Management Question

Question:
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? (Reading:
Chapter 1)
Embed course material concepts, principles, and theories (which require
supporting citations), along with two scholarly peer-reviewed references in
support of your answer.
Be sure to support your statements with logic and argument, citing all
sources referenced.
Between 480 – 520 words.
>chapter 1
Research Foundations
and Fundamentals
“As big data
increases, we see a
parallel growth in the
need for ‘small data’
to answer the questions
it raises.”
William C. Pink,
senior partner
Creative Analytics
>learningobjectives
After reading this chapter, you should understand . . .
LO1-1
! How business research and data analytics complement each other.
LO1-2 The language of professional researchers.
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>chapter 1 Research Foundations and Fundamentals
>The Role and Process of Research
Every manager in the course of his or her career, regardless of his or her field, will make thousands of
decisions of various types: strategic, tactical, and procedural. Each decision starts with a problem or
opportunity—a dilemma. A strategic decision determines the general approach; a tactical one, a method
for executing the strategic decision; and a procedural one, the specifics for executing the tactical decision. For example, brick-and-mortar retailers have been having significant difficulties as more people
have embraced smartphones. Increasingly, people are bypassing stores to shop online (dilemma). Hobby
Lobby, however, is thriving. This retailer chooses to serve the crafting market (strategic) and emphasizes
supplying the needs of painters, photographers, jewelry makers, quilters, floral designers, and interior
decorators (strategic). To serve photographers, it carries matting boards, tools for cutting such board,
and frames, but not cameras (tactical). It might identify each new product for its photography area
by reviewing an in-person, profit-based pitch (procedural). The company is family-owned (strategic).
It makes its decisions guided by values emphasizing strengthening family (strategic). As a result, its
brick-and-mortar stores are open fewer hours (tactical), and its full-time people are paid three times the
minimum wage (tactical).1 Researchers also make these kinds of decisions, deciding, for example, to use
a communication study (strategic) and choosing a mobile survey (tactical) with participants recruited
by posting an invitation on a company’s Facebook page (procedural). Today, the pressure on managers
to justify their decisions, in an effort to guarantee a return on the investment of the resources (people,
money, time, equipment, facilities) that each decision requires, is enormous. The journey from dilemma
to decision uses information as fuel.
Companies have always collected data. Each organization is not equally adept, however, at using
those data to develop meaningful information and insights useful for making good decisions. Over the
last decade, some organizations have used newly available tools (faster computing power, better data
analytic software) to tap into data it has already collected, data that have been languishing in departmental silos or company data warehouses. What once was a pool of data has become a veritable ocean of
data. Some firms are drowning. Others are barely staying afloat. Still others are grasping the opportunity
to use this ocean of data as a foundation for their strategic direction and gain competitive advantage.2
Those companies that have been successful have found fuel for their decisions. But fuel comes in different grades; think regular gasoline verses rocket fuel. Using only historic data to make a current decision
is one approach to decision making; enriching that fuel mixture by collecting new data specific to a given
dilemma, is another.
The field you are about to study is in the midst of upheaval and disruption.3 For almost a century,
researchers have been viewed by managers as technical support. These specialists were brought in on
projects when technical expertise in research methodology and data analysis were needed. But, within
the last few years, that has been changing. New technology, new and better computing tools (artificial
intelligence, virtual reality, better mobile equipment, Internet of Things), and even a new computing
environment (the cloud) are adding to industry chaos. In the midst of this sea change in business, new
pressures are being put on the researcher. It is no longer acceptable to merely add to the data pool; business managers need clearly communicated insights from each new data addition. Researchers are now
expected to not only be technologically competent, but to have an understanding of how businesses and
organizations work. And managers, who once delegated research projects to specialists, are expected
to be conversant with research methodologies and tools. Welcome to the new world of research, where
researchers are data storytellers and insight providers, critical to helping provide strategic and tactical
direction.
Research versus Data Analytics
Facing each new dilemma, it is the manager’s decision whether he or she has sufficient information—
drawn from data previously collected, either internal or external to the firm—or needs more information
to make an appropriate decision. Managers draw on data from existing internal data sources (called
a decision support system) when engaging in data analytics. For example, Amazon, in an attempt to
increase our order, mines its data to provide us with a list of products that others—who bought what we
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>part I Building the Foundation for Research
>snapshot
Analytics Under-delivers Compared to its Hype
According to the latest report from MIT/Sloan Management
Review and SAS, data analytics is not living up to its hype. The
report classifies analytic users on three levels of maturity: ana-
Some examples of analytical innovators, however, give us
role models for the practice. Bank of England (BoE) is an analytic innovator; to fulfill its regulatory role in the British economy,
lytical innovators (who apply analytics strategically), analytical
practitioners (who apply analytics operationally), and the analytically challenged (who rely more on management experience
it is aggregating datasets, both microeconomic and macroeconomic, for the first time. BoE has “hired a chief data officer, created a data lab, established an advanced analytics group and
than analytics to make decisions). Analytical innovators—those
benefiting the most from the application of analytics (both the
extraction of insights and their dissemination to affect organizational actions)—have, rather than growing in the last four years,
formed a bank-wide data community.” General Electric, also an
analytic innovator, created a new business unit, as well as a huge
software division, to manage a cloud-based platform that aggregates and analyzes sensor data from industrial machines. “GE’s
basically remained stagnant. They propose several reasons for
this, including a lack of senior management commitment and a
focus on operational rather than strategic use of data.
strategy for data and analytics has become tightly linked to its
corporate strategy, a tremendous corporate shift for what was
once a traditional manufacturing conglomerate.”
State of Data Analytics
Analytical Innovators
Senior management
drives an analytics culture;
strategic use of data
Higher levels of
data management
& analytic skills
10%
Analytically Challenged
Focus on prediction
and prescription
Analytical Practitioners
Working to become data
driven; operational use
Have ‘just good enough’
data; more of what they
need.
Use complex analytics
with some prediction
49%
41%
Rely more on management
experience than data
Lack appropriate data and
analytic skills
Simple analytics; mostly
descriptive; focus on cost
reduction
Source: Sam Ransbotham, David Kiron, and Pamela Kirk Prentice, “Beyond the Hype: The Hard Work Behind Analytic Success,” MIT/Sloan Management Review,
April 2016, downloaded April 29, 2016 (
utm_medium=email&utm_campaign=darpt16&utm_content=Download+the+Report+%28PDF%29&cid=1).
are ordering—also bought. Such data is often referred to as big data due to the extensive size of many of
these databases. Exhibit 1-1 provides some ideas for sources.
When existing data is mined, it may be used for a purpose other than that for which it was originally intended. In its mobility division, Siemens AG, the engineering powerhouse with almost 350,000 employees in
200 countries,4 builds systems into its trains. These systems generate more than 1 billion data points5 per train
per year. They are used to track that train’s performance and maintenance activity and to learn from any train
malfunctions or accidents. Collecting train data to understand and improve that train’s performance employs
repetitive, ongoing observation research. However, drawing insights from data accumulated from European
and U.S. trains to help design new rolling stock for China and Russia employs data analytics.
In another example, customer relationship management (CRM) software may initially be used to
facilitate the sales process and improve the effectiveness of sales appeals to various customer groups.
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>chapter 1 Research Foundations and Fundamentals
>Exhibit 1-1 Where Business Collects Information
Type of Data
Where/How
Data Source
Transactional
Online and in-store purchases
Online, phone, in-store inquiries
Warehouse and shipping manifests
Machine performance
Customer
Potential customer, customer
Logistic partners, employee
Machine data log
Observational
Online Web visits and in-store shopping trips
Competitor interactions
Click-through paths on Web
In-store customer service interactions
Stock price valuations
Biometric measures (e.g., neuromarketing,
fMRI, PET, eye tracking)
Customer, employee
Customer
Potential customer, customer
Customer, employee
Investors
Potential customer, customer, employee
Conversational
(Touch points)
Surveys, online and in-store intercepts
Call center interactions
In-store customer service interactions
Web chat interactions
In-store checkout
Candidate interviews
Performance reviews
Exit interviews
Annual stockholder meetings
Financial performance presentations
Listening tours
Potential customer, customer, employee
Customer, employee
Customer, employee
Customer, employee
Customer, employee
Potential employee
Employee
Employee
Investor
Financial analyst, institutional investor
Customer, supplier, logistic partner, employee,
decision influencer
Customer, employee, competitor, trade
associations, distributor
Customer, employee, trade associations, distributor
Customer, employee, competitor, trade
associations, distributor
Customer, employee, competitor, trade
associations, distributor
Twitter posts
Facebook posts (company site)
Blog activity
Other social media posts or discussions
Internet Analytics
Keyword searches
Click analysis
Google+
Potential customer, customer
Potential customer, customer
Potential customer, customer
Our interviews and research for this edition revealed several sources of research data. This table is adapted from that research and author experience as well as from material by Cynthia Clark, “5 Ways to Learn What Customers Aren’t Telling You,”1to1 Magazine, March 5, 2012, accessed March 8, 2012 ( and
“Harness the Conversation: Business in Today’s Social World,”Cvent, accessed March 8, 2012 (
Additionally, a call center is designed to answer questions, provide technical support, or funnel prospects
into the sales process; many calls to a call center are recorded to improve performance. Using data analytics, a firm might mine these two datasets to extract insights that help design a new customer landing page
for the firm’s website. Businesses are getting better at data blending,6 combining data from separate data
files (e.g., financial, human resources [HR], CRM, inventory management, and manufacturing) into a new
composite data file, and then querying that composite data file to help make decisions. While the information that comes from data blending has an important role in decision making, it is not the same as research.
Assume for the moment that you are the manager of a full-service restaurant. You are experiencing
significant turnover in your server pool, and some long-time customers have commented that the friendly atmosphere, which has historically drawn them to your door, is changing. Where will you begin to try to solve
this problem? Your business intelligence system is designed to provide ongoing information about events and
trends in the technological, economic, political-legal, demographic, cultural/social, and competitive arenas
(see Exhibit 1-2). It reveals that wait-staff turnover is high in your industry, regulations on restaurant operations have become more stringent, and some area competitors are experimenting with increasing wait-staff
wages while eliminating tips. You also review your firm’s financial records and HR records to determine
pay, tips, pre-hire experience, and work hours of those who left and compare that information with those
who stayed. Is this sufficient information, or is this a problem for which additional research should be used?
5
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>part I Building the Foundation for Research
>snapshot
Big versus Small Data
In his book, Small Data: The Tiny Clues That Uncover Huge
Trends, author Martin Lindstrom talks about the importance of
knowing why. Lindstrom isn’t an advocate of only using big data,
indicating big data lacks insight because it focuses on analysis
rather than emotional connection. His book focuses on what
he’s learned as he has visited or lived in more than 2,000 homes
throughout the world and how those ethnographic observations
paid big dividends. In a Knowledge@Wharton interview, Lindstrom described financially troubled Danish toymaker Lego. In
2002, Lego had ventured away from its core small blocks, instead
emphasizing movies, theme parks, apparel, and large building
blocks (based on big data about Millennials) only to discover—via
interviews and ethnographic observations in homes across Europe—that it was taking away the major reason children play with
the toy: the sense of accomplishment. In explanation, Lindstrom
writes, “children attain social currency among their peers by playing and achieving a level of mastery at their chosen skill.” Lego refocused on the small blocks based on its collection of small data.
These actions brought Lego back from near bankruptcy. “You
have to remember that Big Data is all about analyzing the past,
but it has nothing to do with the future. Small Data, . . . seemingly
©Cr-Management GmbH & Co. KG/Getty Images
insignificant observations you identify in consumers’ homes, is . . .
the emotional DNA we leave behind.”
Sources: Martin Lindstrom, Small Data: The Tiny Clues That Uncover Huge Trends,
St. Martin’s Press (February 23, 2016), pp 1-2; and “Why Small Data Is the New
Big Data,” Knowledge@Wharton, March 24, 2016. Downloaded March 25, 2016
(
_source=daily_email&utm_medium=newsletter&utm_campaign=adage&ttl
=1459463433).
>Exhibit 1-2 Some Sources of Business Intelligence
Speeches by
elected officials
Website of agency
or department
Press releases or
press events
Recordings of public
proceedings
Presentations at
conferences
Press releases or
press events
Government/
Regulatory
Literature
searches
Government
reports
Records of public
proceedings
Competitive
Syndicated
studies
Clipping
services
Business
Intelligence
Demographic
Syndicated industry
studies
Website
Business
research
Literature
searches
Business
research
Economic
Government
reports
Patent filings
Syndicated industry
studies
Presentations at
conferences
Websites
Syndicated
studies
Technological
Cultural &
Social
Press releases or
press events
Literature
search
Clipping
services
Government
reports
Public opinion
organizations
Business
research
6
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>chapter 1 Research Foundations and Fundamentals
Perhaps you are the head of your state’s department of transportation, charged with determining
which roads and bridges will be resurfaced or replaced in the next fiscal year. You have data on which
roads and bridges handle the most traffic, as well as those roads/bridges representing the greatest economic disaster if closed. However, the state’s manager of public information has expressed concern about
the potential for public outcry if work is once again directed to more affluent regions of the state. The
manager suggests using new research to assist in making your decision because the decision is one with
numerous operational, financial, and public relations ramifications. Should you authorize new research?
The Research Process
A deep dive into historical data rarely illuminates the ‘why’ behind actions. And the whys change over
time; what was true a year ago might not be true today. To fulfill this new role of insight provider, you’ll
need an understanding of both the process and the tools used by a researcher. Business research is defined as a systematic inquiry that provides information to guide a specified managerial decision. More
specifically, it is a set of processes that include planning, acquiring, analyzing, and reporting relevant
data, information, and insights to decision makers in ways that mobilize the organization to take appropriate actions. These actions are designed to maximize performance and help accomplish organizational
goals. Typically, the overall process is divided into the following stages:
1. Clarify the research question.
2. Design the research.
3. Collect and prepare the data.
4. Analyze and interpret the data.
5. Report insights and recommendations.
Exhibit 1-3 provides a graphic of the process that we will develop in this text. At times, a manager may
start his or her journey at the beginning and proceed stage-by-stage to its culmination. At other times, a
>Exhibit 1-3 The Research Process
Clarify the Research Question
Stage 1
Exploration
Exploration
Design the Research
Data Collection
Design
Sampling
Design
Stage 2
Collect and Prepare Data
Stage 3
Analyze and Interpret the Data
Stage 4
Report Insights and Recommendations
Stage 5
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>part I Building the Foundation for Research
>snapshot
Research on Cyber Security
How does research keep an organization secure from criminal
“bad actors” in the cyber security arena? It’s used to spot the
threat before it happens, to understand an organization’s vulnerabilities, to spot the attack venues, and more.
Over the last decade, cyber attacks have become more frequent, more sophisticated, more complex, and easier for the bad
actors, all at the same time. New digital tools make it possible
for these criminals to do tasks that just recently would require
sophisticated programming expertise. Today, they can purchase
whatever tools they need from criminal networks using Bitcoin,
a digital currency that makes tracking the purchase and finding the criminal very difficult. As Richard Cassidy, cyber security
evangelist with Alert Logic, one of the nation’s leading managed
security providers, explains, “Companies are vulnerable from
three types of bad actors. Not all pose the same degree of damage.” Hacktivists have a political or social agenda, and garnering
media attention is their goal; cyber criminals may also want the
media attention, but they seek monetary gain from the data they
capture; advanced persistent threats (APTs) are the most dangerous and spend significant time, money, and resources prior
to crafting a target-specific attack and do so for significant monetary gain and/or damage to the target.
From research, Alert Logic discovered that passwords (49.9
percent) and email addresses (45.5 percent) remain the prize
target of bad actors, along with usernames (37.7 percent) and
names (29.4 percent). Using this stolen information in combination with information readily available on company websites and
social media sites like LinkedIn, a bad actor can obtain access to
servers, databases, web domains, and more.
Attacks via phishing emails and software application
plug-ins are the chief avenues of access to deliver malware.
A phishing email is disguised to appear as though from a
trusted source—for example, another employee or your boss.
©solarseven/Shutterstock
A plug-in is a software component that adds a specific feature
to an existing computer program. Employees today bring their
own mobile devices to work and often use software that hasn’t
been rigorously evaluated for inappropriate plug-ins. Through
these portals, malware (malicious software such as viruses,
worms, Trojan horses, and spyware) can be injected into an
organization’s system.
“Bad actors often try out their approach before launching
the real attack,” claimed Cassidy. So catching them in this preliminary test should be the goal. The problem is that it takes an
organization, on average, 205 days to identify that it has been
compromised, and by then, the purpose of the attack has been
accomplished.
www.alertlogic.com
Source: Richard Cassidy, “Behind The Scenes: Cybercrime Threat Landscape,” Brightalk
webcast, April 27, 2016, downloaded May 26, 2016 (
587/201299?autoclick=true&utm_medium=web&utm_source=brighttalk-promoted&utm
_campaign=player-page-feed&utm_content=promoted).
manager may need only a portion of the process, given information that is available from a variety of other
sources. Research is often characterized by much smaller datasets than big data. Once the research is presented, the manager has one very important decision: How shall he or she resolve the management problem?
Research and the Scientific Method
Intelligent, curious people who have a driving need to seek answers are at the heart of great research. The
foundation of the business research process is the scientific method. The essential tenets of the scientific
method are:
• Clearly defined concepts, constructs, variables, methods, and procedures.
• Empirically testable hypotheses: a way exists to gather evidence that directly supports/refutes any
hypothesis.
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>chapter 1 Research Foundations and Fundamentals
• Direct observation of phenomena (facts).
• Conclusions drawn from statistical evidence rather than inferred justification (educated guesses).
• The self-correcting process: ability to replicate and reassess validity of conclusions.
>The Language of Research
So where do we start to understand the preceding material? We start with the language of research.
When we do research, we seek to know “what is” in order to understand, explain, or predict phenomena. We might want to answer the question “What will be the department employees’ reaction to a 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
employees of the department: clerical or professional? What reaction? What are normal indicators? These
questions require the use of concepts, constructs, operational definitions, and variables.
Concepts
Concepts are used to understand and communicate information. The success of research hinges on
(1) how clearly we conceptualize and (2) how well others understand the concepts we use. We design
hypotheses using concepts. We devise measurement scales using concepts by which to test these hypotheses. We gather and analyze data using measurement concepts.
A concept is a generally accepted collection of meanings or characteristics associated with certain
events, objects, conditions, situations, or behaviors:
• Concepts are created when we classify and categorize events, objects, conditions, situations, or
behaviors—identifying common characteristics beyond any single observation.
• Concepts are acquired through personal experience or the experience of others.
• Concepts use words as labels to designate them; these words are derived from our experiences.
• Concepts have progressive levels of abstraction—that is, the degree to which the concept does or
does not have something objective to refer to. At one extreme are objective concepts; at the other,
abstractions. Table is an objective concept. We have images of tables in our mind. Personality is an
abstract concept as it is much more difficult to visualize.
Think of a movie ticket as a concept. What comes to mind is not a single example, but your collected
memories of all movie tickets from which you define a set of specific and definable characteristics (material, movie title use, perforation, multiple parts, screen location, etc.). For another example, assume you
see a man passing and identify that he is running rather than walking, skipping, crawling, or hopping.
Each movement represents a different concept. We also use concepts to identify that the moving object
is an adult male rather than a truck or a horse.
Ordinary concepts make up the bulk of communication in research. Ordinary, however, does not
mean unambiguous. We might, for example, ask research participants for an estimate of their family’s
total income. Income may seem to be a simple, unambiguous concept, but we will receive varying answers and confusing data unless we restrict or narrow the concept by specifying:
• Time period, such as weekly, monthly, or annually.
• Before or after income taxes are deducted.
• For head of household only or for all household members.
• For salary and wages only or also including tips, bonuses, dividends, interest, and capital gains.
• To include or not include in-kind income (e.g., free rent, employee discounts, vacations, or food
stamps).
We run into difficulty when trying to deal with less ordinary phenomena or advance new ideas. One
way to handle this problem is to borrow a concept from another language or from another field. Assume we are researching a brand logo’s design strength. We can borrow the term gestalt from German,
which translates as form or shape and means an organized whole more than the sum of its parts.7 Or we
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>part I Building the Foundation for Research
might use the physics concept of gravitation to explain why people shop where they do or the geographic
concept of distance to describe degree of variability between the attitudes of employees on a new work
schedule.
Sometimes we need to make a word cover a different concept or develop new labels for a concept.
When we adopt new meanings or develop new labels, we begin to develop a specialized language or jargon. While jargon contributes to efficiency of communication among specialists or a particular group,
it excludes everyone else. Jargon is often avoided in business research for this reason unless the sample
is very narrowly defined.
Constructs
When research requires us to work with abstract concepts, we define one or more constructs. A construct
is an abstract idea specifically invented for a given research and/or theory-building purpose. We build
constructs by combining simpler, more concrete concepts, especially when the idea or image we intend
to convey is not subject to direct observation. 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.
Exhibit 1-4 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) define a construct that Heather calls “presentation quality.” Presentation quality is not directly observable. It is an
invented construct, used to communicate the combination of meanings presented by the three objective,
measurable concepts that Heather has discovered are related empirically. She is able to observe keyboarding speed, for example, by timing a person’s entry of a paragraph.
Concepts at the next higher level of abstraction in Exhibit 1-4 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 label 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.
>Exhibit 1-4 Constructs Composed of Concepts in a Job Redesign
Most
abstract
“Job Interest Construct”
(Components unknown by analyst)
Level of abstraction
“Language Skill Construct”
Vocabulary
Syntax
Spelling
“Presentation Quality
Construct”
Manuscript
errors
Format
accuracy
Most
concrete
Keyboarding
speed
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>chapter 1 Research Foundations and Fundamentals
Heather has not yet defined 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.
Highly abstract constructs can be inferred only from the data; these are presumed to exist but must await
further testing and definition. Heather will have the beginning of a conceptual scheme if research shows
the concepts and constructs in this example to be interrelated and if their connections can be supported.
In graphic form, the conceptual scheme depicts the relationships among the knowledge and skill requirements necessary to clarify the job redesign effort.
Operational Definitions
Confusion about the meaning of constructs or concepts can destroy a research study’s value without the
knowledge of the researcher or its sponsor. Definitions are one way to reduce this danger.
Researchers distinguish between 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 organization or a patron of any shop.8 These circular definitions
may be adequate for general communication but not for research. In research, we measure concepts and
constructs, and this requires more rigorous operational definitions.
An operational definition is a definition stated in terms of specific criteria for measurement or testing. 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.
To do this, you need operational definitions. Operational definitions may vary, depending on your
purpose and the way you choose to measure them. College undergraduates are grouped by class. No one
has much trouble understanding such terms as senior, junior, sophomore, and so forth. But the task may
not be that simple if you must determine which students comprise each class. Here are two different
situations involving a survey among students where we want to classify their answers by their class level.
Each uses a different definition of the same concept:
1. You ask them to report their class status and you record it. In this case, class is freshman, sophomore, junior, or senior; you accept the answer each respondent gives as correct. The operational
definition for class: how the student themselves classify their class.
2. You ask them to define their class by registrar guidelines. The operational definition for class:
semester hours of credit completed by the end of the prior spring semester and recorded in each
student’s record in the registrar’s office:
• Freshman
• Sophomore
• Junior
• Senior
Fewer than 30 hours’ credit
30 to 59 hours’ credit
60 to 89 hours’ credit
90 or more hours’ credit
These examples deal with relatively concrete concepts, but operational definitions are even more
critical for constructs. Suppose one tries to measure a construct called “socialization.” Are we referring
to someone’s level of activity on Facebook and other social media, whether they like to entertain, or
whether they can converse with others? We would probably develop questions on access, posts, sharing,
likes, etc., in the first instance; the number of times and the types of entertaining they do for the second;
and the number, types of language used, and types of reactions received for the third as we grapple
with the operational definition. We may use a measurement approach already developed and validated
by other researchers or create our own. The measurement approach chosen operationally defines the
construct.
We may need to provide operational definitions for only a few critical concepts or constructs, but
these will almost always be the definitions used to develop the relationships found in hypotheses and
theories.
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>part I Building the Foundation for Research
>snapshot
Identifying and Defining Constructs
When you read in the business press or attend a business
presentation, you are exposed to many constructs. If you
don’t struggle to define these, you miss out on a lot of what
agencies). “The [industry trade associations] can’t agree on what
transparency is and, clearly, they are not speaking the same
language. And that leads to a lack of trust,” shared Ron Amram,
the article is telling you. Let’s see how good you are at spotting these.
During the most recent Ad Age Digital Conference, in a
vice president of media at Heineken USA. What are the constructs
and operational definitions for these constructs? What is the
management problem(s) confronting advertisers and advertising
panel discussion on major issues advertisers and marketers
know they have but aren’t ready to confront—the elephants
in the room—Lou Paskalis, senior vice president of enterprise
media at Bank of America shared, “We’re optimizing advertising
media?
Paskalis also shared, “We need to go back to what [the advertising] business is about. We need to ask, ‘Is this compelling?’
Clients need to step up and pay full value for things that are
and people want storytelling. People are ad blocking because
they don’t like what we are doing.” What are the constructs in
this statement? What might be the operational definition of these
constructs? What is(are) the management problem(s) confronting
advertisers and their agencies?
The panel also revealed there are challenges beyond digital
advertising’s oft-cited viewability, ad blocking, and transparency
(which refers to kickbacks and rebates from media to advertising
great, but we need to stop commoditizing everything.” What
are the constructs and the operational definitions for these constructs? What is the management problem revealed for advertisers and advertising agencies?
Source: George Slefo, “Digital Conference: Marketers Chime in on Elephants in the
Room, Known and Unknown,” Ad Age, April 05, 2016, downloaded April 6, 2016
(
-digital/303413/?utm_source=daily_email&utm_medium=newsletter&utm
_campaign=adage&ttl=1460499287).
Variables
A variable is a measurable symbol of an event, act, characteristic, trait, or attribute.9 In practice, one or
more variables are used as a substitute for a concept or construct. As researchers are interested in relationships among concepts and constructs, researchers are interested in relationships among variables.
Variables come in various types: independent, dependent, moderating, and extraneous (including control, confounding, and intervening). We’ll expand this list when we get into the concept of measurement.
Independent and Dependent Variables
The dependent variable (DV) is of primary interest to the researcher; it is measured, predicted, or otherwise monitored and is expected to be affected by manipulation of an independent variable (IV), another
variable of primary interest. In each relationship, there is at least one independent variable (IV) and one
dependent variable (DV). As one writer notes:
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.10
The assignment of the variable type (dependent vs. independent) depends on the assumed relationship the researcher is studying. If you were interested in studying the impact of the length of the working
week on productivity, you would make the length of working week the IV. If you were focusing on the
relationship between age of the worker and productivity, then age would be the IV. Exhibit 1-5 lists some
terms that have become synonyms for independent variable and dependent variable. 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.
Exhibit 1-6 summarizes the many variable types, while Exhibit 1-7 graphically shows their relationship to each other using an example. Researchers recognize that there are often several independent
variables that might be studied and that they are probably at least somewhat related and, therefore, not
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>chapter 1 Research Foundations and Fundamentals
>Exhibit 1-5 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
independent among themselves. When we study simple relationships, all other variables are considered
unimportant and are ignored by the researcher. But for more complex relationships, researchers must
identify and measure those variables.
In Exhibit 1-7a, a causal relationship is illustrated by an arrow pointing from the independent variable
to the dependent variable.
Moderating Variables
A moderating variable (MV) is a second independent variable believed to have a significant contributory
effect on the original IV–DV relationship. 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 Exhibit 1-7a, the arrow pointing from the moderating variable to the arrow between the IV and
DV shows the difference between an IV directly affecting the DV and an MV affecting the relationship
between an IV and the DV. In this case, the researcher hypothesizes that a different relationship between
the four-day week and productivity might result from age differences among the workers. Hence, after
introduction of a four-day working week, the productivity gain for younger workers is compared with that
for older workers. For example, let’s assume that the productivity of younger workers is 12 percentage
points higher than that for older workers before the introduction of the four-day working week. Assume
that the productivity of all workers having a four-day working week is six percentage points higher than
for 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 an older worker, there
is no moderating effect (12 + 6 = 18), because the 18 percentage points are the sum of the main effects
and the moderating effect should show a surplus. However, if the productivity of younger workers was 25
percentage points higher, then the moderating effect of a worker’s age would be obvious.
Other Extraneous Variables
An almost infinite number of extraneous variables (EVs) exist that might conceivably affect a given
relationship. Taking the example of the effect of the four-day working week again, one would normally
>Exhibit 1-6 A Summary of Variable Types
Variable Type
Symbol
Presumed Effect on IV-DV Relationship
Action Needed
Dependent
DV
Concept/construct of interest
Measure
Independent
IV
Primary variable believed to have significant effect on DV
Manipulate
Moderating
MV
Alternative IV; possible significant contributory effect on IV-DV
Measure
Control
CV
Might influence the IV-DV, but effect is not at the core of the
problem studied
Ignore; effect is
randomized
Confounding
CFV
Alternative IV; unknown effect on IV-DV
Measure
Intervening
IVV
Theoretically might affect; effect can’t be determined
Infer effect from
IV and MV on DV
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>part I Building the Foundation for Research
>Exhibit 1-7 Relationships among Types of Variables
(a)
IV:
four-day
working
week
DV:
productivity
MV:
workers’ age
(b)
CV:
weather
IV:
four-day
working
week
DV:
productivity
CFV:
type work
MV:
workers’ age
(c)
CV:
weather
IV:
four-day
working
week
IVV:
job
satisfaction
IVV:
special project
DV:
productivity
MV:
workers’ age
CFV:
type work
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 work-week length and productivity. Most can 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.
Control variables (CV) are extraneous variables that we measure to determine whether they influence
our results, as we want to make sure our results are not biased by excluding them. In Exhibit 1-7b,
weather is shown as a CV; the broken line indicates that we included it in our research hypothesis
because it might influence the DV, but we consider it irrelevant for the investigation of our research
problem.
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 workweek length on productivity; productivity gains might not be universal across
all types of work. This might lead you to introducing type of work as a confounding variable (CFV). In
our example, we would study the effect of the four-day working week within groups (e.g., office workers
vs. manufacturing workers vs. distribution plant workers). In Exhibit 1-7b, we included the type of work
as a CFV, with a broken line.
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>chapter 1 Research Foundations and Fundamentals
>snapshot
Radio Chips versus Retinal Scans: Which Theory Offers
the Best Protection?
When the first confirmed case of bovine spongiform encepha-
certain these devices don’t end up in the meat. “All you need
lopathy (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 U.S. Department of
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
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 com-
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 hand-
parison, 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
held 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 third-largest 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; www.jbsswift.com
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 workweek
results in higher productivity, we might think that this is not the whole story—that workweek length
affects some other variable that, in turn, results in higher productivity. The intervening variable (IVV)
is 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 moderating variables on the observed
phenomenon.11 In our example, 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 work week increases job satisfaction; similarly, we can assume that the
introduction of special project work might influence productivity. Exhibit 1-7c illustrates how theoretical
constructs, which are not directly observed, fit into our model.
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>part I Building the Foundation for Research
Hypotheses, Theories, and Models
Hypotheses, theories, and models serve researchers in different ways but are related.
• A hypothesis is an unsubstantiated assumption about the relationship between concepts and constructs; it drives the research.
• A theory is comprised of data-tested, supported hypotheses; it is derived from research.
• A model is a visualization of a theory; it is used for clarification and to enhance understanding.
Hypotheses
At the core of any good research, then, is a carefully constructed hypothesis. A hypothesis can be
phrased as a declarative statement (descriptive) or a question about the relationship between two or
more concepts or constructs that may be judged as true or false. It is always conjecture and formulated
for empirical testing/measurement.
Descriptive format
Question format
American cities are experiencing budget difficulties due
to a decline in manufacturing.
Are American cities experiencing budget difficulties due
to a decline in manufacturing?
Because if drives the research, crafting a hypothesis serves several important functions:
• It encourages researchers to think about the likely relationships to be found.
• It encourages researchers to think about the relevant facts (those needed to support or reject the
hypothesis) and those facts that are not relevant.
• It suggests which research design is likely to be most appropriate.
• It is useful for testing statistical significance.
• It provides a framework for organizing the conclusions that result from the research.
To consider specifically the role of the hypothesis in determining the direction of the research, suppose we use this example:
Husbands and wives agree on their respective roles in vehicle purchase decisions.
The hypothesis specifies who shall be studied (married couples), in what context they shall be studied
(their vehicle purchase decision making), and what shall be studied (their individual perceptions of their
roles). This hypothesis suggests that the best research design is a communication-based study, either 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 vehicle purchase decision. The study should not, therefore, seek information about roles
husbands and wives might assume in other purchase decisions—say, electronics or furniture or movies.
A study based on this hypothesis might reveal that husbands and wives disagree on their perceptions of
roles, but the differences may be explained in terms of some factor(s) other than gender (i.e., mechanical
knowledge, passion for cars, age, social class, religion, personality, etc.).
Types of Hypotheses There are numerous types of hypotheses. A descriptive hypothesis states the
existence, size, form, or distribution of some concept/construct. For example, “The life expectancy of
airplane Model 707 exceeds 16 years.”
A relational hypothesis describes a relationship between two or more concepts/constructs. Each relationship describes a correlational or causal relationship. With causal hypotheses one variable being
studied is assumed to cause a specific effect on other variables studied. With correlational hypotheses the
variables being studied occur together, but there is no assumption of causation. For example:
• Women older than 50 (concept) purchase (concept) more or less of our product (concept) than
those younger than 25 (concept). [correlational (neutral) relationship]
• Students who attend class regularly (construct) earn higher grades (concept) than those who do
not attend regularly (construct). [correlational (positive) relationship]
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>chapter 1 Research Foundations and Fundamentals
• U.S. brand cars (concept) are perceived by American consumers (construct) to be of lesser quality
(construct) than foreign brand cars (concept). [correlational (negative) relationship]
In the first example we hypothesize that the two groups purchase different levels of our product. In
the second, there may be other factors that influence grades; it might be the amount of studying. In the
last example, we hypothesize that “country of origin” (concept) influences “perceived quality” (construct), but we don’t know what factors cause “perceived quality.” Correlational hypotheses are often
made when we believe there are more basic causal forces that affect the concepts/constructs (e.g., maybe
a lack of product recalls causes high perceived quality) or when we do not have enough evidence to claim
a stronger linkage.
Causal hypotheses not only predict the cause (cause means roughly to “help make happen”) but also
the effect. Here are three examples of causal hypotheses:
• An increase in the price of salvaged copper wire (concept/cause) leads to an increase in scavenging in abandoned homes (construct/effect).
• Exposure to a company’s message concerning recent injuries (concept/cause) leads to more favorable attitudes among employees toward safety (construct/effect).
• Loyalty to a particular grocery store (construct/cause) leads to purchasing that store’s private
brands (concept/effect).
In proposing or interpreting causal hypotheses, the researcher must consider the direction of influence. One would assume the price of copper wire influences scavenging rather than the reverse. Once
would also assume that the measure of attitude follows the release of the information about on-the-job injuries. Sometimes our ability to identify the direction of influence depends on the research design. Store
loyalty and purchasing of store brands appear to be interdependent. Loyalty to a store may increase the
probability of someone buying the store’s private brands, but satisfaction with the store’s private brand
may also lead to greater store loyalty.
Reasoning and Hypotheses Every day we reason with varying degrees of success. Reasoning—
gathering facts consistent with the problem, proposing and eliminating rival hypotheses, measuring
outcomes, developing crucial empirical tests, and deriving the conclusion—is pivotal to much of a researcher’s success. Two types of reasoning are of great importance to research in forming and testing
hypotheses: induction and deduction.
Induction Researchers use induction to craft hypotheses. In induction, you start by drawing 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 $10 million on a regional promotional campaign and sales do not increase; these are facts. Under such circumstances, we ask, “Why
didn’t sales increase?”
One likely answer to this question is that the promotional campaign was poorly executed (conclusion). 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:
• A strike by the employees of our trucking firm prevented stock from arriving at retailers; regional
retailers did not have sufficient stock to fulfill customer orders during the promotional period.
• A competitor lowered its price during the promotional period; customers bought their brand
rather than ours.
• 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 leap beyond the evidence presented—that is, although one conclusion explains the fact of no
sales increase, other conclusions also might explain the fact. It may even be that none of the conclusions
we advanced correctly explain the failure of sales to increase.
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>part I Building the Foundation for Research
©Erik Isakson/Blend Images
Researchers often use observation when evaluating a
customer’s use of a product.
Apply deductive reasoning to
this image. Develop your own
conclusions concerning what will
happen next.
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):
• Tracy makes too few sales calls per day to build a good sales record.
• 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 pricecutting 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 has 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.
Deduction Researchers use deduction to plan research and draw insights from data that will test hypotheses. Deduction is a form of reasoning that starts with one or more true premises and the conclusion
flows from the premises given. For a deduction to be correct and sound, 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. 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 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, trillions of dollars each year
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>chapter 1 Research Foundations and Fundamentals
in confirmed global employee theft12 will challenge premise 1. If one premise fails the acceptance test,
then the conclusion is not a sound deduction. It should be apparent that a conclusion that results from
deduction is, in a sense, already “contained in” its premises.13
Combining Induction and Deduction Induction and deduction are used together in research
reasoning. John Dewey, psychologist and educational reformer, 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 fact (event or condition) 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 1-8:
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 propose a 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 1-8. 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)
How would the double movement of reflective thought work when applied to the Tracy Nelson problem? The process is illustrated in Exhibit 1-9. 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 manager’s problem. 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 to any given hypothesis.
>Exhibit 1-8 Why Didn’t Sales Increase
Induction
Fact 1:
We promote a product
but sales do not increase.
Why?
Deduction: Ineffective promotion
Fact 2:
We run an effective
promotion and
sales increase.
will not increase sales.
n
otio
prom
e
v
.
cti
ales
Effe
se s
on:
i
a
t
e
c
r
u
inc
Ded
will
Hypothesis:
The promotion was
poorly executed.
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>part I Building the Foundation for Research
>Exhibit 1-9 Why Is Tracy Nelson’s Performance So Poor?
Induction
Fact 1:
Tracy has a poor
performance record.
Fact 2:
Tracy is regularly
late to work.
Fact 3:
Tracy makes fewer
calls per day than the
average salesperson.
Why?
Deduction: Laziness results
in excessive tardiness.
er
few
lts in
u
s
s re
day.
nes
per
Lazi
s
l
:
l
n
o
r ca
ucti
ome
Ded
cust
Hypothesis:
Tracy is lazy.
The steps that follow represent one approach to assessing the validity of conclusions about observable events.15 These steps 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 (one plausible explanation) to explain the facts that are believed to be logically related to the problem.
4. Deduces outcomes or consequences of that 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.
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 detailed more fully in Exhibit 1-10.
Theories
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 (empirically supported) and fit the situation, we are successful in
our explanations and predictions. For example, it is midday and you note that outside the natural light
is dimming, dark clouds are moving rapidly in from the west, the breeze is freshening, the barometric
pressure is falling, and the air temperature is cooling but above 50 degrees. Would your understanding
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>chapter 1 Research Foundations and Fundamentals
>Exhibit 1-10 Checklist for Developing a Strong Hypothesis
Criteria
Interpretation
Adequate for Its Purpose
1. Does the hypothesis reveal the original problem condition?
2. Does the hypothesis clearly identify facts that are relevant and those that
are not?
3. Does the hypothesis clearly state the condition, size, or distribution of some
variable in terms of values meaningful to the research problem (descriptive)?
4. Does the hypothesis explain facts that gave rise to the need for explanation
(explanatory)?
5. Does the hypothesis suggest which form of research design is likely to be
most appropriate?
6. Does the hypothesis provide a framework for organizing the conclusions
that result?
Testable
1. Does the hypothesis use acceptable techniques?
2. Does the hypothesis require an explanation that is plausible given known
physical or psychological laws?
3. Does the hypothesis reveal consequences or derivatives that can be deduced for testing purposes?
4. Is the hypothesis simple, requiring few conditions or assumptions?
Better Than Its Rivals
1. Does the hypothesis explain more facts than its rivals?
2. Does the hypothesis explain a greater variety or scope of facts than its
rivals?
3. Is the hypothesis one that informed judges would accept as being the most
likely?
of the relationship among these concepts/constructs (your weather theory) lead you to predict that
it will rain?
A theory is an empirically supported description of the relationships among concepts, constructs,
and hypotheses that are advanced to explain or predict phenomena. A theory, therefore, is comprised of
data-tested, supported hypotheses; it is derived from research. Our ability to make rational decisions is
based on our ability to develop theory. But a caution: No theory can ever be considered final because it
is subject to challenge by new data.
In marketing, for example, the product life cycle theory describes the stages that a product category
goes through in the marketplace.16 It was developed based on observing thousands of product introductions
and their success path over time. The generalized product life cycle has four stages: introduction, growth,
maturity, and decline. In each stage, many concepts, constructs, and hypotheses describe the influences
that change revenue and profit. Definitions are used for communicating the claims of the theory. In the
growth stage of this theory, for example, companies in the category spend heavily on promotion to create
product awareness (construct). In the early period of this stage these expenditures may be made to fuel primary demand (construct), improving product category awareness (construct) rather than brand awareness
(construct). In this stage, sales (concept) increase rapidly because many customers (concept) are trying
the product; and those who are satisfied and purchase again—repeat purchasers (concept)—are swelling the
ranks. According to product lifecycle theory, if a given company is unable to attract repeat purchasers, this
may mean death for its particular brand (hypothesis), even though the product category may endure. This
theory has endured for more than 50 years.
Models
If you are an architect, a client might hire you to design their dream home. You might build them a physical model so that they could better visualize your design. Such models, while time-consuming to create,
often save time and money during the construction process by avoiding costly onsite design changes.
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>part I Building the Foundation for Research
>Exhibit 1-11 Model of the Traditional Product Life Cycle Theory
Stage of the product life cycle
Sales revenue or profit
Introduction
Growth
Decline
Total industry
sales revenue
Total industry
profit
+
0
–
Marketing
objective
Maturity
Time
Gain
awareness
Stress
differentiation
Maintain brand
loyalty
Harvesting,
deletion
Source: Adapted from Roger Kerin, Eric Berkowitz, Steven Hartley, and William Rudelius, Marketing, 7th ed. (Burr Ridge, IL: McGraw-Hill, 2003), p. 295.
©John Lund/Marc Romanelli/Blend Images LLC
A model, therefore, is a representation of a theory or
system that is constructed to study some aspect of that system or the system as a whole. A research model’s purpose
is to increase our understanding, prediction, and control of
the complexities of the environment. While theory’s role
is explanation or prediction, a model’s role is representation. Models allow researchers and managers to characterize present or future conditions: the effect of a raise on
employee engagement, the effect of higher interest on bond
purchase rates, the effect of advertising on purchase, the
effect of scheduled maintenance on manufacturing defects.
Exhibit 1-11 provides the model for the product life-cycle
theory. Models are an important means of advancing theories and aiding decision makers.
>summary
LO1-1 ” “Managers make thousands of decisions (strategic, tactical, and procedural) that use data as fuel. Business
research and data analytics complement each other, but
they are not synonymous. Data analytics needs data,
some of which are provided by research. Managers
often draw on data from existing internal data sources
(called a decision support system) when engaging in
data analytics. When these data are mined, they may be
used for a purpose other than that for which they were
originally collected. Collecting data to understand and
improve performance employs ongoing observation or
communication research. However, using such data for
a different purpose employs data analytics. Businesses
are getting better at data blending—combining data from
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>chapter 1 Research Foundations and Fundamentals
separate datasets into a new actionable dataset and then
querying that composite data to help make decisions.
While the information that comes from data blending has
a role in decision making, it is not the same as research.
Business research is a systematic inquiry that provides information to guide decisions. More specifically,
it is a process of determining, acquiring, analyzing and
synthesizing, and disseminating relevant data, information, and insights to decision makers in ways that
mobilize the organization to take appropriate actions
that, in turn, maximize performance and help achieve
organizational goals. The research process is a model
for the development and interpretation of research
studies. Several subprocesses or stages are included
in the process, including (1) clarify the research,
(2) design the research, (3) collect and prepare the
data, (4) analyze and interpret the data, and (5) report
insights and recommendations. Despite variations, the
idea of a sequence is useful for developing a project
and for keeping the project orderly as it unfolds.
The research process is based on the tenets of the
scientific method: clearly defined concepts, constructs,
methods, and procedures; empirically testable hypotheses; direct observation of phenomena; conclusions
drawn from statistical evidence rather than inferred
justification (educated guesses); and the self-correcting
process (ability to replicate and reassess validity of
conclusions). 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.
LO1-2 ” Understanding the language of professional researchers is crucial to doing great research. Researchers
use concepts, constructs, operational definitions, and
variables to form hypotheses. A concept is a generally accepted collection of meanings or characters
associated with an event, object, condition, situation, or behavior. When we work with more abstract
“”””””””
phenomena, we invent constructs. When we are
trying to understand the relationships between concepts and constructs, we often form a conceptual
scheme or map. Each concept or construct needs an
operational definition, its specific criteria for measurement. Variables serve as measurable substitutes for
concepts and constructs in research. There are many
different types: independent, dependent, moderating,
and extraneous (including control, confounding, and
intervening).
Hypotheses are the core of good research and are
crafted as descriptive statements or questions. Each
hypothesis is an unsubstantiated assumption about
a relationship between two or more concepts or
constructs; it drives the research study. Hypotheses are
either descriptive or relational. Relational hypotheses
are either correlational or causal. The research process
is grounded in reasoning. The reasoning process is
used for the development and testing of various hypotheses 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.
Hypotheses are often crafted using inductive reasoning
and then tested using deductive reasoning. Induction
starts by drawing a conclusion from one or more facts
or pieces of evidence. Deduction starts by proposing
one or more true premises and draws a conclusion
based on those premises. A theory is a data-supported
description of the relationships between concepts and
constructs in hypotheses; it is derived from research. Its
role is prediction. A model is the visualization
(representation) of a theory; it is used for clarification
and to enhance understanding. Models allow
researchers and managers to characterize present or
future conditions.
>keyterms
business research 7
descriptive hypothesis 16
control variable (CV) 14
concept 9
relational hypothesis 16
confounding variable (CFV) 14
conceptual scheme 11
induction 17
construct 10
model 16
data blending 5
operational definition 11
deduction 18
reasoning 17
hypothesis 16
scientific method 8
causal hypothesis 16
theory 16
correlational hypothesis 16
variable 12
dependent variable (DV)
(criterion variable) 12
extraneous variable (EV) 13
independent variable (IV)
(predictor variable) 12
intervening variable (IVV) 15
moderating variable (MV) 13
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>part I Building the Foundation for Research
>discussionquestions
Terms in Review
1 What is business research?
a Propose the concepts and constructs you might use to
study this phenomenon.
2 Distinguish among the following sets of items, and suggest
the significance of each in a research context:
b How might any of these concepts and/or constructs be
related to explanatory hypotheses?
a Concept and construct.
b Deduction and induction.
c Operational definition and dictionary definition.
d Concept and variable.
e Hypothesis and theory.
3 Describe the characteristics of the scientific method.
4 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 Leadership.
f Union democracy.
g Ethical standards.
5 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?
6 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
7 You observe the following condition: “Our female sales representatives have lower customer defections than do our
male sales representatives.”
8 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.
From Concept to Practice
9 Using Exhibits 1-8 an 1-9 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
10 Chipotle Mexican Grill continues to suffer from perception
issues after a string of outbreaks, including E.coli, worried
customers about the safety of eating at the fast casual
chain. Chipotle’s strategy for getting customers back into its
restaurants was to give away free tacos, burritos, and chips.
And while its customer survey scores are improving, Chipotle is still operating at a loss. What concepts, constructs, and
operational definitions should any future research deal with?
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>chapter 1 Research Foundations and Fundamentals
>cases*
Campbell-Ewald: R-E-S-P-E-C-T Spells Loyalty
Open Doors: Extending Hospitality to Travelers with Disabilities
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. Cases and case supplements are available
in Connect.
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