REFEREED PUBLICATIONS
Ding, X., Hu, P.J., Verma, R. and D.G. Wardell (2010). “The Impact of Service System Design and Flow Experience on Customer Satisfaction in Online Financial Services,” forthcoming in Journal of Service Research (scheduled to appear in the February 2010 issue).
Victorino, L., Verma, R. and D.G. Wardell (2008), “Service Scripting: A Customer’s Perspective of Quality and Performance,” Cornell Center for Hospitality Research Managerial Report, 8, 20, 4-13.
Tsai, W. and Wardell, D.G. (2006).
"Creating Individualized Data Sets for Student
Exercises Using Microsoft Excel and Visual Basic," INFORMS
Transactions on Education, 7, 1, http://archive.ite.journal.informs.org/Vol7No1/TsaiWardell2/.
Tsai, W. and Wardell, D.G. (2006).
"An Interactive Excel VBA Example For Teaching Statistics
Concepts," INFORMS
Transactions on Education, 7, 1, http://archive.ite.journal.informs.org/Vol7No1/TsaiWardell/
Ding, X., Wardell, D.G. and
Verma, R. (2006). "An Assessment of Statistical Process
Control-Based Approaches for Charting Student Evaluation Scores," forthcoming
in the Decision Sciences
Journal of Innovative Education, 4, 2, 259-272.
Chesteen, S., Helgheim, B.,
Randall, T. and Wardell, D.G. (2005). "Comparing quality
of care in non-profit and for-profit nursing homes: A process perspective,"
Journal
of Operations Management, 23, 2, 229-242.
Pullman, M., Moore, W. and Wardell,
D. G. "A Comparison of Quality Function Deployment and Conjoint
Analysis In New Product Design," to appear in Journal
of Product Innovation Management.
Wardell, D. G. (1997). "Small
Sample Interval Estimation of Bernoulli and Poisson Parameters," The
American Statistician, 51, 4, 321-325.
Wardell, D. G. and M. R. Candia
(1996). "Statistical Process Monitoring of Customer Satisfaction
Survey Data," Quality
Management Journal, 3, 4, 36-50.
Wardell, D. G., H.
Moskowitz and R.D.
Plante (1994). "Run Length Distributions of Residual
Control Charts for Autocorrelated Processes," Journal
of Quality Technology, 26, 4, 308-317.
Wardell, D. G., H.
Moskowitz and R.D.
Plante (1994). "Run Length Distributions of Special-Cause
Control Charts for Correlated Processes," Technometrics,
36, 1, 3-17.
Moskowitz,
H., R.D.
Plante and D. G. Wardell (1994). "The Use of Run Length
Distributions of Statistical Process Control Charts to Detect False Alarms,"
Production and Operations
Management, 3, 3, 217-239.
Wardell, D. G., H.
Moskowitz and R.D.
Plante (1992). "Control Charts in the Presence of
Data Correlation," Management Science,
38, 8, 1084-1105.
Prior research examines customer satisfaction in retailing and e-commerce settings, yet online financial services have received little research attention. To understand customer satisfaction with this fast-growing service, we investigate the role of flow experience, a sensation that occurs as a result of significant cognitive involvement. We examine how service system characteristics affect the cognitive states of the flow experience, which determines customer satisfaction. The flow construct and total experience design suggest a structural model that we empirically test using responses from a large sample of online investors. In support of the model and most of the hypotheses it suggests, our empirical results clarify the important antecedents and consequence of flow experience in online financial services and suggest the viability of using a dual-layer experience construct to investigate customer satisfaction. Our findings can help researchers and service providers understand when, where, and how flow experience is formulated in online financial services.
KEYWORDS: Satisfaction, service system, flow experience, online financial serviceKEYWORDS: Spreadsheets, Student Assessment, Education Customization, Computer-based
Testing, Algorithmic-styled Exam Generation
It is often challenging for business students to learn abstract statistical
concepts and apply these concepts to their work. Three concepts in particular
that we have found difficult to communicate effectively are the Central Limit
Theorem, interval estimation and hypothesis testing. To improve the effectiveness
of teaching these fundamental statistical concepts, we developed a Visual Basic
for Applications (VBA) driven Excel spreadsheet that is built around one simple
business scenario. The scenario involves setting the filling speed in a cereal
filling plant. The faster the filling speed, the larger the variation in cereal
box weights and the higher the chance of having an out-of-control filling process.
On the other hand, the lower the filling speed, the less efficient the plant
is at utilizing capacity. Through interactively finding the optimal filling
speed, students are exposed to these key statistics concepts as well as random
sampling techniques. Hence, we integrate the illustration of three important
statistical concepts in one simple yet practical business scenario. Moreover,
the Excel VBA-driven example demonstrates several Excel statistical formulae
that are useful to business students. We conducted an in-class open-book quiz
to two sections of professional MBA students to assess teaching effectiveness
of this interactive example. The results showed that the scores of those using
the interactive VBA demo were superior to those exposed to more traditional
techniques at 10% significance level. A follow-up on-line feedback survey further
supported the usage of the Excel VBA-driven example in enhancing student learning.
KEY WORDS: Excel VBA Macro, Visualize Statistics Concepts, Central Limit Theorem, Confidence Interval, Hypothesis Testing, Random Sampling
We compare three control charts for monitoring data from student evaluations of teaching (SET) with the goal of improving student satisfaction with teaching performance. The two charts that we propose are a modified p chart and a z-score chart. We show that these charts overcome some of the shortcomings of the more traditional charts for analyzing SET data. A comparison of three charts (an individuals chart, the modified p chart and the z-score chart) reveals that the modified p chart is the best approach for analyzing SET data because it utilizes distributions that are appropriate for categorical data, and its interpretation is more straightforward. We conclude that administrators and faculty alike can benefit by using the modified p chart to monitor and improve teaching performance as measured by student evaluations.
Using data from a sample of nursing homes, this paper uses an
operations centric approach to test the economic hypothesis asserting that
quality in non-profit healthcare entities will exceed quality in for-profit
counterparts (Arrow 1963). For-profit healthcare entities face an inherent
conflict between providing profits to investors and health welfare to patients.
Thus, non-profit entities exist as a signal and evidence of higher quality
services. To date, research examining differences in quality between for-profit
and non-profit nursing homes has focused on a direct link between outcome
quality and non-profit status. These studies have produced inconclusive or
mixed results. We argue that non-profit or for-profit status and outcome quality
are linked via two intermediate factors, namely process quality and input
quality. Consistent with many prior studies, we report no direct link between
non-profit status and outcome quality. However, we report that process quality
is indeed higher at non-profit nursing homes than for-profit nursing homes,
but that input quality is lower. We also examine the association between outcome
quality with process and input quality. We report that different aspects of
process quality are tied to better outcome quality, but report several notable
exceptions. This research provides support for Arrow's hypothesis at the process
level and gives insights into the link between process quality, input quality
and outcome quality in the nursing home environment.
KEYWORDS: Process quality, hierarchical linear models,
Baldrige Award
We compare two product design approaches, quality function
deployment (QFD) and conjoint analysis, by applying each to the design of
a new all-purpose climbing harness for the beginning/intermediate ability
climber that would complement a leading manufacturer's existing product line.
While many of the optimal design features were the same under both approaches,
the differences allow us to highlight the strengths of each approach. With
conjoint analysis, it was easier to compare the most preferred features (i.e.,
ones that maximized sales) to profit maximizing features and also to develop
designs that optimize product line sales or profits. On the other hand, QFD
was able to highlight the fact that certain engineering characteristics or
design features had both positive and negative aspects. This tradeoff could
point the way to "out of the box" solutions. QFD also highlighted
the importance of starting explicitly with customer needs, regardless of which
method is used.
Rather than competing, we view them as complementary approaches
that should be conducted simultaneously; each providing feedback to the other.
When the two approaches differed on the optimal level or importance of a feature,
it appeared that conjoint analysis better captured customers' current preferences
for product features while QFD captured what product developers thought would
best satisfy customer needs. Looking at the problem through these different
lenses provides a useful dialogue that should not be missed. QFD's ability
to generate creative or novel solutions should be combined with conjoint analysis'
ability to forecast market reaction to design changes.