ONE POINT OF VIEWPaul N. Friga and Richard B. ChapasMA.docx

ONE POINT OF VIEW
Paul N. Friga and Richard B. Chapas
MAKE BETTER BUSINESS DECISIONS
Decision-making in today’s environment is difficult, and
new managers in R&D and other technical positions are
often shocked at the lack of systematic decision-making
they find in their interactions with upper management
and their peers in other parts of the organization.
However, there is a well-tested source of insight into how
to improve the decision-making in business: the scien-
tific method. Although it has revolutionized our lives and
the ability to manipulate our material world, the scien-
tific method has not been widely adapted for business
executives. Nevertheless, we believe it can improve the
efficiency and effectiveness of decision-making for
executives, research managers, and business leaders in
general.
In this article, we first examine the typical decision-
making environment in organizations, highlighting the
challenges executives face in their quest for better per-
formance. Next, we introduce some of the basic tenets
from the scientific method and describe how they can
play a role in overcoming several of the key decision-
making deficiencies. We then describe a five-step
process that can assist in the implementation of scientific
method techniques in daily decision-making, illustrated
by a case study relating to new technology develop-
ment.

Challenges Executives Face
Three key macro-level elements that differentiate the
daily decision-making of today include information
overload, shareholder pressure, and shortened business
cycle time:
• The search tools in use for problem solving by execu-
tives at most companies today yield a quantity of infor-
mation that can be overwhelming. This situation has
increased the importance of knowledge management
skills to sort the data, identify what is truly relevant, and
then to create value from it.
• Shareholder pressure, a result of the rise in worldwide
capital markets, has led to a relentless drive to achieve
short-term financial results, often at the expense of long-
term considerations. A number of well-known corporate
failures may have resulted from the pressure to achieve
consistent growth at any cost.
• Finally, the time-to-market and overall business cycles
have shortened to a level unimaginable 50 years ago.
Decisions must be made faster than ever before (1).
Ultimately, decision-making is done on an individual
level. Alarmingly, much of the research suggests that
humans are extremely limited in their decision-making
Paul Friga was clinical associate professor of strategic
management at the Kelley School of Business at
Indiana University in Bloomington, Indiana, when this
article was written. He is now a professor at the
Kenen-Flager School of Business, Chapel Hill, North
Carolina. He researches strategic decision-making,
knowledge transfer, intuition, management consulting

practices, and entrepreneurship. His work has been
published in The Academy of Management Learning
and Education, Research � Technology Management,
and The McKinsey Mind (McGraw-Hill, 2001). He pre-
viously worked as a management consultant for Price-
waterhouseCoopers and McKinsey & Company. He
received his Ph.D. in strategic management and M.B.A.
from the University of North Carolina at Chapel Hill.
[email protected]; www.kelley.iu.edu/pfriga.
Richard Chapas is senior market manager at Battelle
and responsible for business development for Battelle’s
Environmental Technologies, Aberdeen, Maryland.
Prior to joining Battelle, he ran his own consulting
business and held the following positions: Rayonier,
vice president of Research & Development; Kimberly-
Clark, senior R&D manager; Johnson & Johnson,
group leader; and Eastman Kodak, senior scientist.
While operating his own business, he served as chief
operating officer for Cara Plastics, a University
of Delaware start-up company producing bio-
based materials, and as industrial liaison for the Parti-
cle Engineering Research Center at the University
of Florida. Chapas received his Ph.D. in chemistry
from the University of Illinois.
[email protected]
Research � Technology Management8
0895-6308/08/$5.00 © 2008 Industrial Research Institute, Inc.
abilities. For example, “bounded rationality” is a concept
used to describe specific cognitive limitations of humans
to process information and make sense of complex envi-
ronmental conditions (2). Essentially, our rationality is
our tool for decision-making that relies on simplistic and

routine categorization systems to deal with otherwise
overwhelming situations. This approach can result in
rational decisions, but often fails to use relevant informa-
tion not within those bounds.
One major problem in organizational decision-making is
the lack of objectivity. “Intellectual honesty” is a hot
topic as companies struggle to increase the ethical nature
of their financial reporting. Another common application
would be the setting of decision agendas and more
importantly, interpretation of data, based on confirma-
tion of pre-set planning without adequate consideration
of disconfirming evidence.
Enter the Scientific Method
Three key concepts impacting the subjectivity of
decision-making include invalid assumptions, data
filtering and representativeness (3). The individual-level
constraints often manifest themselves in decisions that
are not adequately objective, are inefficient and lack gen-
eralizability. The scientific method, on the other hand,
attacks each of these issues specifically, as we describe
next.
Intrinsic to American management systems are practices
that discourage strategic and long-range investments. By
expensing research expenditures, they become vulner-
able to cost-cutting. By emphasizing quarterly reporting
on earnings, manipulation of the numbers can become
more important than doing the right things (effectiveness
argument), or doing things the right way (efficiency
argument).
A system of decision-making that is more objective and
strategic is needed to overcome these obstacles. We are

proposing the use of the scientific method to accomplish
this goal. The method allows for both evolutionary and
revolutionary change, as well as exploitative and explor-
ative contexts. It is conservative, based on existing
models, yet is open to taking a risk when the existing
model does not provide for a valid solution. It is
objective, not dependent on large egos and bureaucratic
thinking. The method requires dialogue and challenging
the knowledge base, while simultaneously leveraging
existing knowledge. It requires taking a risk and present-
ing a hypothesis, an explanation for the existing facts, yet
realizing the difference between a hypothesis and the
ultimate answer. In short, the scientific method can
transform business decision-making and increase the
effectiveness, efficiency and innovativeness thereof.
The scientific method, which has faithfully served the
academic and scientific communities for eons, is clearly
anchored in data-driven analysis. There is, however, a
role for intuition in the process and the same will be true
for applying it to a business context. The topic of
intuition and its impact on decision-making in organiza-
tions has surfaced as an important research stream related
to decision-making. While hard to define, intuition is
generally regarded as a non-linear, subconscious thought
process and is contrasted to the rational, step-wise infor-
mation process. Intuition provides answers that are inte-
grated, non-obvious and difficult to justify (4).
In our discussion of the scientific method, there are two
key opportunities for intuition to influence the process.
The first is the development of the hypothesis. Early in
the process, the scientific method requires the develop-
ment of the possible solution—the hypothesis. This is
done to focus the analysis and to provide assertions that

may be proved or disproved through subsequent data col-
lection. The development of the hypothesis is often aided
by some preliminary secondary data collection (via lit-
erature search), but also includes the presence of
intuition, in essence a “hunch” that provides the impetus
for the direction of the potential solution. The second
opportunity to use intuition is in the results stage, where
scientists generally apply a big picture, reasonableness or
“smell” test to ensure that the results make sense and,
more importantly, look for non-obvious connections.
Implementing the Scientific Method
To test the relevance of our work and illustrate the appli-
cations, we developed a brief case study for this article.
Although it is based on our actual experiences, it should
be considered as “realistic fiction”; the primary goal is to
present realistic scenarios related to the theoretical
concepts presented herein and to replicate decision-
making situations common to executives.
The case is presented now in discrete steps (like chapters
in a story) that correspond to the key elements in the
evolution of the technical development. Within each
step, we show how application of the scientific method
could improve the decision reached at that step.
Case Study: A team of technologists (three scientists, one
engineer and one team leader) have discovered unique
The scientific method
can transform
business
decision-making.

July—August 2008 9
consequences of engineering on the microscale. With the
onset of nanotechnology, this team has been driving the
size of reactors to smaller and smaller dimensions. As
they achieved micrometer dimensions (0.000001 meter),
reaction rates increased by orders of magnitude, running
a thousand and even ten thousand times faster. The team
knows that there is a tremendous opportunity here and is
now deciding how to move forward.
Having discussed the issues related to decision-making
generally, the next question is, “How can I use this
method to improve my decision-making in business?”
Based upon interviews with over 70 executives in middle
to top management positions throughout the world, and
our own experiences, we identified five processes in
business with distinct opportunities to apply principles
from the scientific method. The implementation plan is
shown graphically in the diagram below. Within each
step, we take a finer-grain view of the case, followed with
a discussion of the implementation process.
Step 1—Frame the Problem
The first stage of decision-making often suffers from a
lack of thoroughness and explicitness. It is quite common
for executives to move through the framing process
quickly, given several of the macro- and micro-factors
described above. For example, the macro-factor of
shareholder pressure often dictates the criteria for
decision-making, which often leads to priority of short-
term results over long-term sustainability. Additionally,

decision-makers often move through this stage quickly
as the temporal pressure of speed-to-market and product
development time has increased.
On a micro level, executives often find themselves
hindered in their ability to address the “real” issues in the
organization; instead, they are forced to address top-
management-team issues (including those of a political
nature) and frame them using data filtering as described
earlier. The scientific method can push the development
and explicit discussion of the critical issues in an organi-
zation by removing some of this subjectivity early in the
process.
Case Study—Step 1: The three scientists are pursuing
different reaction systems, involving homogeneous and
heterogeneous reactions with and without catalysts. Each
scientist is becoming increasingly excited about the
Five-step plan for implementing the scientific method can lead
to better decisions.
many application opportunities. Resources are becoming
an issue. The engineer is concerned about how to
uniformly distribute the catalyst at the micro scale. The
team leader feels that the work is out of control with these
divergent approaches and realizes his role is to optimize
the use of resources. He also feels pressure to move
forward as soon as possible.
The case illustrates a good example of framing the
problem. While this is widely recognized as one of the

most important aspects of the scientific method, execu-
tives often move right past this stage to get to the all-
important analysis. But the truth is that this is where
analysis begins. Decisions made at this stage of the
process have important efficiency and effectiveness con-
sequences for the life of each particular decision-making
event. The benefit of using the scientific method in the
example cited was not achieved, because a careful for-
mulation of the question based on analysis of the data and
clearly defining what success would be did not occur. At
this stage, no attempt was made to define the solution,
because the focus was on the opportunity.
As shown in the diagram, we suggest four specific
actions for implementing Step 1 in your organization.
First—identify the problem—may sound elementary but
is important. Next, this is the time to specify how success
or failure will be determined and to set the parameters
that essentially bound the range of elements for consid-
eration (temporal, geographic, etc.). Finally, reviewing
the history is akin to the literature search in the scientific
method, and in business has become the all-important
goal of effective knowledge management. Before
beginning new analysis, it is helpful to learn from past
experiences and avoid the need for redundant experien-
tial learning, i.e., reinventing the wheel.
In our case, it is important for the team to step back and
review each reaction system, laying out the technical
challenges with each. From the business and leadership
side, it is important to determine how this invention
could influence allocation of resources, namely: how big
an idea is this? Consequently, the problem can be
re-stated as how to design and use micro-reactors to sig-
nificantly improve existing chemical processes. By
stating it this way, we are no longer arguing about which

process to study, but rather looking at the opportunity
holistically. Here is the revised case study, using the sci-
entific method.
With the scientific method approach: The team leader
asks each of the scientists to do a primary literature
search, including patents and publications about com-
mercial applications. He asks the engineer to work with
each scientist to list the key challenges and opportunities
for each system. Each scientist is then asked to define the
most significant problem and how it would be tackled,
keeping in mind the opportunity for new intellectual
property and commercial impact. A brief discussion is
held on the potential impact of the work.
Step 2—Develop Hypotheses
Here the challenge is to first diverge in looking for
possible solutions, but then to converge on what appears
to be the best solution. Although scientists do not always
formally employ brainstorming, it is becoming more
common, particularly in innovative organizations that
see the value in taking the time to look beyond the
“obvious.” Since we had already framed the problem in
the last step, diverging now means finding the best means
to demonstrate the value of this invention.
Case Study—Step 2: The team leader allocates resources
to the three approaches, giving each scientist adequate
resources to further develop the system by extending the
time line. He has the engineer split his time as best as
possible. He is feeling good about getting everyone on
the same page and keeping his options open.
Hypotheses should not be construed as the truth. Rather,

hypothesizing is a technique to determine the most likely
course of action that will maximize the anticipated return
given the decision criteria. Critical to developing a
hypothesis in product development is matching technol-
ogy and market need. In our example, intuition required
a broad experience base to select from all the market
opportunities, because the options were so extensive. In
the first approach, the team leader refused to converge,
thereby lengthening the development cycle.
It can be argued that more time should be spent exploring
options before deciding on one, but in today’s context,
this argument rarely holds and, in most cases, is not the
best approach because upstream problems are not being
explored. In the better approach using the scientific
method, stated below, a specific market area was selected
and the hypothesis became: micro-reactors can signifi-
cantly improve reforming hydrocarbons, thereby
reducing capital cost and increasing yields. Now the sci-
entists and engineer can focus on this problem.
The first stage of
decision-making
often suffers from
lack of thoroughness
and explicitness.
The implementation actions include the surfacing of all
options that have potential to address the problem articu-
lated in Step 1. Many firms (especially consulting firms)

use templates or frameworks to generate the initial list of
issues for consideration. The prioritization process is
clearly driven by the objective decision criteria, again
from Step 1.
Finally, the generation of hypotheses may be the most
unique feature of this model. The hypothesis is a
statement of the possible solution that is falsifiable—i.e.,
can be proved or disproved with further analysis. Conse-
quently, we recommend that it be articulated as clearly as
possible so that its validity can be readily determined.
We summarize the improved method below.
With the scientific method approach: After brainstorm-
ing and then analyzing the results, they decide that
applying this technology to reforming hydrocarbons for
the oil and gas industry should be their initial focus. The
technical team then targets its work on demonstrating
that the technology can create a significant increase in
reaction rates, so that capital cost can be reduced by a
factor of ten. This change would result in saving millions
of dollars when constructing new refineries and refitting
existing ones.
Step 3—Gather Data
The real challenge of this next stage is to approach data
gathering systematically and with a focus that, while not
overlooking relevant information, judiciously includes
only key information related to the most important
issues at hand. Top strategic consulting firms deal
with this issue by implementing a technique referred to
as “MECE”—Mutually Exclusive and Collectively
Exhaustive (5). Careful planning and systematic analysis
of large data populations are tenets of the scientific
method that offer another opportunity for improvement

in business decision-making, specifically its efficiency.
Case Study—Step 3: The team has now generated signif-
icant data on the three approaches. The team leader is
even further confused and knows the clock is ticking. His
best friend has just moved over to the business side and
he calls him. His friend asks him one question: “If you
had to choose one option to pursue, which would it be?”
The leader convenes the team and asks them the same
question. As a result, the team decides to bring additional
expertise to better understand each option. A decision is
finally reached after extensive debate.
Gathering data may be the unglamorous part of the
process, but the best organizations are the ones that know
how to gather data and use it effectively, and moreover,
what not to gather. The scientific method reinforces the
objective examination of data, enabling cross-functional
teams with different backgrounds to come together to
create solutions that are data-driven.
The insights from the scientific method suggest the
importance of a carefully crafted “research” plan or
methodology for gathering data. The first step is to
design the workplan, which is followed by the accumu-
lation of relevant data. Relevance is one of the biggest
organizational hurdles discovered in our interviews with
executives. As mentioned earlier, one of today’s biggest
decision-making challenges is wading through the ever-
growing plethora of data. Focusing on data that are
related to the key issues under study, and in particular,
the proving or disproving of the hypotheses, is the insight
from the scientific method.
By contrasting the case study with the scientific method
approach below, it is evident that data gathering is sig-

nificantly improved because of the focus that comes
from the hypothesis. Time and effort are saved; most
important, the team is able to bring a diversity of
viewpoints—engineering, marketing, chemistry, eco-
nomics—to the same problem.
With the scientific method approach: The technical team
reviews the literature about typical reaction rates for
reforming petroleum to produce gasoline. They also
determine whether they can run the process on small-
scale and fully leverage the new technology in the
process. Fortunately, both inquiries come back positive.
Producing gasoline should work on lab scale. Mean-
while, the business development team is looking at the
assumptions on capital expenditure reductions as well as
the importance of being able to increase the rate of pro-
duction. From annual reports of the big players in oil and
gas, it is determined that they are investing collectively
over $100 million in research in this area. When the full
team reconvenes, they decide that they can move
forward. This decision will require the team to request
$250,000 to build the necessary reactor and to expand
the team to include marketing expertise, so that a plan
can be formulated as to how best to market this technol-
ogy either as a start-up or licensing-contract research
approach.
Step 4—Interpret Findings
The interpretation of results is often underemphasized;
yet, even in the best case, the importance of looking
The generation of
hypotheses may be
the most unique

feature of this model.
beyond the obvious should be recognized. The critical
question should be asked in two ways: What do the
findings mean? What else could they mean? Once again,
it is important to fully explore the connections, and for
that reason, we stressed the use of intuition in this step.
Case Study—Step 4: After six months to set up the new
process, the technical team generated significant data
showing promise, but not the quantum change in perfor-
mance that they needed. The lead scientist feels it’s just a
matter of time and continues to try different reaction con-
ditions. The engineer is concerned that the system is not
optimized. The team leader is back on the phone to his
friend who reminds him that initial selection of process
might be wrong and they should be open to revisiting
their focus. The team agrees to go back and review all
aspects of the process relating to the second-best choice
from their initial analysis. They continue to analyze the
experiments that were run. What they find is that the
catalyst system was not working as expected and they
would be required to find a new way to synthesize this
catalyst on the nanoscale. Fortunately, they had done
similar work in another application. However, time and
money will be required.
The interpretation of data is generally where “the rubber
meets the road.” Too often in business, the overarching
focus is on the confirming evidence, at the expense of
reconciling the impact of disconfirming data. The ability
to focus on the “truth” of the data, even in the presence of

senior management, is the key benefit of the scientific
method in this step.
In this case, we see how developing new technology has
additional degrees of complexity. Exploitation of
existing technology and knowledge limits the new data
obtained. Exploration of a new product introduces more
data about the market needs and ability to meet those
needs. Exploration of a new technology goes even
further in determining whether or not the new technology
can accomplish a specific purpose, possibly in even more
than one market application, and then gathering data
about the ability to meet the specific market require-
ments if a product or process already exists.
In the case study, the findings were not what the team had
hoped for. The system they chose to pursue was not
analyzed with the same degree of detail and, as a result,
they built the reactor without understanding all the
nuances of the catalyst system. Now they had to sort
through the question of whether it was the wrong
reaction to study or whether they had not engineered it
properly.
With the scientific method approach, below, the findings
were very encouraging. The question the team faced was
on the business side as to who would be the best partner
and what avenue would they pursue in partnering.
With the scientific method approach: The team spends a
great deal of time understanding how to design the
reactor. It learned the importance of catalyst location and
distribution for the reforming process, and as a result, the
team tackled this problem upfront. It took them nine
months to complete the design, but the results came back
just as predicted with a ten-fold increase in reaction rates.

They have investigated further the landscape of oil and
gas companies, hiring an expert to determine interest by
the various companies. These data clearly indicate that
Company X is the leader in this field, while Company Y
has a strong interest and a record of working well with
partners.
Step 5—Make Decision
Perhaps the most important part of the decision-making
process relates to the final steps of making the decision.
This process is essentially the sorting of data according
to some dominant logic (6). The problem is that the
dominant logic can be overly analytical or slow, overly
intuitive and unfounded. Worse, the logic can drive
unethical and misleading behavior. The scientific
method addresses this issue directly by mandating the
consideration of confirming and disconfirming data
with an objective perspective and a push toward “intel-
lectual honesty.” The balancing of differing types of
confirming/disconfirming data and analysis/intuition
represents an area of common deficiencies in typical
decision-making and an opportunity for improvement by
using the scientific method.
Case Study—Step 5: The team decides to move forward,
requesting additional funds for a redesigned reactor and
further experimentation. They also want to add another
reaction system which they believe will be less sensitive
to catalyst distribution. They will get more business and
competitive intelligence on the two markets for these
systems.
In the case study, the degree of financial commitment
becomes a major factor in focusing decision-making.
The team’s burn rate of the initial capital will determine

when the decision will have to be reviewed and addi-
tional decisions made. There is no clear commercial
outcome to this work.
In the scientific method approach (below), the clear con-
nection between investment and commercial outcome is
present, justifying a business plan to move forward. The
decision to spin the company out is clearly a bold move,
but ensures that the team will not be pulled into other
short-term projects. The intuitive connection here is to go
to Company Y instead of X, because of its openness to
external technology. So the excellent scientific results
and the thorough business analysis have provided the
leadership team the information they need to make this
decision.
With a start-up, it is important to use the full scientific
method process we have demonstrated here when any
new decision has to be made, starting with framing the
problem in light of the new knowledge that has been
obtained. As additional expertise is added to the team,
providing more knowledge of manufacturing and
marketing, the decision-making process becomes more
complicated. The tendency, as mentioned earlier, is to
take short cuts and make assumptions, which can be
disastrous for a start-up company.
With the scientific method approach: The team decides
to go forward, putting together a comprehensive business
plan that specified milestones and investment require-
ments. When the plan was complete, it was presented to
senior management who agreed to spin this technology

out into a new company and provide the initial financing
to do so. This decision required the involvement of many
groups, particularly legal, human resources and licens-
ing, in order that the new company have access to the
right people and intellectual property.
Meanwhile the marketing group had concluded that a
direct marketing approach of licensing the technology
and providing implementation support would probably
not work. The “Not Invented Here” (NIH) reaction
would be too strong. A company would need to be
formed to obtain capital for scale-up to demonstrate that
the technology was scalable, and simultaneously identify
a less capital-intensive application for the technology to
generate sales. However, it was also decided to approach
Company Y, which appeared to have an open innovation
model, to discuss a possible partnership.
What We Learned
What are the “take-aways” from this discussion? In the
first section of this paper, we discussed the typical
aspects and deficiencies of decision-making. We found
during our analysis that the importance of such deficien-
cies varied according to the context under examination.
In exploitative settings, for example, where a company is
working to take advantage of its existing strengths and
capabilities, the biggest barriers may be complacency,
knowledge transfer issues due to functional silos, and the
challenge of incorporating an external perspective in the
data collection (especially to determine a sense of
“value”). The primary benefits of employing the scien-
tific method in these situations would be efficiency gains
from increasing the focus of analysis through the use of
hypotheses and the organization of knowledge flows
within the organization.

In exploratory situations, where a company may be
developing new capabilities, the primary barriers to good
decision-making center on the inability to tap into unmet
market needs due to limited vision or environmental
restrictions. Of these barriers, the vision is subject to
managerial control, suggesting that improvement oppor-
tunities do exist.
We suggest that by employing certain aspects of the sci-
entific method, the effectiveness of exploration can
increase. Common situations in business may be the
overly influential direction of top-management teams in
a creative agenda-setting or the interpretation of discon-
firming data. This is especially worrisome in times of
intense shareholder pressure and disappointing financial
results, a seedbed for unethical decision-making.
Another relevant stream of research is related to the con-
sideration of different contexts in the development of
strategies. One theory posits that strategic activities
generally group around exploitation and exploration (7).
Exploitation is argued to exist when managerial activi-
ties are focused on efficiency gains related to the
leverage of existing knowledge or capabilities. An
example would be where a team is challenged with
applying past knowledge and analytical tools to solve a
different, but not entirely unfamiliar problem, such as
developing a new product in an existing market. Usually
these decisions tend to be more iterative, and the risk is
diminished. Yet the danger here is complacency and
inefficiencies that may result from relying on extrapo-
lated data to make the decision.
Exploration, on the other hand, is generally used to

describe stretch activities where company employees are
charged with conquering unfamiliar or new territories.
An example would be where a team is developing a new
technology, such as the case study in this article. In
exploratory decision-making, the risk is higher because
new ground is being broken. Exploitation and explora-
tion considerations are compared in “Applying the Sci-
entific Method,” next page.
To non-scientists, the scientific method may appear at
first glance to have nothing to do with business
processes, being seen as abstract and detached. As a
result, changing this perspective is an important consid-
eration and possible limitation. Critical elements of the
method, such as dialogue, openness and being data-
driven, may seem to be already present in the organiza-
tion, but the challenge here is to provide a clear
distinction between what typically exists in business
today and what is required for success by the scientific
method.
Clearly, training will be required to overcome these limi-
tations. Total Quality Management (TQM), Six Sigma,
and diversity initiatives, which are still transforming the
workplace (in some cases unsuccessfully), require such
training. We believe that the scientific method can
achieve the same transformations and build upon the
learning of the previous initiatives. Both TQM and Six
Sigma preach the importance of detail, testing and veri-
fication. However, the critical importance of being
hypothesis-driven is missing in these initiatives and

generally in business today. We are seeking the solution;
it is not dictated from above or hidden behind the mask of
functional expertise.
An example of an organization employing elements of
the scientific method in business processes is Toyota,
where a set of rules mandates improvement efforts in
accordance with the scientific method (8). However, the
connection of diversity with business results is lacking.
The scientific method makes that connection more clear.
Only through openness to the diversity of thought
present within the organization can the debate required to
achieve solutions to today’s pressing problems be
achieved.
We have provided the elements of a training plan with
our 5-Step Process, which can be implemented with real
problems. The concept in Six Sigma of having “black
belts” to spearhead the initiative also applies to the sci-
entific method. Many of the anticipated champions of the
scientific method can emerge from the R&D organiza-
tion.
One other interesting area is the use of inductive and
deductive approaches to problem solving. While not a
focus in this study, we observed through our analysis,
that the determination of an approach is best if consid-
ered on a case-by-case basis. For example, in the exploit-
ative business setting, we see more successful
application of a deductive type of decision-making. In
scientific terminology, the deductive approach is derived
from theory, moving from generalities to specifics.
Exploratory situations are a better home for inductive
reasoning, which moves from the specifics to more
general applications. New technology development, for

example, often includes mini-experiments to generate
data needed to evaluate the potential applicability to new
markets.
From a narrower perspective, we have seen that the sci-
entific method is applicable across a broad range of
problems and is independent of the source of data or
thought process. Whether the problems are exploitative
or exploratory, whether inductive or deductive processes
are used, the scientific method provides the discipline to
sort through the maze of data and opportunities by using
its incisive hypothesis-driven focus.
The scientific method brings discipline to decision-
making, while still allowing for innovation, openness
and dialogue. We believe the use of the scientific method
as outlined in this paper opens the door to the pursuit of a
science of business management. What this science
brings is the ability to handle and address the overabun-
dance of information that information technology now
provides the decision maker. In fact, the scientific
method is built around a wealth of information. The sci-
entific method thrives on debate with opposing camps
presenting the merits of their approach. The solution
emerges from such open debate, unlike the silence in
those meeting and board rooms where concern for job
and status dominate. Finally, the scientific method opens
the door to innovation and risk-taking, because within its
confines there is control and objectivity.
Efficient and Objective
Our research suggests that business and research leaders
who successfully implement theories from the scientific
method cite two primary benefits: efficiency and objec-
tivity. The efficiency results from the use of less

resources throughout the entire business-making process
as a result of investing more effort upfront in “framing”
and “developing hypotheses.” Framing allows us to
identify and focus on the key question and set appropri-
Applying the Scientific Method
Exploitation Exploration
Focus Leveraging existing knowledge and/or
capabilities.
Experimenting with new, unrelated
knowledge and/or capabilities.
Example Existing technologies are leveraged for scale
advantage or application to similar
environments.
New-product team must find new platform
for growth, which leverages developing
technologies and expertise.
Decision-Making Barriers • Complacency • Limited paradigms
• Inefficiencies • Regulatory approval
• Knowledge transfer inhibitors (silos) • Standardization issues
• Market perceptions of value • Market specifications
Potential Benefits • Efficiencies through leverage of existing
knowledge.
• Recognition and attention to disconfirming
evidence before making decisions.
• Focus on most relevant areas of analysis. • Focus on value-
creating opportunities.

• Organizational buy-in. • Inter-functional cooperation.
ate parameters. A hypothesis eliminates prejudice,
promotes transparency and dialogue, and requires verifi-
cation through experimentation and data gathering. The
hypothesis leads to a decision based on experiments run
in the marketplace.
The objectivity in the process is designed to overcome
executive blindspots, biases and heuristics by focusing
on confirming and, perhaps even more importantly, dis-
confirming evidence. In our model, the last three steps
(“gather data,” “interpret results” and “make decision”)
are all based on honest and objective analysis. While not
revolutionary, we believe that reviewing the scientific
method, and applying the concepts as we discuss in this
paper, can provide valuable insights for making better
business decisions. ��
Acknowledgement
Special thanks to Jeffrey Covin and Michael Friga for
insightful comments on earlier drafts of this paper. We
would also like to recognize previous research and col-
laborative work with Ethan Rasiel in the development of
The McKinsey Mind which was the impetus for many of
the ideas contained herein.
References and Notes
1. The acceleration of decision-making is theorized to be a
result of a

more complex and competitive macro-environment. Leading
research investigates the speed of the decisions. Eisenhardt, K.
1989.
Making fast decisions in high-velocity environments. The
Academy
of Management Journal 32, pp. 543–576; Challenges in unstable
environments. Fredrickson, J. and Mitchell, T. 1984. Strategic
decision processes: Comprehensiveness and performance in an
industry with an unstable environment. The Academy of
Management
Journal 27, pp. 399–423; and Chaos theory. Levy, D. 1994.
Chaos
theory and strategy: Theory, applications, and managerial
implica-
tions. The Strategic Management Journal 15, pp. 167–178.
2. March, J. and Simon, H. 1958. Organizations. New York,
John
Wiley & Sons, and Williamson, O. E. 1975. The Organizational
Failures Framework. Markets and Hierarchies: Analysis and
Antitrust Implications. New York, The Free Press: Chapter 2,
pp.
20–40.
3. For a thorough and informative summary of blindspots and
the
leading literature thereof, see Fleisher, C. and Bensoussan, B.
2002.
Strategic and Competitive Analysis, Prentice Hall, pp. 122–143.
4. Research on intuition in management is on the rise. See:
Behling,
O. and Eckel, N. 1991. Making sense out of intuition. Academy
of
Management Executive 5(1), pp. 46–53; Burke, L. and Miller,
M.
1999. Taking the mystery out of intuitive decision-making.
Academy

of Management Executive 13(4), pp. 91–99; Covin, J., Slevin,
D. and
Heeley, S. 2001. Strategic decision-making in an intuitive vs.
techno-
cratic mode: Structural and environmental considerations.
Journal of
Business Research 52, pp. 51–67; Mitchell, Ron, Friga, Paul N.
and
Mitchell, Rob. 2005. Untangling the intuition mess: Intuition as
a
construct in entrepreneurial research. Entrepreneurship, Theory
and
Practice. November, pp. 653–679.
5. See a thorough description in Rasiel and Friga. 2001. The
McKinsey Mind. McGraw-Hill, NY, NY.
6. Prahalad, C. and Bettis, R. 1986. The dominant logic: A new
linkage between diversity and performance. The Strategic
Manage-
ment Journal 7, pp. 485–501; Bettis, R. and Prahalad, C. 1995.
The
Dominant Logic: Retrospective and Extension. The Strategic
Man-
agement Journal 16, pp. 5–14.
7. Levinthal, D. and March, J. 1993. The Myopia of Learning.
The
Strategic Management Journal 14, pp. 95–112.
8. Spear, S. and Bowen, H. 1999. Decoding the DNA of the
Toyota
Production System. Harvard Business Review, Sept.–Oct. pp.
97–106.
The scientific method
thrives on debate
with opposing camps
presenting the merits

of their approach.
Reprints
IMPROVING PRODUCT-DEVELOPMENT PROCESSES
Fifty-one RESEARCH � TECHNOLOGY MANAGEMENT
articles on this subject are now available in
paperback. To order, see inside back cover.
Learning from the Best New Product Developers
Portfolio Management in New Product
Development: Lessons from the Leaders
Best Practices for Managing R&D Portfolios
New Problems, New
Solution
s: Making
Portfolio Management More Effective
Assessing Technology Projects Using
Real Options Reasoning
Crafting R&D Project Portfolios
Speeding Development Cycles
What First-to-Market Companies Do

Differently
Satisfy Customers While Speeding R&D and
Staying Profitable
Product Development—Faster, On-Time
Managing Risk as Product Development
Schedules Shrink
. . . AND MORE
Reproduced with permission of the copyright owner. Further
reproduction prohibited without permission.

ONE POINT OF VIEWPaul N. Friga and Richard B. ChapasMA.docx

Recommended

Recommended

More Related Content

Similar to ONE POINT OF VIEWPaul N. Friga and Richard B. ChapasMA.docx

Similar to ONE POINT OF VIEWPaul N. Friga and Richard B. ChapasMA.docx (20)

More from hopeaustin33688

More from hopeaustin33688 (20)

Recently uploaded

Recently uploaded (20)

ONE POINT OF VIEWPaul N. Friga and Richard B. ChapasMA.docx