MGMT501- Research Methods in Management Science Discussion Question

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.

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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.

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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.

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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.

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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.

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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

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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.

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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.

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