded each
participant’s thought processes and transcribed them. To analyze the verbal reports, we followed methods established
in the analysis of qualitative data (Miles and Huberman, 1984): First, we openly coded the data using the qualitative
software analysis NVivo (QSR International, 2012). During this phase, we let codes emerge as we were going through
the transcripts. We then grouped these open codes into higher-order categories and cross-analyzed transcripts to note
similarities and differences in how individuals behave in exploration–exploitation. To complement manual coding, we
analyzed verbal reports on several pre-defined and validated LIWC categories that may be relevant to exploration and
exploitation. These were analytical thinking (Pennebaker et al., 2014), emotional tone (Cohn et al., 2004), positive and
negative emotions, cognitive processes (insight, causation, certainty), core drives and needs (reward, achievement,
risk), and time orientation (past, present, future focus).
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Classification Procedure
When we observed participants’ search behavior and listened to their thought processes, it seemed that
participants relied on different reasoning to choose between exploration and exploitation over time, resulting in
different search patterns. To get a better understanding of these differences, we classified participants based on their
search patterns, using quantitative and qualitative criteria. First, we inspected the graphs plotting participants' search
distance over time and observed differences in when and how much they explored (Appendix A). Second, we
considered participants’ search timing. We plotted the average search distance of participants in the last nine rounds
against their average search distance in the first ten rounds (Figure 3). Participants whose search patterns lie above the
45° line on average explored more in the second half of the experiment, participants whose patterns lie below the 45°
line explored more in the first half. Participants near the 45° line had similar search patterns in the first and second
half of the experiment. Third, we considered search frequency: the number of highly exploratory search decisions. To
differentiate between high and low search distances, our measure of exploration, we chose 10 percent of the
maximum search distance as the low cut-off point since this meant that on average participants did not search the
landscape further than the local neighborhood of already discovered spots. We opted for 25 percent as a high cut-off
point, since this clearly differentiates between individuals who were “outliers” in terms of search distance and those
who were clustered together. Hierarchical cluster analysis confirms this distinction (Appendix C). Based on these
criteria, we placed participants into one of five categories: High Wanderers, Low Wanderers, Late Bloomers,
Focusers, and Staying Local.
Insert Figure 3 here
Analysis
After we classified individuals into categories, we contrasted them in different ways. We conducted chi square
and ANOVA analyses to study differences in categories’ search patterns, individual characteristics, and LIWC
categories. For pairwise comparisons, we conducted Post Hoc tests (Appendix D). We report p-values, following
recent recommendations in management (Bettis, 2012). We used NVivo’s visualization techniques to compare coding
between categories.
Robustness Checks
To check the robustness of our classification procedure, we examined differences in search distance between
categories (Table 3). On average, High Wanderers' search distance was around 31 percent of the maximum search
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distance, in both halves of the experiment. Low Wanderers' search patterns were similar in both halves, but shorter
(14%). The search distances of Late Bloomers and Focusers averaged 10 percent of the maximum, but those who
focused narrowed their search, exploring very little (6%) as time passed, whereas Late Bloomers, as the name
suggests, did the opposite, increasing exploration (14%) as time passed. Finally, those who Stayed Local had the
lowest search distance (6%), effectively exploiting throughout.
We also compared search experiences between categories. High Wanderers explored the entire landscape, so they
discovered some 90 percent of the oil availabilities. Late Bloomers and those who stayed local discovered 15 to 20
percent less than High Wanderers, as would be expected based on our classification. High Wanderers found the
maximum amount of oil the soonest (on average in round 4), whereas those who focused their search found the
maximum in round 10, on average. Categories did not systemically differ in performance or in the average amount of
oil they found in the first round. So our classification is not just capturing differences in overall performance or early
luck, which may also explain different search patterns (Billinger et al., 2014; Denrell et al., 2014).
Insert Table 3 here
RESULTS
Qualitative Evidence for Five Categories of X–X Behavior
We use the data from protocol analysis, the verbalization of decision-making (description above), to analyze
participants’ reasoning, as they described it in real time. Analyzing the data, five decision-making mechanisms
emerged: experimenting, reducing risk, forward looking, backward looking, and building slack. We identified some
overlaps and clear distinctions in the extent to which participants rely on these mechanisms to balance exploration
and exploitation (Table 4).
Insert Table 4 Here
High Wanderers emphasize exploration. Their decision-making processes reveal a desire to understand the
environment by searching across locations in pursuit of the optimum. They understood the role of luck, as
exemplified by the participant who uttered, after experiencing disappointing performance: “I am just confused should
I go with the same one or try my luck again" (ID 16). And they often spoke of randomness as a strategy, using
random numbers to choose drilling locations: “I’m going to try a random pattern and understand first how large I can
go” (ID43). Randomness played a role in their thinking, and so did experimentation: “Now let me try something
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again to see if I can get something better in a different area, so I would like to take on B2 to experiment” (ID81),
explained one participant. They explore because they wanted to maximize their earnings: “I really want to know if I
can get more than 600, I have to know so I have to try” (ID54), and were not discouraged by the risk of falling below
previous performance. As one conceded after a failed attempt: “It was nice experimenting different locations, as it
was mentioned that there can be places with more or less oil” (ID16).
Low Wanderers. Here the pattern was similar but the extent of ambidexterity was different, as participants who
adopted this approach voiced a preference for exploitation. They began with experimenting, attempting to understand
boundaries: “I want to see what is between these patterns, to get an understanding, so let me try establishing an upper
limit and a lower limit” (ID23). Soon after establishing some familiarity with the landscape, they hedged: Alternate
between exploration — drilling in unfamiliar spots — and pure exploitation — returning to spots they experienced to
reduce risks and build slack of earnings. This ambidextrous approach was illustrated by a participant who planned
“for the time being [I’ll] go back to my original place and get some money out of it and then maybe switch later"
(ID95). Another laid down her approach even before learning the results from another location: “Let’s try that spot, if
I don’t find anything, I can always go back to the previous one”, she explained, “you keep finding neighboring spots
and in-between you can take risk” (ID27).
Late Bloomers. As the name implies, Late Bloomers begin by exploiting for several periods, and then switch to
full-fledged exploration. Unlike Low Wanderers, they do not alternate between exploration and exploitation — they
pursue them sequentially. Empirically, they initially stayed close to the first point in which they found oil, and around
it they sought a (local) maximum. They sought in the immediate vicinity, attempting to decipher the pattern in which
the oil was distributed locally: “It is good to rather use something that you have seen, something that is evident and
then go lower, once I know that it is diminishing to a very low extent, I can leave it and continue at some other place”
(ID22). One Late Bloomer rationalized: “Logically, it makes sense to make sure that I am covering up the whole
[local] area” (ID63). It was in the final rounds that they began exploring: “I have [already gone] 15 rounds, so at this
moment I may want to try a different area, moving from the center to try my luck” (ID32). After exploiting, another
commented “I will just randomly choose another spot at this point” (ID15). Although search had no monetary cost
— the constraint was in the number of drills, so the only cost was opportunity — Late Bloomers often referred to
slack: “I will just keep on tapping this point for the next two rounds, but then I can go around here. I am using this
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money to fuel my search” (ID53).
Focusers. In contrast to the Late Bloomers, decision-makers who focus first explore different regions of the
landscape and then persistently exploit the most profitable one. Unlike High Wanderers, their search is not random,
but they systematically segment the landscape in different areas to find out where the optimum might be, as one
explained: "I am trying to understand how this works. I’m trying to go through all the borders so I can figure out how
high it can go" (ID54). After they segmented the landscape, they exploit around their known maximum, either by
drilling in the same spot or staying in its vicinity. One mentioned: "So far the most I get is 1000 and I have 8 rounds
left, so I am going to do all the 8 rounds in the same spot" (ID13). They explore at times, but quickly return to their
optimum when search is not fruitful, to reduce risk. Instead, they choose to play it safe: "Somewhere I’m thinking
1000 is the highest number so I should stick with that, I also tried exploring other areas by random, but it was not
fruitful, so I played it safe" (ID20).
Staying Local. Those who stayed local are closest to exploitation. They attempt to discern local patterns, moving
in small steps from known to neighboring spots. One reasoned, “there is definitely an oil cluster here, although I am
not sure if 800 barrels is the maximum amount of oil, but the safest bet right now is to exploit the potential of this
place over here” (ID66). Unlike High Wanderers, they prefer playing it safe in a known area over making random
choices that may be less profitable. One stated: "I don’t think there is a high probability of getting anything by picking
randomly, so I will dig around this place only" (ID24). They also repeatedly drill in the same spot, since they consider
exploration too risky or even pointless. One said: "I am assuming that 1000 is the maximum, so what is the point of
exploring more, I am going for the same spot" (ID57). Similarly, another participant mentioned: "I think this is the
maximum amount of oil I can get from this neighborhood so I don’t want to waste any effort digging into other
spots" (ID65). Decision-makers who stay local prefer using existing knowledge and are not willing to take risks for
potentially higher profits. One said: "Finding a new oil location would be a risk, and I’d rather reap the benefit of what
I’ve already found, so I’m thinking of digging again in the same one" (ID61).
Examining Text from Protocol Analysis and Individual Characteristics
To confirm the differences, we statistically analyzed the categories on several metrics (Table 5). When we
analyzed the verbal reports on the LIWC categories, some additional differences emerged. High Wanderers and those
who Stay Local both score high on emotional tone and the use of affect words and positive emotions. Even if the
14
two categories differ much in their search behaviors, these behaviors are associated with positive emotions for both.
We can only speculate: in these two categories, the search behavior is closest to pure exploration (for High
Wanderers) and exploitation (for Staying Local), so they were least affected by the tension inherent in exploration–
exploitation. In contrast, Low Wanderers, whom the qualitative analysis shows to oscillate between the local and the
distant, are less likely to express positive emotions. We conjecture that this has to do with the trade-off inherent in the
decisions.
Insert Table 5 Here
Of all categories, High Wanderers are most reward-driven, using words such as “prize” and “benefit” more than
twice, compared to Low Wanderers. This supports the qualitative analysis that indicates High Wanderers seek the
global optimum through exploration. However, there are no differences in the extent to which categories used words
that express achievement, such as “win” or “success”, or words related to risk, such as “doubt” or “danger”. Finally,
qualitative analysis suggests some potential differences in time focus between categories, but these differences are not
borne by the LIWC analysis.
We then compared how categories may differ on individual characteristics. Half of the Low Wanderers had a
background in science, technology, engineering, mathematics or medicine, whereas no High Wanderer or Late
Bloomer had such a background. As can be expected, High Wanderers report to have a higher bias towards action (in
“Instruments and Measures” section), especially compared to Focusers. Also, not surprisingly, participants who stayed
local were slightly more risk averse and more often chose a low certain pay-off over a higher uncertain one in the card
game task. However, members of the categories do not differ on ambiguity aversion. We find no age or gender
differences between categories.
DISCUSSION
Scholars often highlight the importance of balancing tensions, as when entrepreneurs decide between persisting
with existing opportunities or pursue novel ones (Alvarez et al., 2013) or when firms decide on the mix of existing vs.
new knowledge for innovation (Cattani, 2005). Here we examine how decision-makers behave when facing an
exploration–exploitation environment.
Five Types of Ambidexterity
We identify distinct categories of their behavior, showing where they overlap and how they differ. High
15
Wanderers experiment. Rather than following known patterns, they frequently explore to understand the
environment, pursuing best performance. This resonates with Laureiro-Martínez, Brusoni, Canessa and Zollo (2015),
who rely on neurological evidence to show that decision-makers track the value of alternative choices when deciding
to explore. Like High Wanderers, Low Wanderers experiment to understand the environment they are in, but unlike
them they repeatedly fall back on existing knowledge to exploit, reasoning that they should build slack before
exploring. Another category, Late Bloomers, starts by exploitation and ventures into exploration only late, perhaps
when enough slack seems available. In contrast, Focusers begin by broad exploration, as if attempting to map their
environment, and once a potential high-point emerges, they stick to it. Wilson et al. (2014) distinguished between
directed and random exploration. Likewise, we suggest that, among members of this category, exploration is directed
towards information seeking rather than based on random decisions, unlike what High Wanderers do. The empirical
case that is closest to pure exploitation is Stay Local, but even they explore, although much less than the other
categories. This category sticks to a local optimum and uses existing knowledge to make decisions, rather than
evaluating different options in the global environment. Consistent with Laureiro-Martínez and her associates (2015),
we find that those who mostly exploit track the value of current choices. These differences cannot fully be explained
by individual characteristics such as gender, age, or ambiguity aversion.
Different Reasoning Gives Rise to Similar Ambidexterity and Performance Levels
Low Wanderers, Late Bloomers, and Focusers had the same average levels of exploration, but pursued different
paths. The implication: the process cannot be deduced from the outcome. But perhaps due to data limitations, most
studies examine only outcomes such as whether a manager participated in new product development or not (e.g.,
Mom et al., 2009; Rogan and Mors, 2014). Hitherto, mechanisms were rarely discussed. A couple of examples
demonstrate that focusing on outcomes, without considering processes, can be shortsighted: While High Wanderers
may engage in new ventures in search of the optimal opportunity, Late Bloomers may do so only after accumulating
large enough resources. Similarly, decision-makers who Stay Local may focus on existing opportunities due to risk
aversion, whether Focusers may have found that existing opportunities provide the best possibility.
Of course, the ultimate outcome is performance. But even if members of categories pursue different search
patterns, we found no systematic differences in performance. This may be surprising to some: It seems intuitively
appealing that more ambidextrous individuals enjoy higher performance, but we found few empirical tests of the
16
proposition. Billinger, Stieglitz, and Schumacher (2014) suggest that early exploitation, followed by exploration may be
most effective to prevent overexploration in complex environments. Ederer and Manso (2013) seem to conclude the
opposite: Decision-makers who first explore and then exploit show higher performance. In marketing, researchers
found correlation between what they deemed ambidextrous behavior — customer service representatives providing
customer service while making additional sales pitches — and customer satisfaction (Jasmand et al., 2012). Other
researchers show correlations between performance in a computer game and various measures of intelligence,
attention, and other cognitive skills, but it is yet to be established that such measures indeed constitute ambidexterity
(Good and Michel, 2013). In contrast, we used separate measures of characteristics, behavior, and performance and
found no optimal ambidexterity strategy.
The Cognitive Mechanisms Underlying Ambidexterity
Backward- and Forward-Looking. In our experiment, all decision-makers faced the same exploration–
exploitation continuum, but they address it differently. Some heavily rely on past experiences and follow known
patterns (Late Bloomers, Staying Local), others attempt to imagine the problem space (High Wanderers, Focusers),
and some alternate between the two (Low Wanderers). Gavetti and Levinthal (2000) model these as two distinct but
complementary logics: experience-based, backward-looking search around local optima and forward-looking search
based on an actor’s mental model of the problem space. While many studies focus on experience-based search (e.g.,
Billinger et al., 2014), others suggest that models of bounded rationality should take both logics into account (Gavetti
and Levinthal, 2000), because decision-making is constrained by both limited experience and biased representations of
the problem space. Our findings suggest that decision-makers indeed rely on both logics, although differ in the extent
to which they use one over the other.
Slack resources play an important role in decision-makers’ choice to switch from exploitation to exploration.
High Wanderers and Low Wanderers alternate between exploration and exploitation over time and exploit to build
slack for further exploration. Late Bloomers do not even engage in exploration until they have built up enough slack.
This behavior has been termed “slack search” (Levinthal and March, 1981; Rhee and Kim, 2014): Decision-makers
become less risk averse when they have a buffer. However, not all decision-makers engage in slack search; some focus
or stay local. These types get stuck in “success traps” and become more rigid as they accumulate more resources
(Levinthal and March, 1993).
17
Risk aversion differs from exploitative behavior. Through protocol analysis, we heard that that participants
switched from exploration to exploitation to reduce risk. In all categories (somewhat less in the High Wanderers) we
saw participants reusing existing knowledge, a safe option in their eyes. Yet, we found little correlation between
measures of risk aversion, a trait -like individual characteristics, and the choices in exploration–exploitation. The only
exception was the Staying Local category, whose members were shown slightly more risk-averse using our risk and
ambiguity instrument and indeed behaved so in the experiment. Aside from this exception, it seems to us that
exploration–exploitation decisions cannot easily be predicted from individual characteristics, whether risk aversion,
gender, age or other demographic variables we examined. Context, it seems, plays a more central role in affecting
exploration–exploitation behavior. This observation fits with modern theories of judgment and decision-making, such
as Prospect Theory (Fiegenbaum and Thomas, 1988; Tversky and Kahneman, 1992), the description–experience gap
(Hertwig and Erev, 2009), and others.
The Role of Emotions. Drawing on a vast body of research in cognitive psychology, some observers called for
examining the effect of emotions on decision-making in organizational settings (Hodgkinson and Healey, 2011). In an
experimental study on team decision-making, Håkonsson et al. (2016) found that positive emotions, such as
happiness and excitement, are a marginal cause and a greater outcome of adopting a new routine. Positive emotions
are related to exploration, but only when it is successful.
We find that decision-makers who explore most (High Wanderers) indeed report the highest level of positive
emotions, but not very different from those that explore the least and Stay Local. Perhaps positive emotions are
simply the result of perceived success, whether from successful exploration or successful exploitation. We did find
differences between High and Low Wanderers. Following Håkonsson et al. (2016)’s findings, this difference may
mean that High Wanderers experience more successful exploration compared to Low Wanderers and therefore
report more positive emotions. However, we did not find performance differences between the two types. An
alternative explanation, supported by our protocol analysis, therefore could be that Low Wanderers experience a
stronger tension when choosing along the exploration–exploitation continuum, whereas High Wanderers are less
affected by it, and inherently derive more positive emotions from exploring. In other words, happiness may depend
on neglect of opportunity cost.
18
Implications for Entrepreneurship Research
Our findings inform debates among entrepreneurship scholars on the origin of opportunities (Alvarez and
Barney, 2007; Alvarez et al., 2013; Eckhardt and Shane, 2003; Ramoglou and Tsang, 2016). We find that when
opportunities may be present, decision-makers differ in how they interpret and act on them. Those who stay local,
whom can be understood as non-entrepreneurial, do not do so because they lack opportunities or are unaware of
them. They faced the same environment, with the same potential rewards. Apart from slightly higher risk aversion,
those who Stay Local do not have unique set of characteristics that distinguish them from others. Instead, they differ
in how they respond to known opportunities. Rather than exploring the landscape for higher peaks, they choose to
exploit existing ones. These findings offer some empirical support for a realist perspective of entrepreneurship
proposed by Ramoglou and Tsang (2016): Entrepreneurship (i.e., departure from existing ways of doing things) may
result from a combination of preexisting opportunities and decision-makers’ interpretations of these opportunities.
Our findings suggest why entrepreneurs may pursue different paths when they choose to exploit or explore
opportunities over time: various cognitive processes underlie a range of behaviors. Thus, policy makers who aspire to
encourage entrepreneurship should consider cognitive processes, since different interventions (e.g. grants, tax
incentives, support programs) are likely to have different effects depending on the entrepreneur’s cognition. Contrary
to what one might expect, we find that an optimal pathway does not exist. This implies, for instance, that
entrepreneurs who mostly exploit may be as effective as serial entrepreneurs.
Implications for Ambidexterity in Research and Practice
Our methods, novel as far as we know, contribute to a more behavioral measurement of exploration–
exploitation. As noted, most studies on individuals the ambidexterity adopt survey measures, which are often selfreported. Instead of asking about the activities individuals undertake, we identify underlying cognitive processes that
underlie the tension between the two. We thereby add to recent studies who advance a behavioral and cognitive
approach to ambidexterity (Kauppila and Tempelaar, 2016; Laureiro‐ Martínez et al., 2015).
At the same time, our findings can inform studies that take a contextual perspective on ambidexterity. Since
decision-makers may face exploration–exploitation trade-offs differently, organizational mechanisms to encourage
ambidexterity may have different effects depending on a decision-maker’s ambidexterity type. For example, increasing
managers’ decision-making authority may only exacerbate Low Wanderers’ tension between exploration and
19
exploitation, while increased ownership of tasks may give High Wanderers the freedom to experiment. A decisionmaker’s ambidexterity type may therefore act as a moderator that influences the effect of context on ambidexterity.
Based on our findings, we can offer some specific insights into how organizations can balance exploration and
exploitation, depending on a decision-makers’ ambidexterity type. For example, rotation programs may prevent
Focusers or those who Stay Local from exploiting too much by exposing them to new environments. Moreover, slack
resources, such as time to spend on non-routine activities, may encourage Late Bloomers and Low Wanderers to
explore, but others to exploit.
Our findings can also be helpful for researchers that use simulation models to study how organizations balance
exploration and exploitation (Fang et al., 2010; Lazer and Friedman, 2007). The underlying decision-making processes
of how individuals deal with the tension between exploration and exploitation can be translated into decision rules
and support a more accurate reflection of individual heterogeneity in organizations.
Limitations and Future Directions
Several features would benefit from replication and future research. We consider the role of experience in a way
that can be expanded. Participants made repeated decisions and received feedback after each one. Extensive research
shows how experiential learning affects behavior (Denrell et al., 2004) and that decision-makers search or take risks in
response to performance shortfalls relative to an aspiration level (Billinger et al., 2014). We find, for example, that Late
Bloomers search when their performance is reducing, but we did not explicitly take these performance shortfalls into
account. Future research could test the effect of feedback on exploration–exploitation and study if and how decisionmakers respond differently to performance feedback.
Future research may also validate the ambidexterity categories and develop more rigorous classification
procedures. An important next step would be to replicate our study to see whether the same categories emerge and if
these categories are stable over time. One advantage of experiments is that they are easy and cheap to replicate, so
they can sieve true findings from false positives, an urgent need in the social sciences (Bettis et al., 2016).
Although we observed differences in the underlying cognition of exploration and exploitation, we did not find
that these differences stem from individual characteristics. Future research can include other individual characteristics
that may be linked to the different categories. Future work can also manipulate features of the organizational context
and study how they affect ambidextrous behavior. Our proposition that these organizational features may affect
20
decision-makers in different ways can be formally tested.
CONCLUSION
We take a behavioral approach to examine the cognitive differences that underlie different explorationexploitation paths. We deploy three methods that allow us to observe decisions along a continuum of exploration–
exploitation over time whilst revealing some of the cognitive processes that underlie these decisions. From the data,
we identify five categories, which are distinguished by the reasoning used to choose exploration–exploitation. We
suggest that decision-makers who follow different paths do not share individual traits or respond alike to contextual
factors. Instead, we offer a cognitive foundation to study exploration-exploitation paths. By shedding light on these
cognitive differences, we advance a cognitive foundation to study entrepreneurship and propose that cognitive
differences need to be considered alongside personal and contextual factors.
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24
Figure 1. A measure of exploration–exploitation.
Figure 2. Measures of risk and ambiguity preferences.
25
Figure 3. Ambidexterity categories. We used the following classification rules to create five groups: (1) High
Wanderers: average search distance in the first or second half exceeds 25 percent of maximum search distance, (2)
Low Wanderers: average search distance in the first and second half is between 10 and 25 percent of search distance,
(3) Late Bloomers: average search distance in first half is lower than 10 percent of maximum search distance, in the
second half it exceeds 10 percent, (4) Focusers: average search distance in first half exceeds 10% of maximum
distance, but is lower than 10 percent in the second half, (5) Staying Local: average search distance during all rounds is
lower than 10 percent of the maximum search distance.
26
Conceptual descriptions
Contextual factors
Personal traits
Host contradictions; multitask, refine and
renew knowledge (Mom et al., 2009)
A business unit’s level
of discipline, support,
and trust may support
managers to balance
conflicting tensions
(Gibson and
Birkinshaw, 2004)
Self-efficacy
(Kauppila and
Tempelaar, 2016)
Resource allocation, cross-fertilization of
knowledge, resource mobilization, and
new opportunity development (Rogan
and Mors, 2014)
Leader coaching,
frequent interactions,
rewards (Smith and
Tushman, 2005)
Locomotion
orientation
(Jasmand et al.,
2012)
Differentiate and integrate between old
and new (Smith and Tushman, 2005)
Incentives (Ederer and
Manso, 2013; Lee and
Meyer-Doyle, 2017)
Building and maintaining boundaryspanning relationships; avoiding
becoming trapped solely in exploitation
by preserving time for exploration;
nurturing platforms for discussing
exploration-exploitation; engaging in
divergent and convergent thinking;
switching between task-oriented and
change-oriented activities; shifting focus
from exploration and exploitation as the
situation requires (Volery et al., 2015)
Networks (Rogan and
Mors, 2014)
Divergent thinking,
focused attention,
cognitive flexibility
(Good and Michel,
2013)
Passion, discipline
(Andriopoulos and
Lewis, 2009)
Cognitive
processes
Reward seeking,
tracking the value of
current choices,
attentional control,
tracking the value of
alternative choices
(Laureiro‐ Martínez
et al., 2015)
Decision-making
authority
Participation in crossfunctional interfaces
Connectedness to other
organizational members
(Mom et al., 2009)
Table 1. Overview of research on individual ambidexterity.
27
Theory: Entrepreneurship as per (evolutionary)
realist approach
Individuals begin to act through blind variation
(Campbell, 1974)
Individuals go through several iterative actions,
evaluations, reactions and may go back (Cyert and
March, 1963)
Through trial and error, certain actions are selected and
tested against an objective reality (Alvarez et al., 2010, p.
30)
The world exists objectively. Entrepreneurs may use
their imagination, but reality imposes constraints
(Ramoglou and Tsang, 2016, p. 413)
Entrepreneurs do not have a “god’s eye” view or ex
ante knowledge of the opportunities (Alvarez et al.,
2010, p. 26)
Entrepreneurs make decisions under uncertainty
(McMullen and Shepherd, 2006)
The more novel the opportunity that is ultimately
formed, the more knowledge the entrepreneur has to
create through series of experiments (Galbraith, 1977)
Opportunities can remain unperceived and unexploited
(Ramoglou and Tsang, 2016, p. 424)
Experimental design
Participants can choose their starting point, but without
information about outcomes, it is essentially random
(Winter, 2012).
In each round, a participant chooses on a continuum
(Lavie et al., 2010): operating in a known location,
where outcomes are known; staying in its vicinity,
where outcomes are correlated with the known
outcomes; or trying an unexplored move, where
outcomes are completely ambiguous. After each
decision, the participant receives feedback and chooses
again.
The wealth scattered in the landscape remain static
throughout, so the environment offers objective
feedback (March, 1991).
Participants have little concrete information about their
environment and cannot define an optimal approach
(Alchian, 1950; Knight, 1921). They can only learn
through experience.
Choices are made on a rugged landscape, so outcomes
are correlated in space (Kauffman and Levin, 1987;
Levinthal, 1997). To form new information,
participants have to experiment by searching across the
landscape.
Under time pressure, a participant can only search a
small percentage of the environment.
Table 2. How the task captures exploration–exploitation.
28
n (percent)
High
Low
Late
Wandere Wandere bloomer
rs
rs
Focuser
s
Staying
Local
11
(19.29%)
31.12%
Average search distance
Maximum search distance
In rounds 31.73%
1–10 only
In rounds 30.45%
11–19
only
Comp
arison
of
means
(pvalue)
14
(24.56%)
13.95%
6
(10.53%)
10.07%
12
(21.05%)
11.13%
14
(24.56%)
6.31%
0.000
14.16%
6.98%
15.50%
7.47%
0.000
13.70%
13.51%
6.27%
5.02%
0.000
Search distance plotted
over time
Multiple
high
peaks
Multiple
Skewed
low peaks towards
the final
rounds
5.50
Skewed
Flat
towards
the
beginning
rounds
9.83
6.79
Average round in
which maximum was
found
Oil discovered
Oil available
Amount recovered in first round
Maximum availability
Total performance
3.64
7.23
0.014
90.91%
82.17%
72.22%
81.94%
73.81%
0.025
65.50%
50.00%
50.00%
51.70%
50.00%
0.692
12818
14867
13929
15300
14214
0.196
Table 3. Ambidexterity categories and differences in search patterns. The rightmost column reports p-values from a
comparison of multiple means using ANOVA. To assess magnitude and order of differences in means, we conducted
post-hoc analysis using Tukey’s HSD (equal variances) or Tamhane’s T2 (no equal variances), as detailed in Appendix
D.
29
Experimenting
Reducing risk
Understanding the
landscape
“forward looking”
Following familiar
patterns
“backward looking”
Building slack
High
Wanderers
√
√
Low
Wanderers
√
√
Late
Bloomers
Focusers Staying
Local
√
√
√
√
√
√
√
√
√
√
Table 4. Overview of cognitive differences between ambidexterity categories.
30
High
Wanderers
n (percent)
11 (19.29%)
Low
Wande
rers
14
(24.56
%)
Late
bloomer
Focuse
rs
Staying
Local
6
(10.53%)
12
14
(21.05% (24.56%)
)
pvalu
e
LIWC
analysis
Analytical thinking
Emotional tone
Affect (happy, cried)
Positive emotions (love,
nice, sweet)
Negative emotions (hurt,
ugly, nasty)
Drives
Reward (take, prize,
benefit)
Achievement (win,
success, better)
Risk (danger, doubt)
Cognitive processes
(cause, know, ought)
Focus past (ago, did,
talked)
Focus present (today, is,
now)
Focus future (may, will,
soon)
27.84
76.61
4.20
3.86
17.68
53.79
2.38
1.96
13.24
56.23
2.69
2.16
21.64
63.34
2.62
2.37
19.45
76.08
3.63
3.27
0.395
0.011
0.008
0.006
0.31
0.40
0.53
0.25
0.34
0.658
6.16
3.35
5.26
1.38
6.26
1.60
4.96
1.88
5.31
1.90
0.405
0.001
1.70
2.14
2.11
1.75
1.58
0.694
0.19
15.23
0.25
15.75
0.44
17.67
0.19
15.00
0.29
15.38
0.471
0.550
3.07
2.71
2.43
2.59
2.37
0.786
15.75
17.01
15.50
15.42
15.78
0.572
3.38
4.35
4.42
4.39
3.46
0.296
Gender (% male)
Age
Science education
Confidence
Action bias
Risk aversion
Ambiguity aversion
54.54
24.36
0
3.02
3.38
0.43
0.35
71.43
24.93
50%
3.09
2.98
0.43
0.37
66.67
25.83
0
2.97
3.08
0.50
0.42
66.67
25.92
17%
2.77
2.75
0.42
0.46
85.71
25.00
21%
3.10
3.13
0.62
0.47
0.555
0.471
0.019
0.556
0.107
0.150
0.692
Individual
characterist
ics
Table 5. Overview linguistic and individual differences across ambidexterity categories. The rightmost column
reports p-values from a comparison of multiple means using ANOVA. To assess magnitude and order of differences
in means, we conducted post-hoc analysis using Tukey’s HSD (equal variances) or Tamhane’s T2 (no equal
variances), as detailed in Appendix D.
31
APPENDIX A: SEARCH DISTANCE OVER TIME
Visual representation of participant’s search patterns over time reveals five different patterns, which supported the
classification of participants in five groups: (1) High Wanderers (ID 16, 21, 40, 41, 43, 46, 47, 48, 59, 81, 92), (2) Low
Wanderers (ID 9, 10, 17, 23, 29, 38, 39, 42, 49, 56, 60, 78, 82, 95), (3) Late Bloomers (ID 15, 22, 32, 53, 63, 98), (4)
Focusers (ID 12, 13, 18, 20, 25, 27, 28, 30, 54, 58, 73, 103), (5) Staying Local (ID 11, 24, 31, 37, 50, 57, 61, 62, 65, 66,
67, 69, 75, 76).
32
APPENDIX B: SELF-REPORT MEASURES
Confidence
Five-point Likert scale: 1 = not at all, 5 = to a very great extent.
1. When making drilling decisions, I was confident
2. I knew from the start which areas contain the most oil
3. It was easy to figure out where the oil was
4. I did not know how the oil reservoirs were distributed (Reverse)
5. It was easy to know when I found the most profitable drilling spot
Action Bias
1. Five-point Likert scale: 1 = not at all, 5 = to a very great extent.
2. It was hard to decide between drilling in the same spot or choosing a new one (Reverse)
3. I felt conflict when thinking about drilling in the same spot
4. It was very easy for me to choose moving to a different drilling spot
5. I felt compelled to drill in a different spot each round
33
APPENDIX C: HIERARCHICAL CLUSTER ANALYSIS
We used participants’ average search distances in the first half of the experiment and second half in a hierarchical
cluster analysis. We used Ward’s method and clustered participants based on the squared Euclidean distance between
them. We let the range of solutions vary between two and five clusters. The cluster analysis first differentiates between
two clusters: those in the red and white area below. It then further splits up the red cluster into three subgroups. To
come to five clusters, the white cluster is further split up into two groups.
34
APPENDIX D: POST HOC ANALYSES
Levene
Test of
Statistic Homogeneity of
variances
Search patterns
P Value
Average search distance 11.32
0.000
Maximum search distance
Post Hoc
Test
Pairwise Comparison High Wanderers (HW), Low Wanderers (LW), Late
Bloomers (LB), Focusers (F), Staying Local (SL)
Tamhane
HW is higher than LW (p = 0.000), LB (p = 0.001), F (p = 0.002), SL (p = 0.000)
LW is higher than LB (p = 0.012), SL (p = 0.000)
LB is higher than SL (p = 0.008)
F is higher than SL (p = 0.000)
In rounds 1–10 only
8.29
0.000
Tamhane
HW is higher than LW (p = 0.003), LB (p = 0.000), F (p = 0.006), SL (p = 0.000)
LW is higher than LB (p = 0.000) and SL (p = 0.000)
LB is lower than F (p = 0.024)
F is higher than SL (p = 0.017)
In rounds 11–19 only
19.08
0.000
Tamhane
HW is higher than LW (p = 0.061), LB (p = 0.057), F (p = 0.005), SL (p = 0.004)
LW is higher than F (p = 0.000), SL (p = 0.000)
LB is higher than F (p = 0.001), SL (p = 0.000)
0.57
0.688
Tukey
HW is lower than F (p = 0.006)
3.36
0.016
Tamhane
HW is higher than LB (p = 0.015) and SL (p = 0.079)
0.956
0.439
Tukey
5.145
5.180
0.001
0.001
Tamhane
Tamhane
0.684
0.606
Tukey
HW is higher than LW (p = 0.038)
LW is lower than SL (p = 0.028)
LW is lower than SL (p = 0.027)
HW is higher than LW (p = 0.008)
LW is lower than SL ( p = 0.090)
HW is higher than LW (p = 0.000), LB (p = 0.021), F (p = 0.018), SL (p = 0.015)
1.389
0.251
Tukey
HW is higher than HI (p = 0.062)
Average round in which
maximum was found
Oil range discovered
Oil range available
LIWC analysis
Emotional tone
Affect
Positive emotions
Reward
Individual characteristics
Action bias
35