(Draft: not to be quoted or cited without written permission of the author)
Cognitive Style, Innovation and Attitude Toward Dreamsi
John E. Ettlie, Ph.D,
Saunders College of Business
Rochester Institute of Technology
107 Lomb Memorial Drive
Rochester, NY 14623
585 261 7789, [email protected]
October 2012
Revised data November 2012
Revised January 2013
Revised February 2013
Revised March 2013
1
Draft: Do not quote or cite without written permission from the author
Cognitive Style, Innovation, and Attitude Toward Dreams
Abstract
Anecdotes proliferate about how dreams at night become reality during the day. Is there
evidence that there a general relationship between dreams and acquired behavioral
predispositions—especially those related to creativity? This question is explored with selfreport data on the relationship between attitude toward dreams, cognitive style and
innovation behaviors. For a sample of MBAs and M.S. students, we found a significant
relationship between balanced thinking style, innovation, and attitude toward dreams.
Implications and future research are presented.
2
Cognitive Style, Innovation and Attitude Toward Dreams
In a recent interview, Francis Ford Coppola said that he has given up on the Hollywood of today
for funding movie projects and has turned to “student films.” That is, he is using his other
enterprises like wine making to fund smaller, artistic films like Twixt, a recent example of his
new approach. Coppola is the man who directed blockbuster movies like the Godfather and
Apocalypse Now. There seems little doubt that he qualifies as a creative director. Here is what
he says about his new movie Twixt:
“I’ve now done three small films—student films, I like to call them. The latest is Twixt. I
had planned to make it in Turkey. In Istanbul, I drank raki with the lawyer who was
advising us about subsidies. I stumbled home to my room totally drunk and had this
dream: a strange girl is leading me through the forest to a hotel where the ghosts of
children play in the moonlight. It was exhilarating and I realized I live in a forest like
that. I could just stay home and sleep in my own bed and make the movie. I love the
strangeness of Twixt. It surprised me how personal it got,” (Brady, 2012,p. 88).
This is not an isolated example of how dreams stimulate the creative process, and lead to real
behaviors and real outcomes. Inventions like flaked cereals, the sewing machine needle
(Domino, 1982), musical works by Wagner, Beethoven, Mozart, and Schumann (Dreistadt,
1971; Sylvia, et al, 1978; Dave, 1978), poets and writers like Henry James, Tolstoy, Hawthorne,
Shelley, Poe, and Robert Louis Stevenson have all reported that their dreams were raw material
for creative output (Kwiatkowski, 2003).
3
Systematic evaluations of the relationship between dreaming and waking creativity are
relatively rare, but there are exceptions. One of the earliest attempts to study dreams and
paper and pencil measures of independence of judgment and creativity was reported by
Schechter, et al. (1965). They found for a sample of 105 students in various majors that positive
relationship between dream imaginativeness and test scores. The proportion of dream
recallers was greatest among art students and least among engineering students. Kwiatkowski
(2003) found that greater involvement in the creative process is significantly associated with
greater incorporation of dreams into waking behavior for a sleep laboratory sample of 517
individuals. Tonay (1995) studied a sample of 155 anonymous subjects and found that
creativity was negatively correlated with dream sexuality and positively correlated with the
number of dreams reported and originality. Hill (1998) conducted a secondary analysis of
interviews with 26 writers and found that dreams provided starting points for creative work via
visual imagery and primary process material. Writers start by literally reproducing dreams and
fictionalizing their content, often reporting anxiety surrounding the creative process.
One of the most recent, large-sample studies of the self-reported effects of dreams on
waking-life creativity reports on over 1000 subjects in two samples: students (n=444) and an
on-line sample (n=636). These subjects were not selected for their creative abilities but it was
found that among ordinary people, 8% of those sampled reported that dreams stimulated
waking-life creativity (Schredl and Erlacher, 2007). The authors report that “The main factors
influencing frequency of creative dreams were dream recall frequency and the thin boundaries
personality dimension,” (Schredl and Erlacher, 2007, p. 35). The thin boundaries personality
dimension was coded from The Boundary Questionnaire (Hartmann, 1991). “Respondents with
4
thin boundaries, are sensitive, creative, and vulnerable; and involve themselves quickly in
relationships, (Schredl and Erlacher, 2007, p. 38).
Intuition and New Product Development
Why bother to even think about dreams and cognitive style? First, there has always been
resurging interest in intuition and new product development, and the general proposition that
intuition and the subconscious are connected has always been a topic of interest in psychology
(Dearborn, 1916; McGrath, 2000). Learning and the intuition are related, so the outcomes of
behaviors that result are important (Lieberman, 2000).
Mumin and Di Benedetto (2011) report on a study of 310 new product/project
developers and 155 project managers and found a positive and linear relationship between
turbulent conditions (both market and technical) and team intuition. They also predicted and
found an inverted U-shaped team intuition-new product creativity relationship for teams with
high experience and low stress.
Further, there is evidence that deep sleep and, therefore, dreaming are related to
conscious behavior, especially creativity and innovation on the job (Lagace, 2012). The
subconscious and creativity have always been a topic of study and speculation, but with rare
exception (Tonay, 2006), little work outside the dream laboratory in applied settings has been
published on this subject. Tonay (1996) found that dream theme of creative individuals was
significantly different than the other participants in her study.
5
More recently, Dorfler and Ackermann (2012) have proposed that for applied purposes
one ought to distinguish between two types of intuition: intuitive judgment and intuitive
insight. For our purposes here, we will assume that regardless of how the intuitive construct is
parsed, it is an intervening variable in our empirical testing of the relationship between dreams,
cognitive style and innovation. We take up these topics next.
Cognitive Style and Innovative Intentions
Linear and Nonlinear Thinking Style
Thinking style has been defined as an individual’s preferred approach when using cognitive
abilities to direct daily activities, including understanding and solving challenges and problems
(Sternberg, 1988). Thinking styles are at least partially influenced by socialization, and may vary
according to the unique constraints and conditions of a given situation. In addition, recent
research suggests that specific thinking style patterns can be associated with different professions
(Groves et al., 2008). Prior theoretical and empirical research suggests that individuals tend to
adopt as a dominant approach one of two primary thinking styles or patterns of cognition and
decision-making. Extending Kolb’s learning style model (Kolb, 1984), Honey and Mumford
identified distinct individual styles related to cognitive processes in managerial problem-solving
and decision-making--activist, reflector, theorist, versus pragmatist--and emphasized the
importance of versatility in using these styles as conditions warrant for generating solutions
(Honey & Mumford, 1992).
6
As one primary thinking style, linear thinking consists of the more traditional cognitive
pattern of logical, rational, analytical, and data-driven decision-making that relies on
conventional information sources or inputs such as rational analysis, logic, reason, and causeeffect predictability. A complementary and increasingly valued thinking style or cognitive
counterpart to linear thinking is nonlinear thinking, characterized as including intuitive and
emotional assessments (Sadler-Smith & Shefy, 2004), creativity (Csikszentmihalyi, 1996),
lateral thinking (De Bono, 1992), holistic/total systems appraisal (Maani & Maharaj, 2004),
integrative and synergistic thinking, perceptual flexibility, imagination and visualization, and
insight (Groves & Vance, 2011). Below we review major components of nonlinear thinking,
namely, the distinct yet complementary nonlinear dimensions of intuition, creativity, and
emotions.
Intuition has been defined as a holistic hunch or judgment obtained from a subconscious
synthesis of information and knowledge across one’s diverse experiences, and has gained
scholarly attention and broad support for its value in executive decision-making (Dane & Pratt,
2007). Miller and Ireland (2005) observed that intuition-related decisions involve “novel
approaches, changes in directions, and/or actions that run counter to prevailing thinking or data”
(p. 21), and frequently are described as “gut feelings.” Intuition is holistic in nature, and often is
the result of an unconscious, automatic scan of the interrelated parts of a complex, seemingly
chaotic (i.e., nonlinear) system or environment resulting in an integrated “big picture” that
informs intentions and guides behaviors rather than getting delayed and overwhelmed in detailed
data analysis.
As an appropriate thinking style for nonlinear systems that are by nature unpredictable,
the nonlinear dimension of creativity or lateral thinking is characterized by spontaneity and
7
flexibility, whereby individuals consciously and purposefully adopt new perspectives and
reassemble interrelated parts or components of a system in novel ways leading to viable
solutions. The use of metaphors also can be helpful in increasing flexibility and facilitating
creative outcomes by making a comparison of a problem situation with a seemingly unrelated
object or system, providing a new perspective for gaining a better understanding of a challenging
problem, and generating a creative solution. A growing body of literature on the affective
domain demonstrates that the nonlinear dimension of emotion, beyond linear rational thinking
and logical reasoning, also can play a critical and productive role in individual and group
decision-making. Recent theoretical and empirical research on emotions and emotional
intelligence provides ample evidence that feelings and emotions affect thinking and decisionmaking at both unconscious and conscious levels (Allinson, Chell & Hayes, 2000).
There has been a growing interest in applications of the nonlinear dimension in general
decision-making contexts of leadership and entrepreneurship. In particular, there is increased
attention devoted to examining emotional intelligence as a predictor of exemplary leadership
(Goleman, 1998). Marcy and Mumford (2010) also have examined holistic and contextually-rich
processes of leadership decision-making in complex environments that involve the nonlinear
dimension. Moreover, there has been growing interest in balanced and flexible linear and
nonlinear thinking as a strong predictor of exemplary leadership, as suggested by Rowe’s (2001)
model of “strategic leadership,” featuring a blend of visionary (i.e., nonlinear) and managerial
(i.e., linear) leadership efforts for creating value in organizations. Besides creative idea
generation, the effective calculated and planned implementation of creative ideas and directions
for organizational innovation and change might also require complementary linear skills.
8
Often corporate entrepreneurs, and others involved in the innovative process facing
complex challenges with a seemingly endless array of data and probabilities utilize emotions to
reduce the number of plausible options and inform behavioral intent. Allinson, Chell, and Hayes’
(2000) empirical study demonstrated that successful entrepreneurs were more intuitive in
cognitive style than middle and junior managers, and did not differ in cognitive style from senior
managers and executives. Similarly, empirical studies by Blume and Covin (2011) and Corbett
(2002) found that entrepreneurs demonstrate a greater intuitive thinking style, while general
managers prefer an analytical approach to information-processing and decision-making. Cardon
et al. (2009) identified enduring subconscious and cognized emotion as a primary source of
entrepreneur persistence and perseverance, problem solving, and absorption of external market
data toward successful decision-making. These unconscious feelings-based mental processes,
related to intuition, may scan an otherwise overwhelming presentation of data surrounding a
difficult problem and help an individual to feel comfortable with focusing attention more deeply
on a more realistic amount and assembly of data.
Despite the very widespread, popular notion that employee and managerial cognition
associated with entrepreneurial behavior and innovativeness adopts a predominantly nonlinear
thinking style framework, we argue that innovative intentions and behaviors are more likely
associated with a thinking style that emphasizes a balance of both linear and nonlinear cognition
and decision-making. Indeed, research on entrepreneurs and the entrepreneurial process suggests
that successful innovations often are driven by individuals who demonstrate the ability to
effectively utilize linear and nonlinear thinking in tandem. Besides linear, rational analysis, the
new-market-opportunity recognition-and-realization process relies heavily upon such nonlinear
thinking style patterns as intuition, insight, creativity, imagination, and optimism that support
9
risk-taking and perseverance in the face of failure (Groves et al., 2008). Simultaneously, the
innovation process relies heavily upon highly rational, scientific skills that demand analytical,
data-driven, linear thinking and decision-making. For example, Fiet (2002) suggests that
entrepreneurs consistently apply rational and data-driven thinking to the opportunity discovery
process. Despite the stereotypes of successful entrepreneurs as intuitive, independent,
unconventional thinkers, careful analysis of successful innovations suggests that such individuals
succeed “…not by bucking the odds, but by selecting an environment that they view as having an
appropriate set of security arrangements, which probably includes being in close proximity to an
information channel” (p. 53). Fiet argues that entrepreneurial success cannot be attributed
primarily to chance or luck since successful innovators are well aware of the types of
information cues that have been historically useful, and based on attentive readings of these
signals will make initial decisions and course corrections to the venture process as necessary.
Indeed, such an incremental fine-tuning process suggests that entrepreneurs and innovators also
benefit from a rational, analytical, and linear approach to thinking and decision-making.
Fiet moreover asserts that this linear approach to innovative thinking and decisionmaking is balanced by the ability to quickly recall previous relevant experiences and associated
“deposits of specific information” (p. 58) from memory to inform an entrepreneur’s assessment
of a discovery’s potential viability. Such an emphasis on the rapid recall of relevant experiences
to support the discovery process is consistent with the nature and utility of intuition in innovative
thinking whereby intuition allows an entrepreneur to quickly and subconsciously recall previous
experiences and transform signals into useful knowledge for arriving at innovative conclusions.
Thus, based on the preponderance of research examining entrepreneurship and the new venture
10
process, we argue that innovative intentions and resulting behavior should be associated with the
tendency to utilize both linear and nonlinear thinking.
Innovation: Intentions and Behaviors
We now examine the role of innovative intentions in the new product and service development
process. There is some evidence that beneficial program changes come more frequently in
organizations where key decision-makers have more innovative values (Hage & Dewar, 1973), whereas
other research has shown that innovative attitudes and intentions often are influenced by the situation
rather than by an organization’s cultural values (Rokeach & Kliejunas, 1972).
In an effort to search for a measure of individual innovative tendency, Ettlie and O’Keefe (1982)
developed and validated a diagnostic instrument, based on both the academic and practice literatures,
that identifies creative employees. This measure incorporates consideration of such tendencies and
actions as combining several known ideas into a new combination to solve a problem, seeking out
difficult problems to solve, placing value on being first to try out a new use of an old method, and having
a sense of humor. One of the surprises of this line of research, and something that tends to contradict
common sense, is that when one evaluates the risk-taking culture of an organization or functional group
where innovative people work, there is no consistent relationship regardless of their occupation. That is,
one would expect that only a work environment that supports calculated risk-taking behavior would
have many innovative people employed there. On the contrary, innovative people, regardless of their
job—R&D scientist, software engineer, accountant, etc.—seem to be driven to innovate and stand out in
their workplace. Thus, a work environment culture that supports risk-taking is only half the formula for
innovation. What appears to be more important is the blend of people working together—meshing their
innovative gears, so to speak—with many roles for different kinds of personalities, and collaboration
that converts good ideas into successful new products and services.
11
Glynn and Webster (1992) have refined the concept of adult play and developed a measure of
playfulness. They define adult playfulness as an “individual trait, a propensity to define (or redefine) an
activity in an imaginative, non-serious or metaphoric manner so as to enhance intrinsic enjoyment,
involvement, and satisfaction” (p. 85). They found scores on this scale to be positively correlated with
creativity and spontaneity, while the concept is not related to gender, age, or occupational discipline. In
addition, playfulness was found to be positively correlated with work performance, as well as innovative
intention attitudes and intrinsic motivational orientation (Glynn and Webster, 1992). These relationships
suggest that there is a central core personal value, validated by the playfulness characteristic and
innovative acquired behavioral predisposition, which can have substantial practical importance
regardless of the job that a person occupies (Campbell, 1996).
It would appear that a nonlinear thinking style would be closely related to the highly correlated
constructs of innovative intention and playfulness with their emphasis upon creativity, imagination,
spontaneity, and pleasurable emotion. This presumption of the central importance of creativity and the
nonlinear dimension of thinking style for supporting innovative and entrepreneurial behavior is clearly
behind increasing recommendations for significant business education curriculum change
(Schwagler, 2011; Cheng & Chen, 2010; Kerr & Lloyd, 2008; Titus, 2007). Nevertheless, an excessive
emphasis upon creativity and other components of the nonlinear thinking style in the development of
entrepreneurial and innovative thinking skills may actually result in sub-optimized learning outcomes in
business education since, as is discussed below, successful entrepreneurial thinking appears to involve a
balance of linear and nonlinear thinking styles.
Thus, although nonlinear thinking likely would be more closely associated with innovative intention than
would linear thinking, it is possible that balanced linear and nonlinear thinking would have an even
greater fit with innovative intention that leads to beneficial innovations. Moreover, a clear
12
understanding of the optimal thinking style pattern corresponding to innovative intention has important
implications for business education, and for the effective selection and training of employees tasked
with product and service innovation for enhancing organization viability and competitive advantage.
Cognitive Style, Innovation and Dreams
There is now considerable evidence that dream measures need not be confined to the
sleep laboratory (Schredl, et al, 2003; 1996), and we take advantage of this work here. We have
already reported success in validating the balance thinking style cognitive measure with
innovation intentions and behaviors (Ettlie, 2011). What we are focused on here is the intuition
that results from dreams, which we hope is captured, in part, by the attitude toward dreams
measure (Schredl, 2003), and our two proximal measures of on the job creativity: balanced
thinking and innovation. Therefore, we test two hypotheses:
H1: There is a significant association between balanced thinking style and attitude
toward dreams.
H2: There is a significant association between innovation, as measured by intentions &
behaviors and attitude toward dreams.
13
We will evaluate these measures and relationships with the methodology described next. We
also will control for other factors that have been included in previous studies: major
(occupation), work experience, and scholarship venue.
Method
Sample
The data were collected in the fall of 2012 and winter of 2013 from U.S. MBA and M.S. students
in two evening classes: one on technology strategy and one on project management. Questionnaires
measuring cognitive style, innovative intention & behaviors and attitude towards dreams were
distributed primarily for the purpose of group formation for course project simulation, and directions
were standardized across all course sections. Only complete questionnaires were included for analysis,
and one case was eliminated for incomplete data, as well as outliers on the three primary construct
measures yielding a total complete sample n=82. The students were in graduate business, engineering
or software and computer science colleges.
Measures
All participants completed both the Linear/Nonlinear Thinking Style Profile (LNTSP) (Vance et al.,
2007) and the innovative intention and behavior scale by Ettlie and O’Keefe (1982), and the attitude
toward dream scale (Schredl, et al, 2003; 1996). The questionnaire is reproduced in the Appendix. Scale
analysis for 20-item innovation scale produced a Cronbach’s alpha = .75 (n=100), similar to earlier
reliability tests for this scale. The ten-item dream scale had a Cronbach’s alpha= .87 (n=101).
The LNTSP is a 26-item, four-dimensional, forced-choice self-report measure of decision-making
style (Vance et al., 2007). The four LNTSP subscales include external information sources (EIS, 8 items)
14
and inner information sources (IIS, 8 items), which comprise the eight pairs of alternative words or
phrases; and linear decision-making (LDM, 5 items) and nonlinear decision-making (NDM, 5 items),
which comprise the five pairs of alternative behaviors.
We also included questions on work experience, major (technical coded 1 vs. nontechnical, 0),
class (project management was coded 1, and strategy was coded 0) and gender (1=male, 0=female).
Results
The correlation matrix of Pearson r statistics are presented in Table 1 below. Hypothesis one is
supported by these results. That is there is a significant correlation between balanced thinking and
attitude toward dreams, with r=.232, p=.02 (two-tailed test, n=101). The test of hypothesis two, that is
the prediction that attitude toward dreams and innovation intentions & behaviors, also yielded a
significant correlation in the predicted direction, r=.254, p=.010 (two-tailed test, n=101). Both
hypotheses were strongly supported.
Other results were as would be expected. Work experience did not matter but major (proxy for
occupation) does significantly interact with the cognitive style (Table 1, Balanced thinking) constructs as
reported elsewhere (Vance, et al, 2007). Work experience was not significantly related to gender, major,
innovation or dream attitudes. However, the gender/dream correlation does approach significance with
a small subsample of these data (p=.061) suggesting women might have a more positive attitude toward
dreams. More data will be required to sort out gender, and other contextual variables although these
relationships are statistically significant. Mulitvariate tests will also make sense when more data become
available, but these preliminary results are very encouraging.
Discussion
15
This study represents the very beginning of a research stream on dreams, cognitive style,
and innovation. More data, perhaps more measures and more experimental methodologies needs
to be added to this stream. However, early, preliminary results are very encouraging that a valid
link exists between these constructs and the practical and theoretical implications could be
substantial should they pan out.
It appears that the research on intuition in new product development is highly likely to
lead to an investigation of cognitive style and innovation as the outcome of the subconscious
represented by the residue of the dream state. The strong support of the two hypotheses in this
preliminary study of attitude towards dreams, balanced thinking style and innovative intentions
and behaviors is very encouraging and future research would be advised given these resultsii.
Dream content reports could be added to this research stream as well as longitudinal data on the
outcomes of dream therapy and applications of these results in practice such as group formation
and facilitation of the creative process.
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20
Table 1: Correlations
GENDER
GENDER
Pearson Correlation
MAJOR
1
-.299
.900
.034
.063
.061
40
40
40
40
40
40
Pearson Correlation
.236
1
.078
**
-.147
-.065
Sig. (2-tailed)
.143
.487
.008
.142
.517
.263
40
101
82
101
101
101
Pearson Correlation
.021
.078
1
.095
.026
.029
Sig. (2-tailed)
.900
.487
.396
.817
.796
40
82
82
82
82
82
*
**
.095
1
-.083
.254
.410
.010
.337
Sig. (2-tailed)
.034
.008
.396
40
101
82
101
101
101
-.297
-.147
.026
-.083
1
.232
.063
.142
.817
.410
40
101
82
101
-.299
-.065
.029
.254
.061
.517
.796
.010
.020
40
101
82
101
101
Pearson Correlation
Sig. (2-tailed)
N
Pearson Correlation
Sig. (2-tailed)
N
.263
*
101
101
*
1
.232
**. Correlation is significant at the 0.01 level (2-tailed).
i
This study was supported in part by the Saunders College of Business, and the research assistant on this project
was Laura Conde Escano. The author is solely responsible for content.
Thanks to Dr. Veronica Tonya for the suggestion of this next step as well as the inclusion of gender in the
questionnaire protocol.
21
*
.020
*. Correlation is significant at the 0.05 level (2-tailed).
ii
*
Pearson Correlation
N
Dream
-.297
.143
N
BALANCED
Dream
.337
N
INNOVATION
BALANCED
*
.021
N
WORK.EX
INNOVATION
.236
Sig. (2-tailed)
MAJOR
WORK.EX
101