1. The terminology used by professional researchers employing scientific thinking.
2. What you need to formulate a solid research hypothesis.
3. The need for sound reasoning to enhance business research results.
After reading this chapter, you should understand...
>learningobjectives
Brand communities play a pivotal role for a brand
connecting with its consumers, and as one of our
Never Ending Friending focus group respondent
notes: “I want brands to be my friends,” which
means that consumers would like to have common
ideas, conversations and benefits delivered to
them on their own terms.
Judit Nagy, vice president, consumer insights,
MySpace/Fox Interactive Media
“
”
Thinking Like a Researcher
>chapter 3
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MindWriter
“Myra, have you had any experience with research
suppliers?” asks Jason.
“Some. Actually, I worked for one of your
competitors for a short time after college, on a project
with the U.S. Army. That project helped me decide that
research wasn’t my life’s work—not that it wasn’t and
isn’t an important field and an important part of my
new responsibilities.”
“No need to apologize. Some of us have what it
takes and others don’t.”
“Actually, there wasn’t anything missing in my
ability to observe data, or build rapport with study
participants, or find insights,” shares Myra. “The project
made all the papers; you probably read about it.”
“Refresh my memory.”
“The death rate near one Army munitions testing
area was unexplainably high. Local activists were
trying to shut it down, fearing it was an environmental
hazard. The Army had a vested interest in keeping it
open. Besides, it didn’t think the civilian deaths had
anything to do with the firing range. U.S. Senator Sly
forced the Army to investigate. Since the Army thought
it had a public relations job on its hands, my firm was a
logical choice; PR campaigns were a specialty.
“The firing range was a played-out mine, stripmined
until it was worse than a moonscape. The area
had once been a prosperous mining region, where the
people were known for fearlessly and proudly going out
to dig and produce. The nearest town was so severely
economically depressed that, for the pitifully few jobs
the Army provided, the folks welcomed the military in
to bomb their backyard to cinders.
“The cannon the Army was testing was impressive.
Troops armed it with 3-inch shells, put on ear protectors
and goggles, and lobbed shells into the range. There
would be a tremendous flash and boom, and the shells
would go roaring and soaring out of sight. We would
soon hear a tremendous boom coming back to us and
see dust and ash kicked up several hundred feet. We
were all very happy not to be downrange. When we
went downrange later, we found a huge crater and a
fused puddle of iron, but nothing else but slag and
molten rocks.
“There was one problem. About every 20th shell
would be a dud. It would fly off and land, and maybe
kick up some dust, but explode it would not.
“On paper, this was not supposed to be a problem.
TheArmy sent an officious second lieutenant to brief us.
He showed us reports that the Army had dropped such
duds from hundred-foot platforms, from helicopters,
had applied torches to them—everything—and had
discovered them to be completely inert. The only thing
he claimed would ignite one of these duds was to drop
another live bomb on it.
“Regrettably, this proved not to be the case. My
team had barely finished its initial briefing when in
the middle of the night we heard one of these so-called
duds explode. We rushed out at dawn and, sure enough,
found a new crater, molten slag, molten rock, and so
forth. It was quite a mystery.
“Our team took shifts doing an all-night observation
study. During my two-hour stint, my partner and I saw
people with flashlights moving around in there.
Truly effective research is more likely to result when a marketing research supplier works
collaboratively with its client throughout the research process. However, not all clients are
trained in research methodology, and some come from backgrounds other than marketing.
The supplier needs to understand the client’s background in order to effectively develop the
collaboration. We rejoin Henry and Associates as Jason Henry strives to profile the research
knowledge, if any, of his client’s representative—Myra Wines—the individual with whom he will
be working on the MindWriter CompleteCare laptop servicing assessment project.
>bringingresearchtolife
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54
>bringingresearchtolifecont’d
“We didn’t know if the people were military or
civilian. We learned later that locals were coming
in at night, intending to crack open the bombs and
scavenge for copper wire or anything they thought
was salvageable. Except, of course, their actions
occasionally ignited one of the beauties and erased any
evidence of a crime being committed by vaporizing the
perpetrators on the spot.
“Part of our research was to measure public
sentiment about the firing range among the locals.
During our stay in the area, we discovered the locals
were involved in every kind of thrill sport. It was
not unusual to see a 50-mile auto race with four
ambulances on hand on the edge of the oval, to cart off
the carnage to the surgical hospital in the next county.
I saw men leap into cars with threadbare tires, loose
wheels, malfunctioning brakes, with brake fluid and
transmission fluid drooling all over the track. They
could wheel their cars out onto the track on a tire they
knew was thin as tissue, and if it blew out and put them
in the hospital, their reaction was ‘Some days you can’t
win for losin’.’ Nobody thought anything of this. If we
asked, their answer was, ‘I’ll go when my number is
up,’ or ‘It’s not in my hands.’
“Their attitude made sense, from a culturaleconomic
view. That attitude had permitted the men
to go down in the mines year after year. Even the local
sheriff wouldn’t stop their daredevil behavior. ‘They
are going to die anyway,’ he was overheard remarking.
‘We all are going to die. People die every month that
never go out on that dirt track.’ Of course, unlike
driving a car, messing with a potentially live bomb
didn’t leave much to skill but left everything to chance.
“The Army had considered an educational
campaign to keep the scavengers out but, given our
findings, decided it couldn’t deal with such thinking
by applying logic. Instead, it changed its procedure.
The troops would now fire the shells in the morning
and spend the afternoon finding the duds, to which
they attached kerosene lanterns. At dusk, a fighterbomber
would fly over the area and bomb the
lanterns—and the duds—to a molecular state. It was
neat and it worked. And the death rate of the locals
dropped dramatically.”
As Myra finished her story, Jason asks, “It sounds like
a successful project. By studying the locals’ attitudes
and behavior, you could discard the alternative of the
education campaign. Why did you decide research
wasn’t for you?”
“My boss didn’t like the idea that I broke
confidentiality and told a local reporter what the locals
were doing. I’d seen someone’s dad or brother blown
to pieces and felt I had to act. My dismissal taught me
one of the rules of good research—the client always
gets to choose whether to use, or release, the findings
of any study.”
> The Language of Research
When we do research, we seek to know what is in order to understand, explain, and predict phenomena.
We might want to answer the question “What will be the department’s reaction to the new flexible work
schedule?” or “Why did the stock market price surge higher when all normal indicators suggested it
would go down?” When dealing with such questions, we must agree on definitions. Which members
of the department: clerical or professional? What kind of reaction? What are normal indicators? These
questions require the use of concepts, constructs, and definitions.
Concepts
To understand and communicate information about objects and events, there must be a common ground
on which to do it. Concepts serve this purpose. A concept is a generally accepted collection of meanings
or characteristics associated with certain events, objects, conditions, situations, and behaviors.
Classifying and categorizing objects or events that have common characteristics beyond any single
observation creates concepts. When you think of a spreadsheet or a warranty card, what comes to mind
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>chapter 3 Thinking Like a Researcher 55
is not a single example but your collected memories of all spreadsheets and warranty cards, from which
you abstract a set of specific and definable characteristics.
We abstract such meanings from our experiences and use words as labels to designate them. For
example, we see a man passing and identify that he is running, walking, skipping, crawling, or hopping.
These movements all represent concepts. We also have abstracted certain visual elements by which we
identify that the moving object is an adult male, rather than an adult female or a truck or a horse. We
use numerous concepts daily in our thinking, conversing, and other activities.
Sources of Concepts
Concepts that are in frequent and general use have been developed over time through shared language
usage. We acquire them through personal experience. Ordinary concepts make up the bulk of communication
even in research, but we often run into difficulty trying to deal with an uncommon concept
or a newly advanced idea. One way to handle this problem is to borrow from other languages (e.g.,
gestalt) or to borrow from other fields (e.g., from art, impressionism). The concept of gravitation is
borrowed from physics and used in marketing in an attempt to explain why people shop where they
do. The concept of distance is used in attitude measurement to describe degree of variability between
the attitudes of two or more persons. Threshold is used effectively to describe a concept about the way
we perceive.
Sometimes we need to adopt new meanings for words (make a word cover a different concept)
or develop new labels for concepts. The recent broadening of the meaning of model is an example of
the first instance; the development of concepts such as sibling and status-stress is an example of the
second. When we adopt new meanings or develop new labels, we begin to develop a specialized jargon
or terminology. Jargon no doubt contributes to efficiency of communication among specialists, but it
excludes everyone else.
Importance to Research
In research, special problems grow out of the need for concept precision and inventiveness. We design
hypotheses using concepts. We devise measurement concepts by which to test these hypothetical statements.
We gather data using these measurement concepts. The success of research hinges on (1) how
clearly we conceptualize and (2) how well others understand the concepts we use. For example, when
we survey people on the question of customer loyalty, the questions we use need to tap faithfully the
attitudes of the participants. Attitudes are abstract, yet we must attempt to measure them using carefully
selected concepts.
The challenge is to develop concepts that others will clearly understand. We might, for example, ask
participants for an estimate of their family’s total income. This may seem to be a simple, unambiguous
concept, but we will receive varying and confusing answers unless we restrict or narrow the concept
by specifying:
• Time period, such as weekly, monthly, or annually.
• Before or after income taxes.
• For head of family only or for all family members.
• For salary and wages only or also for dividends, interest, and capital gains.
• Income in kind, such as free rent, employee discounts, or food stamps.
Constructs
Concepts have progressive levels of abstraction—that is, the degree to which the concept does or does
not have something objective to refer to. Table is an objective concept. We can point to a table, and
we have images of the characteristics of all tables in our mind. An abstraction like personality is much
more difficult to visualize. Such abstract concepts are often called constructs. A construct is an image
or abstract idea specifically invented for a given research and/or theory-building purpose. We build
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56 >part I Introduction to Business Research
constructs by combining the simpler, more concrete concepts, especially when the idea or image we intend
to convey is not subject to direct observation. When Jason and Myra tackle MindWriter’s research
study, they will struggle with the construct of satisfied service customer.
Concepts and constructs are easily confused. Consider this example: Heather is a human resource
analyst at CadSoft, an architectural software company that employs technical writers to write product
manuals, and she is analyzing task attributes of a job in need of redesign. She knows the job description
for technical writer consists of three components: presentation quality, language skill, and job interest.
Her job analysis reveals even more characteristics.
Exhibit 3-1 illustrates some of the concepts and constructs Heather is dealing with. The concepts
at the bottom of the exhibit (format accuracy, manuscript errors, and keyboarding speed) are the most
concrete and easily measured. We are able to observe keyboarding speed, for example, and even with
crude measures agree on what constitutes slow and fast keyboarders. Keyboarding speed is one concept
in the group that defines a construct that the human resource analyst calls “presentation quality.” Presentation
quality is really not directly observable. It is a nonexistent entity, a “constructed type,” used
to communicate the combination of meanings presented by the three concepts. Heather uses it only as
a label for the concepts she has discovered are related empirically.
Concepts at the next level in Exhibit 3-1 are vocabulary, syntax, and spelling. Heather also finds
them to be related. They form a construct that she calls “language skill.” She has chosen this term
because the three concepts together define the language requirement in the job description. Language
skill is placed at a higher level of abstraction in the exhibit because two of the concepts it comprises,
vocabulary and syntax, are more difficult to observe and their measures are more complex.
Heather has not yet measured the last construct, “job interest.” It is the least observable and the
most difficult to measure. It will likely be composed of numerous concepts—many of which will be
quite abstract. Researchers sometimes refer to such entities as hypothetical constructs because they
can be inferred only from the data; thus, they are presumed to exist but must await further testing
to see what they actually consist of. If research shows the concepts and constructs in this example to
be interrelated, and if their connections can be supported, then Heather will have the beginning of a
“Job Interest Construct”
(Components unknown by analyst)
“Language Skill Construct”
“Presentation Quality
Construct”
Most
abstract
Most
concrete
Levelofabstraction
Vocabulary
Syntax
Spelling
Manuscript
errors
Format
accuracy
Keyboarding
speed
>Exhibit 3-1 Constructs Composed of Concepts in a Job Redesign
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>chapter 3 Thinking Like a Researcher 57
conceptual scheme. In graphic form, it would depict the relationships among the knowledge and skill
requirements necessary to clarify the job redesign effort.
Definitions
Confusion about the meaning of concepts can destroy a research study’s value without the researcher
or client even knowing it. If words have different meanings to the parties involved, then the parties are
not communicating well. Definitions are one way to reduce this danger.
Researchers struggle with two types of definitions: dictionary definitions and operational definitions.
In the more familiar dictionary definition, a concept is defined with a synonym. For example,
a customer is defined as a patron; a patron, in turn, is defined as a customer or client of an establishment;
a client is defined as one who employs the services of any professional and, loosely, as a patron
of any shop.1
Circular definitions may be adequate for general communication but not for research. In
research, we measure concepts and constructs, and this requires more rigorous definitions.
Operational Definitions
An operational definition is a definition stated in terms of specific criteria for testing or measurement.
These terms must refer to empirical standards (i.e., we must be able to count, measure, or in some other
way gather the information through our senses). Whether the object to be defined is physical (e.g., a
can of soup) or highly abstract (e.g., achievement motivation), the definition must specify the characteristics
and how they are to be observed. The specifications and procedures must be so clear that any
competent person using them would classify the object in the same way.
During her research project with the military, Myra observed numerous shells that, when fired, did
not explode on impact. She knew the Army attached the operational definition “a shell that does not explode
on impact” to the construct dud shell. But if asked, Myra would have applied the operational term
dud shell only to “a shell that, once fired from a cannon, could not be made to explode by any amount of
manipulation, human or mechanical.” Based on her operational definition, the town’s residents rarely
encountered “duds” during their excursions onto the firing range.
Suppose college undergraduates are classified by class. No one has much trouble understanding
such terms as freshman, sophomore, and so forth. But the task may not be that simple if you must determine
which students fall in each class. To do this, you need operational definitions.
Operational definitions may vary, depending on your purpose and the way you choose to measure
them. Here are two different situations requiring different definitions of the same concepts:
1. You conduct a survey among students and wish to classify their answers by their class levels.
You merely ask them to report their class status and you record it. In this case, class is
freshman, sophomore, junior, or senior; and you accept the answer each respondent gives as
correct. This is a rather casual definition process but nonetheless an operational definition. It is
probably adequate even though some of the respondents report inaccurately.
2. You make a tabulation of the class level of students from the university registrar’s annual
report. The measurement task here is more critical, so your operational definition needs
to be more precise. You decide to define class levels in terms of semester hours of credit
completed by the end of the spring semester and recorded in each student’s record in the
registrar’s office:
Freshman Fewer than 30 hours’ credit
Sophomore 30 to 59 hours’ credit
Junior 60 to 89 hours’ credit
Senior 90 or more hours’ credit
Those examples deal with relatively concrete concepts, but operational definitions are even
more critical for treating abstract ideas. Suppose one tries to measure a construct called “consumer
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58 >part I Introduction to Business Research
socialization.” We may intuitively understand what this means, but to attempt to measure it among
consumers is difficult. We would probably develop questions on skills, knowledge, and attitudes; or
we may use a scale that has already been developed and validated by someone else. This scale then
operationally defines the construct.
Whether you use a definitional or operational definition, its purpose in research is basically the
same—to provide an understanding and measurement of concepts. We may need to provide operational
definitions for only a few critical concepts, but these will almost always be the definitions used
to develop the relationships found in hypotheses and theories.
Variables
In practice, the term variable is used as a synonym for construct, or the property being studied. In this
context, a variable is a symbol of an event, act, characteristic, trait, or attribute that can be measured
and to which we assign categorical values.2
For purposes of data entry and analysis, we assign numerical value to a variable based on the variable’s
properties. For example, some variables, said to be dichotomous, have only two values, reflecting
the presence or absence of a property: employed–unemployed or male–female have two values,
generally 0 and 1. When Myra Wines observed the cannon shells, they were exploded or unexploded.
Variables also take on values representing added categories, such as the demographic variables of race
or religion. All such variables that produce data that fit into categories are said to be discrete, because
only certain values are possible. An automotive variable, for example, where “Chevrolet” is assigned a
5 and “Honda” is assigned a 6, provides no option for a 5.5.
Income, temperature, age, and a test score are examples of continuous variables. These variables
may take on values within a given range or, in some cases, an infinite set. Your test score may range
from 0 to 100, your age may be 23.5, and your present income could be $35,000. The procedure for
assigning values to variables is described in detail in Chapter 11.
Using Scientific Definitions to Shape Political Debate over BioMed
>snapshot
When politics trumps science in defining critical research terminology,
legislators intentionally or unwittingly fail to communicate
information accurately. This could be critical in discovering products
for breakthrough cures, tracking the progression of diseases
(thus affecting decisions about hospital staffing and insurance),
and finding better ways to test new drugs to discover their various
applications.
One example is The National Academies, which advises the
federal government and public on scientific issues. It has “created
voluntary guidelines for embryonic stem cell research.”
These guidelines also “provide a comprehensive definition
of terms that are accepted by every major research body in
the U.S.” Because stem-cell research and human cloning are
such volatile political issues, the federal government hasn’t
proposed countrywide guidelines. As a result, individual states
opportunistically exploit scientific terminology and fill the void
with altered definitions that operationally join humans with embryos
and add overarching definitions of human cloning.
Another example relates to the Kansas House of Representatives,
which has two bills in process. H.B. 2098 claims “to define
terms related to human cloning.” The companion bill, H.B. 2255,
seeks to ban public funding for somatic cell nuclear transfer
(SCNT), the bill’s term for creating cloned embryonic stem cells.
Opponents of embryonic stem-cell research (who contend that
embryos are human beings and wish to ban such research) find
the definition credible. While “68 percent of Kansans support
somatic cell nuclear transfer, there is also strong opposition to
reproductive cloning.” Thus, by combining both techniques in
the public’s mind in a single operational definition, opponents
aim to ban SCNT.
Paul Terranova, vice chancellor for research at Kansas University
Medical Center, is critical of the many scientific inaccuracies
in the definitions used in both bills. When politics collide with
science, should politics triumph?
www.kumc.edu; www.kslegislature.org
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>chapter 3 Thinking Like a Researcher 59
Independent and Dependent Variables
Researchers are most interested in relationships among variables. For example, does a newspaper coupon
(independent variable) influence product purchase (dependent variable), or can a salesperson’s
ethical standards influence her ability to maintain customer relationships? As one writer notes:
There’s nothing very tricky about the notion of independence and dependence. But there is something tricky about the
fact that the relationship of independence and dependence is a figment of the researcher’s imagination until demonstrated
convincingly. Researchers hypothesize relationships of independence and dependence: They invent them, and
then they try by reality testing to see if the relationships actually work out that way.3
Many textbooks use the term predictor variable as a synonym for independent variable (IV). This variable
is manipulated by the researcher, and the manipulation causes an effect on the dependent variable.
We recognize that there are often several independent variables and that they are probably at least somewhat
“correlated” and therefore not independent among themselves. Similarly, the term criterion variable
is used synonymously with dependent variable (DV). This variable is measured, predicted, or otherwise
monitored and is expected to be affected by manipulation of an independent variable. Exhibit 3-2 lists
some terms that have become synonyms for independent variable and dependent variable.
In each relationship, there is at least one independent variable (IV) and one dependent variable (DV).
It is normally hypothesized that, in some way, the IV “causes” the DV to occur. It should be noted, however,
that although it is easy to establish whether an IV influences a DV, it is much harder to show that
the relationship between an IV and DV is a causal relationship (see also Chapter 6). In Exhibit 3-3a, this
relationship is illustrated by an arrow pointing from the independent variable to the dependent variable.
For simple relationships, all other variables are considered extraneous and are ignored.
Moderating or Interaction Variables
In actual study situations, however, such a simple one-to-one relationship needs to be conditioned or
revised to take other variables into account. Often, we can use another type of explanatory variable
that is of value here: the moderating variable (MV). A moderating or interaction variable is a second
independent variable that is included because it is believed to have a significant contributory or contingent
effect on the original IV–DV relationship. The arrow pointing from the moderating variable to
the arrow between the IV and DV in Exhibit 3-3a shows the difference between an IV directly impacting
the DV and an MV affecting the relationship between an IV and the DV. For example, one might
hypothesize that in an office situation:
The introduction of a four-day working week (IV) will lead to higher productivity (DV), especially among younger workers (MV).
In this case, there is a differential pattern of relationship between the four-day week and productivity
that results from age differences among the workers. Hence, after introduction of a four-day working
week, the productivity gain for younger workers is higher than that for older workers. It should be noted
that the effect of the moderating or interaction variable is the “surplus” of the combined occurrence of
introducing a four-day working week and being a younger worker. For example, let’s assume that the
>Exhibit 3-2 Independent and Dependent Variables: Synonyms
Independent Variable Dependent Variable
Predictor Criterion
Presumed cause Presumed effect
Stimulus Response
Predicted from... Predicted to...
Antecedent Consequence
Manipulated Measured outcome
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60 >part I Introduction to Business Research
productivity of younger workers is 12 percentage points higher than that for older workers, and that
the productivity of workers having a four-day working week is 6 percentage points higher than those
of workers having a five-day working week. If the productivity of a younger worker having a four-day
working week is only 18 percentage points higher than the productivity of a older worker with a five-day
working week, there is no interaction effect, because the 18 percentage points are the sum of the main
effects. There would be an interaction effect if the productivity of the younger worker on a four-day week
was, say, 25 percentage points higher than the productivity of the older worker on a five-day week.
Whether a given variable is treated as an independent or moderating variable depends on the hypothesis
under investigation. If you were interested in studying the impact of the length of the working
week, you would make the length of week the IV. If you were focusing on the relationship between age
of worker and productivity, you might use working week length as an MV.
Extraneous Variables
An almost infinite number of extraneous variables (EVs) exists that might conceivably affect a given
relationship. Some can be treated as IVs or MVs, but most must either be assumed or excluded from the
study. Fortunately, an infinite number of variables has little or no effect on a given situation. Most can
(a)
(b)
(c)
CFV:
meeting
attendance
CFV:
meeting
attendance
IVV:
routine
work
MV:
workers' age
MV:
workers' age
DV:
productivity
DV:
productivity
MV:
workers' age
DV:
productivity
IV:
four-day
working
week
IV:
four-day
working
week
IVV:
job
satisfaction
CV:
weather
CV:
weather
IV:
four-day
working
week
>Exhibit 3-3 Relationships among Types of Variables
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>chapter 3 Thinking Like a Researcher 61
safely be ignored because their impact occurs in such a random fashion as to have little effect. Others
might influence the DV, but their effect is not at the core of the problem we investigate. Still, we want
to check whether our results are influenced by them. Therefore, we include them as control variables
(CVs) in our investigation to ensure that our results are not biased by not including them. Taking the
example of the effect of the four-day working week again, one would normally think that weather conditions,
the imposition of a local sales tax, the election of a new mayor, and thousands of similar events
and conditions would have little effect on working week and office productivity.
Extraneous variables can also be confounding variables (CFVs) to our hypothesized IV–DV relationship,
similar to moderating variables. You may consider that the kind of work being done might
have an effect on the impact of working week length on office productivity. This might lead you to introducing
time spent in a meeting to coordinate the work as a confounding variable (CFV). In our office
example, we would attempt to control for type of work by studying the effect of the four-day working
week within groups attending meetings with different intensity. In Exhibit 3-3b, weather is shown as
an extraneous variable; the broken line indicates that we included it in our research because it might
influence the DV, but we consider the CV as irrelevant for the investigation of our research problem.
Similarly we included the type of work as a CFV.
Intervening Variables
The variables mentioned with regard to causal relationships are concrete and clearly measurable—that
is, they can be seen, counted, or observed in some way. Sometimes, however, one may not be completely
satisfied by the explanations they give. Thus, while we may recognize that a four-day working
week results in higher productivity, we might think that this is not the whole story—that working
Forrester Research: Can an Auto Dealership Go Lean?
>snapshot
Not all research is driven by a specific client problem. Some firms
specialize in researching emerging issues when the issue is more
idea than reality. Forrester Research is one such research firm.
As senior analyst Mark Bunger explains, research problems often
come from taking an issue in one field and transplanting it into
another arena. “The genesis of Forrester’s ‘Making Auto Retail
Lean’ study was a book I was reading by James Womack and
Daniel Jones, Lean Thinking: Banish Waste and Create Wealth in
Your Corporation.” In their book the authors describe lean thinking
as the “elimination of unnecessary waste in business” and
explain that if lean principles are applied to the whole product
cycle, from suppliers to customers, firms can demonstrate significant
increases in productivity and sales. “I knew lean principles
were being applied in the manufacturing of cars. I wondered if
such principles were applied at the level of the auto dealership
and with what effect.” Bunger’s question led Forrester to launch
a study that had Bunger and his team of research associates
conducting hour-long phone interviews with vendors of products
and services related to supply chain enhancement (e.g.,
IBM), followed by a 15-minute, 20-question phone survey of 50
auto dealer CEOs. Bunger also visited dealers in his immediate
area to flesh out ideas from the phone interviews. Data revealed
that “dealers have the wrong cars 40 percent of the time.” Yet if
they applied the lean principles so effective for car manufacturers,
they could lower their demand chain–related costs up to
53 percent.
The Forrester study followed a fairly standard model for the
firm: approximately two weeks to define and refine the problem—
a stage that involves significant secondary data analysis; two to
four weeks for data collection—a stage that involves selecting
at least two sample segments (usually “experts” and users; for
this study, vendors and dealers); and 2 to 30 hours to prepare
a brief or report. Forrester’s research is purchased by subscription.
Subscribing companies related to the automotive industry
have “whole-view” access to any report on any study that Forrester
develops at an approximate cost of $7,000 per seat. When
a subscriber wants numerous people to have direct access to
Forrester research, a firm’s subscription could be worth several
million dollars.
Should auto dealers go lean? What reasoning approaches
did you use to reach your conclusion? What concepts and constructs
are embedded in this example? What hypotheses could
you form from this example?
www.forrester.com
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62 >part I Introduction to Business Research
week length affects some intervening variable (IVV) that, in turn, results in higher productivity. An
IVV is a conceptual mechanism through which the IV and MV might affect the DV. The IVV can be
defined as a factor that theoretically affects the DV but cannot be observed or has not been measured;
its effect must be inferred from the effects of the independent and moderator variables on the observed
phenomenon.4
In the case of the working week hypothesis, one might view the intervening variable (IVV) to be job
satisfaction, giving a hypothesis such as:
The introduction of a four-day working week (IV) will lead to higher productivity (DV) by increasing job satisfaction (IVV).
Here we assume that a four-day working week increases
job satisfaction; similarly, we can assume that
attending internal meetings is an indicator negatively
related to the routine character of work. Exhibit 3-3c
illustrates how theoretical constructs, which are not directly
observed, fit into our model.
Propositions and Hypotheses
We define a proposition as a statement about observable phenomena (concepts) that may be judged
as true or false. When a proposition is formulated for empirical testing, we call it a hypothesis. As a
declarative statement about the relationship between two or more variables, a hypothesis is of a tentative
and conjectural nature.
Hypotheses have also been described as statements in which we assign variables to cases. A case is
defined in this sense as the entity or thing the hypothesis talks about. The variable is the characteristic,
trait, or attribute that, in the hypothesis, is imputed to the case.5
For example, we might create the following
hypothesis:
Brand Manager Jones (case) has a higher-than-average achievement motivation (variable).
If our hypothesis was based on more than one case, it would be a generalization. For example:
Brand managers in Company Z (cases) have a higher-than-average achievement motivation
(variable).
Descriptive Hypotheses
Both of the preceding hypotheses are examples of descriptive hypotheses. They state the existence,
size, form, or distribution of some variable. Researchers often use a research question rather than a
descriptive hypothesis. For example:
Descriptive Hypothesis Format Research Question Format
In Detroit (case), our potato chip market share
(variable) stands at 13.7 percent.
What is the market share for our potato
chips in Detroit?
American cities (cases) are experiencing
budget difficulties (variable).
Are American cities experiencing budget
difficulties?
Eighty percent of Company Z stockholders
(cases) favor increasing the company’s cash
dividend (variable).
Do stockholders of Company Z favor an
increased cash dividend?
Seventy percent of the high school–educated
males (cases) scavenge in the Army firing
range for salvageable metals (variable).
Do a majority of high school–educated male
residents scavenge in the Army firing range
for salvageable metals?
The percent of executives who admitted
that their companies do not have an
official policy for social networks.
55
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>chapter 3 Thinking Like a Researcher 63
Either format is acceptable, but the descriptive hypothesis format has several advantages:
• It encourages researchers to crystallize their thinking about the likely relationships to be found.
• It encourages them to think about the implications of a supported or rejected finding.
• It is useful for testing statistical significance.
Relational Hypotheses
The research question format is less frequently used with a situation calling for relational hypotheses.
These are statements that describe a relationship between two variables with respect to
some case. For example, “Foreign (variable) cars are perceived by American consumers (case) to
be of better quality (variable) than domestic cars.” In this instance, the nature of the relationship
between the two variables (“country of origin” and “perceived quality”) is not specified. Is there
only an implication that the variables occur in some predictable relationship, or is one variable
somehow responsible for the other? The first interpretation (unspecified relationship) indicates
a correlational relationship; the second (predictable relationship) indicates an explanatory, or
causal, relationship.
Correlational hypotheses state that the variables occur together in some specified manner without
implying that one causes the other. Such weak claims are often made when we believe there are more
basic causal forces that affect both variables or when we have not developed enough evidence to claim
a stronger linkage. Here are three sample correlational hypotheses:
Young women (under 35 years of age) purchase fewer units of our product than women who
are 35 years of age or older.
The number of suits sold varies directly with the level of the business cycle.
People in Atlanta give the president a more favorable rating than do people in St. Louis.
By labeling these as correlational hypotheses, we make no claim that one variable causes the other to
change or take on different values.
With explanatory (causal) hypotheses, there is an implication that the existence of or a change in
one variable causes or leads to a change in the other variable. As we noted previously, the causal variable
is typically called the independent variable (IV) and the other the dependent variable (DV). Cause
means roughly to “help make happen.” So the IV need not be the sole reason for the existence of or
change in the DV. Here are four examples of explanatory hypotheses:
An increase in family income (IV) leads to an increase in the percentage of income saved (DV).
Exposure to the company’s messages concerning industry problems (IV) leads to more
favorable attitudes (DV) by employees toward the company.
Loyalty to a particular grocery store (IV) increases the probability of purchasing the private
brands (DV) sponsored by that store.
An increase in the price of salvaged copper wire (IV) leads to an increase in scavenging (DV)
on the Army firing range.
In proposing or interpreting causal hypotheses, the researcher must consider the direction of influence.
In many cases, the direction is obvious from the nature of the variables. Thus, one would
assume that family income influences savings rate rather than the reverse. This also holds true for the
Army example. Sometimes our ability to identify the direction of influence depends on the research
design. In the worker attitude hypothesis, if the exposure to the message clearly precedes the attitude
measurement, then the direction of exposure to attitude seems clear. If information about both
exposure and attitude was collected at the same time, the researcher might be justified in saying that
different attitudes led to selective message perception or nonperception. Store loyalty and purchasing
of store brands appear to be interdependent. Loyalty to a store may increase the probability of
one’s buying the store’s private brands, but satisfaction with the store’s private brand may also lead
to greater store loyalty.
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64 >part I Introduction to Business Research
The Role of the Hypothesis
In research, a hypothesis serves several important functions:
• It guides the direction of the study.
• It identifies facts that are relevant and those that are not.
• It suggests which form of research design is likely to be most appropriate.
• It provides a framework for organizing the conclusions that result.
Unless the researcher curbs the urge to include additional elements, a study can be diluted by trivial
concerns that do not answer the basic questions posed by the management dilemma. The virtue of the
hypothesis is that, if taken seriously, it limits what shall be studied and what shall not. To consider
specifically the role of the hypothesis in determining the direction of the research, suppose we use this:
Husbands and wives agree in their perceptions of their respective roles in purchase decisions.
The hypothesis specifies who shall be studied (married couples), in what context they shall be studied
(their consumer decision making), and what shall be studied (their individual perceptions of their roles).
The nature of this hypothesis and the implications of the statement suggest that the best research design
is a communication-based study, probably a survey or interview. We have at this time no other practical
means to ascertain perceptions of people except to ask about them in one way or another. In addition, we
are interested only in the roles that are assumed in the purchase or consumer decision-making situation.
The study should not, therefore, involve itself in seeking information about other types of roles husbands
and wives might play. Reflection upon this hypothesis might also reveal that husbands and wives disagree
on their perceptions of roles, but the differences may be explained in terms of additional variables, such as
age, social class, background, personality, and other factors not associated with their difference in gender.
What Is a Strong Hypothesis? A strong hypothesis should fulfill three conditions:
• Adequate for its purpose.
• Testable.
• Better than its rivals.
The conditions for developing a strong hypothesis are developed more fully in Exhibit 3-4.
>Exhibit 3-4 Checklist for Developing a Strong Hypothesis
Criteria Interpretation
Adequate for Its Purpose Does the hypothesis reveal the original problem condition?
Does the hypothesis clearly identify facts that are relevant and those that are not?
Does the hypothesis clearly state the condition, size, or distribution of some variable in terms of
values meaningful to the research problem (descriptive)?
Does the hypothesis explain facts that gave rise to the need for explanation (explanatory)?
Does the hypothesis suggest which form of research design is likely to be most appropriate?
Does the hypothesis provide a framework for organizing the conclusions that result?
Testable Does the hypothesis use acceptable techniques?
Does the hypothesis require an explanation that is plausible given known physical or
psychological laws?
Does the hypothesis reveal consequences or derivatives that can be deduced for testing purposes?
Is the hypothesis simple, requiring few conditions or assumptions?
Better Than Its Rivals Does the hypothesis explain more facts than its rivals?
Does the hypothesis explain a greater variety or scope of facts than its rivals?
Is the hypothesis one that informed judges would accept as being the most likely?
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>chapter 3 Thinking Like a Researcher 65
Theory
Hypotheses play an important role in the development of theory. How theory differs from hypothesis
may cause confusion. We make the general distinction that the difference between theory and hypothesis
is one of degree of complexity and abstraction. In general, theories tend to be complex and abstract
and to involve multiple variables. Hypotheses, on the other hand, tend to be more simple, limitedvariable
statements involving concrete instances.
A person not familiar with research uses the term theory to express the opposite of fact. In this sense,
theory is viewed as being speculative or “ivory tower.” One hears that managers need to be less theoretical
or that some idea will not work because it is too theoretical. This is an incorrect picture of the relationship
between fact and theory to the researcher. In truth, fact and theory are each necessary for the
other to be of value. Our ability to make rational decisions, as well as to develop scientific knowledge,
is measured by the degree to which we combine fact and theory. We all operate on the basis of theories
we hold. In one sense, theories are the generalizations we make about variables and the relationships
among them. We use these generalizations to make decisions and predict outcomes. For example, it is
midday and you note that the outside natural light is dimming, dark clouds are moving rapidly in from
the west, the breeze is freshening, and the air temperature is cooling. Would your understanding of the
relationship among these variables (your weather theory) lead you to predict that something decidedly
wet will probably occur in a short time?
Radio Chips versus Retinal Scans: Which Theory Offers the Best Protection?
>snapshot
When the first confirmed case of bovine spongiform encephalopathy
(BSE—known as “mad cow” disease) was discovered
in a Washington state dairy cow in December 2003, numerous
countries banned U.S. beef imports, bringing the $3.2 billion
export industry to a standstill. That year, the United States Department
of Agriculture (USDA) performed random tests on approximately
0.03 percent of all slaughtered cattle, about 20,000
cows of the nearly 40 million head of cattle slaughtered annually.
In comparison, western European countries tested 10 million
cows and Japan tested each of its 1.2 million slaughtered cows.
Theories are essential to a researcher’s quest to explain and
predict phenomena while creating business opportunities and
informing public policy. One USDA theory is that the best way to
identify sources of cattle-born disease is to monitor a cow from
birth to slaughter. Thus, the USDA wanted a national livestock
database. After evaluating the options, the USDA proposed another
theory: Cows tagged with radio frequency identification
devices (RFID) would create the most accurate database.
About the size of a quarter, the RFID tag is stapled to the
base of the animal’s ear. It is programmed with a numeric code
that is scanned by a stationary or handheld device when a cow
reaches a new location in the production process. As cows
move from farm to feeding lot to slaughterhouse, each animal’s
origin and location can be updated in the national database.
But RFID tags can be damaged, dislodged, or tampered
with. Slaughterhouses need additional safeguards to be certain
these devices don’t end up in the meat. “All you need is one
chip in someone’s burger and you’ve got a problem,” says Brian
Bolton, vice president of marketing for Optibrand. This Colorado
company offers a different theory for the best identification and
tracking: A camera that records the unique vascular patterns in
a cow’s retina at each stage of the beef production chain is the
most reliable. With retinal scanning, Bolton says, “the tracking
technology is contained in the handheld reader. It takes a tiny
picture of a cow’s retina and then links it to that animal’s computerized
record.” Meatpacker Swift & Co., the nation’s thirdlargest
beef processor, has been using Optibrand’s devices for
several years. Retinal scan wands also read RFID tags, access
global positioning receivers, and stamp each scan with a location
record. However, retinal scanning is not always practical because
scans must be taken about an inch from an animal’s eye.
In addition to RFID and retinal scanning, beef producers and
processors implement other tracking systems, thus implementing
their own theories. Some use implantable computer chips
and others use DNA matching systems. While still preferring
RFID technology, the USDA’s director of national animal identification,
John F. Wiemers, concedes, “We think there’s room for
all these technologies.”
Which tracking theory do you favor? What are the most important
variables you would consider in justifying your decision?
www.usda.gov; www.optibrand.com;
http://www.jbsswift.com/
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66 >part I Introduction to Business Research
A theory is a set of systematically interrelated concepts, definitions, and propositions that are advanced
to explain and predict phenomena (facts). In this sense, we have many theories and use them
continually to explain or predict what goes on around us. To the degree that our theories are sound and
fit the situation, we are successful in our explanations and predictions.
In marketing, the product life cycle describes the stages that a product category goes through in
the marketplace.6
The generalized product life cycle has four stages (although the length and shape
of product life cycles differ): introduction, growth, maturity, and decline (Exhibit 3-5). In each stage,
many concepts, constructs, and hypotheses describe the influences that change revenue and profit.
Definitions are also needed for communicating about the claims of the theory and its consistency in
testing to reality.
For example, in the growth stage, companies spend heavily on advertising and promotion to create
product awareness. In the early period of this stage these expenditures may be made to fuel primary
demand (construct), improving product class awareness rather than brand awareness. Also, high pricing
may reflect skimming (concept) to help the company recover developmental costs. The product
manager may alternatively use low pricing, or penetration pricing (concept), to build unit volume. In
the growth stage, sales increase rapidly because many consumers are trying or actually using the product;
and those who tried (were satisfied) and bought again—repeat purchasers (concept)—are swelling
the ranks. If the company is unable to attract repeat purchasers, this usually means death for the
product (proposition). The maturity stage is a good time for the company in terms of generating cash
(proposition). The costs of developing the product and establishing its position in the marketplace are
paid and it tends to be profitable. Firms will often try to use extension strategies (constructs). These are
attempts to delay the decline stage of the product life cycle by introducing new versions of the product.
In the decline stage, “products will consume a disproportionate share of management time and financial
resources relative to their potential future worth”7
(hypothesis). To make this hypothesis fully testable,
we would need operational definitions for disproportionate share, time, resources, and future worth.
The challenge for the researcher in this example is to build more comprehensive theories to explain
and predict how modifying the product and other variables will benefit the firm.
Marketing
objective
Gain
awareness
Stress
differentiation
Maintain brand
loyalty
Harvesting,
deletion
0
+
–
Introduction Growth Maturity Decline
Total industry
profit
Total industry
sales revenue
Salesrevenueorprofit
Stage of the product life cycle
>Exhibit 3-5 Traditional Product Life Cycle
Source: Adapted from Roger Kerin, Eric Berkowitz, Steven Hartley, and William Rudelius, Marketing, 7th ed. (Burr Ridge, IL: McGraw-Hill,
2003), p. 295.
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>chapter 3 Thinking Like a Researcher 67
Models
The term model is used in business research and other fields of business to represent phenomena
through the use of analogy. A model is defined here as a representation of a system that is constructed
to study some aspect of that system or the system as a whole. Models differ from theories in that a
theory’s role is explanation whereas a model’s role is representation.
Early models (and even those created as recently as the 1990s for mainframe computers) were enormously
expensive and often incomprehensible to all but their developers. Modeling software, such as
Excel, has made modeling more inexpensive and accessible.
Models allow researchers and managers to characterize present or future conditions: the effect of
advertising on consumer awareness or intention to purchase, a product distribution channel, brand
switching behavior, an employee training program, and many other aspects of business. A model’s purpose
is to increase our understanding, prediction, and control of the complexities of the environment.
Exhibit 3-6 provides an example of a maximum-flow model used in management science. In this example,
a European manufacturer of automobiles needs an increased flow of shipping to its Los Angeles
distribution center to meet demand. However, the primary distribution channel is saturated and alternatives
must be sought. Although this is a geographic model, more sophisticated network, mathematical,
and path diagrams are subsequently created so that researchers can create hypotheses about the nature,
relationship, and direction of causality among variables.
Descriptive, predictive, and normative models are found in business research.8
Descriptive models
are used frequently for more complex systems, such as the one in Exhibit 3-6. They allow visualization
of numerous variables and relationships. Predictive models forecast future events (e.g., the Fourt
and Woodlock model could be used to forecast basketball shoes for a market segment).9
Normative
models are used chiefly for control, informing us about what actions should be taken. Models may also
be static, representing a system at one point in time, or dynamic, representing the evolution of a system
over time.
Models are developed through the use of inductive and deductive reasoning, which we suggested
previously is integral to accurate conclusions about business decisions. As illustrated in Exhibit 3-7,
a model may originate from empirical observations about behavior based on researched facts and
relationships among variables. Inductive reasoning allows the modeler to draw conclusions from the
LA
NY
NO
RO
BO ST
LI
Los Angeles
New York
New Orleans
[70 units
max.]
[80
units
m
ax.]
[60 units max.]
[40 units max.]
[50
units
m
ax.]
[30 units max.] Lisbon
[40 units max.]
[70 units max.]
[50 units max.]
Stuttgart
Rotterdam
Bordeaux
>Exhibit 3-6 A Distribution Network Model
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68 >part I Introduction to Business Research
facts or evidence in planning the dynamics of the model. The modeler may also use existing theory,
managerial experience, judgment, or facts deduced from known laws of nature. In this case, deductive
reasoning serves to create particular conclusions derived from general premises.
Models are an important means of advancing theories and aiding decision makers. Because the
inputs are often unknown, imprecise, or temporal estimates of complex variables, creating and using
models in the decision-making process can be a time-consuming endeavor.
Inductive
reasoning
Develop premises
Test/anticipate outcomes
Specify relationships
among variables
D
eductive
reasoning
Conclusions about
Behavior
Model Development
Theory/Experience
Empirical Data
>Exhibit 3-7 The Role of Reasoning in Model Development
> Research and the Scientific Method
Good business research is based on sound reasoning. Competent researchers and astute managers alike
practice thinking habits that reflect sound reasoning—finding correct premises, testing the connections
between their facts and assumptions, making claims based on adequate evidence. In the reasoning
process, induction and deduction, observation, and hypothesis testing can be combined in a systematic
way. In this chapter we illustrate how this works and why careful reasoning is essential for producing
scientific results.
If the tools of thinking are the mind of science, then the scientific attitude is the spirit. The scientific
attitude unleashes the creative drive that makes discovery possible. The portraits of scientists involved
in some of the most spectacular discoveries of the last century—Crick, Watson, and Pauling (who
developed the foundations of DNA structure), and others—are the stories of imagination, intuition,
curiosity, suspicion, anguish, the rage to know, and self-doubt. A good business researcher must also
possess these essential predispositions. Each must exercise imagination in the discovery process in capturing
the most essential aspect of the problem, or in selecting a technique that reveals the phenomenon
in its most natural state.
Curiosity in its many forms characterizes the persistent effort to understand relationships. For example,
consider the Hawthorne studies discovering a relationship between the seemingly unrelated
entities of productivity and levels of light in the workplace. Exemplars like Weber, Taylor, Fayol,
Gulick, Mayo, Maslow, McGregor, Argyris, Simon, Likert, Katz, and Kahn (among others in organizational
studies) have all typified the curiosity to ask questions with the passion not to quit and a
discomfort with existing answers. From applied researchers addressing managers’ practical needs to
academics fascinated with the construction of grand theories, the attitude of science is the enabling
spirit of discovery.
The scientific method, as practiced in business research, guides our approach to problem solving.
The essential tenets of the scientific method are:
• Direct observation of phenomena.
• Clearly defined variables, methods, and procedures.
• Empirically testable hypotheses.
• The ability to rule out rival hypotheses.
• Statistical rather than linguistic justification of conclusions.
• The self-correcting process.
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>chapter 3 Thinking Like a Researcher 69
Business and Battlefield: Scientific Evidence Supports “Gut-Hunches”
>snapshot
A team of researchers at Leeds University Business School conducted
studies on how intuition and hunches result from the
way our brains store, process, and retrieve information on a
subconscious level.a
Their research is important to executives
and managers, who often claim that hunches are preferred over
deliberate analysis when a quick decision is required. Gerald
Hodgkinson, the lead researcher remarked, “People usually experience
true intuition when they are under severe time pressure
or in a situation of information overload or acute danger,
where conscious analysis of the situation may be difficult or
impossible.”b
If we consider the analogy of the business executive attempting
to avoid fatal decisions in a fast-moving, turbulent environment
with a soldier scanning the landscape for any evidence of
improvised explosive devices (IEDs) in a treacherous neighborhood,
then the importance to organizations comes into focus.
This aspect of decision-making research seeks to understand
how to channel and fine-tune intuitive skills. Being able to identify
when managers and executives switch from an intuitive mode to
deliberate analysis, and why, may shed light on which decisions
are likely to be correct for their environments.
In an article on the importance of hunches in battle, Benedict
Carey described how hunches were critical to military survival in
threat environments like Iraq and Afghanistan, especially clearing
roads of IEDs. Fighting a war-within-a-war, insurgents and U.S.
bomb teams have improved their IED tactics: insurgents by better
placing, concealing, and detonating and U.S. teams with better
recognition and diffusion. Reduced casualties, it was found,
could be attributed to some soldiers who could sense danger in
a life-or-death situation well before others.c
Army research psychologist Steven Burnett led a study involving
approximately 800 military men and women that focused
on how some soldiers see what others miss. They found that two
types of personnel are particularly good at spotting anomalies:
those with hunting backgrounds and those from tough urban
neighborhoods, where knowing what gang controls a block is
a survival necessity. These latter troops also seemed to have
innate “threat-assessment” abilities.d
Martin P. Paulus, a psychiatrist at the University of California,
San Diego found that the brains of elite military units appear to
record apparent threats differently from the average enlistee.e
When presented with the sight of angry faces, Navy Seals show
significantly higher activation in the insula (the brain location that
collects sensations from around the body and interprets them
cohesively) than regular soldiers.
Not long ago, management academics thought of hunches
and intuition as folklore, or as just feelings. Feelings have “little
to do with rational decision making, or that got in the way of
it,” said Dr. Antonio Damasio, director of the Brain and Creativity
Institute.f
Notwithstanding that hunches are still not part of
scientific orthodoxy,g
they are supported by strong evidence in
neuroscience and psychology.
The technical evidence is complicated but fascinating. Here
is a glimpse.
Each of us was born with two brains—the cranial brain
between our ears, and a second brain with just as many
neurons and neurotransmitters as the first, but located in
the sheaths of tissue lining our stomach, small intestine,
and colon. During early fetal development the same clump
of embryonic tissue constituted both our primary brain and
our gut brain. In later development the two brains separated
yet remained connected (and in communication)
through the vagus nerve extending from the brain stem
through the enteric nervous system, otherwise known as
our gut brain.h
Receptors for the gut that process serotonin (a neurotransmitter)
are identical to those found in the bilateral part of the
brain where intuitive thinking is believed to originate. Professor
Wolfgang Prinz of the Max Planck Institute in Munich reveals
that our gut brain “may be the source for unconscious decisions
which the main brain later claims as conscious decisions
of its own.”i
There is a lot of research to support gut brain intuition; however,
a few examples are illustrative.j
At the Institute of Noetic
Sciences, researchers showed how the human gut reacts to
emotionally alarming information seconds before the conscious
mind is aware of the information. Previous experiments found
similar evidence of reaction times four to seven seconds before
conscious awareness of emotionally disturbing images.k
In another
study by Professor Ronald Rensink of the University of
British Columbia, one-third of the subjects could sense changes
in the patterns of a series of images before the actual changes
occurred.l
“It’s like a gut feeling,” said Rensink. “It’s like using the
force. The point of this is that these kinds of feelings are often
correct.”m
Stephen Jay Gould, the eminent paleontologist, biologist,
and historian of science said, “Science . . . progresses by hunch,
vision, and intuition.”n
Let’s not forget the hunches.
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70 >part I Introduction to Business Research
An important term in this list is empirical. Empirical testing or empiricism is said “to denote observations
and propositions based on sensory experience and/or derived from such experience by methods
of inductive logic, including mathematics and statistics.”10
Researchers using this approach attempt to
describe, explain, and make predictions by relying on information gained through observation. This
book is fundamentally concerned with empiricism—with the design of procedures to collect factual
information about hypothesized relationships that can be used to decide if a particular understanding of
a problem and its possible solution are correct.
The scientific method, and scientific inquiry generally, is described as a puzzle-solving activity.11
For the researcher, puzzles are solvable problems that may be clarified or resolved through reasoning
processes. The steps that follow represent one approach to assessing the validity of conclusions about
observable events.12
They are particularly appropriate for business researchers whose conclusions result
from empirical data. The researcher:
1. Encounters a curiosity, doubt, barrier, suspicion, or obstacle.
2. Struggles to state the problem—asks questions, contemplates existing knowledge, gathers facts,
and moves from an emotional to an intellectual confrontation with the problem.
3. Proposes a hypothesis, a plausible explanation, to explain the facts that are believed to be
logically related to the problem.
4. Deduces outcomes or consequences of the hypothesis—attempts to discover what happens
if the results are in the opposite direction of that predicted or if the results support the
expectations.
5. Formulates several rival hypotheses.
6. Devises and conducts a crucial empirical test with various possible outcomes, each of which
selectively excludes one or more hypotheses.
7. Draws a conclusion (an inductive inference) based on acceptance or rejection of the hypotheses.
8. Feeds information back into the original problem, modifying it according to the strength of the
evidence.
Clearly, reasoning is pivotal to much of the researcher’s success: gathering facts consistent with the
problem, proposing and eliminating rival hypotheses, deducing outcomes, developing crucial empirical
tests, and deriving the conclusion.
Sound Reasoning for Useful Answers
Every day we reason with varying degrees of success and communicate our meaning in ordinary
language or, in special cases, in symbolic, logical form. Our meanings are conveyed through
one of two types of discourse: exposition or argument. Exposition consists of statements that
describe without attempting to explain. Argument allows us to explain, interpret, defend, challenge,
and explore meaning. Two types of argument of great importance to research are deduction
and induction.
Deduction
Deduction is a form of argument that purports to be conclusive—the conclusion must necessarily follow
from the reasons given. These reasons are said to imply the conclusion and represent a proof. This
is a much stronger and different bond between reasons and conclusions than is found with induction.
For a deduction to be correct, it must be both true and valid:
• Premises (reasons) given for the conclusion must agree with the real world (true).
• The conclusion must necessarily follow from the premises (valid).
A deduction is valid if it is impossible for the conclusion to be false if the premises are true. Logicians
have established rules by which one can judge whether a deduction is valid. Conclusions are not
logically justified if one or more premises are untrue or the argument form is invalid. A conclusion may
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>chapter 3 Thinking Like a Researcher 71
still be a true statement, but for reasons other than those given. For example, consider the following
simple deduction:
All employees at BankChoice can be trusted to observe the ethical code. (Premise 1)
Sara is an employee of BankChoice. (Premise 2)
Sara can be trusted to observe the ethical code. (Conclusion)
If we believe that Sara can be trusted, we might think this is a sound deduction. But this conclusion
cannot be accepted as a sound deduction unless the form of the argument is valid and the premises
are true. In this case, the form is valid, and premise 2 can be confirmed easily. However, more than a
billion dollars each year in confirmed retail employee theft will challenge the premise “All employees
can be trusted to observe an ethical code.” And instances of employee fraud among professionals make
any specific instance questionable. If one premise fails the acceptance test, then the conclusion is not
a sound deduction. This is so even if we still have great confidence in Sara’s honesty. Our conclusion,
in this case, must be based on our confidence in Sara as an individual rather than on a general premise
that all employees of BankChoice are ethical.
This Synovate ad reinforces
that one trait—curiosity—is
necessary for someone to be
a good researcher.
www.synovate.com
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72 >part I Introduction to Business Research
As researchers, we may not recognize how much we use deduction to understand the implications of
various acts and conditions. For example, in planning a survey, we might reason as follows:
Inner-city household interviewing is especially difficult and expensive. (Premise 1)
This survey involves substantial inner-city household interviewing. (Premise 2)
The interviewing in this survey will be especially difficult and expensive. (Conclusion)
On reflection, it should be apparent that a conclusion that results from deduction is, in a sense, already
“contained in” its premises.13
Induction
Inductive argument is radically different. There is no such strength of relationship between reasons
and conclusions in induction. In induction you draw a conclusion from one or more particular facts
or pieces of evidence. The conclusion explains the facts, and the facts support the conclusion. To illustrate,
suppose your firm spends $1 million on a regional promotional campaign and sales do not
increase. This is a fact—sales did not increase during or after the promotional campaign. Under such
circumstances, we ask, “Why didn’t sales increase?”
One likely answer to this question is a conclusion that the promotional campaign was poorly executed.
This conclusion is an induction because we know from experience that regional sales should go
up during a promotional event. Also we know from experience that if the promotion is poorly executed,
sales will not increase. The nature of induction, however, is that the conclusion is only a hypothesis. It
is one explanation, but there are others that fit the facts just as well. For example, each of the following
hypotheses might explain why sales did not increase:
• Regional retailers did not have sufficient stock to fill customer requests during the promotional
period.
• A strike by the employees of our trucking firm prevented stock from arriving in time for the
promotion to be effective.
• A category-five hurricane closed all our retail locations in the region for the 10 days during the
promotion.
In this example, we see the essential nature of inductive reasoning. The inductive conclusion is an
inferential jump beyond the evidence presented—that is, although one conclusion explains the fact of
no sales increase, other conclusions also can explain the fact. It may even be that none of the conclusions
we advanced correctly explain the failure of sales to increase.
For another example, let’s consider the situation of Tracy Nelson, a salesperson at the Square Box
Company. Tracy has one of the poorest sales records in the company. Her unsatisfactory performance
prompts us to ask the question “Why is she performing so poorly?” From our knowledge of Tracy’s
sales practices, the nature of box selling, and the market, we might conclude (hypothesize) that her
problem is that she makes too few sales calls per day to build a good sales record. Other hypotheses
might also occur to us on the basis of available evidence. Among these hypotheses are the following:
• Tracy’s territory does not have the market potential of other territories.
• Tracy’s sales-generating skills are so poorly developed that she is not able to close sales effectively.
• Tracy does not have authority to lower prices and her territory has been the scene of intense
price-cutting by competitive manufacturers, causing her to lose many sales to competitors.
• Some people just cannot sell boxes, and Tracy is one of those people.
Each of the above hypotheses is an induction we might base on the evidence of Tracy’s poor
sales record, plus some assumptions or beliefs we hold about her and the selling of boxes. All of
them have some chance of being true, but we would probably have more confidence in some than in
others. All require further confirmation before they gain our confidence. Confirmation comes with
more evidence. The task of research is largely to (1) determine the nature of the evidence needed to
confirm or reject hypotheses and (2) design methods by which to discover and measure this other
evidence.
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>chapter 3 Thinking Like a Researcher 73
Combining Induction and Deduction
Induction and deduction are used together in research reasoning. Dewey describes this process as the
“double movement of reflective thought.”14
Induction occurs when we observe a fact and ask, “Why is
this?” In answer to this question, we advance a tentative explanation (hypothesis). The hypothesis is
plausible if it explains the event or condition (fact) that prompted the question. Deduction is the process
by which we test whether the hypothesis is capable of explaining the fact. The process is illustrated in
Exhibit 3-8:
1. You promote a product but sales don’t increase. (Fact 1)
2. You ask the question “Why didn’t sales increase?” (Induction)
3. You infer a conclusion (hypothesis) to answer the question: The promotion was poorly
executed. (Hypothesis)
4. You use this hypothesis to conclude (deduce) that sales will not increase during a poorly
executed promotion. You know from experience that ineffective promotion will not increase
sales. (Deduction 1)
This example, an exercise in circular reasoning, points out that one must be able to deduce the
initiating fact from the hypothesis advanced to explain that fact. A second critical point is also illustrated
in Exhibit 3-8. To test a hypothesis, one must be able to deduce from it other facts that
can then be investigated. This is what research is all about. We must deduce other specific facts
or events from the hypothesis and then gather information to see if the deductions are true. In this
example:
5. We deduce that a well-executed promotion will result in increased sales. (Deduction 2)
6. We run an effective promotion, and sales increase. (Fact 2)
Researchers often use
observation when evaluating toys
and children. Apply deductive
reasoning to this image. Develop
your own conclusions concerning
what will happen next.
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74 >part I Introduction to Business Research
How would the double movement of reflective thought work when applied to Tracy Nelson’s problem?
The process is illustrated in Exhibit 3-9. The initial observation (fact 1) leads to hypothesis 1 that
Tracy is lazy. We deduce several other facts from the hypothesis. These are shown as fact 2 and fact 3.
We use research to find out if fact 2 and fact 3 are true. If they are found to be true, they confirm our
hypothesis. If they are found to be false, our hypothesis is not confirmed, and we must look for another
explanation.
In most research, the process may be more complicated than these examples suggest. For instance,
we often develop multiple hypotheses by which to explain the problem in question. Then we design
a study to test all the hypotheses at once. Not only is this more efficient, but it is also a good way to
reduce the attachment (and potential bias) of the researcher for any given hypothesis.
Fact 1:
We promote a product
but sales do not increase.
Induction
Deduction: Ineffective promotion
will not increase sales.
Deduction: Effective promotion
will increase sales.
Fact 2:
We run an effective
promotion and
sales increase.
Hypothesis:
The promotion was
poorly executed.
Why?
>Exhibit 3-8 Why Didn’t Sales Increase?
Fact 1:
Tracy has a poor
performance record.
Fact 2:
Tracy is regularly
late to work.
Induction
Deduction: Laziness results
in excessive tardiness.
Deduction: Laziness results in fewer
customer calls per day.
Fact 3:
Tracy makes fewer
calls per day than the
average salesperson.
Hypothesis:
Tracy is lazy.
Why?
>Exhibit 3-9 Why Is Tracy Nelson’s Performance So Poor?
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>chapter 3 Thinking Like a Researcher 75
1 Scientific methods and scientific thinking are based on
concepts, the symbols we attach to bundles of meaning
that we hold and share with others. We invent concepts
to think about and communicate abstractions. We also
use higher-level concepts—constructs—for specialized
scientific explanatory purposes that are not directly observable.
Concepts, constructs, and variables may be
defined descriptively or operationally. Operational definitions
must specify adequately the empirical information
needed and how it will be collected. In addition, they
must have the proper scope or fit for the research problem
at hand.
Concepts and constructs are used at the theoretical
levels; variables are used at the empirical level. Variables
accept numerals or values for the purpose of testing and
measurement. They may be classified as explanatory,
independent, dependent, moderating, extraneous, and
intervening.
2 Propositions are of great interest in research because
they may be used to assess the truth or falsity of relationships
among observable phenomena. When we advance
a proposition for testing, we are hypothesizing. A hypothesis
describes the relationships between or among
variables. A good hypothesis is one that can explain what
it claims to explain; is testable; and has greater range,
probability, and simplicity than its rivals. Sets of interrelated
concepts, definitions, and propositions that are
advanced to explain and predict phenomena are called
theories. Models differ from theories in that models are
analogies or representations of some aspect of a system
or of the system as a whole. Models are used for description,
prediction, and control.
3 Scientific inquiry is grounded in the inference process.
This process is used for the development and testing of
various propositions largely through the double movement
of reflective thinking. Reflective thinking consists of
sequencing induction and deduction in order to explain
inductively (by hypothesis) a puzzling condition. In turn,
the hypothesis is used in a deduction of further facts
that can be sought to confirm or deny the truth of the
hypothesis.
Researchers think of the doing of science as an orderly
process that combines induction, deduction, observation,
and hypothesis testing into a set of reflective thinking activities.
Although the scientific method consists of neither
sequential nor independent stages, the problem-solving
process that it reveals provides insight into the way research
is conducted.
>summary
argument 70
case 62
concept 54
conceptual scheme 57
construct 55
deduction 70
empiricism 70
exposition 70
hypothesis 62
correlational 63
descriptive 62
explanatory (causal) 63
relational 63
hypothetical construct 56
induction 72
model 67
operational definition 57
proposition 62
scientific attitude 68
scientific method 68
sound reasoning 68
theory 66
variable 58
control 61
confounding (CFV) 61
dependent (DV) (criterion
variable) 59
extraneous (EV) 60
independent (IV) (predictor
variable) 59
intervening (IVV) 62
moderating (MV) 59
>keyterms
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76 >part I Introduction to Business Research
Terms in Review
1 Distinguish among the following sets of items, and suggest
the significance of each in a research context:
a Concept and construct.
b Deduction and induction.
c Operational definition and dictionary definition.
d Concept and variable.
e Hypothesis and proposition.
f Theory and model.
g Scientific method and scientific attitude.
2 Describe the characteristics of the scientific method.
3 Below are some terms commonly found in a management
setting. Are they concepts or constructs? Give two different
operational definitions for each.
a First-line supervisor.
b Employee morale.
c Assembly line.
d Overdue account.
e Line management.
f Leadership.
g Union democracy.
h Ethical standards.
4 In your company’s management development program,
there was a heated discussion between some people who
claimed, “Theory is impractical and thus no good,” and others
who claimed, “Good theory is the most practical approach to
problems.” What position would you take and why?
5 An automobile manufacturer observes the demand for its
brand increasing as per capita income increases. Sales
increases also follow low interest rates, which ease credit
conditions. Buyer purchase behavior is seen to be dependent
on age and gender. Other factors influencing sales appear to
fluctuate almost randomly (competitor advertising, competitor
dealer discounts, introductions of new competitive models).
a If sales and per capita income are positively related,
classify all variables as dependent, independent, moderating,
extraneous, or intervening.
b Comment on the utility of a model based on the
hypothesis.
Making Research Decisions
6 You observe the following condition: “Our female sales representatives
have lower customer defections than do our
male sales representatives.”
a Propose the concepts and constructs you might use to
study this phenomenon.
b How might any of these concepts and/or constructs be
related to explanatory hypotheses?
7 You are the office manager of a large firm. Your company
prides itself on its high-quality customer service. Lately
complaints have surfaced that an increased number of
incoming calls are being misrouted or dropped. Yesterday,
when passing by the main reception area, you noticed the
receptionist fiddling with his hearing aid. In the process, a
call came in and would have gone unanswered if not for
your intervention. This particular receptionist had earned
an unsatisfactory review three months earlier for tardiness.
Your inclination is to urge this 20-year employee to retire or
to fire him, if retirement is rejected, but you know the individual
is well liked and seen as a fixture in the company.
a Pose several hypotheses that might account for
dropped or misrouted incoming calls.
b Using the double movement of reflective thought, show
how you would test these hypotheses.
Bringing Research to Life
8 Identify and classify all the variables in the Army’s dud shell
research.
9 What was Myra’s hypothesis for the Army’s dud shell research?
What was the Army’s hypothesis?
From Concept to Practice
10 Using Exhibits 3-1 and 3-9 as your guides, graph the
inductions and deductions in the following statements.
If there are gaps, supply what is needed to make them
complete arguments.
a Repeated studies indicate that economic conditions vary
with—and lag 6 to 12 months behind—the changes in
the national money supply. Therefore, we may conclude
the money supply is the basic economic variable.
b Research studies show that heavy smokers have a
higher rate of lung cancer than do nonsmokers; therefore,
heavy smoking causes lung cancer.
c Show me a person who goes to church regularly, and I
will show you a reliable worker.
From the Headlines
11 That investment manager Bernard Madoff’s hedge fund
Ascot Partners was a giant scam will likely be the finance
story remembered from the last decade. It is estimated that
Madoff stole an estimated $50 billion from noteworthy individuals
and institutional investors, and he covered the crime
by creating fictional financial statements for each investor.
If you were an institutional investor, how might employing
scientific attitude and scientific method have protected
your organization from this Ponzi scheme?
>discussionquestions
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>chapter 3 Thinking Like a Researcher 77
Campbell-Ewald: R-E-S-P-E-C-T Spells Loyalty Open Doors: Extending Hospitality to Travelers
with Disabilities
>cases*
HeroBuilders.com
* You will find a description of each case in the Case Abstracts section of this textbook. Check the Case Index to
determine whether a case provides data, the research instrument, video, or other supplementary material. Written cases
are downloadable from the text website (www.mhhe.com/cooper11e). All video material and video cases are available
from the Online Learning Center.
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