Working Capital ManagementAlternative Working Capital Policies.docx

Working Capital Management
Alternative Working Capital Policies
Cash Management
Inventory and A/R Management
Trade Credit
Bank Loans
Chapter 16
INTRO
INV & A/R MGMT
ALT WC POLICIES
BANK LOANS
CASH MGMT
TRADE CREDIT
16-1
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1
Working Capital Terminology
Working capital: current assets.
Net working capital: current assets minus current liabilities.
Net operating working capital: current assets minus (current
liabilities less notes payable).

Current assets investment policy: deciding the level of each
type of current asset to hold, and how to finance current assets.
Working capital management: controlling cash, inventories,
and A/R, plus short-term liability management.
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Selected Ratios for SKI Inc.SKIInd. AvgCurrent
ratio1.75x2.25xDebt/Assets58.76%50.00%Turnover of cash &
securities16.67x22.22xDays sales
outstanding45.6332.00Inventory turnover4.82x7.00xFixed
assets turnover11.35x12.00xTotal assets
turnover2.08x3.00xProfit margin2.07%3.50%Return on
equity10.45%21.00%
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How does SKI’s current assets investment policy compare with
its industry?
Current assets investment policy is reflected in the current ratio,
turnover of cash and securities, inventory turnover, and days
sales outstanding.
These ratios indicate SKI has large amounts of working capital
relative to its level of sales.
SKI is either very conservative or inefficient.
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Is SKI inefficient or conservative?
A conservative (relaxed) policy may be appropriate if it leads to
greater profitability.
However, SKI is not as profitable as the average firm in the
industry.
This suggests the company has excessive current assets.
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Working Capital Financing Policies
Moderate: Match the maturity of the assets with the maturity of
the financing.

Aggressive: Use short-term financing to finance permanent
assets.
Conservative: Use permanent capital for permanent assets and
temporary assets.
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Moderate Financing Policy
Years
Lower dashed line would be more aggressive.
$

Perm C.A.
Fixed Assets
Temp. C.A.
S-T
Loans
L-T Fin:
Stock,
Bonds,
Spon. C.L.
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Conservative Financing Policy
$
Years
Perm C.A.
Fixed Assets
Marketable
securities
Zero S-T
Debt
L-T Fin:
Stock,
Bonds,

Spon. C.L.
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Cash Conversion Cycle
The cash conversion cycle focuses on the length of time
between when a company makes payments to its creditors and
when a company receives payments from its customers.
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Cash Conversion Cycle
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Minimizing Cash Holdings

Use a lockbox
Insist on wire transfers and debit/credit cards from customers
Synchronize inflows and outflows
Reduce need for “safety stock” of cash
Increase forecast accuracy
Hold marketable securities
Negotiate a line of credit
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Cash Budget
Forecasts cash inflows, outflows, and ending cash balances.
Used to plan loans needed or funds available to invest.
Can be daily, weekly, or monthly, forecasts.
Monthly for annual planning and daily for actual cash
management.
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SKI’s Cash Budget for January and
FebruaryJanuaryFebruaryCollections $67,651.95
$62,755.40Purchases 44,603.75 36,472.65Wages
6,690.56 5,470.90Rent 2,500.00 2,500.00Total
payments $53,794.31 $44,443.55Net cash flows
$13,857.64 $18,311.85
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SKI’s Cash BudgetJanuaryFebruaryCash at start if no borrowing
$ 3,000.00 $16,857.64Net cash flows 13,857.64
18,311.85Cumulative cash $16,857.64
$35,169.49Less: Target cash 1,500.00
1,500.00Surplus $15,357.64 $33,669.49
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How could bad debts be worked into the cash budget?
Collections would be reduced by the amount of the bad debt
losses.
For example, if the firm had 3% bad debt losses, collections

would total only 97% of sales.
Lower collections would lead to higher borrowing requirements.
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Analyze SKI’s Forecasted Cash Budget
Cash holdings will exceed the target balance for each month,
except for October and November.
Cash budget indicates the company is holding too much cash.
SKI could improve its EVA by either investing cash in more
productive assets, or by returning cash to its shareholders.
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Why might SKI want to maintain a relatively high amount of
cash?
If sales turn out to be considerably less than expected, SKI
could face a cash shortfall.
A company may choose to hold large amounts of cash if it does
not have much faith in its sales forecast, or if it is very
conservative.
The cash may be used, in part, to fund future investments.
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Inventory Costs
Types of inventory costs
Carrying costs: storage and handling costs, insurance, property
taxes, depreciation, and obsolescence.
Ordering costs: cost of placing orders, shipping, and handling
costs.
Costs of running short: loss of sales or customer goodwill, and
the disruption of production schedules.
Reducing inventory levels generally reduces carrying costs,
increases ordering costs, and may increase the costs of running
short.
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Is SKI holding too much inventory?

SKI’s inventory turnover (4.82x) is considerably lower than the
industry average (7.00x).
The firm is carrying a large amount of inventory per dollar of
sales.
By holding excessive inventory, the firm is increasing its costs,
which reduces its ROE.
Moreover, this additional working capital must be financed, so
EVA is also lowered.
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If SKI reduces its inventory without adversely affecting sales,
what effect will this have on the cash position?
Short run: Cash will increase as inventory purchases decline.
This will reduce financing or target cash balance.
Long run: Company is likely to take steps to reduce its cash
holdings and increase its EVA.
The “excess” cash can be used to make investments in more
productive assets such as plant and equipment resulting in an

increase in operating income increasing its EVA.
Alternately, can distribute “excess” cash to its shareholders
through higher dividends or repurchasing shares resulting in a
lower cost of capital increasing its EVA.
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Do SKI’s customers pay more or less promptly than those of its
competitors?
SKI’s DSO (45.6 days) is well above the industry average (32
days).
SKI’s customers are paying less promptly.
SKI should consider tightening its credit policy in order to
reduce its DSO.
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Elements of Credit Policy
Credit Period: How long to pay? Shorter period reduces DSO
and average A/R, but it may discourage sales.
Cash Discounts: Lowers price. Attracts new customers and
reduces DSO.
Credit Standards: Restrictive standards tend to reduce sales,
but reduce bad debt expense. Fewer bad debts reduce DSO.
Collection Policy: How tough? Restrictive policy will reduce
DSO but may damage customer relationships.
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Does SKI face any risk if it restricts its credit policy?
Yes, a restrictive credit policy may discourage sales.
Some customers may choose to go elsewhere if they are
pressured to pay their bills sooner.
SKI must balance the benefits of fewer bad debts with the cost
of possible lost sales.
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If SKI reduces its DSO without adversely affecting sales, how
would this affect its cash position?

Short run: If customers pay sooner, this increases cash
holdings. This will reduce financing or target cash balance
needed.
Long run: Over time, the company would hopefully invest the
cash in more productive assets, or pay it out to shareholders.
Both of these actions would increase EVA.
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What is trade credit?
Trade credit is credit furnished by a firm’s suppliers.
Trade credit is often the largest source of short-term credit,
especially for small firms.
Spontaneous, easy to get, but cost can be high.
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Terms of Trade Credit
A firm buys $3,000,000 net ($3,030,303 gross) on terms of
1/10, net 40.
The firm can forego discounts and pay on Day 40, without
penalty.
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Breaking Down Trade Credit
Payables level, if the firm takes discounts
Payables = $8,219.18(10) = $82,192
Payables level, if the firm takes no discounts
Payables = $8,219.18(40) = $328,767
Credit breakdown
Total trade credit$328,767Free trade credit- 82,192Costly
trade credit$246,575
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Nominal Cost of Trade Credit
The firm loses 0.01($3,030,303) = $30,303 of discounts to

obtain $246,575 in extra trade credit:
rNOM = $30,303/$246,575
= 0.1229 = 12.29%
The $30,303 is paid throughout the year, so the effective cost of
costly trade credit is higher.
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Nominal Cost of Trade Credit Formula
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Effective Cost of Trade Credit
Periodic rate = 0.01/0.99 = 1.01%
Periods/year = 365/(40 – 10) = 12.1667
Effective cost of trade credit
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Bank Loans
The firm can borrow $100,000 for 1 year at an 8% nominal rate.
Interest may be set under one of the following scenarios:
Simple annual interest
Installment loan, add-on, 12 months
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Simple Annual Interest
Simple interest means no discount or add-on.
Interest = 0.08($100,000) = $8,000
rNOM = EAR = $8,000/$100,000 = 8.0%
For a 1-year simple interest loan, rNOM = EAR.
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Add-on Interest
Interest = 0.08($100,000) = $8,000
Face amount = $100,000 + $8,000 = $108,000
Monthly payment = $108,000/12 = $9,000
Avg. loan outstanding = $100,000/2 = $50,000
Approximate cost = $8,000/$50,000 = 16.0%
To find the exact effective rate, recognize that the firm receives
$100,000 and must make monthly payments of $9,000 (like an
annuity).
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33
Add-on Interest
From the calculator output below, we have:
rNOM = 12 (0.012043)
= 0.1445 = 14.45%
EAR = (1.012043)12 – 1 = 15.45%
INPUTS
OUTPUT
N
I/YR
PMT
PV
FV
12
1.2043
100
-9
0
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period
deferral
Payables
period
collection
Average
period
conversion
Inventory
CCC
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+
=
days
92
30

46
76
CCC
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46
4.82
365
CCC
period
deferral
Payables
g
outstandin
sales
Days
turnover
Inventory
year
per
Days

CCC
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period
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Average
period
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Inventory
CCC
=
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=
18
.
219

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12
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Discount
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Student Satisfaction with Online Learning: Is it a
Psychological Contract?
Charles Dziuban, Patsy Moskal, Jessica Thompson, Lauren
Kramer, Genevieve DeCantis and Andrea Hermsdorfer
Research Initiative for Teaching Effectiveness
University of Central Florida
Abstract
The authors explore the possible relationship between student
satisfaction with online learning and the
theory of psychological contracts. The study incorporates latent
trait models using the image analysis
procedure and computation of Anderson and Rubin factors
scores with contrasts for students who are
satisfied, ambivalent, or dissatisfied with their online learning
experiences. The findings identify three
underlying satisfaction components: engaged learning, agency,
and assessment. The factor score
comparisons indicate that students in the general satisfaction
categories characterize important differences
in engaged learning and agency, but not assessment. These

results lead the authors to hypothesize that
predetermined, but unspecified expectations (i.e., psychological
contracts) for online courses by both
students and faculty members are important advance organizers
for clarifying student satisfaction.
Introduction
From its inception, online learning has been confronted by
concerns about quality from the
established educational community and society at large
(Carnaghan & Webb, 2007; Akdemir & Koszalka,
2008). Often, in addressing these concerns students’ perceptions
of their course experience becomes a
surrogate for learning engagement in the context of satisfaction
(Swan, 2001; Arbaugh, 2001; Richardson
& Swan, 2003; Bolliger, 2004). Because contemporary students
view information as a commodity which
can be traded openly among a community of learners,
collaboration becomes fundamental to a variety of
educational outcomes (Shirky, 2010; Dziuban et al., 2013).
Online Learning Vol. 19 Issue 2 (2015) 122
Modern technologies are contributing to the dissolution of
traditional classroom boundaries
(Shirky, 2008). Students connect with their instructors and each
other through modalities of almost every
variety, greatly expanding avenues of communication. Norberg,
Dziuban and Moskal’s (2011)
development of a time-based blended learning model, for
instance, modifies the instructor’s role (Liu &
Hwang, 2010) in learning environments based on students’
synchronous and asynchronous learning

preferences. The need for new and more authentic assessment
techniques in addition to challenges to
traditional educational structures (e.g. semester length time
boundaries) raises issues about what
moderates students’ academic expectations and satisfaction.
Studies suggest that online students wish to decrease their
ambivalence toward formal education
by gaining some sense of a carefully delineated path to success
(Dziuban & Dziuban, 1998; Dziuban,
Moskal & Dziuban, 2000; Long, 2011; Young & Dziuban,
2000). Students prefer active, rather than
passive learning environments, and, because they participate in
a highly interactive world, they expect the
same in their classes (Dziuban et al., 2003). Today’s learners
require more outlets for creativity and
collaboration which online learning environments can
accommodate through a variety of instructional
models that are provided anytime, anyplace.
Researchers should not be surprised that identifying the
defining elements for satisfaction has
become much more dynamic and complex. The construct has
multiple facets that tend to be stochastic as
a particular course progresses. In this study, we attempt to
clarify the underlying (latent) elements of
student satisfaction in the context of overall course evaluation
for students who respond positively to
online experiences on end-of-course evaluation protocols.
Feldman (1993) describes the assessment
challenges we encounter as distributions of considerations when
he argues that responses to survey
questions provide only an estimate of the central tendency of an
individual’s attitude or belief about a
subject or object. Craig and Martinez (2005) summarize the
issue: “in retrospect, it seems rather

simplistic to think of attitudes as always being unidimensional.
After all, who hasn’t experienced mixed
feelings about people, places and things that we have
encountered or visited in our lives?” (p. 1)
Recent Studies on Student Satisfaction with Online Courses
Multiple approaches define and assess student satisfaction.
Rubin, Fernandes & Avgerinou
(2013) extended research on the Community of Inquiry
(Garrison, Anderson & Archer, 2000) which
defines social, cognitive, and teaching presence as being
essential to the student learning experience and,
thus, student satisfaction. They determined that learning
management system (LMS) features greatly
impact perceptions of community according to the inquiry
framework. In a related study, Mahmood,
Mahmood and Malik (2012) argued that teaching presence plays
the most critical role in how students
evaluate online learning.
The interaction construct plays an important role in both face-
to-face and online learning
modalities (Kuo, Walker, Belland & Schroder, 2013). In fact,
many studies have found that both quantity
and quality of student interactions are highly correlated with
student satisfaction in almost any learning
environment. However, investigators have noted that
demographic and cultural considerations also impact
the design of appropriate interaction techniques in online
learning (González-Gómez, Guardiola, Martín
Rodríguez & Montaro Alonso, 2012).
Ke and Kwak (2013) identified five elements of student
satisfaction: learner relevance, active
learning, authentic learning, learner autonomy, and technology

competence. Kuo et al. (2013) determined
that learner-instructor interaction and learner-content
interaction combined with technology efficacy are
valid indicators of students’ positive perceptions. However
Battalio (2007), using a criterion approach,
argued that a positive course rating requires effective learner-
instructor interaction.
Keengwe, Diteeyont and Lawson-Body (2012) argued that
students’ expectations influence the
instructor’s design of effective technology tools in online
courses and are the key to understanding the
satisfaction construct. The authors concluded that satisfaction
was most impacted by learning
convenience combined with the effectiveness of e-learning
tools. Dziuban, Moskal, Brophy-Ellison and
Shea (2007) found six key elements that contribute to students’
satisfaction: an enriched learning
environment, well-defined rules of engagements, instructor
commitment, reduced ambiguity, an engaging
environment, and reduced ambivalence about the value of the
course.
Because colleges and universities have to be much more
responsive to their student client base
(Long, 2011; Bordelon, 2012; Allen & Seaman, 2013),
ambivalence becomes particularly important. This
implies satisfaction is an underlying indicator of success in
various learning environments, especially
online modalities. Satisfied students appear to be engaged,
motivated and responsive; contribute to an

effective learning climate; and achieve at higher levels.
Dissatisfied or ambivalent students contribute to
environments where instructors appear to have much more
difficulty facilitating effective learning
situations. Faculty members in such circumstances have trouble
relating to their students and may
incorrectly assume that such difficulties are related primarily to
student dissatisfaction with online
learning (Dziuban et al., 2007).
A precise configuration of student satisfaction with online
learning is proving to be elusive
because it might be context dependent (e.g., college, discipline,
course level, institution, and, of course,
instructor). Bowker and Star (1999) use the term “boundary
object” to suggest that these items or ideas
adapt to specific needs and constraints while maintaining a
common identity. While bringing a
community of practice together for communication and inquiry
purposes, they are generally are weak in
the large cohort. According to these researchers, however, the
object (student satisfaction, in this case) is
much more well-defined within individual constituencies. These
definitional issues appear to reflect what
Watts (2011) calls confirmation bias—that is, accepting
information that confirms our existing beliefs
much more readily than information that does not. To express
their degree of satisfaction, students react
only to things that they expect, but are never expressly stated
(i.e., their predetermined psychological
contract) or to what they have already assumed about the
course. However, should dissonance with these
expectations develop, students may encounter ambivalence
characterized by simultaneous positive and
negative feelings. These are the mixed emotions described by
Weigert (1991) and Long (2011).

Factor Studies of Student Satisfaction with Online Learning
A small number of studies conducted by investigators seeking to
identify the dimensionality of
student satisfaction with online learning have emerged in the
past few years. This work has been a natural
extension of inquiry into student satisfaction in higher
education (Abrami & d’Apollonia, 1991; Feldman,
1976; Feldman, 1993; Greenwald & Gilmore, 1997; Kim,
Damewood & Hodge, 2000; Marsh & Roche,
1997; McKeachie, 1997). While prior studies have focused
primarily on face-to-face teaching
environments, online learning has provided a new dynamic and
has re-energized interest in the topic.
Arbaugh (2007) adopted factoring methods to validate the
Community of Inquiry framework (Garrison et
al., 2000) incorporating social, cognitive, and teaching
presences. He retrieved these primary constructs
and demonstrated that they exhibited excellent reliability. His
work extended the original Community of
Inquiry framework to a fourth dimension: course design and
organization. Stewart, Hong, and Strudler
(2004), using principal components analysis, found a fairly
complex underlying dimensionality that
defines the pattern of student satisfaction in online learning: the
evaluative construct for student involved
issues such as web page appearance, hyperlinks and navigation
facility, technical constraints, online
applications, instructional techniques and expectations, content
delivery, and the interaction environment.
Bangert (2006) found four underlying elements related to the
evaluation of online and blended courses:
interaction, active learning, time on task, and student
cooperation. In a later study, he validated his
previous findings using both exploratory and confirmatory

factor methods (Bangert, 2008).
In a somewhat different approach to the identification of
underlying dimensionality, Wang,
Dziuban, Cook, and Moskal (2009) used classification and
regression trees to predict student assessment
of online courses and identified a series of validated if-then
decision rules for predicting students’
perceptions of excellent teaching based on three constructs:
facilitation of learning, ability of the
instructor to communicate information and concepts, and the
instructor’s respect and concern for students.
Dziuban and Moskal (2011) conducted a study of the factor
invariance in student satisfaction
across online, blended, and face-to-face courses. Using
Guttman’s (1954) image analysis, they found a
single general component that remained constant across all
modalities. The authors concluded that
students do not use the modality of a course to differentiate
elements of excellent instruction and course
satisfaction.
In a later study, Dziuban, Moskal, Kramer and Thompson
(2013) used the alpha factoring
procedure (Kaiser & Caffery, 1965) to identify the underlying
dimensionality of satisfaction under
varying conditions of student ambivalence toward their online
courses. Using overall satisfaction with the
course, they classified students into five categories: negative
non-ambivalent, negative ambivalent,

ambivalent, positive ambivalent and positive non-ambivalent,
corresponding with the 5-item Likert scale.
By factoring the remaining items of the instrument stratified by
those categories, they found the highest
dimensionality for students in the ambivalent categories and the
lowest dimensionality in the non-
ambivalent classifications. The factors in the extreme categories
(either positive or negative) were
identical as were the factors in the positive ambivalent and
negative ambivalent categories. The authors
hypothesized that the students who appear to be least engaged
in their courses (i.e., ambivalent) may be
the most reflective and thoughtful about evaluating their
educational experience.
Psychological Contracts as a Basis for Understanding
Satisfaction
By definition, factor analysis studies imply latent dimensions–
constructs that cannot be directly
observed. Therefore, the underlying components identified in
these kinds of studies relate closely to
Argyris’s (1960) notion of a psychological contract. These
contracts are formed by implicit understanding
and are not bound by written or legal agreements of two parties
within a reciprocal relationship. They
consist of perceived obligations and expectations, and thus, are
subjective and vary from person to person
(Bordia, Hobman, Restubog, & Bordia, 2010). When broken, or
breached, (due to perceived unfairness,
inequality, or mistrust), satisfaction and performance decline
and workforce turnover increases,
consequently impacting attitudes and behaviors (Bordia et al.,
2010).
All workplace psychological contracts contain six features:

voluntary choice, mutual agreement,
incompleteness, presence of numerous contract makers, plan for
managing unsuccessful contract losses,
and a relational model between employer and employee
(Rousseau, 1990). Relational, transactional and
balanced define these six features contained within three
different types as outlined by Rousseau (1990).
Relational agreements are based on loyalty and stability and,
thus, foster satisfaction (Raja, Johns &
Ntalianis, 2004). Transactional agreements include fewer duties,
are usually temporary or short in
duration, and usually result in behaviors that align or are
consistent with the contributions in which one is
rewarded. These contributions are economic in nature and
viewed as less significant. The balanced
agreement: a hybrid contract that is generally open-ended
includes a mutual concern between both parties,
but with clear expectations (Raja et al., 2004).
When analyzing and assessing psychological contracts, the three
forms of measurement include
content-oriented assessment, feature and evaluation. The
content-oriented assessment examines the
obligations of the agreement; the feature-oriented assessment
compares one agreement to another’s based
upon attributes of the contracts; and the evaluation-oriented
assessment assesses the degree of fulfillment
and the amount of change that results (Rousseau & Tijoriwala,
1998).
Psychological Contracts in Education
Although Argyris (1960) developed the theory of a
psychological contract for the workplace, the
idea has important implications for educational environments.
Wade-Benzoni, Rousseau, and Li (2006),

for instance, contend that students view psychological contracts
as a form of mutual exchange in the
education process. The interactions between student and
instructor are crucial and telling about ongoing
perceptions of obligations (Wade-Benzoni et al., 2006). Often
there is little to no explicit communication
about these arrangements because they are informal and
temporary. The power in the relationship within
these contracts is predominately asymmetric, favoring faculty
members who hold expectations about
student performance and control resources such as grades,
letters of recommendation, advice on careers,
and, in some cases, dissertation research approval (Wade-
Benzoni et al., 2006).
Prior to viewing a syllabus, students begin to form expectations
as they assess course offerings
for academic development, decision-making input, challenges,
feedback, and support (Spies et al., 2010).
According to Spies et al. (2010), students pay close attention to
the following categories: faculty,
futuristic factors, student development, course and curricular
content, learning opportunities,
involvement, and facilities. These agreements tend to change
and become more elaborate as the course
progresses.
Within a particular class, both students and faculty form a large
number of contracts that present
satisfaction challenges if, in the participants’ judgment, their
implicit expectations are not met. This

suggests that student satisfaction with online learning is, as
Lakoff (1987) termed, a function of an
idealized cognitive model—a construct fabricated to bring
clarity and structure to a situation. Kahneman
(2011) describes this thinking as “what you see is all there is.”
Because of the complex interaction of
these many constructs, however, student satisfaction with online
learning appears to be an example of
“there is much more than you can see directly.”
The Survey and Sample
The Research Initiative for Teaching Effectiveness (RITE) at
the University of Central Florida
(UCF) has been surveying UCF’s online students as part of an
ongoing impact evaluation since 1996,
when the university began offering Web courses. The
longitudinal nature of the university’s examination
of student attitudes has allowed for refinement and validation
from the original survey. Ongoing
evaluation allows researchers to accommodate rapid change in
the online course environments and
provide baseline data on items that may contribute to student
satisfaction with these courses (Roberts,
2007).
Response rates for online surveys are always a concern (Sax,
Gilmartin & Bryant, 2003). The art
of a student survey is the development of an instrument that
addresses the target, yet requires a minimal
time to complete. The current RITE instrument focuses
specifically on the dynamics of student
satisfaction with online learning and is presented in a 5-point
Likert scale format, ranging from strongly
agree to strongly disagree. Items related to the learning
management system or to technology itself have

been excluded in an effort to minimize survey fatigue. Survey
items were validated by examining their
psychometric properties from previous surveys in terms of
central tendency, variability, skewness and
kurtosis, looking for anomalies, and for their relevance to the
current state of the online initiative.
Once the original item pool was selected the survey was
developed using Google Forms
(https://drive.google.com). Students were sent an announcement
through the UCF Knights student email
platform. Student directions included the purpose of the study,
their rights as survey participants, and
contact information for both the survey administrators and the
University’s Institutional Review Board.
Students were advised that the survey was open only to
undergraduates 18 years of age and older, and
were reminded that the survey was voluntary. Students were
free to omit any question they were not
comfortable answering or stop at any time with no penalty.
Students received no rewards for participation
and there were no risks for non-participation. All student data
remained anonymous when aggregated.
Overall 1,217 surveys were returned.
An examination of student responses indicated that 84% of
students represented the millennial
generation, 72% were female, and 76% were unmarried. Almost
half of the responding students worked at
least 10 hours per week and data reflected the ethnic
demography of the university, with 70% of students
having a grade point average of at least 3.0. Respondents were
experienced with online learning—97% of
students indicated taking at least one online course, with a
median of five online courses. Students were
predominately upperclassmen, with 83% of respondents being

juniors or seniors. The university has
targeted the majority of its online offerings to the upper
undergraduate level, thereby allowing for the
transition of freshmen and sophomores to university life prior to
extensive online learning. Our
respondent sample of predominately upper undergraduates
reflects this philosophy. Students who
indicated they had not taken an online course were excluded
from analyses, reducing the usable sample to
1,197 responses.
Methodology
Reliability and Domain Sampling
Prior to any analysis of the item responses collected in this
sample, the psychometric quality of
the information yielded by the instrument was assessed with
validated techniques. Next, coefficient alpha
(Cronbach, 1951) was used to determine the survey reliability.
The psychometric sampling issue of how
well the items comprise a reasonable sample from the domain of
interest is an important aspect of
analyzing constructs such as student satisfaction. Addressing
this issue, Guttman (1953) developed a
theoretical solution illustrating that the domain sampling
properties of items improve when the inverse of
the correlation matrix approaches a diagonal. Kaiser and Rice
(1974) used this property to develop their
measure of sample adequacy. The index has an upper bound of
one with Kaiser offering some decision
rules for interpreting the value of measure of sampling
adequacy (MSA). If the value of the index is in

the range .80 to .99, the investigator has evidence of an
excellent domain sample. Values in the .70s
signal an acceptable result, and those in the .60s indicate data
that are unacceptable. MSA has been used
for data assessment prior to the application of any factoring
procedures. Computation of the MSA index
gives the investigators a benchmark for the construct validity of
the items. This procedure was
recommended by Dziuban and Shirkey (1974) prior to any latent
dimension analysis. An individual MSA
for each variable gives the investigators an indication of
whether or not a particular item belongs in the
particular domain.
Dimensionality of Student Responses
The investigators sought to determine whether multiple
dimensions underlie students’ satisfaction
to their online learning environments. This is normally
accomplished by the application of some version
of the generalized factor analysis procedure. In this study the
data were analyzed with Guttman’s (1954)
image analysis. The procedure assumes that the data sets divide
into two components. The first
component is the portion of data that can be predicted from the
remaining variables in the set (the image).
The second component is the data that is not predictable from
the remaining variables (the anti-image).
The method is operationalized by predicting a standardized
score on a variable for each individual from
the remaining variables in the set. The image procedure derives
underlying components found in the
covariance matrix (the image matrix) of the standardized
variables.
The number of factors (components) retained in the final

solution was determined by a procedure
originally proposed by Dziuban and Shirkey (1993) and later
validated by Hill (2011). The method
involves the initial assessment of the dataset with the MSA
followed by subsequent MSA computation on
the matrix of partial correlations once the impact of the first,
second, third etc. number of factors have
been removed from the system. Once a value in the .60s has
been reached, the majority of information
from the system has been attained. The initial pattern matrix
was transformed (rotated) according to the
promax (Hendrickson & White, 1964) procedure. Pattern
coefficients absolutely larger than .40 were used
for interpretation purposes (Stevens, 2002).
Once the final dimensionality of the data set was determined,
factor scores for each subject in the
sample were derived using the Anderson and Rubin (1956)
method. These scores have a mean of zero and
a standard deviation of one and are uncorrelated with each
other. They also have a reasonably good
relationship to the estimated factor validity. The final step in
the handling of the data involved deriving a
linear transformation of the standardized factor scores with T =
(Z x 10) + 50 giving the scores a mean of
50 and standard deviation of 10 for ease of interpretation.
The scores for each factor were used as dependent measures for
a rescaled comparison variable
related to overall online course satisfaction. Because the
number of dissatisfied students was small, the
comparison variable was declassified into satisfied, ambivalent,
and dissatisfied and used as a factor in
the hypothesis test. The investigators were concerned with
trends and effect size differences among the
dissatisfied (4%), ambivalent (5%), and satisfied (91%) groups

followed by Bonferroni post hoc
comparisons (Hochberg, 1988).
Results
The promax transformed pattern matrix may be found in Table
1. The overall MSA for the
variables set was .94 with an overall alpha reliability coefficient
of .96. These values indicate excellent
domain sampling and reliability. The individual MSAs indicate
each item belongs to the family
psychometrically. Upon extraction of three dimensions from the
system using the Dziuban-Shirkey
procedures, the MSA on the residual correlation matrix was .58
indicating that what remained in the
system was essentially noise.
Table 1 Pattern Matrix for the Promax Transformed Image
Analysis
Items
Engaged
Learning Agency Assessment MSA
Generally, I am more engaged in my online
courses .84 .04 -.07 .94
I have more opportunities to reflect on what I
have learned in online courses .79 -.05 .04 .94
Online learning helps me understand course
material .76 .03 .05 .95

There are more opportunities to collaborate
with other students in an online course .67 -.14 -.03 .93
My online experience has increased my
opportunity to access and use information .66 .11 .06 .95
I am more likely to ask questions in an online
course .65 -.11 .01 .94
Generally, I understand course requirements
better in an online course .64 -.09 .19 .96
Because of online courses, I am more likely to
get a degree .56 .09 -.03 .94
I can manage my own learning better in online
courses .54 .18 .17 .95
Take more online courses? .47 .22 .04 .96
I am motivated to succeed -.12 .56 -.03 .81
I have strong time management skills .05 .53 -.07 .85
I am a multitasker -.05 .57 .05 .87
Assessment of my academic progress is more
accurate in online courses -.19 -.04 .56 .92
I can more easily monitor my academic
progress in online courses .14 .11 .51 .92
Response time from teachers and assistants is
quicker in online courses .24 -.12 .43 .94
MSA = .94
Residual MSA = .58
Average Item Correlation = .70

=.96
From Table 1, the reader may observe that the first factor
appears very general, covering a
number of issues associated with online courses ranging from
engagement through willingness to take
another online course. However, upon closer examination, it is
clear what appears to be very general is
quite specific in relation to what students evaluate in online
courses. These elements include students’
abilities to engage, reflect, understand material, collaborate,
find information, question, understand course
requirements, manage their own learning, and increase
opportunities for degree completion. This finding
suggests students simultaneously evaluate multiple aspects of
online courses to make decisions about
their class experience. Furthermore, students may evaluate each
element separately, especially when they
are unsure of their satisfaction levels. We name this factor
engaged learning (74% factor variance) and in
many respects, it conforms to Tolstoy’s (1878/2004) opening
argument and Diamond’s (1999) contention
that many elements must be accommodated if conditions are to
be resolved satisfactorily. Conversely, any
one or more of these elements might cause students to be less
than satisfied with their educational
experience.
The second factor (17% factor variance) in the pattern matrix
involves motivation, time
management skills, and multitasking ability. This dimension

suggests that students’ sense of agency—that
is, students’ ability to initiate and control their own actions in
the learning environment—plays a role in
their satisfaction with their online learning experience. Students
with a strong sense of agency assume
responsibility for their learning and bring a sense of
empowerment to their classes. Since the majority of
students in this study indicated higher levels of satisfaction
with online learning, we might reasonably
assume they bring a higher sense of agency as well. Agency,
however, may not be specifically related to
course modality.
The final factor (9% factor variance) depicts the manner in
which the assessment process evolves
in the online environment. Satisfied students are characterized
by an ability to assess and monitor their
progress, and indicate that a timely response by the instructor
plays an important role in their satisfaction.
Therefore, we find online students incorporate three dimensions
into their evaluation process of online
learning experiences: 1) engaged learning with various course
elements, 2) a sense of agency, and 3) an
efficient assessment of academic progress.
The factor correlation matrix in Table 2 indicates that these
student dimensions are highly and
positively related in a generally satisfied population. This
suggests that engaged learning, agency, and
assessment factors form a highly interrelated lexicon for student
satisfaction, with engaged learning most
highly related to agency (r =.86) and agency most highly related
to assessment (r =.77).
Table 2 Factor Correlation Matrix

Factors
Engaged
Learning Agency
Engaged Learning
Agency .86
Assessment .59 .77
The average correlation among the factors in Table 3 is .74
when computed by the method
developed by Kaiser (1958). Table 3 contains the means,
standard deviations, and significance levels
for the three sets of factor scores for the declassified overall
satisfaction variable. In addition, the table
contains the pairwise combinations that proved significant on
the Bonferroni comparison and the
associated effect size calculated by the method outlined by
Hedges and Olkin (1985). The factor scores
for engaged learning and agency lead to the null hypothesis
rejection, however assessment did not. For
engaged learning, dissatisfied versus ambivalent ratings
produced an effect size of .53, dissatisfied versus
satisfied ratings yielded values of 2.01, and ambivalent versus
satisfied ratings equaled 1.43. Bonferroni
comparisons for the agency factors showed two significant
differences with dissatisfied versus satisfied
ratings producing an effect size value of 1.03, while ambivalent
versus satisfied ratings yielded .77. Each
of the above effects sizes by most standards is considered
substantial.

Table 3 Factor Score Difference by Overall Satisfaction
Dissatisfied (D)
(n=46)
Ambivalent (A)
(n=56)
Satisfied (S)
(n=1016)
Sig.Factors Scores S.D. S.D. S.D.
Engaged Learning 33.63 10.86 38.92 8.92 51.59 8.86
.00
Agency 41.46 11.27 43.74 13.84 50.98 9.15 .00
Assessment 48.93 9.24 49.21 8.56 50.13 10.19 .63
Significant Bonferroni Pairwise Differences
Categories Engaged Learning Agency Assessment
Dissatisfied vs. Ambivalent .00 ns ns
Dissatisfied vs. Satisfied .00 .00 ns
Ambivalent vs. Satisfied .00 .00 ns
Effect Sizes - Hedges’ g
Categories Engaged Learning Agency Assessment
Dissatisfied vs. Ambivalent 0.53 0.18 0.03
Dissatisfied vs. Satisfied 2.01 1.03 0.12
Ambivalent vs. Satisfied 1.43 0.77 0.09
Limitations
There are a number of limitations in this study. Initially it

should be noted that the factors derived
resulted from a one-time administration of the survey
instrument during the semester. Therefore, the
stability of the satisfaction factors over an entire semester has
not been validated. Second, the study was
conducted on individual item responses rather than scales.
Although this has precedent in literature, single
items with presumed low reliability can be problematic in factor
studies such as this because of their
instability. Third, many aspects of exploratory factor analysis
involve arbitrary decisions, for instance,
number of factors to extract, values for salience in the pattern
matrix, rotational strategy, and naming the
final dimensions. Fourth, online survey research using mass e-
mailings to students has the possibility of
introducing response bias into the data. This makes replication
of studies much more difficult. Finally,
although the investigators collected extensive demographic data
on the responding students, there was no
possibility for controlling for many of the student
characteristics that might have influenced the results.
This raises a more general limitation resulting from the ease
with which survey instruments can be
distributed in the electronic environment. This causes many
students to suffer “survey fatigue” that can
adversely impact response rates.
Conclusion
Student Satisfaction in the Online Environment
From its inception, the Sloan-Consortium (now the Online

Learning Consortium) established
student satisfaction with online leaning as one of its founding
metaphoric pillars. In doing so, the
organization demonstrated a commitment to the student voice as
a component for understanding effective
teaching and learning. This commitment by the Online Learning
Consortium resulted in two decades of
research devoted to understanding how students define
excellence in their learning space. Satisfaction
with online learning is becoming increasingly important in
higher education for a number of reasons. The
most important is the rapid adoption of this teaching and
learning modality in colleges, universities, and
community colleges across the country. However, another
mediating issue is the growing sense of student
agency in the educational process. Students are able and do
express their opinions about their educational
experiences in formats ranging from end of course evaluation
protocols to social networks of all varieties
making their voice more important than ever before.
Factor Studies
Online learning has redefined student satisfaction research. It
has caused the education research
community to reexamine traditionally held assumptions that
learning primarily takes place within a
metaphoric container called a “course.” In reviewing the studies
that address student satisfaction, from a
factor analytic perspective, one point becomes obvious: this is a
complex system with very little
agreement. Even the most recent factor analytic studies have
done little to resolve the lack of consensus
about the dimensions that underlie satisfaction with online
learning. This appears to be the factor
invariance problem in full bloom, where differing contexts

mediate how students relate to their learning
experiences because a common prototype for online courses has
been elusive at best. There exists the
possibility that each course incorporates several unique
characteristics that make it difficult to identity
common factors that are robust across context. Although the
results of these studies differ in how many
and what dimensions constitute satisfaction, their unifying
objective was the same: identify the
underlying theoretical perspective of student perception of
online learning. In addition, all of them
subscribed to latent trait theory, recognizing that the important
dimensions that students differentiate
when they express their opinions about online learning are
formed by the combination of the original
items that cannot be directly observed—that which underlies
student satisfaction.
Psychological Contracts as a Lens for Student Satisfaction
Very often theories developed in on one discipline inform work
in another area. We contend that
this is the case with the psychological contracts and factors that
define student satisfaction with online
learning. The theory of psychological contracts explains
employee satisfaction through the perspectives of
expectations for the work place and employee interactions.
These contracts may be common across
employees, for instance safety on the job, or they may be unique
to individual employees such as
promotion. The elements of the contract are implicit in that they
are never formally stated, but they are
assumed by the individual holding them to be mutually agreed
upon between the employee and the
employer. Of course, this may or may not be so. Most
importantly, a violation of the psychological

contract, either real or perceived, by either party, leads to
workplace dissatisfaction.
In factor analytic studies, items about student satisfaction with
online learning correspond to the
formation of a psychological contract. The survey responses are
reconfigured into a smaller number of
latent (non-observable) dimensions that are never really
articulated by the students, but are, nonetheless,
fully expected to be satisfied. Of course, instructors have
contracts for students as well. Studies such as
this identify the student psychological contact after the fact, not
prior to the class, however, nothing
prevents both from happening and/or a comparison of the two.
The prior contract might be identified
before the class and the satisfaction factors after the class.
Table 4 depicts the relationship between online student
satisfaction factors and the features of a
psychological contract specified in the literature. Each factor
translates into how it might be expressed in
the student voice followed by a corresponding contract feature
and an assessment strategy. Engaged
learning, the largest contributor to the factor pattern, indicates
that students expect instructors to adopt a
facilitative role in their teaching. This dimension corresponds to
the relational contract where the learning
environment is stable and organized with a clearly delineated
path to success. Assessment in this situation
is evaluation oriented, indexing fulfillment and change (i.e.,
students achieving learning outcomes).

The second factor, agency, characterizes satisfied students who
recognize their abilities and
accomplishments in a balanced contract arrangement that they
assessed by the degree of agreement
between them and the instructor (feature oriented). The final
factor, assessment, corresponds to that
transactional contract with its evaluation determined by the
degree to which the obligations of the course
have been met (content oriented).
Although they have been developed in different contexts,
workplace contracts and student
satisfaction factors are similar. Both attempt to explain the
underlying cause of satisfaction or lack
thereof. Both are general and non-nonspecific, becoming more
complex as the job, class, or classes
evolve over time. They are limited in their scope and at best
index a kind of average perception of the
workplace or educational environment. Rarely are employees
fully satisfied with their jobs or students
completely satisfied with their classes. However, both contracts
and factors frame blueprints for
developing instructional design strategies that can improve
learning climates. Online learning has
unbundled the classroom and the same technologies have done
precisely the same to the workplace: no
longer are either bound by physical space.
Perhaps in a more traditional time, psychological contracts
(predispositions) and student
satisfaction elements (post dispositions) were somewhat
separate in their context and orientation.
However, it seems clear that information and instructional
technologies are migrating into the same
orientation space. This makes the questions “What did you
expect on your way in?” and “Now that you

are finished, how was your experience?” part of the same
climate assessment paradigm. By coalescing
factors and psychological contracts, we might gain insights into
more effective learning environments that
are not possible when each theory is considered separately.
Blending the two takes best features of both
and results in something entirely new--something more than you
can see in either theory.
Table 4 Correspondence Between Satisfaction and
Psychological Contracts
Student Satisfaction
Factors Student Voice Contract Features Contract Assessment
Engaged Learning “Facilitate my learning” Relational
Evaluation Oriented
Agency “Recognize my abilities and accomplishments”
Balanced Feature Oriented
Assessment “Let me know where I stand” Transactional Content
Oriented
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Online Instruction, E-Learning, and Student
Satisfaction: A Three Year Study
(SNnOnlineCourses
Michele T. Cole, Daniel J. Shelley, and Louis B. Swartz
Robert Morris University, United States
Abstract

This article presents the results of a three-year study of
graduate and undergraduate
students’ level of satisfaction with online instruction at one
university. The study
expands on earlier research into student satisfaction with e-
learning. Researchers
conducted a series of surveys over eight academic terms. Five
hundred and fifty-three
students participated in the study. Responses were consistent
throughout, although
there were some differences noted in the level of student
satisfaction with their
experience. There were no statistically significant differences in
the level of satisfaction
based on gender, age, or level of study. Overall, students rated
their online instruction
as moderately satisfactory, with hybrid or partially online
courses rated as somewhat
more satisfactory than fully online courses. “Convenience” was
the most cited reason for
satisfaction. “Lack of interaction” was the most cited reason for
dissatisfaction.
Preferences for hybrid courses surfaced in the responses to an
open-ended question
asking what made the experience with online or partially online
courses satisfactory or
unsatisfactory. This study’s findings support the literature to
date and reinforce the
significance of student satisfaction to student retention.
Keywords: E-learning; instructional design; online education;
student retention;
student satisfaction

Online Instruction, E-Learning, and Student Satisfaction: A
Three Year Study
Cole, Shelley, and Swartz
Vol 15 | No 6 Creative Commons Attribution 4.0
International License Dec/14
112
Introduction
In their ten-year study of the nature and extent of online
education in the United States,
Allen and Seaman (2013) found that interest on the part of
universities and colleges in
online education shows no sign of abating. Online education
continues to expand at a
rate faster than traditional campus-based programs. The authors
reported the number
of students enrolled in at least one online course to be at an all-
time high of 32% of all
enrollments in participating institutions, representing an
increase of 570,000 students
from the previous year. Allen and Seaman also found that 77%
of university leaders
responding to the survey rated learning outcomes to be the
same, if not better, with
online education when compared with face-to-face learning.

Their results support the
no significant difference phenomenon that Russell (1999) found
in his comparative
study of student learning in the online and traditional classroom
environments.
Acknowledging that learning outcomes are equivalent, the
question of how satisfied
students are with their experiences with e-learning persists.
This is important from the
stand point of student retention which is, of course, relevant to
enrollment and
maintaining institutional revenue streams. Also, analysis of
student satisfaction may
point to improvements in e-learning practices which in turn
could improve outcomes.
Literature Review
The Allen and Seaman (2013) report looked at online education,
including the growing
presence of massive open online courses (MOOCs), from the
institutional perspective,
not from the student’s. In their report, the authors noted that
the remaining barriers to
widespread acceptance of online education were lack of faculty
and employer
acceptance, lack of student discipline and low retention rates.
Of these, student
retention in online programs is particularly relevant to the
discussion of student
satisfaction with their online experience. Reinforcing the
instructor’s role in designing
satisfying online curricula, Kransow (2013) posited that if
students were satisfied with
their online experiences, they would be more likely to remain in
the program.

Kransow (2013) poses a critical question for instructors
working in the online
environment. How can online courses be designed to maximize
student satisfaction as
well as student motivation, performance and persistence?
Drawing on the literature,
Kransow emphasizes the importance of building a sense of
community in the online
environment. Yet, building an online community that fosters
involves strategies that go beyond facilitating interaction with
course components.
Building community also requires, among other elements,
interaction with each other,
that is, between student and instructor and among students in
the course. Sher (2009),
in his study of the role such interactions play in student
learning in a Web-based
environment, found interaction between student and instructor
and among students to
be significant factors in student satisfaction and learning.
Interaction—between the student and the instructor, among
students, and with course
content and technology—was the focus of Strachota’s (2003)
study of student
Three Year Study

113
satisfaction with distance education. In her study, learner-
content interaction ranked
first as a determinant of student satisfaction, followed by
learner-instructor and learner-
technology interaction. Interaction between and among students
was not found to be
significantly correlated with satisfaction. Bollinger (2004)
found three constructs to be
important in measuring student satisfaction with online courses:
interactivity,
instructor variables and issues with technology.
Palmer and Holt (2009) found that a student’s comfort level
with technology was
critical to satisfaction with online courses. Secondary factors
included clarity of
expectations and the student’s self-assessment of how well they
were doing in the online
environment. Drennan, Kennedy, and Pisarski (2005) also found
positive perceptions of
technology to be one of two key attributes of student
satisfaction. The second was
autonomous and innovative learning styles. Richardson and
Swan (2003) focused on
the relationship of social presence in online learning to
satisfaction with the instructor.
They found a positive correlation between students’ perceptions
of social presence and
their perceptions of learning and satisfaction. For Sahin (2007),
the strongest predictor

of student satisfaction was personal relevance (linkage of
course content with personal
experience), followed by instructor support, active learning and,
lastly, authentic
learning (real-life problem-solving).
Kleinman (2005) looked at improving instructional design to
maximize active learning
and interaction in online courses. Over a period of ten years,
Kleinman studied online
communities of learning, concluding that an online environment
which fosters active,
engaged learning and which provides the interactive support
necessary to help students
understand what is expected, leads to a satisfied learning
community. Swan (2001), too,
found that interactivity was essential to designing online
courses that positively affect
student satisfaction. Wang (2003) argued that to truly measure
researchers must first assess the effectiveness of online
education.
Online education represents a major shift in how people learn
and in turn, how learners
are taught. The argument is made that, therefore, there is an
increasing need to
understand what contributes to student satisfaction with online
learning (Sinclaire,
2011). Student satisfaction is one of several variables
influencing the success of online
learning programs, along with the institutional factors that Abel
(2005) listed in his
article on best practices (leadership, faculty commitment,
student support, and
technology). Sener and Humbert (2003) maintained that

satisfaction is a vital element
in creating a successful online program.
There have been a number of studies of student satisfaction with
e-learning (Swan,
2001; Shelley, Swartz, & Cole, 2008, 2007), fully online as well
as with blended learning
models (Lim, Morris, & Kupritz, 2007). There have also been a
number of studies by
Arbaugh and associates on the predictors of student satisfaction
with online learning
(Arbaugh, 2000; Arbaugh, & Benbunan-Fich, 2006; Arbaugh, et
al., 2009; Arbaugh, &
Rau, 2007). Results from this study both support and expand on
earlier work.
Three Year Study
114
Discussion about the role that MOOCs are destined to play in
higher education (Deneen,
2013; Shirky, 2013) serves to heighten educators’ interest in
providing quality online
courses that maximize student satisfaction. The controversy
over granting credit for

MOOC courses (Huckabee, 2013; Jacobs, 2013; Kolowich,
2013a; Kolowich, 2013b;
Kolowich, 2013c; Lewin, 2013; Pappano, 2012) reinforces the
relevance of student
satisfaction to successful online education.
This study reports on research into student satisfaction with
online education conducted
over three years. The research has focused largely on business
students at one university
in Southwestern Pennsylvania. The emphasis on student
satisfaction with e-learning
and online instruction is increasingly relevant for curriculum
development which in
turn is relevant for student retention. Understanding what makes
online instruction and
e-learning satisfactory helps to inform instructional design.
This study is an extension of previous research on student
satisfaction with online
education (Cole, Shelley, & Swartz, 2013, Swartz, Cole, &
Shelley, 2010, Shelley, Swartz,
& Cole, 2008, 2007). Researchers used a multi-item survey
instrument to assess how
well student expectations were met in selected online courses.
Graduate and
undergraduate students were asked first whether they were
satisfied with their
experience with e-learning. Following that, they were asked to
explain what made the
experience satisfactory or unsatisfactory. Student satisfaction is
defined as “the learner’s
perceived value of their educational experiences in an
educational setting” (Bollinger &
Erichsen, 2013, p. 5).

Research Questions
This study focused on two survey questions:
1. Please rate your level of satisfaction with the online and/or
partially online
courses you have taken.
2. What made your experience with the online course/s
satisfactory or
unsatisfactory?
Both survey questions were broken into two separate questions
for purposes of analysis,
resulting in four research questions:
1. How satisfied were students with their fully online courses?
2. How satisfied were students with their partially online
courses?
3. What factors contributed to students’ satisfaction with e-
learning?
4. What factors contributed to students’ dissatisfaction with e-
learning?
This paper presents the results of that analysis.
Three Year Study

115
Method
Researchers used a Web-based survey created in Vovici, an
online survey software
program. Following a pilot study in spring, 2010, surveys were
sent to students in
graduate and undergraduate business courses over a period of
three years. Researchers
used a mixed-method analysis to evaluate responses to the
selected questions.
Descriptive statistics were used to summarize demographic data
and survey responses.
Results were transferred from Vovici to, and combined in, SPSS
to analyze the first two
research questions. Independent samples t-tests were conducted
on the scaled items.
Keyword analysis was used for the third and fourth research
questions. The survey was
anonymous.
Students in each of the business classes were offered extra
credit for taking the survey.
Credit was given based on notification to the instructor by the
student. The same
instructor taught each of the 19 courses in the second and third
study samples as well as
the business courses included in the initial study.
The initial survey instrument was approved by the University’s

Institutional Review
Board in 2010. Subsequent modifications to the survey were
minor and did not require
separate approvals in 2011/2012 or 2012/2013. The same script
was used seeking
participation in each of the surveys. Participation was solicited
via an e-mail from the
instructor. Each e-mail included the link to the Web-based
survey developed in Vovici.
Data from the completed surveys were transferred from Vovici
into SPSS. Independent
samples t-tests were conducted on the questions asking students
to rate their level of
satisfaction with online learning. Responses from males and
females, “Generation X”
and “Generation Y,” and from graduate and undergraduate
students were compared to
determine if there were any statistically significant differences
in the level of satisfaction
with online and partially online courses. Responses to the
question asking what
contributed to the respondents’ satisfaction or dissatisfaction
with online learning were
tabulated in Vovici. To analyze these responses, researchers
grouped keywords under
themes to form categories. The categories were: convenience,
interaction, structure,
learning style, and platform. “Interaction” included
“communication.” “Structure”
included “clarity” and “instructor’s role.” “Other” was included
to capture responses that
did not fall into any of the stated categories.
Sample and Participant Selection

The sample from the pilot study in spring, 2010 included
graduate students from the
MS in Instructional Technology and the MS in Nonprofit
Management programs,
undergraduate business majors, and Masters of Business
Administration (MBA)
students. No changes to the survey design were indicated as a
result of the pilot study.
The second study was conducted over three terms, summer,
2010, fall, 2010, and spring,
2011. This sample was composed of undergraduate students
enrolled in Legal
Environment of Business (BLAW 1050), taught in the fall 2010
term, and graduate
Three Year Study
116
students enrolled in Legal Issues of Executive Management
(MBAD 6063), which was
taught in the summer 2010 and spring 2011 terms. The third
study was conducted over
four terms, fall, 2011, spring, 2012, fall, 2012, and spring,
2013. This sample was
composed of undergraduates in BLAW 1050 taught in the fall

2011, fall 2012, and spring
2013 terms and graduate students in MBAD 6063, taught in the
spring 2012 and spring
2013 terms. Both the graduate and undergraduate business
courses chosen for the study
were taught by the same instructor.
Thirty-three students participated in the spring 2010 survey, a
response rate of 58%.
One hundred and sixty-four students participated in the second
study, a response rate of
92%. Three hundred and fifty-six students participated in the
third study, a response
rate of 97%. Combined, the total number of participants was
553 of 603 enrolled
students, for a response rate of 92%.
Twelve males and 21 females participated in the first survey.
One hundred and three
males and 61 females responded to the survey in the second
study group. Two hundred
and seventeen males and 135 females responded to the survey in
the third study group
for a total of 332 males (60.5%) and 217 females (39.5%) who
participated in the
surveys. Not all participants in the third sample responded to
the question on gender.
Participants were asked to identify themselves as belonging to
one of the following age
groups:
• Traditional Workers (born before 1946)
• Baby Boomers (born between 1946 and 1960),

• Generation X (born between 1961 and 1979) and,
• Generation Y (born after 1979) (Recursos Humanos, 2010).
Eight participants identified themselves as belonging to the
Baby Boomer or the
Traditional Worker categories. Nine people checked “Other.”
Three participants did not
respond to the question on age. The remaining respondents self-
identified as belonging
to “Generation X” or “Generation Y.” Due to the limited sample
sizes for “Baby
Boomers” and “Traditional Workers,” only responses from
participants in the
Generation X and Generation Y categories were compared for
this study.
In the first survey, 22 respondents self-identified as members of
“Generation Y.” Eleven
respondents classified themselves as members of “Generation
X.” In the second study
group, 136 respondents self-identified as “Generation Y.”
Twenty-two respondents self-
identified as “Generation X.” In the third study group, 303
respondents self-identified
as “Generation Y.” Thirty-nine respondents self-identified as
“Generation X.” The total
number of respondents who self-identified as belonging to
“Generation Y” was 461.
Seventy-two respondents self-identified as “Generation X.” The
total number of
respondents belonging to either “Generation X” or “Generation
Y” was 533.

Three Year Study
117
Two hundred and sixty graduate students participated in the
surveys. Two hundred and
eighty-one undergraduate students participated, for a total of
541. Some respondents
did not identify themselves clearly as being either graduate or
undergraduate students.
Table 1 presents the respondents’ demographic information.
Table 1
Respondent Sample Demographics*
Study N/Response
%
Male Female Gen X Gen Y Grad UG
I
II
III

Total
33 – 58%
164 -92%
365-97%
553-92%
12
103
217
332
21
61
135
217
11
22
39

72
22
136
303
461
33
89
138
260
0
73
208
281
* Not all respondents answered each question on gender, age,
or level of study
Procedure
Responses to the two questions on student satisfaction from

three surveys, Designing
Online Courses, Students’ Perceptions of Academic Integrity
and Enhancing Online
Learning with Technology, provided the data for the analysis.
Although survey
instruments used in the second and third studies were modified
slightly to gather data
for the studies on academic integrity and use of technology,
each survey asked:
1. Please rate your level of satisfaction with the online and/or
partially online
courses you have taken.
2. What made your experience with the online course/s
satisfactory or
unsatisfactory?
Researchers used a 5 point Likert scale for the first survey
question, asking students to
rate their level of satisfaction with fully online and/or partially
online courses. Zero was
equal to “very satisfied;” four was equal to “very dissatisfied.”
The second survey
question was designed as a follow-up query, asking what
contributed to the student’s
satisfaction or dissatisfaction with online learning.
To help inform the analysis of responses to the research
questions, researchers asked
students how many online or partially online courses they had
taken. To enable
comparisons by gender, age group, and level of study,
demographic questions were
included in each of the surveys.

Three Year Study
118
Designing Online Courses was administered in the spring 2010
term. The survey was
composed of 12 questions. Students’ Perceptions of Academic
Integrity was conducted
in the summer 2010, fall 2010, and spring 2011 terms. This
survey was composed of 13
questions. The third survey, Enhancing Online Learning with
Technology, was
composed of 12 questions. This survey was administered in the
fall 2011, spring 2012,
fall 2012, and spring 2013 terms.
Results
The first survey question sought to capture respondents’ level of
experience with e-
learning. In the first two studies, students were asked if they
had taken or were taking
one or more fully online graduate courses, partially online
graduate courses, fully online
undergraduate courses, and/or partially online undergraduate

courses. Responses from
both studies were combined for analysis. There were 198
student responses. Since the
response categories were not mutually exclusive, a student
could select more than one
response. Some students had taken both graduate and
undergraduate-level fully online
and/or partially online courses. As a result, the total number of
responses to the
question (255) exceeds the number of respondents (198). Table
2 presents the results.
Table 2
Level of Experience with E-Learning – Studies I & II
Response Count
N=255
% Student
responses
N=198
As a graduate student in fully online courses
As an under graduate student in fully online
courses
As a graduate student in partially online courses
As an undergraduate student in partially online
courses
As a student taking courses outside of a degree
program
None
Other

65
28
73
50
5
24
10
32.8
14.1
36.8
25.2
2.5
12.1
5.0
Elaboration on “other” included four instances of some
experience with online courses
that did not fit the categories in the question, and two
references to having had online
assignments. Four were unresponsive to the question.
The question asking for the respondent’s level of experience
with online or partially
online was phrased differently in the third study. In the final
surveys (from fall, 2011,

Three Year Study
119
spring, 2012, fall, 2012, and spring, 2013), researchers asked
how many fully or partially
online courses the student had taken. There were 391 responses.
Students could choose
only one response. Table 3 illustrates the results.
Table 3
Level of Experience with E-Learning – Study III
Responses Count
N= 391
%of
Responses
1 course
2-4 courses
5-10 courses
More than 10 courses
None
Oher

89
154
56
20
35
37
22.7
39.3
14.3
5.1
8.9
9.4
RQ1 How satisfied were students with their fully online
courses?
In a two part survey question, students were asked to rate their
level of satisfaction with
fully online courses taken and with partially online courses
taken. Students could
respond to either part of the question or to both. To the first
part, level of satisfaction
with fully online courses, there were 472 responses, 85% of the
total 553 participants. A
5 point Likert scale was used to measure responses ranging from
0 (very satisfied) to 4
(very dissatisfied). One hundred and six students or 22.5% of
the total responding said
that they were “very satisfied.” One hundred and seventy-one
(36.2%) said that they

were “satisfied.” One hundred and twenty-six (26.7%) were
“neutral.” Fifty–one (10.8%)
said that they were “dissatisfied.” Eighteen (3.8%) respondents
were “very dissatisfied”
with their experience with fully online courses.
Independent samples t-tests were conducted on this question to
determine if there were
any significant differences in the levels of satisfaction based on
gender, age, or level of
study with regard to satisfaction with fully online learning.
There were no statistically
significant differences between males and females, between
members of “Generation X”
and “Generation Y,” or between graduate and undergraduate
students on the question.
Females, members of Generation X, and upper-level
undergraduate students were more
likely than males, members of Generation Y, and graduate
students to rate their
experiences with fully online courses as satisfactory. The mean
score for females was
1.31; the mean score for males was 1.41. The mean score for
members of Generation X
was 1.24; the mean score for members of Generation Y was
1.40. The mean score for
upper-level undergraduate students was 1.19; the mean score for
graduate students was
1.23. Table 4 presents the results.
Three Year Study

120
Table 4
Student Satisfaction with Fully Online Courses
Variable n M t Sig.(2-
tailed)
Female 177 1.31 -1.052 .293
Male 294 1.41
Generation X 63 1.24 -.989 .326
Generation Y 396 1.40
Undergraduates 26 1.19 -.146 .884
Grad. Students 105 1.23
RQ2 How satisfied were students with their partially online
courses?
There were 420 responses, 76% of the total 553 participants, to
the second part of the
question asking students to rate their level of satisfaction with
partially online courses.
The same 5 point Likert scale was used to measure both parts.
Ninety-nine students or
23.6% of the total responding said that they were “very
satisfied.” One hundred and
thirty-six (32.4%) said that they were “satisfied.” One hundred

and thirty-seven (32.6%)
were “neutral.” Forty-three (10.2%) said that they were
“dissatisfied.” Five students
(1.2%) said that they were “very dissatisfied” with their
experience with partially online
courses.
Independent samples t-tests were conducted on this question to
determine if there were
any significant differences in the levels of satisfaction based on
gender, age, or level of
study with regard to satisfaction with partially online learning.
As with the first research
question, there were no statistically significant differences
between males and females,
between members of “Generation X” and “Generation Y,” or
between graduate and
undergraduate students with regard to satisfaction with partially
online courses.
However, unlike satisfaction with fully online courses taken,
males were somewhat
more satisfied than females, and graduate students were more
satisfied than upper-level
undergraduates with partially online courses taken. The mean
score for males was 1.32;
for females, the mean was 1.34. The mean for graduate students
was 1.11; for upper-level
undergraduates, the mean was 1.35. As was the case with fully
online courses, older
students, members of Generation X, were more satisfied with
their partially online
courses than were members of Generation Y. The mean score
for “Generation X” was
1.09; for “Generation Y,” the mean was 1.37. Table 5 presents
the results.

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