08832323.2013.806885
Journal of Education for Business
ISSN: 0883-2323 (Print) 1940-3356 (Online) Journal homepage: http://www.tandfonline.com/loi/vjeb20
Developing and Teaching an Online MBA
Marketing Research Class: Implications for Online
Learning Effectiveness
Qin Sun & Gopala Ganesh
To cite this article: Qin Sun & Gopala Ganesh (2014) Developing and Teaching an Online MBA
Marketing Research Class: Implications for Online Learning Effectiveness, Journal of Education
for Business, 89:7, 337-345, DOI: 10.1080/08832323.2013.806885
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Date: 11 January 2016, At: 20:46
JOURNAL OF EDUCATION FOR BUSINESS, 89: 337–345, 2014
Copyright Ó Taylor & Francis Group, LLC
ISSN: 0883-2323 print / 1940-3356 online
DOI: 10.1080/08832323.2013.806885
Developing and Teaching an Online MBA
Marketing Research Class: Implications for Online
Learning Effectiveness
Qin Sun
Downloaded by [Universitas Maritim Raja Ali Haji] at 20:46 11 January 2016
Trident University International, Cypress, California, USA
Gopala Ganesh
University of North Texas, Denton, Texas, USA
The authors intend to describe the experience of developing and teaching an online marketing
research class for master of business administration students. The class has been taught for
four fall semesters. Each time, the class also completed an online survey, analyzed the
resulting data, and wrote a detailed report for a real client. The course content, topic
sequence, class assignments, and student feedback about the experience are all described in
great detail herein. This web implementation incorporates several useful features such as a
very large number of audio and video segments that have been used to teach difficult
concepts like sampling and data analysis techniques. The implications for student online
learning effectiveness conclude the remark.
Keywords: MBA education, marketing marketing research, online teaching, pedagogy
With the increasing popularity of online learning in higher
education industry, more and more attention has been paid
to the quality and effectiveness of online courses. In particular, due to inadequate mathematics knowledge among
master of business administration (MBA) students and the
difficulty of delivering quantitative courses online, there is
a need to provide empirical data to evaluate the feasibility
of online statistics classes such as marketing research class
required for Association to Advance Collegiate Schools of
Business (AACSB)–accredited MBA program (Sebastianelli & Tamimi, 2011). Generally speaking, marketing
research involves with systematic data collection and analysis for a particular market segment in order to improve decision making in marketing (Malhotra, 2007). Therefore, it is
not surprising that the teaching of marketing research has
typically involved with topics introducing students to sources of secondary and primary data, the issues in data collection and the various techniques used for analyzing raw data
and turning it into actionable and valuable information
(Ganesh, 1992.) Although the marketing research course is
Correspondence should be addressed to Qin Sun, Trident University
International, College of Business Administration, 5757 Plaza Dr., Ste.
100, Cypress, CA 90630, USA. E-mail: [email protected]
a required class for undergraduate and graduate marketing
majors in most business schools, it imposes challenges to
both students and professors, mainly because of its significant statistical and data analysis content (Bradstreet, 1996;
Bridges, 1999; Castleberry, 2001; Dobni & Links, 2008;
Giacobbe & Segal, 1994). In addition, hands-on experience
in using the computer and statistical software such as SPSS
(IBM, Armonk, NY), Minitab (Minitab Inc., State College,
PA), and Excel (Microsoft, Seattle, WA) have been an integral part of teaching marketing research (Bove & Davies,
2009), posing additional challenges to the students.
From the practitioners’ perspective, marketing research
should be taught emphasizing the practice and quantitative
analysis rather than the theory (Bellenger & Bernhardt,
1977). Higher caliber statistical coverage such as multivariate analysis is seen as necessary even in the undergraduate
marketing research course (Stern & Tseng, 2002). For the
MBA marketing research class, canonical correlation, discriminant analysis, factor analysis, logit or probit regression, multivariate analysis of variance (ANOVA), multiple
regression, conjoint analysis, decision analysis, and multidimensional scaling are in the priority list of practitioners
(Anderson, 1982). Due to the popularity of Internet and
World Wide Web, distance learning and online education
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338
Q. SUN AND G. GANESH
are becoming popular platforms to teach college students
(Steiner & Hyman, 2010) and the digital millennial college
students are becoming more comfortable with the online
teaching methods and online communities (Matulich, Papp,
& Haytko, 2008). Again this backdrop, it is important to
explore the online teaching of marketing research and the
related learning effectiveness issues.
For several years now, marketing professors have been
exploring ways to bring the rapid advances in information
technology and the personal computer revolution for teaching marketing research. While Castleberry (2001) introduced hands-on project to sharpen students’ secondary data
research skills using web and off-web sources, Malhotra,
Dixit, and Uslay (2002) recommended the use of Internet
technology in all aspects of marketing research education.
High-tech electronic devices such as personal digital assistants and video cameras have been used in the undergraduate marketing research courses to conduct interviews and
collect observational data (McGorry, 2006; Smith & Fisher,
2006). Multimedia and podcasting have also been tried to
help the tech-savvy students effectively learn marketing
research (Zahay & Fredricks, 2009) and simulations were
used to teach international marketing research to undergraduates and MBA students with mostly positive feedback
(Peterson, 2006).
Courses such as marketing research also provide ample
opportunity to bridge the gap between theory and practice
and apply what is learned in class to the real business situations. For example, Bove and Davies (2009) used clientsponsored project in the graduate marketing research class
over a three-year period and the positive student feedback
acknowledged the practical benefits of the live case to the
students’ research and consulting skills. Bhattacharya and
Sheth (1996) designed the marketing research course with
field project for cause-oriented nonprofit organization in
order to instill social responsibility among undergraduate
and graduate business students. This so called experiential
learning is promoted and required by AACSB International
to better prepare the business students for a career in the
business professions (AACSB, 2003, Ganesh & Sun, 2009).
Although there are debates on the effectiveness of online
education, teaching marketing research via the Internet provides a viable and flexible option, especially to U.S. graduate students who are typically constrained by the time
demands of their full-time jobs, commuting requirements,
and sometimes by language constraints, as in the case of
international students (Liu, Gomez, Khan, & Yen, 2007;
Stewart, 2004). As a result, marketing professors have begun
to teach this class online to undergraduate students (Steiner
& Hyman, 2010). Even though there is a need to have an
online marketing research course for MBA students, the
advanced topics are difficult to handle in an online environment, especially the significant hands-on components: client
project, survey design, data collection, data analysis, and
writing a report. In this article, we discuss the experience of
building and teaching an online MBA marketing research
class at a large public university in the southwest United
States, with the hope that this endeavor would benefit others
contemplating similar or other forays online.
THE COURSE DESIGN
This online MBA-level marketing research course is
unique in many ways, including its concept, execution and
implementation. Basic familiarity with principles of marketing management and elementary business statistics are
assumed. Incoming students are required to have completed the core course in marketing and statistics. While
the traditional face-to-face MBA marketing research class
has been taught for a very long time, the online version
has been offered only five times in the fall semester, as
part of a fairly recent online MBA program. Data collection took place during the first four semesters during
which the enrollment ranged between 14–15 students a
semester, typical for a MBA marketing elective class.
One of the fundamental objectives in designing the web
course was to make its content nearly identical to the traditional course. Therefore, the students would exit not only
with a good overview of marketing research, but also
enhanced interpretational quantitative skills essential for a
managerial decision maker. They would be comfortable
with concepts such as sampling methods, the factors that
drive sample size including confidence intervals, and handon familiarity with advanced data analysis techniques.
They would also brush up on effective communication
through charts, graphs and good presentation through a living case market research project for a real client.
The mandatory textbook for the class was chosen after
careful review of the available options for an MBA-level
course. It would have substantial managerial, as opposed to
mathematical, coverage of advanced statistical emphasis,
written for SPSS-based data analysis and come with a good
supply of datasets with adequate data for analysis and
assignments. The authors settled on Malhotra’s Marketing
Research book, starting with the 4th edition (Malhotra,
2004) and presently using the 6th edition (Malhotra, 2007).
The online marketing research class probably has more
topic and detailed content than the traditional class, since
time constraint was neither assumed nor taken into account
in its design! There was no limitation such as a 3-hr class
that met once a week! While the class is delivered using
university’s customized Blackboard (Blackboard Inc.,
Washington, DC), it does not use the typical Blackboard
course delivery method. Instead, students access each of 14
lessons as a PowerPoint (Microsoft, Seattle, WA) file that
is prepared by substantially shortening and then modifying
as well as customizing the presentation resources that were
part of the instructor materials for the text book. The resulting lesson is such that students would need to access the
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DEVELOPING AND TEACHING AN ONLINE MBA MARKETING RESEARCH CLASS
textbook to refer to the various tables and figures commented or elaborated upon. A substantial number of electronic resources are also hyper-linked within the outlines.
In this online course, students have a choice of accessing
each lesson outline as either html PowerPoint (which
works very well with the Internet Explorer browser) or
enhanced Adobe (Adobe Systems Incorporated, San Jose,
CA) PDF PowerPoint (which works better with Mozilla
[The Mozilla Foundation, Mountain View, CA] Firefox
and Apple’s [Apple, Cupertino, CA] Safari browsers).
Besides helping with the enhanced PDF topic presentations, the Adobe Acrobat (AA) version 9 is also extensively used for creating PDFs of hyperlinkable content
from text (.doc), worksheets (.xls), and other presentations (.ppt), making it possible to incorporating these
into the online class.
Another key resource is Camtasia from TechSmith
(Okemos, MI), a software tool that helps create audio
and video (ACV) segments in which the students listen
to the professor’s explanation in very clear audios while
watching the action on the professor’s computer screen
in very high resolution videos. The *.camrec file produced by Camtasia can be rendered in a web browsercompatible, hyper-linkable format such as a flash .swf
file. One major advantage of these ACV tools for the
professor is the convenience without the major hassle of
having to leave the office and going to a campus or
other recording facility at an appointed time. The only
major additional hardware requirement is a high quality
microphone such as the easy to use and flawlessly functional USB microphone from Samson (Samson Technologies, Hauppauge, NY). Using these tools, the authors
recorded a very large number of unscripted ACV segments to create crucial contents such as the following:
How to use the electronic library resources such as
ABI-Inform and Lexis-Nexis: One of the business
librarians came to the professor’s office and demonstrated a couple of search sessions that were captured
as ACVs.
Sample size and other demonstrations of by-hand calculations: For this, an inexpensive digital writing tablet was purchased and used as an online overhead
projector, capturing the work as an ACV.
Explanations of routine PowerPoint content that could
spread over multiple slides.
Demonstration of how to use software tools. This
came in handy for training students in using Qualtrics
(Qualtrics, Provo, UT) for creating online surveys.
They were asked to follow the ACV and create the
web version of a short paper and pencil survey that
had been administered on campus in the late 1990s.
The same technique was also used to teach students
the basics of drawing decision trees using the TreePlan add-in for Excel and proper cell and print
339
formatting of Excel worksheets. It can be used for
illustrating just about any other computer-based demo.
Orientation to SPSS: This ACV demonstrated the
basics of analyzing the SPSS dataset that resulted
from the campus survey referred to above.
Data analyses: Several ACVs were recorded to supplement the topic outlines. These included descriptive
statistics; t-tests; one-way ANOVA; correlation and
regression; discriminant, factor, and cluster analyses;
multidimensional scaling, and conjoint analysis. Data
sets that came with the book or resulted from the
authors’ research were used in preparing these
demonstrations.
MEASURING STUDENT PERFORMANCE
The requirements for this course include chapter quizzes
(10%), four graded assignments (40%), midterm (MT)
examination (20%), and final examination (30%). The
online quizzes are made up of multiple-choice questions on
key concepts and hence can be easily answered by going
through each chapter of textbook. The 12 quizzes cover the
first 17 chapters, essentially all the way through basic data
analysis. Each quiz is a random selection of 10 questions
drawn from a pool of between 25 and 30 questions from the
relevant chapters. Students are allowed two attempts of 2 hr
each and the better score is retained. Likewise, the best 10
quiz scores comprise the semester score for this grade component. The four graded assignments (GA), worth 40% of
final grade, are described as the following:
GA1: 5.0%. Online secondary sources, preparation
for GA3b topic;
GA2: 5.0%. Value of information, use TreePlan to
draw decision trees;
GA3a: 2.5%. Orientation to Qualtrics online survey
tool, preparation for GA3b;
GA3b: 7.5%. Design and publish an online survey
using Qualtrics;
GA4a: 2.5%. Orientation to data analysis basics of
SPSS, preparation for GA4b; and
GA4b: 17.5%. Analyze GA3b data, prepare charts
and write a report.
GA1, GA3a&b, and GA4a and GA4b all focus on the
live case market research project incorporating an online
survey prepared using Qualtrics. The client usually sent
two or three survey emails (notification of selection, survey
activation, and reminder to participate) directly to its
patrons and thereby avoided confidentiality issues in sharing the email addresses with the professor or class. During
the recent semesters, the online students have completed
projects that dealt with the proposed new business administration building, the area market for MBA programs, the
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340
Q. SUN AND G. GANESH
own test in detail (question by question, with correct
answers and explanation provided). They could then retake
MT test (another random selection of questions) after they
submitted their final exams. A half to one letter grade curve
was added to their original MT exam percentage, if they
improved their raw score on the second attempt by five
questions or more.
The final exam is a comprehensive take home effort on
data analysis. In the first two semesters, it was based on a
case that came with the book and its associated dataset. In
recent semesters, it has been based on the client project
data for that semester. The test starts with a set of data
transformations (recode and compute) followed by basic
data analysis (e.g., frequencies, descriptive, means, cross
tabs, single-sample t-test, t-tests of independent samples
and paired samples). Next, students are asked to perform
one-way ANOVA, correlation, simple and multiple regression analysis, discriminant analysis, factor analysis, and
cluster analysis. Each of these is covered in detail in the
various course modules using one or more ACVs of different data sets. Therefore, the diligent student has plenty of
resources to refer to and complete the examination for a
very satisfactory outcome. To facilitate grading, detailed
guidelines are provided including standardized formats for
organizing the voluminous results of SPSS analysis.
local public library, the semiautonomous performing arts
center of the university, and the student health and wellness
center on campus. In each of these projects, GA1 provided
the opportunity to improve their familiarity and/or expand
the initial list of professor-provided objectives for the
research. These were then fine tuned with input from one or
two online chat sessions with the client representatives who
were given guest access to the class. The finalized objectives were then incorporated into the GA3b online survey
design assignment. The professor either picked the best
effort or melded the best questions and then, after smoothening the rough edges, produced the final survey, which
was field tested by the class as sample participants. Afterwards, the survey was placed online and the patrons were
notified by the client via emails. During the 7–10-day survey data collection window, students prepared for and then
took the mid-term examination. Once the test was over,
they worked on completing GA4b over 10 to14 days, analyzing the survey data, preparing the charts, and writing a
report, conforming to a set of detailed guidelines provided
by the professor.
Except for the first semester when it was given on campus, the MT exam is prepared using Respondus software
and administered within Blackboard. It is made up of a set
of 75 multiple-choice questions pulled from a pool of
nearly 200 questions. Therefore, each student was presented with a randomly selected set of questions with
answer choices in random order as well. The questions are
all application micro-cases written to test the student’s
understanding of basic marketing research concepts and
covered material from the first half of the semester through
basic data analysis (descriptives, crosstabs, and hypothesis
testing; i.e., Modules 1–8).
Despite liberal test conditions (open book and notes, at
least a 72-hr window with breaks for taking the test), student performance on MT test was quite mediocre. Therefore, after the conclusion of the test, a deliberate attempt
was made each time to encourage better performance and
keep the motivation level high. For example, once the test
window closed, students have been allowed to review their
STUDENT CHARACTERISTICS AND CLASS
PERFORMANCE
This paper intends to describe an online course after it had
been taught, demographic data were not collected and
hence unavailable to describe the students in these online
classes. Therefore, the authors describe the students using
three years of data (135 students) from the end of semester
voluntary exit survey in the mostly evening, part time master’s programs at the university. Nearly 60% of the master’s
students completing the exit survey over the three-year
period are in the MBA program, while the rest are in the
master of science program. The average age of the students
TABLE 1
Performance Comparison of the Two Instructional Formats, by Assignment
Face to face
Semester 1
Assignment
Secondary sources
Value of information
Qualtrics
SPSS
Midterm exam
Final exam
Semester total
M
94%
86%
92%
90%
79%
80%
80%
Semester 3
Median n
95%
85%
95%
90%
80%
75%
82%
Online
10
10
10
10
11
11
11
M
93%
78%
88%
88%
83%
86%
86%
Total F2F
Median n
94%
80%
88%
88%
83%
88%
85%
11
11
11
11
11
11
11
M
93%
82%
90%
89%
81%
83%
83%
Semester 2
Median n
94%
80%
90%
90%
81%
87%
85%
21
21
21
21
22
22
22
M
Median n
Semester 4
M
95% 100% 13 70%
88% 90% 13 70%
93% 96% 13 92%
93% 97% 13 92%
81% 81% 13 70%
85% 83% 13 78%
87% 87% 13 83%
Total INET
Median n
70%
70%
91%
93%
71%
83%
82%
20
20
21
21
21
21
21
M
80%
77%
92%
92%
74%
81%
84%
Total
Median n
90%
80%
93%
93%
73%
83%
86%
33
33
34
34
34
34
34
M
85%
79%
91%
91%
77%
82%
84%
Median n
90%
80%
93%
93%
79%
83%
85%
54
54
55
55
56
56
56
341
DEVELOPING AND TEACHING AN ONLINE MBA MARKETING RESEARCH CLASS
TABLE 2
Performance Comparison of the Two Instructional Formats, by Semester Grade
Face to face
Downloaded by [Universitas Maritim Raja Ali Haji] at 20:46 11 January 2016
Semester 1
Online
Semester 3
Total F2F
Semester 2
Semester 4
Total INET
Total
Semester grade
n
%
n
%
n
%
n
%
n
%
n
%
n
%
A
B
C
D
F
Total
3
5
1
1
1
11
27%
46%
9%
9%
9%
100%
2
8
1
0
0
11
18%
73%
9%
0%
0%
100%
5
13
2
1
1
22
23%
59%
9%
5%
5%
100%
4
7
2
0
0
13
31%
54%
15%
0%
0%
100%
6
6
8
0
1
21
29%
29%
38%
0%
5%
100%
10
13
10
0
1
34
29%
38%
29%
0%
3%
100%
15
26
12
1
2
56
27%
46%
21%
2%
4%
100%
and 2, it is apparent that students in the online and face to
face formats have performed quite similarly although the
online students have the disadvantage of not being in a regular face to face class that allows for a great deal of synchronous interaction among the students and professor.
is 30 years with a median of 28. Male students comprised
53% of the group. Nearly 40% worked full-time while
another 19% were working at least half time. About a third
planned to continue in their present position or move to
another position with the same employer while 40% were
seeking a new career path. The median income before
obtaining the MBA was $30,000–40,000 while after earning it the expected median was $50,000–60,000. Almost all
the students lived and worked in the two zip code clusters
of the metro area of the university.
Tables 1 and 2 use recent data that compares two online
and two face to face sections taught by one of the authors in
different semesters. Based on the results shown in Table 1
STUDENT FEEDBACK ABOUT THE CLASS
Detailed attitudinal results by semester, shown in Table 3,
are quite encouraging. Looking at information on hours
spent on graded assignments in Table 3, it is easy to draw
the conclusion that the data analysis assignments,
TABLE 3
Graded Assignments
Online semester
Semester 1
M
Hours spent on graded assignments
GA1 decision trees
8.58
GA2 secondary sources
.
GA3 web survey design
9.50
GA4 basic data analysis
10.17
take home final
20.91
Perceptions of usefulness of graded assignments
GA1 decision trees
5.75
GA2 secondary sources
.
GA3 web survey design
7.83
GA4 basic data analysis
7.58
Midterm exam
.
Take home final
6.67
Perceptions of difficulty of graded assignments
GA1 decision trees
5.33
GA2 secondary sources
.
GA3 web survey design
5.08
GA4 basic data analysis
6.00
Midterm exam
.
Take home final
8.00
Median
5.50
.
8.00
8.00
15.00
6.00
.
7.50
8.00
.
6.50
5.50
.
5.00
6.00
.
8.00
Semester 2
n
12
12
12
12
12
12
12
Semester 4
Total
Median
n
M
Median
n
M
Median
n
M
Median
n
7.00
5.00
5.00
10.00
24.00
15
15
15
15
15
22.67
15.20
18.33
29.87
36.20
10.00
10.00
10.00
24.00
32.00
15
15
15
15
15
12.08
10.83
10.92
33.92
38.08
7.00
4.50
6.00
35.00
31.00
12
12
12
12
12
13.69
12.26
12.22
21.67
30.09
7.50
9.00
8.00
20.00
30.00
54
42
54
54
53
5.93
7.67
8.20
8.20
5.73
6.73
6.00
8.00
8.00
8.00
6.00
7.00
15
15
15
15
15
15
6.00
6.73
8.40
7.80
6.40
7.60
7.00
7.00
9.00
8.00
6.00
8.00
15
15
15
15
15
15
6.23
5.69
7.54
7.69
6.00
6.92
6.00
6.00
8.00
8.00
6.00
8.00
13
13
13
13
13
13
5.98
6.74
8.02
7.84
6.05
7.00
6.00
7.00
8.00
8.00
6.00
7.00
55
43
55
55
43
55
6.60
4.73
5.87
7.20
8.53
9.07
7.00
4.00
6.00
7.00
9.00
10.00
15
15
15
15
15
15
6.93
4.64
5.36
7.07
8.57
8.07
7.50
5.00
5.50
8.00
9.50
8.50
14
14
14
14
14
14
6.08
4.92
5.62
8.15
8.69
9.00
7.00
6.00
5.00
8.00
9.00
9.00
13
13
13
13
13
13
6.28
4.76
5.50
7.13
8.60
8.56
7.00
5.00
5.00
7.00
9.00
9.00
54
42
54
54
42
54
M
12 10.07
10.47
12 9.33
12 12.87
11 24.33
12
Semester 3
Note: Perceptions of usefulness and difficulty were evaluated using a 10-point Likert-type scale with responses ranging from 1 (very low) to 10 (very high),
with only end points labeled.
342
Q. SUN AND G. GANESH
TABLE 4
Perceptions of Module Usefulness and Difficulty, Usefulness of Audio and Videos, and Satisfaction With Communication Resources
Online semester
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Semester 1
Module usefulness
MR 01: Introduction to MR
MR 02: The MR problem and research design
MR 03: Exploratory research
MR 04: Descriptive and causal research
MR 05: Measurement, scaling and questionnaire
MR 06: Sampling concepts
MR 07: Fieldwork and data preparation
MR 08: Basic data analysis
MR 09: Analysis of variance and covariance
MR 10: Correlation and regression
MR11: Discriminant, factor and cluster analysis
MR 12: MD scaling and conjoint analysis
MR 13: Report preparation and presentation
MR 14: International marketing research
Module difficulty
MR 01: Introduction to MR
MR 02: The MR problem and research design
MR 03: Exploratory research
MR 04: Descriptive and causal research
MR 05: Measurement, scaling and questionnaire
MR 06: Sampling concepts
MR 07: Fieldwork and data preparation
MR 08: Basic data analysis
MR 09: Analysis of variance and covariance
MR 10: Correlation and regression
MR11: Discriminant, factor and cluster analysis
MR 12: MD scaling and conjoint analysis
MR 13: Report preparation and presentation
MR 14: International marketing research
Usefulness of audio and videos
Module introductions
Topic explanation
Sample size calculation
Data analysis demonstrations
Qualtrics orientation
Special topics or "nuggets"
SPSS orientation
How to do this
Summary judgment
Professor’s accent
Satisfaction with communication resources
Hours online per week
Rate: BB email
Rate: BB redirect email
Rate: BB DA
Semester 2
Semester 3
Semester 4
Total
M
Median
n
M
Median
n
M
Median
n
M
Median
n
M
Median
n
5.83
6.92
7.17
7.42
7.08
7.08
6.75
6.67
6.55
6.64
6.67
5.55
6.08
5.83
5.50
7.00
7.50
8.00
7.00
7.50
6.50
7.00
7.00
7.00
7.00
6.00
7.00
6.50
12
12
12
12
12
12
12
12
11
11
12
11
12
12
7.43
7.43
7.50
7.57
7.79
7.93
7.21
7.57
7.43
7.43
7.57
7.21
6.38
7.00
7.50
8.50
8.00
8.50
8.50
8.50
7.50
8.50
8.00
8.00
8.50
7.00
7.00
7.50
14
14
14
14
14
14
14
14
14
14
14
14
13
14
6.93
7.13
7.40
7.60
7.71
7.93
7.60
8.20
7.73
7.67
7.67
6.71
7.07
6.07
8.00
7.00
7.00
7.00
7.50
8.00
8.00
8.00
7.00
7.00
7.00
7.00
7.00
7.00
15
15
15
15
14
15
15
15
15
15
15
14
15
15
7.33
7.42
7.58
7.92
8.08
7.82
7.58
8.50
8.33
8.25
8.17
8.00
7.83
7.50
7.50
7.50
8.00
8.00
8.00
8.00
8.00
9.50
8.50
8.50
8.50
8.00
8.00
7.50
12
12
12
12
12
11
12
12
12
12
12
12
12
12
6.91
7.23
7.42
7.62
7.67
7.71
7.30
7.75
7.54
7.52
7.53
6.90
6.85
6.58
7.00
7.00
8.00
8.00
8.00
8.00
7.00
8.00
8.00
8.00
8.00
7.00
7.00
7.00
53
53
53
53
52
52
53
53
52
52
53
51
52
53
4.58
4.92
4.83
4.75
4.92
5.25
5.17
6.58
7.17
7.33
7.67
6.82
5.00
5.00
4.50
5.00
5.00
5.00
5.00
5.00
5.00
6.50
7.00
7.00
7.50
7.00
5.00
5.00
12
12
12
12
12
12
12
12
12
12
12
11
11
11
3.93
3.64
3.57
3.79
3.93
4.43
4.36
7.00
7.14
7.29
7.36
7.29
4.93
4.93
4.00
4.00
3.50
4.00
4.00
4.50
4.50
8.00
8.00
8.00
8.00
8.00
5.00
5.00
14
14
14
14
14
14
14
14
14
14
14
14
14
14
4.87
4.71
4.47
4.87
5.07
5.80
5.40
6.93
7.29
7.47
7.80
7.87
5.60
5.27
5.00
5.00
5.00
5.00
5.00
7.00
5.00
7.00
7.50
8.00
8.00
8.00
6.00
6.00
15
14
15
15
15
15
15
15
14
15
15
15
15
15
4.92
4.92
4.91
5.00
5.17
5.25
6.00
8.00
8.00
8.25
8.25
8.25
6.58
6.36
5.00
4.50
4.00
5.00
5.00
5.00
6.50
8.00
8.00
9.00
9.00
9.00
7.00
7.00
12
12
11
12
12
12
12
12
12
12
12
12
12
11
4.57
4.52
4.40
4.58
4.75
5.19
5.21
7.11
7.38
7.57
7.75
7.58
5.52
5.35
5.00
5.00
5.00
5.00
5.00
5.00
5.00
8.00
8.00
8.00
8.00
8.00
5.50
5.00
53
52
52
53
53
53
53
53
52
53
53
52
52
51
4.64
5.27
5.64
6.82
6.36
4.64
6.64
6.18
4.36
5.00
5.00
5.00
7.00
7.00
5.00
6.00
5.00
5.00
no data
11
11
11
11
11
11
11
11
11
6.86
7.57
7.64
8.14
7.93
7.50
8.29
7.29
7.86
4.29
7.50
8.50
8.50
9.00
9.00
8.50
9.00
7.50
8.50
3.50
14
14
14
14
14
14
14
14
14
14
5.77
7.07
7.50
8.00
7.33
6.79
7.79
6.64
8.00
3.87
7.00
7.00
7.50
9.00
8.00
6.50
9.00
7.50
8.50
3.00
13
14
14
15
15
14
14
14
14
15
4.73
7.25
7.50
8.08
7.25
7.50
8.50
7.00
8.58
3.46
3.00
8.00
8.00
8.50
8.00
8.00
9.50
8.00
9.50
3.00
11
12
12
12
12
12
12
12
12
13
5.59
6.86
7.14
7.81
7.27
6.69
7.84
6.80
7.31
3.88
5.00
7.00
8.00
8.50
8.00
7.00
9.00
7.00
8.00
3.00
49
51
51
52
52
51
51
51
51
42
8.75
7.92
8.00
7.42
7.50
9.00
8.00
8.00
12 10.14
12 8.00
11 4.77
12 6.62
9.00
8.00
5.00
7.00
14 22.27
14 7.64
13 4.93
13 7.93
15.00
8.50
3.00
9.00
15 12.75
14 7.69
15 5.23
15 8.54
12.00
9.00
6.00
9.00
12 13.85
13 7.81
13 5.62
13 7.64
10.00
9.00
6.00
8.00
53
53
52
53
Note: Perceptions of usefulness and difficulty were evaluated using a 10-point Likert-type scale with responses ranging from 1 (very low) to 10 (very high),
with only end points labeled. Usefulness of audio and videos was evaluated using two 10-point Likert-type scales, a scale for usefulness ranging from 1 (not at
all useful) to 10 (very useful) and a scale for accent ranging from 1 (not at all difficult) to 10 (very difficult). Satisfaction was evaluated using a 10-point Likerttype scale ranging from 1 (not at all satisfied) to 10 (very satisfied).
especially the take home final, are the time hogs. Table 3
presents the results of analysis of student perceptions of the
graded assignments and examinations. The only item to
show significant difference across semesters is the difficulty
of GA4b (basic data analysis) and this is perhaps due to a
strengthening of the requirements after the first offering of
the online course. Also, when the Total column is examined, across the semesters, the overall mean for most of the
DEVELOPING AND TEACHING AN ONLINE MBA MARKETING RESEARCH CLASS
Table 4 shows the results of analysis of student perceptions of usefulness and difficulty of the course content modules. This time, there are no significant differences across
semesters. When the overall means across the semesters
(the Total column) is examined, all modules are viewed
positively (significantly useful with mean > 5.50, while
only the data analysis modules 08 through 12 are seen as
significantly difficult (mean > 5.50). As expected, the first
four modules (introduction to market research, the market
research problem and research design, exploratory research,
and descriptive and causal research) are seen as significantly easier while four modules on sampling, field work,
assignments is significant and positive. GA3 (web survey
design) and GA4 (basic data analysis) were cited as the
most useful assignments, while GA1 (decision trees) was
uniformly seen as the least useful. As expected, the takehome final involving basic as well as multivariate analysis
was clearly the most difficult assignment, easily outdistancing the others. Students also overwhelmingly felt that the
time given for each graded assignment was just right. The
only exception was the take home final, for which students
expressed a lack of time during the first semester. This was
corrected and now it also displays a pattern just like the
other assignments.
Downloaded by [Universitas Maritim Raja Ali Haji] at 20:46 11 January 2016
343
TABLE 5
Other General Perceptions of Course
Online semester
Scale used (only
end points
labeled)
Benefit versus cost
Comparison with
other classes
Comparison with
other online
classes
Comparison with
other online
classes
(experienced
students)
Meet or exceed
bare minimum
expectations
Experience versus
expectation
Push to limit?
Good refresher/
orientation for
execs?
1 (falls far short of
its cost) to 10
(greatly
exceeds its cost)
1 (much worse
than other
classes) to 10
(much better
than other
classes)
1 (much worse
than other
classes) to 10
(much better
than other
classes)
1 (much worse
than other
classes) to 10
(much better
than other
classes)
1 (met the bare
minimum) to 10
(greatly
exceeded the
bare minimum)
1 (much worse
than
expectations) to
10 (much better
than
expectations)
1 (definitely no) to
10 (definitely
yes)
1 (definitely no) to
10 (definitely
yes)
Semester 1
Semester 2
Semester 3
Semester 4
Total
M
Median
n
M
Median
n
M
Median
n
M
Median
n
M
Median
n
6.50
7.00
12
5.64
5.00
14
7.00
7.00
15
7.00
7.00
13
6.54
7.00
54
6.25
6.00
12
6.50
7.00
14
6.47
6.00
15
6.23
6.00
13
6.37
6.00
54
5.75
6.50
12
7.43
8.00
14
6.87
7.00
15
7.15
8.00
13
6.83
7.00
54
5.60
6.00
5
6.33
7.50
6
7.00
7.00
11
7.00
8.00
11
6.67
7.00
33
6.08
6.00
12
7.79
8.00
14
7.53
8.00
15
7.31
8.00
13
7.22
8.00
54
5.75
5.50
12
6.86
8.00
14
6.60
7.00
15
6.15
6.00
13
6.37
6.50
54
7.08
7.50
12
7.64
8.50
14
7.93
8.00
15
7.69
8.00
13
7.61
8.00
54
7.58
8.00
12
8.43
10.00
14
8.13
10.00
15
7.69
8.00
13
7.98
9.00
54
Downloaded by [Universitas Maritim Raja Ali Haji] at 20:46 11 January 2016
344
Q. SUN AND G. GANESH
report preparation, and international market research fall in
between (mean not significantly different from 5.50).
Perceptions of the ACV content, shown in Table 4, are
of particular interest as so much time was spent in creating
these. It is gratifying to note that overall, most of the ACVs
are perceived significantly positively (total mean > 5.50).
There are no significant differences across semesters,
except in the case of the special topic nugget ACVs and
overall summary judgment. The data analysis and how-to
demonstration ACVs were consistently rated as the best.
The students expressed a preference for accessing the
ACVs as links within modules or as a separate ACV cluster
on Blackboard. Both options are currently implemented.
Table 4 confirms that satisfaction with the communication resources used in the online course do not differ by
semester and the two main ones (email and the discussion
area) are viewed significantly positively. The redirect email
feature is passive (e.g., simply forwarding Blackboard email to the student’s preferred email address) and it is therefore not surprising that it was viewed as not significantly
different from the neutral point.
None of the general perceptions of the online course,
shown in Table 5, differ across the four semesters and it is
interesting to note that all the comparative ratings are significantly positive (total mean >5.50). In particular, the
comparison of the online marketing research class to other
online classes favors the former, for all students and the
subgroup of students who have taken a significant number
(median of 5) of online classes. Likewise, students overwhelmingly endorsed the custom methods used for accessing the basic topic outlines online, preferring to keep
current look and feel as opposed to change to resemble
other Blackboard courses.
CONCLUSIONS
This article described in detail the experience of developing
and teaching an online MBA marketing research course.
Detailed feedback from students suggests that they performed comparably to their face-to-face peers, reacted quite
positively to the class, perceived it as something valuable
and were comfortable with various aspects of its implementation. There have also been voluntary feedback letters and
comments from former students appreciative of what they
learned in this class. In particular, the positive evaluations
of ACVs highlights the importance of resembling the
online courses with face-to-face classroom by utilizing videos. The live project with a real client also ensures the
experiential learning in online courses, as required by
AACSB. As a result, the results of this study provide empirical data on the feasibility of teaching statistical contents
online, as well as the effectiveness of online learning.
One issue not addressed here is a detailed attitudinal
comparison of the online class with the face-to-face class.
However, that would require collecting identical data from
both formats taught by the same professor using a nearidentical syllabus. The authors propose to explore this further in the future. The online course could also be used by
doctoral students to learn or refresh basics of marketing
research, especially to those who have not taken such a
course before joining the doctoral program. In addition,
this online course has just been adapted and taught at the
undergraduate level, primarily by excluding the more
advanced topics. The authors propose to collect data
from the undergraduate class for at least 4–5 iterations
before making a formal assessment about the undergraduate experience and sharing the results. There is also a
need to collect data from the students one year after they
finished this course compare and evaluate the effectiveness of this online marketing research course. Future
researchers should compare student perceptions of course
usefulness and difficulty as well as their satisfaction for
online and face-to-face formats.
In addition, the detailed explanation of the online
MBA marketing research course in this study would
hopefully help marketing educators at other institutions
design and implement a similar course effectively and
efficiently. The longitudinal analysis of course evaluations provide insights on what contents are most important to the students and how other marketing educators
can effectively teach online market research at MBA
level. In particular, the ACV contents and live case could
help bridge the gap of online teaching and traditional
face-to-face communication.
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ISSN: 0883-2323 (Print) 1940-3356 (Online) Journal homepage: http://www.tandfonline.com/loi/vjeb20
Developing and Teaching an Online MBA
Marketing Research Class: Implications for Online
Learning Effectiveness
Qin Sun & Gopala Ganesh
To cite this article: Qin Sun & Gopala Ganesh (2014) Developing and Teaching an Online MBA
Marketing Research Class: Implications for Online Learning Effectiveness, Journal of Education
for Business, 89:7, 337-345, DOI: 10.1080/08832323.2013.806885
To link to this article: http://dx.doi.org/10.1080/08832323.2013.806885
Published online: 29 Sep 2014.
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Date: 11 January 2016, At: 20:46
JOURNAL OF EDUCATION FOR BUSINESS, 89: 337–345, 2014
Copyright Ó Taylor & Francis Group, LLC
ISSN: 0883-2323 print / 1940-3356 online
DOI: 10.1080/08832323.2013.806885
Developing and Teaching an Online MBA
Marketing Research Class: Implications for Online
Learning Effectiveness
Qin Sun
Downloaded by [Universitas Maritim Raja Ali Haji] at 20:46 11 January 2016
Trident University International, Cypress, California, USA
Gopala Ganesh
University of North Texas, Denton, Texas, USA
The authors intend to describe the experience of developing and teaching an online marketing
research class for master of business administration students. The class has been taught for
four fall semesters. Each time, the class also completed an online survey, analyzed the
resulting data, and wrote a detailed report for a real client. The course content, topic
sequence, class assignments, and student feedback about the experience are all described in
great detail herein. This web implementation incorporates several useful features such as a
very large number of audio and video segments that have been used to teach difficult
concepts like sampling and data analysis techniques. The implications for student online
learning effectiveness conclude the remark.
Keywords: MBA education, marketing marketing research, online teaching, pedagogy
With the increasing popularity of online learning in higher
education industry, more and more attention has been paid
to the quality and effectiveness of online courses. In particular, due to inadequate mathematics knowledge among
master of business administration (MBA) students and the
difficulty of delivering quantitative courses online, there is
a need to provide empirical data to evaluate the feasibility
of online statistics classes such as marketing research class
required for Association to Advance Collegiate Schools of
Business (AACSB)–accredited MBA program (Sebastianelli & Tamimi, 2011). Generally speaking, marketing
research involves with systematic data collection and analysis for a particular market segment in order to improve decision making in marketing (Malhotra, 2007). Therefore, it is
not surprising that the teaching of marketing research has
typically involved with topics introducing students to sources of secondary and primary data, the issues in data collection and the various techniques used for analyzing raw data
and turning it into actionable and valuable information
(Ganesh, 1992.) Although the marketing research course is
Correspondence should be addressed to Qin Sun, Trident University
International, College of Business Administration, 5757 Plaza Dr., Ste.
100, Cypress, CA 90630, USA. E-mail: [email protected]
a required class for undergraduate and graduate marketing
majors in most business schools, it imposes challenges to
both students and professors, mainly because of its significant statistical and data analysis content (Bradstreet, 1996;
Bridges, 1999; Castleberry, 2001; Dobni & Links, 2008;
Giacobbe & Segal, 1994). In addition, hands-on experience
in using the computer and statistical software such as SPSS
(IBM, Armonk, NY), Minitab (Minitab Inc., State College,
PA), and Excel (Microsoft, Seattle, WA) have been an integral part of teaching marketing research (Bove & Davies,
2009), posing additional challenges to the students.
From the practitioners’ perspective, marketing research
should be taught emphasizing the practice and quantitative
analysis rather than the theory (Bellenger & Bernhardt,
1977). Higher caliber statistical coverage such as multivariate analysis is seen as necessary even in the undergraduate
marketing research course (Stern & Tseng, 2002). For the
MBA marketing research class, canonical correlation, discriminant analysis, factor analysis, logit or probit regression, multivariate analysis of variance (ANOVA), multiple
regression, conjoint analysis, decision analysis, and multidimensional scaling are in the priority list of practitioners
(Anderson, 1982). Due to the popularity of Internet and
World Wide Web, distance learning and online education
Downloaded by [Universitas Maritim Raja Ali Haji] at 20:46 11 January 2016
338
Q. SUN AND G. GANESH
are becoming popular platforms to teach college students
(Steiner & Hyman, 2010) and the digital millennial college
students are becoming more comfortable with the online
teaching methods and online communities (Matulich, Papp,
& Haytko, 2008). Again this backdrop, it is important to
explore the online teaching of marketing research and the
related learning effectiveness issues.
For several years now, marketing professors have been
exploring ways to bring the rapid advances in information
technology and the personal computer revolution for teaching marketing research. While Castleberry (2001) introduced hands-on project to sharpen students’ secondary data
research skills using web and off-web sources, Malhotra,
Dixit, and Uslay (2002) recommended the use of Internet
technology in all aspects of marketing research education.
High-tech electronic devices such as personal digital assistants and video cameras have been used in the undergraduate marketing research courses to conduct interviews and
collect observational data (McGorry, 2006; Smith & Fisher,
2006). Multimedia and podcasting have also been tried to
help the tech-savvy students effectively learn marketing
research (Zahay & Fredricks, 2009) and simulations were
used to teach international marketing research to undergraduates and MBA students with mostly positive feedback
(Peterson, 2006).
Courses such as marketing research also provide ample
opportunity to bridge the gap between theory and practice
and apply what is learned in class to the real business situations. For example, Bove and Davies (2009) used clientsponsored project in the graduate marketing research class
over a three-year period and the positive student feedback
acknowledged the practical benefits of the live case to the
students’ research and consulting skills. Bhattacharya and
Sheth (1996) designed the marketing research course with
field project for cause-oriented nonprofit organization in
order to instill social responsibility among undergraduate
and graduate business students. This so called experiential
learning is promoted and required by AACSB International
to better prepare the business students for a career in the
business professions (AACSB, 2003, Ganesh & Sun, 2009).
Although there are debates on the effectiveness of online
education, teaching marketing research via the Internet provides a viable and flexible option, especially to U.S. graduate students who are typically constrained by the time
demands of their full-time jobs, commuting requirements,
and sometimes by language constraints, as in the case of
international students (Liu, Gomez, Khan, & Yen, 2007;
Stewart, 2004). As a result, marketing professors have begun
to teach this class online to undergraduate students (Steiner
& Hyman, 2010). Even though there is a need to have an
online marketing research course for MBA students, the
advanced topics are difficult to handle in an online environment, especially the significant hands-on components: client
project, survey design, data collection, data analysis, and
writing a report. In this article, we discuss the experience of
building and teaching an online MBA marketing research
class at a large public university in the southwest United
States, with the hope that this endeavor would benefit others
contemplating similar or other forays online.
THE COURSE DESIGN
This online MBA-level marketing research course is
unique in many ways, including its concept, execution and
implementation. Basic familiarity with principles of marketing management and elementary business statistics are
assumed. Incoming students are required to have completed the core course in marketing and statistics. While
the traditional face-to-face MBA marketing research class
has been taught for a very long time, the online version
has been offered only five times in the fall semester, as
part of a fairly recent online MBA program. Data collection took place during the first four semesters during
which the enrollment ranged between 14–15 students a
semester, typical for a MBA marketing elective class.
One of the fundamental objectives in designing the web
course was to make its content nearly identical to the traditional course. Therefore, the students would exit not only
with a good overview of marketing research, but also
enhanced interpretational quantitative skills essential for a
managerial decision maker. They would be comfortable
with concepts such as sampling methods, the factors that
drive sample size including confidence intervals, and handon familiarity with advanced data analysis techniques.
They would also brush up on effective communication
through charts, graphs and good presentation through a living case market research project for a real client.
The mandatory textbook for the class was chosen after
careful review of the available options for an MBA-level
course. It would have substantial managerial, as opposed to
mathematical, coverage of advanced statistical emphasis,
written for SPSS-based data analysis and come with a good
supply of datasets with adequate data for analysis and
assignments. The authors settled on Malhotra’s Marketing
Research book, starting with the 4th edition (Malhotra,
2004) and presently using the 6th edition (Malhotra, 2007).
The online marketing research class probably has more
topic and detailed content than the traditional class, since
time constraint was neither assumed nor taken into account
in its design! There was no limitation such as a 3-hr class
that met once a week! While the class is delivered using
university’s customized Blackboard (Blackboard Inc.,
Washington, DC), it does not use the typical Blackboard
course delivery method. Instead, students access each of 14
lessons as a PowerPoint (Microsoft, Seattle, WA) file that
is prepared by substantially shortening and then modifying
as well as customizing the presentation resources that were
part of the instructor materials for the text book. The resulting lesson is such that students would need to access the
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DEVELOPING AND TEACHING AN ONLINE MBA MARKETING RESEARCH CLASS
textbook to refer to the various tables and figures commented or elaborated upon. A substantial number of electronic resources are also hyper-linked within the outlines.
In this online course, students have a choice of accessing
each lesson outline as either html PowerPoint (which
works very well with the Internet Explorer browser) or
enhanced Adobe (Adobe Systems Incorporated, San Jose,
CA) PDF PowerPoint (which works better with Mozilla
[The Mozilla Foundation, Mountain View, CA] Firefox
and Apple’s [Apple, Cupertino, CA] Safari browsers).
Besides helping with the enhanced PDF topic presentations, the Adobe Acrobat (AA) version 9 is also extensively used for creating PDFs of hyperlinkable content
from text (.doc), worksheets (.xls), and other presentations (.ppt), making it possible to incorporating these
into the online class.
Another key resource is Camtasia from TechSmith
(Okemos, MI), a software tool that helps create audio
and video (ACV) segments in which the students listen
to the professor’s explanation in very clear audios while
watching the action on the professor’s computer screen
in very high resolution videos. The *.camrec file produced by Camtasia can be rendered in a web browsercompatible, hyper-linkable format such as a flash .swf
file. One major advantage of these ACV tools for the
professor is the convenience without the major hassle of
having to leave the office and going to a campus or
other recording facility at an appointed time. The only
major additional hardware requirement is a high quality
microphone such as the easy to use and flawlessly functional USB microphone from Samson (Samson Technologies, Hauppauge, NY). Using these tools, the authors
recorded a very large number of unscripted ACV segments to create crucial contents such as the following:
How to use the electronic library resources such as
ABI-Inform and Lexis-Nexis: One of the business
librarians came to the professor’s office and demonstrated a couple of search sessions that were captured
as ACVs.
Sample size and other demonstrations of by-hand calculations: For this, an inexpensive digital writing tablet was purchased and used as an online overhead
projector, capturing the work as an ACV.
Explanations of routine PowerPoint content that could
spread over multiple slides.
Demonstration of how to use software tools. This
came in handy for training students in using Qualtrics
(Qualtrics, Provo, UT) for creating online surveys.
They were asked to follow the ACV and create the
web version of a short paper and pencil survey that
had been administered on campus in the late 1990s.
The same technique was also used to teach students
the basics of drawing decision trees using the TreePlan add-in for Excel and proper cell and print
339
formatting of Excel worksheets. It can be used for
illustrating just about any other computer-based demo.
Orientation to SPSS: This ACV demonstrated the
basics of analyzing the SPSS dataset that resulted
from the campus survey referred to above.
Data analyses: Several ACVs were recorded to supplement the topic outlines. These included descriptive
statistics; t-tests; one-way ANOVA; correlation and
regression; discriminant, factor, and cluster analyses;
multidimensional scaling, and conjoint analysis. Data
sets that came with the book or resulted from the
authors’ research were used in preparing these
demonstrations.
MEASURING STUDENT PERFORMANCE
The requirements for this course include chapter quizzes
(10%), four graded assignments (40%), midterm (MT)
examination (20%), and final examination (30%). The
online quizzes are made up of multiple-choice questions on
key concepts and hence can be easily answered by going
through each chapter of textbook. The 12 quizzes cover the
first 17 chapters, essentially all the way through basic data
analysis. Each quiz is a random selection of 10 questions
drawn from a pool of between 25 and 30 questions from the
relevant chapters. Students are allowed two attempts of 2 hr
each and the better score is retained. Likewise, the best 10
quiz scores comprise the semester score for this grade component. The four graded assignments (GA), worth 40% of
final grade, are described as the following:
GA1: 5.0%. Online secondary sources, preparation
for GA3b topic;
GA2: 5.0%. Value of information, use TreePlan to
draw decision trees;
GA3a: 2.5%. Orientation to Qualtrics online survey
tool, preparation for GA3b;
GA3b: 7.5%. Design and publish an online survey
using Qualtrics;
GA4a: 2.5%. Orientation to data analysis basics of
SPSS, preparation for GA4b; and
GA4b: 17.5%. Analyze GA3b data, prepare charts
and write a report.
GA1, GA3a&b, and GA4a and GA4b all focus on the
live case market research project incorporating an online
survey prepared using Qualtrics. The client usually sent
two or three survey emails (notification of selection, survey
activation, and reminder to participate) directly to its
patrons and thereby avoided confidentiality issues in sharing the email addresses with the professor or class. During
the recent semesters, the online students have completed
projects that dealt with the proposed new business administration building, the area market for MBA programs, the
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340
Q. SUN AND G. GANESH
own test in detail (question by question, with correct
answers and explanation provided). They could then retake
MT test (another random selection of questions) after they
submitted their final exams. A half to one letter grade curve
was added to their original MT exam percentage, if they
improved their raw score on the second attempt by five
questions or more.
The final exam is a comprehensive take home effort on
data analysis. In the first two semesters, it was based on a
case that came with the book and its associated dataset. In
recent semesters, it has been based on the client project
data for that semester. The test starts with a set of data
transformations (recode and compute) followed by basic
data analysis (e.g., frequencies, descriptive, means, cross
tabs, single-sample t-test, t-tests of independent samples
and paired samples). Next, students are asked to perform
one-way ANOVA, correlation, simple and multiple regression analysis, discriminant analysis, factor analysis, and
cluster analysis. Each of these is covered in detail in the
various course modules using one or more ACVs of different data sets. Therefore, the diligent student has plenty of
resources to refer to and complete the examination for a
very satisfactory outcome. To facilitate grading, detailed
guidelines are provided including standardized formats for
organizing the voluminous results of SPSS analysis.
local public library, the semiautonomous performing arts
center of the university, and the student health and wellness
center on campus. In each of these projects, GA1 provided
the opportunity to improve their familiarity and/or expand
the initial list of professor-provided objectives for the
research. These were then fine tuned with input from one or
two online chat sessions with the client representatives who
were given guest access to the class. The finalized objectives were then incorporated into the GA3b online survey
design assignment. The professor either picked the best
effort or melded the best questions and then, after smoothening the rough edges, produced the final survey, which
was field tested by the class as sample participants. Afterwards, the survey was placed online and the patrons were
notified by the client via emails. During the 7–10-day survey data collection window, students prepared for and then
took the mid-term examination. Once the test was over,
they worked on completing GA4b over 10 to14 days, analyzing the survey data, preparing the charts, and writing a
report, conforming to a set of detailed guidelines provided
by the professor.
Except for the first semester when it was given on campus, the MT exam is prepared using Respondus software
and administered within Blackboard. It is made up of a set
of 75 multiple-choice questions pulled from a pool of
nearly 200 questions. Therefore, each student was presented with a randomly selected set of questions with
answer choices in random order as well. The questions are
all application micro-cases written to test the student’s
understanding of basic marketing research concepts and
covered material from the first half of the semester through
basic data analysis (descriptives, crosstabs, and hypothesis
testing; i.e., Modules 1–8).
Despite liberal test conditions (open book and notes, at
least a 72-hr window with breaks for taking the test), student performance on MT test was quite mediocre. Therefore, after the conclusion of the test, a deliberate attempt
was made each time to encourage better performance and
keep the motivation level high. For example, once the test
window closed, students have been allowed to review their
STUDENT CHARACTERISTICS AND CLASS
PERFORMANCE
This paper intends to describe an online course after it had
been taught, demographic data were not collected and
hence unavailable to describe the students in these online
classes. Therefore, the authors describe the students using
three years of data (135 students) from the end of semester
voluntary exit survey in the mostly evening, part time master’s programs at the university. Nearly 60% of the master’s
students completing the exit survey over the three-year
period are in the MBA program, while the rest are in the
master of science program. The average age of the students
TABLE 1
Performance Comparison of the Two Instructional Formats, by Assignment
Face to face
Semester 1
Assignment
Secondary sources
Value of information
Qualtrics
SPSS
Midterm exam
Final exam
Semester total
M
94%
86%
92%
90%
79%
80%
80%
Semester 3
Median n
95%
85%
95%
90%
80%
75%
82%
Online
10
10
10
10
11
11
11
M
93%
78%
88%
88%
83%
86%
86%
Total F2F
Median n
94%
80%
88%
88%
83%
88%
85%
11
11
11
11
11
11
11
M
93%
82%
90%
89%
81%
83%
83%
Semester 2
Median n
94%
80%
90%
90%
81%
87%
85%
21
21
21
21
22
22
22
M
Median n
Semester 4
M
95% 100% 13 70%
88% 90% 13 70%
93% 96% 13 92%
93% 97% 13 92%
81% 81% 13 70%
85% 83% 13 78%
87% 87% 13 83%
Total INET
Median n
70%
70%
91%
93%
71%
83%
82%
20
20
21
21
21
21
21
M
80%
77%
92%
92%
74%
81%
84%
Total
Median n
90%
80%
93%
93%
73%
83%
86%
33
33
34
34
34
34
34
M
85%
79%
91%
91%
77%
82%
84%
Median n
90%
80%
93%
93%
79%
83%
85%
54
54
55
55
56
56
56
341
DEVELOPING AND TEACHING AN ONLINE MBA MARKETING RESEARCH CLASS
TABLE 2
Performance Comparison of the Two Instructional Formats, by Semester Grade
Face to face
Downloaded by [Universitas Maritim Raja Ali Haji] at 20:46 11 January 2016
Semester 1
Online
Semester 3
Total F2F
Semester 2
Semester 4
Total INET
Total
Semester grade
n
%
n
%
n
%
n
%
n
%
n
%
n
%
A
B
C
D
F
Total
3
5
1
1
1
11
27%
46%
9%
9%
9%
100%
2
8
1
0
0
11
18%
73%
9%
0%
0%
100%
5
13
2
1
1
22
23%
59%
9%
5%
5%
100%
4
7
2
0
0
13
31%
54%
15%
0%
0%
100%
6
6
8
0
1
21
29%
29%
38%
0%
5%
100%
10
13
10
0
1
34
29%
38%
29%
0%
3%
100%
15
26
12
1
2
56
27%
46%
21%
2%
4%
100%
and 2, it is apparent that students in the online and face to
face formats have performed quite similarly although the
online students have the disadvantage of not being in a regular face to face class that allows for a great deal of synchronous interaction among the students and professor.
is 30 years with a median of 28. Male students comprised
53% of the group. Nearly 40% worked full-time while
another 19% were working at least half time. About a third
planned to continue in their present position or move to
another position with the same employer while 40% were
seeking a new career path. The median income before
obtaining the MBA was $30,000–40,000 while after earning it the expected median was $50,000–60,000. Almost all
the students lived and worked in the two zip code clusters
of the metro area of the university.
Tables 1 and 2 use recent data that compares two online
and two face to face sections taught by one of the authors in
different semesters. Based on the results shown in Table 1
STUDENT FEEDBACK ABOUT THE CLASS
Detailed attitudinal results by semester, shown in Table 3,
are quite encouraging. Looking at information on hours
spent on graded assignments in Table 3, it is easy to draw
the conclusion that the data analysis assignments,
TABLE 3
Graded Assignments
Online semester
Semester 1
M
Hours spent on graded assignments
GA1 decision trees
8.58
GA2 secondary sources
.
GA3 web survey design
9.50
GA4 basic data analysis
10.17
take home final
20.91
Perceptions of usefulness of graded assignments
GA1 decision trees
5.75
GA2 secondary sources
.
GA3 web survey design
7.83
GA4 basic data analysis
7.58
Midterm exam
.
Take home final
6.67
Perceptions of difficulty of graded assignments
GA1 decision trees
5.33
GA2 secondary sources
.
GA3 web survey design
5.08
GA4 basic data analysis
6.00
Midterm exam
.
Take home final
8.00
Median
5.50
.
8.00
8.00
15.00
6.00
.
7.50
8.00
.
6.50
5.50
.
5.00
6.00
.
8.00
Semester 2
n
12
12
12
12
12
12
12
Semester 4
Total
Median
n
M
Median
n
M
Median
n
M
Median
n
7.00
5.00
5.00
10.00
24.00
15
15
15
15
15
22.67
15.20
18.33
29.87
36.20
10.00
10.00
10.00
24.00
32.00
15
15
15
15
15
12.08
10.83
10.92
33.92
38.08
7.00
4.50
6.00
35.00
31.00
12
12
12
12
12
13.69
12.26
12.22
21.67
30.09
7.50
9.00
8.00
20.00
30.00
54
42
54
54
53
5.93
7.67
8.20
8.20
5.73
6.73
6.00
8.00
8.00
8.00
6.00
7.00
15
15
15
15
15
15
6.00
6.73
8.40
7.80
6.40
7.60
7.00
7.00
9.00
8.00
6.00
8.00
15
15
15
15
15
15
6.23
5.69
7.54
7.69
6.00
6.92
6.00
6.00
8.00
8.00
6.00
8.00
13
13
13
13
13
13
5.98
6.74
8.02
7.84
6.05
7.00
6.00
7.00
8.00
8.00
6.00
7.00
55
43
55
55
43
55
6.60
4.73
5.87
7.20
8.53
9.07
7.00
4.00
6.00
7.00
9.00
10.00
15
15
15
15
15
15
6.93
4.64
5.36
7.07
8.57
8.07
7.50
5.00
5.50
8.00
9.50
8.50
14
14
14
14
14
14
6.08
4.92
5.62
8.15
8.69
9.00
7.00
6.00
5.00
8.00
9.00
9.00
13
13
13
13
13
13
6.28
4.76
5.50
7.13
8.60
8.56
7.00
5.00
5.00
7.00
9.00
9.00
54
42
54
54
42
54
M
12 10.07
10.47
12 9.33
12 12.87
11 24.33
12
Semester 3
Note: Perceptions of usefulness and difficulty were evaluated using a 10-point Likert-type scale with responses ranging from 1 (very low) to 10 (very high),
with only end points labeled.
342
Q. SUN AND G. GANESH
TABLE 4
Perceptions of Module Usefulness and Difficulty, Usefulness of Audio and Videos, and Satisfaction With Communication Resources
Online semester
Downloaded by [Universitas Maritim Raja Ali Haji] at 20:46 11 January 2016
Semester 1
Module usefulness
MR 01: Introduction to MR
MR 02: The MR problem and research design
MR 03: Exploratory research
MR 04: Descriptive and causal research
MR 05: Measurement, scaling and questionnaire
MR 06: Sampling concepts
MR 07: Fieldwork and data preparation
MR 08: Basic data analysis
MR 09: Analysis of variance and covariance
MR 10: Correlation and regression
MR11: Discriminant, factor and cluster analysis
MR 12: MD scaling and conjoint analysis
MR 13: Report preparation and presentation
MR 14: International marketing research
Module difficulty
MR 01: Introduction to MR
MR 02: The MR problem and research design
MR 03: Exploratory research
MR 04: Descriptive and causal research
MR 05: Measurement, scaling and questionnaire
MR 06: Sampling concepts
MR 07: Fieldwork and data preparation
MR 08: Basic data analysis
MR 09: Analysis of variance and covariance
MR 10: Correlation and regression
MR11: Discriminant, factor and cluster analysis
MR 12: MD scaling and conjoint analysis
MR 13: Report preparation and presentation
MR 14: International marketing research
Usefulness of audio and videos
Module introductions
Topic explanation
Sample size calculation
Data analysis demonstrations
Qualtrics orientation
Special topics or "nuggets"
SPSS orientation
How to do this
Summary judgment
Professor’s accent
Satisfaction with communication resources
Hours online per week
Rate: BB email
Rate: BB redirect email
Rate: BB DA
Semester 2
Semester 3
Semester 4
Total
M
Median
n
M
Median
n
M
Median
n
M
Median
n
M
Median
n
5.83
6.92
7.17
7.42
7.08
7.08
6.75
6.67
6.55
6.64
6.67
5.55
6.08
5.83
5.50
7.00
7.50
8.00
7.00
7.50
6.50
7.00
7.00
7.00
7.00
6.00
7.00
6.50
12
12
12
12
12
12
12
12
11
11
12
11
12
12
7.43
7.43
7.50
7.57
7.79
7.93
7.21
7.57
7.43
7.43
7.57
7.21
6.38
7.00
7.50
8.50
8.00
8.50
8.50
8.50
7.50
8.50
8.00
8.00
8.50
7.00
7.00
7.50
14
14
14
14
14
14
14
14
14
14
14
14
13
14
6.93
7.13
7.40
7.60
7.71
7.93
7.60
8.20
7.73
7.67
7.67
6.71
7.07
6.07
8.00
7.00
7.00
7.00
7.50
8.00
8.00
8.00
7.00
7.00
7.00
7.00
7.00
7.00
15
15
15
15
14
15
15
15
15
15
15
14
15
15
7.33
7.42
7.58
7.92
8.08
7.82
7.58
8.50
8.33
8.25
8.17
8.00
7.83
7.50
7.50
7.50
8.00
8.00
8.00
8.00
8.00
9.50
8.50
8.50
8.50
8.00
8.00
7.50
12
12
12
12
12
11
12
12
12
12
12
12
12
12
6.91
7.23
7.42
7.62
7.67
7.71
7.30
7.75
7.54
7.52
7.53
6.90
6.85
6.58
7.00
7.00
8.00
8.00
8.00
8.00
7.00
8.00
8.00
8.00
8.00
7.00
7.00
7.00
53
53
53
53
52
52
53
53
52
52
53
51
52
53
4.58
4.92
4.83
4.75
4.92
5.25
5.17
6.58
7.17
7.33
7.67
6.82
5.00
5.00
4.50
5.00
5.00
5.00
5.00
5.00
5.00
6.50
7.00
7.00
7.50
7.00
5.00
5.00
12
12
12
12
12
12
12
12
12
12
12
11
11
11
3.93
3.64
3.57
3.79
3.93
4.43
4.36
7.00
7.14
7.29
7.36
7.29
4.93
4.93
4.00
4.00
3.50
4.00
4.00
4.50
4.50
8.00
8.00
8.00
8.00
8.00
5.00
5.00
14
14
14
14
14
14
14
14
14
14
14
14
14
14
4.87
4.71
4.47
4.87
5.07
5.80
5.40
6.93
7.29
7.47
7.80
7.87
5.60
5.27
5.00
5.00
5.00
5.00
5.00
7.00
5.00
7.00
7.50
8.00
8.00
8.00
6.00
6.00
15
14
15
15
15
15
15
15
14
15
15
15
15
15
4.92
4.92
4.91
5.00
5.17
5.25
6.00
8.00
8.00
8.25
8.25
8.25
6.58
6.36
5.00
4.50
4.00
5.00
5.00
5.00
6.50
8.00
8.00
9.00
9.00
9.00
7.00
7.00
12
12
11
12
12
12
12
12
12
12
12
12
12
11
4.57
4.52
4.40
4.58
4.75
5.19
5.21
7.11
7.38
7.57
7.75
7.58
5.52
5.35
5.00
5.00
5.00
5.00
5.00
5.00
5.00
8.00
8.00
8.00
8.00
8.00
5.50
5.00
53
52
52
53
53
53
53
53
52
53
53
52
52
51
4.64
5.27
5.64
6.82
6.36
4.64
6.64
6.18
4.36
5.00
5.00
5.00
7.00
7.00
5.00
6.00
5.00
5.00
no data
11
11
11
11
11
11
11
11
11
6.86
7.57
7.64
8.14
7.93
7.50
8.29
7.29
7.86
4.29
7.50
8.50
8.50
9.00
9.00
8.50
9.00
7.50
8.50
3.50
14
14
14
14
14
14
14
14
14
14
5.77
7.07
7.50
8.00
7.33
6.79
7.79
6.64
8.00
3.87
7.00
7.00
7.50
9.00
8.00
6.50
9.00
7.50
8.50
3.00
13
14
14
15
15
14
14
14
14
15
4.73
7.25
7.50
8.08
7.25
7.50
8.50
7.00
8.58
3.46
3.00
8.00
8.00
8.50
8.00
8.00
9.50
8.00
9.50
3.00
11
12
12
12
12
12
12
12
12
13
5.59
6.86
7.14
7.81
7.27
6.69
7.84
6.80
7.31
3.88
5.00
7.00
8.00
8.50
8.00
7.00
9.00
7.00
8.00
3.00
49
51
51
52
52
51
51
51
51
42
8.75
7.92
8.00
7.42
7.50
9.00
8.00
8.00
12 10.14
12 8.00
11 4.77
12 6.62
9.00
8.00
5.00
7.00
14 22.27
14 7.64
13 4.93
13 7.93
15.00
8.50
3.00
9.00
15 12.75
14 7.69
15 5.23
15 8.54
12.00
9.00
6.00
9.00
12 13.85
13 7.81
13 5.62
13 7.64
10.00
9.00
6.00
8.00
53
53
52
53
Note: Perceptions of usefulness and difficulty were evaluated using a 10-point Likert-type scale with responses ranging from 1 (very low) to 10 (very high),
with only end points labeled. Usefulness of audio and videos was evaluated using two 10-point Likert-type scales, a scale for usefulness ranging from 1 (not at
all useful) to 10 (very useful) and a scale for accent ranging from 1 (not at all difficult) to 10 (very difficult). Satisfaction was evaluated using a 10-point Likerttype scale ranging from 1 (not at all satisfied) to 10 (very satisfied).
especially the take home final, are the time hogs. Table 3
presents the results of analysis of student perceptions of the
graded assignments and examinations. The only item to
show significant difference across semesters is the difficulty
of GA4b (basic data analysis) and this is perhaps due to a
strengthening of the requirements after the first offering of
the online course. Also, when the Total column is examined, across the semesters, the overall mean for most of the
DEVELOPING AND TEACHING AN ONLINE MBA MARKETING RESEARCH CLASS
Table 4 shows the results of analysis of student perceptions of usefulness and difficulty of the course content modules. This time, there are no significant differences across
semesters. When the overall means across the semesters
(the Total column) is examined, all modules are viewed
positively (significantly useful with mean > 5.50, while
only the data analysis modules 08 through 12 are seen as
significantly difficult (mean > 5.50). As expected, the first
four modules (introduction to market research, the market
research problem and research design, exploratory research,
and descriptive and causal research) are seen as significantly easier while four modules on sampling, field work,
assignments is significant and positive. GA3 (web survey
design) and GA4 (basic data analysis) were cited as the
most useful assignments, while GA1 (decision trees) was
uniformly seen as the least useful. As expected, the takehome final involving basic as well as multivariate analysis
was clearly the most difficult assignment, easily outdistancing the others. Students also overwhelmingly felt that the
time given for each graded assignment was just right. The
only exception was the take home final, for which students
expressed a lack of time during the first semester. This was
corrected and now it also displays a pattern just like the
other assignments.
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343
TABLE 5
Other General Perceptions of Course
Online semester
Scale used (only
end points
labeled)
Benefit versus cost
Comparison with
other classes
Comparison with
other online
classes
Comparison with
other online
classes
(experienced
students)
Meet or exceed
bare minimum
expectations
Experience versus
expectation
Push to limit?
Good refresher/
orientation for
execs?
1 (falls far short of
its cost) to 10
(greatly
exceeds its cost)
1 (much worse
than other
classes) to 10
(much better
than other
classes)
1 (much worse
than other
classes) to 10
(much better
than other
classes)
1 (much worse
than other
classes) to 10
(much better
than other
classes)
1 (met the bare
minimum) to 10
(greatly
exceeded the
bare minimum)
1 (much worse
than
expectations) to
10 (much better
than
expectations)
1 (definitely no) to
10 (definitely
yes)
1 (definitely no) to
10 (definitely
yes)
Semester 1
Semester 2
Semester 3
Semester 4
Total
M
Median
n
M
Median
n
M
Median
n
M
Median
n
M
Median
n
6.50
7.00
12
5.64
5.00
14
7.00
7.00
15
7.00
7.00
13
6.54
7.00
54
6.25
6.00
12
6.50
7.00
14
6.47
6.00
15
6.23
6.00
13
6.37
6.00
54
5.75
6.50
12
7.43
8.00
14
6.87
7.00
15
7.15
8.00
13
6.83
7.00
54
5.60
6.00
5
6.33
7.50
6
7.00
7.00
11
7.00
8.00
11
6.67
7.00
33
6.08
6.00
12
7.79
8.00
14
7.53
8.00
15
7.31
8.00
13
7.22
8.00
54
5.75
5.50
12
6.86
8.00
14
6.60
7.00
15
6.15
6.00
13
6.37
6.50
54
7.08
7.50
12
7.64
8.50
14
7.93
8.00
15
7.69
8.00
13
7.61
8.00
54
7.58
8.00
12
8.43
10.00
14
8.13
10.00
15
7.69
8.00
13
7.98
9.00
54
Downloaded by [Universitas Maritim Raja Ali Haji] at 20:46 11 January 2016
344
Q. SUN AND G. GANESH
report preparation, and international market research fall in
between (mean not significantly different from 5.50).
Perceptions of the ACV content, shown in Table 4, are
of particular interest as so much time was spent in creating
these. It is gratifying to note that overall, most of the ACVs
are perceived significantly positively (total mean > 5.50).
There are no significant differences across semesters,
except in the case of the special topic nugget ACVs and
overall summary judgment. The data analysis and how-to
demonstration ACVs were consistently rated as the best.
The students expressed a preference for accessing the
ACVs as links within modules or as a separate ACV cluster
on Blackboard. Both options are currently implemented.
Table 4 confirms that satisfaction with the communication resources used in the online course do not differ by
semester and the two main ones (email and the discussion
area) are viewed significantly positively. The redirect email
feature is passive (e.g., simply forwarding Blackboard email to the student’s preferred email address) and it is therefore not surprising that it was viewed as not significantly
different from the neutral point.
None of the general perceptions of the online course,
shown in Table 5, differ across the four semesters and it is
interesting to note that all the comparative ratings are significantly positive (total mean >5.50). In particular, the
comparison of the online marketing research class to other
online classes favors the former, for all students and the
subgroup of students who have taken a significant number
(median of 5) of online classes. Likewise, students overwhelmingly endorsed the custom methods used for accessing the basic topic outlines online, preferring to keep
current look and feel as opposed to change to resemble
other Blackboard courses.
CONCLUSIONS
This article described in detail the experience of developing
and teaching an online MBA marketing research course.
Detailed feedback from students suggests that they performed comparably to their face-to-face peers, reacted quite
positively to the class, perceived it as something valuable
and were comfortable with various aspects of its implementation. There have also been voluntary feedback letters and
comments from former students appreciative of what they
learned in this class. In particular, the positive evaluations
of ACVs highlights the importance of resembling the
online courses with face-to-face classroom by utilizing videos. The live project with a real client also ensures the
experiential learning in online courses, as required by
AACSB. As a result, the results of this study provide empirical data on the feasibility of teaching statistical contents
online, as well as the effectiveness of online learning.
One issue not addressed here is a detailed attitudinal
comparison of the online class with the face-to-face class.
However, that would require collecting identical data from
both formats taught by the same professor using a nearidentical syllabus. The authors propose to explore this further in the future. The online course could also be used by
doctoral students to learn or refresh basics of marketing
research, especially to those who have not taken such a
course before joining the doctoral program. In addition,
this online course has just been adapted and taught at the
undergraduate level, primarily by excluding the more
advanced topics. The authors propose to collect data
from the undergraduate class for at least 4–5 iterations
before making a formal assessment about the undergraduate experience and sharing the results. There is also a
need to collect data from the students one year after they
finished this course compare and evaluate the effectiveness of this online marketing research course. Future
researchers should compare student perceptions of course
usefulness and difficulty as well as their satisfaction for
online and face-to-face formats.
In addition, the detailed explanation of the online
MBA marketing research course in this study would
hopefully help marketing educators at other institutions
design and implement a similar course effectively and
efficiently. The longitudinal analysis of course evaluations provide insights on what contents are most important to the students and how other marketing educators
can effectively teach online market research at MBA
level. In particular, the ACV contents and live case could
help bridge the gap of online teaching and traditional
face-to-face communication.
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