Conference Sessions - Jesse H. Jones Graduate School of ...

2 - A Machine Learning Approach to Analyzing Multi-attribute Data: The
OrdEval Algorithm
Sandra Streukens, Hasselt University, Agoralaan-Building D,
BE-3590, Diepenbeek, Belgium, sandra.streukens@uhasselt.be, Koen
Vanhoof, Marko Robnik-Sikonja
Typically, multi-attribute data are analyzed using a multiple regression approach. This
imposes restrictions that may conflict with current marketing theory. First, the
appropriate functional form in terms of possible nonlinearity needs to be specified in
advance. A challenging task as the literature shows disagrees about the optimal
functional form of the relationships among attribute perceptions and higher order
evaluations. Second, several properties cannot be adequately modeled by this class of
techniques. For instance, several models (e.g. Herzberg’s (1966) dual-factor theory)
have been put forward in which a flat relationship between attribute performance
and global judgments is hypothesized for some part of the attribute performance
range. Third, multiple linear regression models implicitly assume symmetric
relationships, meaning that the consequences on the outcome variable of positive
(e.g. increase in performance from 3 to 5) and negative (e.g. decrease in performance
from 5 to 3) performance changes are equal. Research by, for example, Boulding et
al. (1993) demonstrates that this assumption may not reflect reality. These drawbacks
underscore the need for alternative analysis methods that offer enhanced possibilities
and flexibility in modeling functional forms that have been put forward in recent
studies. Therefore, the aim of this research is to develop and demonstrate an
approach (i.e. the OrdEval algorithm) based on machine learning principles for
analyzing multi-attribute models that does not require an
a-priori specification of the functional form in terms of possible nonlinearity, is
capable of adequately capturing a wider set of functional forms, and implicitly takes
asymmetry into account.
3 - Voice Analysis for Measuring Consumer Preferences
Hye-jin Kim, Pennsylvania State University, 421A Business Building,
University Park, PA, 16802, United States of America,
hxk262@psu.edu, Min Ding
Marketing research methods, such as conjoint analysis and customer satisfaction,
usually collect consumer responses in text formats (choice, rating, ranking, etc.). The
estimation, in turn, tend to focus on the response itself (which is one-dimensional)
and often fail to recognize that the response sometimes may not be reflecting what a
subject really thinks. For example, the subject may experience confusion during the
task or give socially desirable answers, which is not possible to identify using text
based response data. Alternatively, marketers have adopted physiological or
neurological measurements (e.g., fMRI) to gain deeper insights into consumers’ true
preferences. However, these methods use expensive equipment and require the
subject to be present in a lab environment. To retrieve richer information from the
respondent while bearing reasonable cost on the marketer and less restriction on the
respondent, we introduce a method of using the consumer’s voice to measure
emotional and cognitive constructs such as excitement, engagement, and uncertainty,
which in turn will be used to supplement responses to allow researchers and
practitioners to obtain more accurate insights. While using the human voice for
emotion recognition purposes has been prevalent in other fields such as computer
science, its use in marketing has been negligible. We conduct an empirical study to
test the validity and implementability of this method. The results and implications for
marketers and academic researchers are discussed.
4 - Estimating Nonresponse Bias in Survey Data
Songting Dong, Lecturer, Australian National University, MMIB,
LF Crisp Bldg 26, Australian National University, Canberra, 0200,
Australia, songting.dong@anu.edu.au, Ujwal Kayande
Detecting nonresponse bias in survey data is important for determining the
representativeness of the sample, which can in turn affect the validity of the survey
findings. Extant methods focus on detecting whether the mean of a variable might be
different for non-respondents relative to those who did respond. Yet, most surveys
used in academic research are not for estimating variable means, but for estimating
the relationship between variables. Therefore, we propose methods that may be able
to detect non-response bias not only in variable means, but also in the variancecovariance
matrix of variables. We use simulations to compare the performance of
the methods. We find that the efficacy of the methods in detecting bias varies,
particularly in terms of the propensity for false negatives and false positives. We
propose a set of guidelines to use when conducting surveys so that researchers are
aware of the possibility of non-response bias in the magnitude of the relationship
between variables.
MARKETING SCIENCE CONFERENCE – 2011 FD11
67
■ FD11
Champions Center I
Sports and Fashion
Contributed Session
Chair: Hema Yoganarasimhan, Graduate School of Management, UC Davis,
3204 Gallagher Hall, Davis, 95616, United States of America,
hyoganarasimhan@ucdavis.edu
1 - An Empirical Investigation of Sports Sponsorship
Yupin Yang, Assistant Professor, Simon Fraser University,
8888 University Drive, Burnaby, BC, V5A 1S6, Canada,
yupin_yang@sfu.ca, Avi Goldfarb
The sponsorship of sports, arts, culture, and charity events has become a popular
promotional tool for organizations of all sizes and across many industry sectors.
According to the International Events Group Report, global sponsorship expenditures
reached $44 billion in 2009, with the majority of corporate sponsorships being sportsrelated.
Although sports sponsorship is a marketing strategy involving a large
economy, relatively little is known about how sponsors and sponsored organizations
address the challenge of evaluating the fitness of partners or how they leverage their
strengths in the negotiation process. Research on the formation and departure of
sponsorships remains scant in the marketing literature. In this research, we
empirically investigate the formation, renewal, and departure of sponsorships in
order to shed light on the decision processes involved. Since sponsorships involve the
mutual agreement of two partners (the sponsoring company and the sponsored
organization), the proposed research will use a two-sided matching model to analyze
a unique dataset—the shirt sponsorships of English Football Leagues. In the
sponsorship literature, researchers use either a case study approach or else work with
data from surveys or experiments. The research is the first empirical work to use
historical data to study the phenomenon of sports sponsorship. Thus, it has the
potential to make an important contribution to the literature. In addition, it is
expected to be of significant interest to industry practitioners (sports teams, sports
leagues, sponsoring companies) as well as policy makers.
2 - The Consumption of Live Sporting Events: Satisfaction of Very
Important Fans
Dennis Ahrholdt, University of Hamburg - Institute for Operations
Reserach, Von-Melle-Park 5, Hamburg, Germany,
Dennis.Ahrholdt@uni-hamburg.de, Claudia Höck, Christian Ringle
Sporting events are an international multi-billion dollar business and economic
aspects have a greater than ever influence on the activities of sports clubs, since
competition is no longer restricted to the sports field, but extends into the
competition for revenues from selling broadcasting and sponsoring rights, tickets, and
merchandise. Hence, the orientation towards the customer (the visitor), who
consumes the product “live sports event”, plays a central role. Creating favorable
experiences for fans and thereby fan satisfaction represent a key success factor for
sports organizations. Particularly customers of business seats and VIP boxes are
important, because revenue is mainly driven by those “very important fans” (VIFs).
In addition to revenues from VIFs through ticket sales and catering, VIFs positively
influence the image of a sports club which in turn has a positive effect on revenues
from merchandising and sponsoring rights. Moreover, VIFs are often sponsors or
potential sponsors themselves. For these key reasons, VIF satisfaction is fundamental
for any sports club’s long-term financial success and, as a consequence, clubs, as
sports businesses, must manage VIF satisfaction proactively by paying increasing
attention to the range and quality of the services they offer. An instrument for
measuring VIF satisfaction is developed innovatively with this research. Structural
equations modeling allows us to empirically test the instrument for VIFs from a major
German soccer club. The partial least squares path modeling analysis reveals deeper
insights of VIFs’ overall satisfaction and its key drivers. Thereby, the instrument
identifies and points towards key areas that require managerial attention to maintain
and further improve VIFs’ overall satisfaction.
3 - Testing Firms’ Conditional Differentiation Behaviour: Quantitative
Evidence in Fashion Advertising
Kitty Wang, Rotman School of Management,
University of Toronto, 105 St. George Street, Toronto, Canada,
kitty.wang@rotman.utoronto.ca
In this paper, I study strategic interactions between firms in their advertising decisionmaking
process. I expand on existing literature by incorporating advertising content
into a structural discrete game model; this approach allows for an empirical
examination of competitive advertising along multiple dimensions. I construct a novel
dataset of print advertising in leading US fashion magazines for a five-year span. For
each advertisement, it contains information on brands, products, and various other
characteristics of the advertisement. Building on the static discrete entry game
framework, I propose a two-stage model to capture the impact that rival firms’
(expected) actions have on own firm’s strategy in terms of when and what to
advertise. The two-stage model allows firms to construct expectations based on what
they learn through rival firms’ past strategies. It also replaces the commonly used
rational expectation assumption with a more general structure for the information
set. While previous studies focus on advertising quantity, I find that firms strategically
coordinate or differentiate from their rivals’ (expected) actions in terms of timing,
location, and content. Furthermore, there appears to be no dominant strategy along
all dimensions – firms’ actions depend crucially on firm characteristics, the product
category, as well as other advertising content.