Misplaced Pages

#996003

69-447: On its introduction, the survey represented a breakthrough in the measurement methods used for service quality research. The diagnostic value of the instrument is supported by the model of service quality which forms the conceptual framework for the development of the scale (i.e. instrument or questionnaire). The instrument has been widely applied in a variety of contexts and cultural settings and found to be relatively robust. It has become

138-563: A {\displaystyle \mathbf {z} _{a}} , F p {\displaystyle \mathbf {F} _{p}} and ε a {\displaystyle {\boldsymbol {\varepsilon }}_{a}} respectively. Since the data are standardized, the data vectors are of unit length ( | | z a | | = 1 {\displaystyle ||\mathbf {z} _{a}||=1} ). The factor vectors define an k {\displaystyle k} -dimensional linear subspace (i.e.

207-445: A "best fit" to the data. In factor analysis, the best fit is defined as the minimum of the mean square error in the off-diagonal residuals of the correlation matrix: This is equivalent to minimizing the off-diagonal components of the error covariance which, in the model equations have expected values of zero. This is to be contrasted with principal component analysis which seeks to minimize the mean square error of all residuals. Before

276-451: A geometrical interpretation. The data ( z a i {\displaystyle z_{ai}} ), the factors ( F p i {\displaystyle F_{pi}} ) and the errors ( ε a i {\displaystyle \varepsilon _{ai}} ) can be viewed as vectors in an N {\displaystyle N} -dimensional Euclidean space (sample space), represented as z

345-426: A hyperplane) in this space, upon which the data vectors are projected orthogonally. This follows from the model equation and the independence of the factors and the errors: F p ⋅ ε a = 0 {\displaystyle \mathbf {F} _{p}\cdot {\boldsymbol {\varepsilon }}_{a}=0} . In the above example, the hyperplane is just a 2-dimensional plane defined by

414-427: A sample estimate of the error covariance which has its off-diagonal components minimized in the mean square sense. It can be seen that since the z ^ a {\displaystyle {\hat {z}}_{a}} are orthogonal projections of the data vectors, their length will be less than or equal to the length of the projected data vector, which is unity. The square of these lengths are just

483-470: A wide range of service industries and contexts, such as healthcare, banking, financial services, and education (Nyeck, Morales, Ladhari, & Pons, 2002). Although the SERVQUAL instrument has been widely applied in a variety of industry and cross-cultural contexts, there are many criticisms of the approach. Francis Buttle published one of the most comprehensive criticisms of the model of service quality and

552-424: Is The numbers 10 and 6 are the factor loadings associated with astronomy. Other academic subjects may have different factor loadings. Two students assumed to have identical degrees of verbal and mathematical intelligence may have different measured aptitudes in astronomy because individual aptitudes differ from average aptitudes (predicted above) and because of measurement error itself. Such differences make up what

621-572: Is a combinatorial model of factor model and regression model; or alternatively, it can be viewed as the hybrid factor model, whose factors are partially known. Explained from PCA perspective, not from Factor Analysis perspective. Researchers wish to avoid such subjective or arbitrary criteria for factor retention as "it made sense to me". A number of objective methods have been developed to solve this problem, allowing users to determine an appropriate range of solutions to investigate. However these different methods often disagree with one another as to

690-438: Is a different method of computing the same model as PCA, which uses the principal axis method. Canonical factor analysis seeks factors that have the highest canonical correlation with the observed variables. Canonical factor analysis is unaffected by arbitrary rescaling of the data. Common factor analysis, also called principal factor analysis (PFA) or principal axis factoring (PAF), seeks the fewest factors which can account for

759-413: Is a linear combination of those two "factors". The numbers for a particular subject, by which the two kinds of intelligence are multiplied to obtain the expected score, are posited by the hypothesis to be the same for all intelligence level pairs, and are called "factor loading" for this subject. For example, the hypothesis may hold that the predicted average student's aptitude in the field of astronomy

SECTION 10

#1732773366997

828-509: Is an expert in the area of services marketing and service quality . Zeithaml earned her Bachelor of Arts degree from Gettysburg College before pursuing further education at the University of Maryland , where she obtained both her Master of Business Administration (MBA) and Doctor of Philosophy (PhD) degrees. In the 1980s, Zeithaml and her co-authors developed SERVQUAL , a multidimensional scale of perceived service quality. She

897-446: Is chosen randomly from a large population , then each student's 10 scores are random variables. The psychologist's hypothesis may say that for each of the 10 academic fields, the score averaged over the group of all students who share some common pair of values for verbal and mathematical "intelligences" is some constant times their level of verbal intelligence plus another constant times their level of mathematical intelligence, i.e., it

966-402: Is collectively called the "error" — a statistical term that means the amount by which an individual, as measured, differs from what is average for or predicted by his or her levels of intelligence (see errors and residuals in statistics ). The observable data that go into factor analysis would be 10 scores of each of the 1000 students, a total of 10,000 numbers. The factor loadings and levels of

1035-433: Is customary to add additional items such as the respondent's demographics, prior experience with the brand or category and behavioural intentions (intention to revisit/ repurchase, loyalty intentions and propensity to give word-of-mouth referrals). Thus, the final questionnaire may consist of 60+ items though the 22 questions are the same. The face to face interview version may take one hour per respondent to administer, but not

1104-552: Is deemed low. When perceptions exceed expectations then service quality is high. The model of service quality identifies five gaps that may cause customers to experience poor service quality. In this model, gap 5 is the service quality gap and is the only gap that can be directly measured. In other words, the SERVQUAL instrument was specifically designed to capture gap 5. In contrast, Gaps 1-4 cannot be measured, but have diagnostic value. The Knowledge Gap The standards Gap The Delivery Gap The Communications Gap The development of

1173-419: Is defined as The goal of factor analysis is to choose the fitting hyperplane such that the reduced correlation matrix reproduces the correlation matrix as nearly as possible, except for the diagonal elements of the correlation matrix which are known to have unit value. In other words, the goal is to reproduce as accurately as possible the cross-correlations in the data. Specifically, for the fitting hyperplane,

1242-401: Is equal to 10 {\displaystyle 10} in the above example. "Factor" indices will be indicated using letters p {\displaystyle p} , q {\displaystyle q} and r {\displaystyle r} , with values running from 1 {\displaystyle 1} to k {\displaystyle k} which

1311-409: Is equal to 2 {\displaystyle 2} in the above example. "Instance" or "sample" indices will be indicated using letters i {\displaystyle i} , j {\displaystyle j} and k {\displaystyle k} , with values running from 1 {\displaystyle 1} to N {\displaystyle N} . In

1380-501: Is often used to help students of marketing remember the five dimensions of quality explicitly mentioned in the research instrument. It is these five dimensions that are believed to represent the consumer's mental checklist of service quality. Nyeck, Morales, Ladhari, and Pons (2002) stated that the SERVQUAL measuring tool “appears to remain the most complete attempt to conceptualize and measure service quality” (p. 101). The SERVQUAL measuring tool has been used by many researchers across

1449-472: Is retained if the associated eigenvalue is bigger than the 95th percentile of the distribution of eigenvalues derived from the random data. PA is among the more commonly recommended rules for determining the number of components to retain, but many programs fail to include this option (a notable exception being R ). However, Formann provided both theoretical and empirical evidence that its application might not be appropriate in many cases since its performance

SECTION 20

#1732773366997

1518-562: Is the Kronecker delta ( 0 {\displaystyle 0} when p ≠ q {\displaystyle p\neq q} and 1 {\displaystyle 1} when p = q {\displaystyle p=q} ).The errors are assumed to be independent of the factors: Since any rotation of a solution is also a solution, this makes interpreting the factors difficult. See disadvantages below. In this particular example, if we do not know beforehand that

1587-402: Is to characterize the correlations between the variables x a {\displaystyle x_{a}} of which the x a i {\displaystyle x_{ai}} are a particular instance, or set of observations. In order for the variables to be on equal footing, they are normalized into standard scores z {\displaystyle z} : where

1656-425: Is used to identify complex interrelationships among items and group items that are part of unified concepts. The researcher makes no a priori assumptions about relationships among factors. Confirmatory factor analysis (CFA) is a more complex approach that tests the hypothesis that the items are associated with specific factors. CFA uses structural equation modeling to test a measurement model whereby loading on

1725-733: Is used when the relevant set of variables shows a systematic inter-dependence and the objective is to find out the latent factors that create a commonality. The model attempts to explain a set of p {\displaystyle p} observations in each of n {\displaystyle n} individuals with a set of k {\displaystyle k} common factors ( f i , j {\displaystyle f_{i,j}} ) where there are fewer factors per unit than observations per unit ( k < p {\displaystyle k<p} ). Each individual has k {\displaystyle k} of their own common factors, and these are related to

1794-1403: The ( i , m ) {\displaystyle (i,m)} th element is simply M i , m = μ i {\displaystyle \mathrm {M} _{i,m}=\mu _{i}} . Also we will impose the following assumptions on F {\displaystyle F} : Suppose C o v ( X − M ) = Σ {\displaystyle \mathrm {Cov} (X-\mathrm {M} )=\Sigma } . Then and therefore, from conditions 1 and 2 imposed on F {\displaystyle F} above, E [ L F ] = L E [ F ] = 0 {\displaystyle E[LF]=LE[F]=0} and C o v ( L F + ϵ ) = C o v ( L F ) + C o v ( ϵ ) {\displaystyle Cov(LF+\epsilon )=Cov(LF)+Cov(\epsilon )} , giving or, setting Ψ := C o v ( ε ) {\displaystyle \Psi :=\mathrm {Cov} (\varepsilon )} , For any orthogonal matrix Q {\displaystyle Q} , if we set L ′ =   L Q {\displaystyle L^{\prime }=\ LQ} and F ′ = Q T F {\displaystyle F^{\prime }=Q^{T}F} ,

1863-434: The variances of the "errors" ε {\displaystyle \varepsilon } must be estimated given the observed data X {\displaystyle X} and F {\displaystyle F} (the assumption about the levels of the factors is fixed for a given F {\displaystyle F} ). The "fundamental theorem" may be derived from the above conditions: The term on

1932-758: The Chairman of the Board of the American Marketing Association . Notably, Zeithaml served in various capacities within the American Marketing Association, including Treasurer in 2015, Incoming Chairman of the Board in 2016, Chairman of the Board in 2017, and Outgoing Chairman of the Board in 2018. She served as a Center for Services Leadership Fellow at Arizona State University from 2016 to 2017. Additionally, from 2001 to 2007, Zeithaml served as an Academic Trustee of

2001-649: The Marketing Science Institute, one of only 12 worldwide. Zeithaml's development of the SERVQUAL model, is a widely adopted measurement instrument across various industries and countries. Her books, including “Driving Customer Equity: How Customer Lifetime Value is Reshaping Corporate Strategy,” Services Marketing: Integrating Customer Focus across the Firm,” and "Delivering Quality Service: Balancing Customer Perceptions and Expectations," have garnered critical acclaim and contributed significantly to

2070-450: The advent of high-speed computers, considerable effort was devoted to finding approximate solutions to the problem, particularly in estimating the communalities by other means, which then simplifies the problem considerably by yielding a known reduced correlation matrix. This was then used to estimate the factors and the loadings. With the advent of high-speed computers, the minimization problem can be solved iteratively with adequate speed, and

2139-453: The associated SERVQUAL instrument in 1996 in which both operational and theoretical concerns were identified. Some of the more important criticisms include: In spite of these criticisms, the SERVQUAL instrument, or any one of its variants (i.e. modified forms), dominates current research into service quality. In a review of more than 40 articles that made use of SERVQUAL, a team of researchers found that “few researchers concern themselves with

SERVQUAL - Misplaced Pages Continue

2208-473: The common variance (correlation) of a set of variables. Image factoring is based on the correlation matrix of predicted variables rather than actual variables, where each variable is predicted from the others using multiple regression . Alpha factoring is based on maximizing the reliability of factors, assuming variables are randomly sampled from a universe of variables. All other methods assume cases to be sampled and variables fixed. Factor regression model

2277-471: The communalities are calculated in the process, rather than being needed beforehand. The MinRes algorithm is particularly suited to this problem, but is hardly the only iterative means of finding a solution. If the solution factors are allowed to be correlated (as in 'oblimin' rotation, for example), then the corresponding mathematical model uses skew coordinates rather than orthogonal coordinates. The parameters and variables of factor analysis can be given

2346-548: The consumer's mental map of service quality dimensions. Both the expectations component and the perceptions component of the questionnaire consist a total of 22 items, comprising 4 items to capture tangibles, 5 items to capture reliability, 4 items for responsiveness, 4 items for assurance and 5 items to capture empathy. The questionnaire may be administered as a paper survey, web survey or in a face-to-face interview. Known studies have published high scores for validity and reliability from small to large size sample sizes. In practice, it

2415-447: The correlation between the latent variables. Principal component analysis (PCA) is a widely used method for factor extraction, which is the first phase of EFA. Factor weights are computed to extract the maximum possible variance, with successive factoring continuing until there is no further meaningful variance left. The factor model must then be rotated for analysis. Canonical factor analysis, also called Rao's canonical factoring,

2484-416: The cosine of the angle between the two data vectors z a {\displaystyle \mathbf {z} _{a}} and z b {\displaystyle \mathbf {z} _{b}} . The diagonal elements will clearly be 1 {\displaystyle 1} s and the off diagonal elements will have absolute values less than or equal to unity. The "reduced correlation matrix"

2553-483: The criteria for being factors and factor loadings still hold. Hence a set of factors and factor loadings is unique only up to an orthogonal transformation . Suppose a psychologist has the hypothesis that there are two kinds of intelligence , "verbal intelligence" and "mathematical intelligence", neither of which is directly observed. Evidence for the hypothesis is sought in the examination scores from each of 10 different academic fields of 1000 students. If each student

2622-459: The diagonal elements of the reduced correlation matrix. These diagonal elements of the reduced correlation matrix are known as "communalities": Large values of the communalities will indicate that the fitting hyperplane is rather accurately reproducing the correlation matrix. The mean values of the factors must also be constrained to be zero, from which it follows that the mean values of the errors will also be zero. Exploratory factor analysis (EFA)

2691-411: The dimensions of service quality across a range of industries and settings. Among students of marketing, the mnemonic RATER , an acronym formed from the first letter of each of the five dimensions, is often used as an aid to recall. Businesses use the SERVQUAL instrument (i.e. questionnaire) to measure potential service quality problems and the model of service quality to help diagnose possible causes of

2760-567: The dominant measurement scale in the area of service quality. In spite of the long-standing interest in SERVQUAL and its myriad of context-specific applications, it has attracted some criticism from researchers. SERVQUAL is a multidimensional research instrument designed to measure service quality by capturing respondents’ expectations and perceptions along five dimensions of service quality. The questionnaire consists of matched pairs of items - 22 expectation items and 22 perceptions items - organised into five dimensions which are believed to align with

2829-435: The example above, if a sample of N = 1000 {\displaystyle N=1000} students participated in the p = 10 {\displaystyle p=10} exams, the i {\displaystyle i} th student's score for the a {\displaystyle a} th exam is given by x a i {\displaystyle x_{ai}} . The purpose of factor analysis

SERVQUAL - Misplaced Pages Continue

2898-486: The factor vectors will define the same hyperplane, and also be a solution. As a result, in the above example, in which the fitting hyperplane is two dimensional, if we do not know beforehand that the two types of intelligence are uncorrelated, then we cannot interpret the two factors as the two different types of intelligence. Even if they are uncorrelated, we cannot tell which factor corresponds to verbal intelligence and which corresponds to mathematical intelligence, or whether

2967-403: The factors allows for evaluation of relationships between observed variables and unobserved variables. Structural equation modeling approaches can accommodate measurement error and are less restrictive than least-squares estimation . Hypothesized models are tested against actual data, and the analysis would demonstrate loadings of observed variables on the latent variables (factors), as well as

3036-482: The factors are linear combinations of both, without an outside argument. The data vectors z a {\displaystyle \mathbf {z} _{a}} have unit length. The entries of the correlation matrix for the data are given by r a b = z a ⋅ z b {\displaystyle r_{ab}=\mathbf {z} _{a}\cdot \mathbf {z} _{b}} . The correlation matrix can be geometrically interpreted as

3105-454: The factors): The sample data z a i {\displaystyle z_{ai}} will not exactly obey the fundamental equation given above due to sampling errors, inadequacy of the model, etc. The goal of any analysis of the above model is to find the factors F p i {\displaystyle F_{pi}} and loadings ℓ a p {\displaystyle \ell _{ap}} which give

3174-478: The first rounds of consumer testing. Preliminary data analysis, using a data reduction technique known as factor analysis (also known as principal components analysis ) revealed that these items loaded onto ten dimensions (or components) of service quality. The initial ten dimensions that were believed to represent service quality were: Further testing suggested that some of the ten preliminary dimensions of service quality were closely related or autocorrelated. Thus

3243-414: The hyperplane. We are free to specify them as both orthogonal and normal ( F p ⋅ F q = δ p q {\displaystyle \mathbf {F} _{p}\cdot \mathbf {F} _{q}=\delta _{pq}} ) with no loss of generality. After a suitable set of factors are found, they may also be arbitrarily rotated within the hyperplane, so that any rotation of

3312-532: The instrument as necessary for context-specific applications. Some researchers label their revised instruments with innovative titles such as LibQUAL+ (libraries) , EDUQUAL (educational context), HEALTHQUAL (hospital context) and ARTSQUAL (art museum). The SERVQUAL questionnaire has been described as "the most popular standardized questionnaire to measure service quality". It is widely used by service firms, most often in conjunction with other measures of service quality and customer satisfaction. The SERVQUAL instrument

3381-525: The left is the ( a , b ) {\displaystyle (a,b)} -term of the correlation matrix (a p × p {\displaystyle p\times p} matrix derived as the product of the p × N {\displaystyle p\times N} matrix of standardized observations with its transpose) of the observed data, and its p {\displaystyle p} diagonal elements will be 1 {\displaystyle 1} s. The second term on

3450-579: The literature on marketing and customer relations. Factor analysis Factor analysis is a statistical method used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables called factors . For example, it is possible that variations in six observed variables mainly reflect the variations in two unobserved (underlying) variables. Factor analysis searches for such joint variations in response to unobserved latent variables . The observed variables are modelled as linear combinations of

3519-420: The mean square error in the off-diagonal components is to be minimized, and this is accomplished by minimizing it with respect to a set of orthonormal factor vectors. It can be seen that The term on the right is just the covariance of the errors. In the model, the error covariance is stated to be a diagonal matrix and so the above minimization problem will in fact yield a "best fit" to the model: It will yield

SECTION 50

#1732773366997

3588-404: The model of service quality involved a systematic research undertaking which began in 1983, and after various refinements, resulted in the publication of the SERVQUAL instrument in 1988. The model's developers began with an exhaustive literature search in order to identify items that were believed to impact on perceived service quality. This initial search identified some 100 items which were used in

3657-423: The model. Thus, no generality is lost by assuming that the standard deviation of the factors for verbal intelligence is 1 {\displaystyle 1} . Likewise for mathematical intelligence. Moreover, for similar reasons, no generality is lost by assuming the two factors are uncorrelated with each other. In other words: where δ p q {\displaystyle \delta _{pq}}

3726-464: The number of factors that ought to be retained. For instance, the parallel analysis may suggest 5 factors while Velicer's MAP suggests 6, so the researcher may request both 5 and 6-factor solutions and discuss each in terms of their relation to external data and theory. Horn's parallel analysis (PA): A Monte-Carlo based simulation method that compares the observed eigenvalues with those obtained from uncorrelated normal variables. A factor or component

3795-1016: The observations via the factor loading matrix ( L ∈ R p × k {\displaystyle L\in \mathbb {R} ^{p\times k}} ), for a single observation, according to where In matrix notation where observation matrix X ∈ R p × n {\displaystyle X\in \mathbb {R} ^{p\times n}} , loading matrix L ∈ R p × k {\displaystyle L\in \mathbb {R} ^{p\times k}} , factor matrix F ∈ R k × n {\displaystyle F\in \mathbb {R} ^{k\times n}} , error term matrix ε ∈ R p × n {\displaystyle \varepsilon \in \mathbb {R} ^{p\times n}} and mean matrix M ∈ R p × n {\displaystyle \mathrm {M} \in \mathbb {R} ^{p\times n}} whereby

3864-423: The potential factors plus " error " terms, hence factor analysis can be thought of as a special case of errors-in-variables models . Simply put, the factor loading of a variable quantifies the extent to which the variable is related to a given factor. A common rationale behind factor analytic methods is that the information gained about the interdependencies between observed variables can be used later to reduce

3933-520: The principal dimensions (or components) of service quality; proposes a scale for measuring service quality (SERVQUAL) and suggests possible causes of service quality problems. The model's developers originally identified ten dimensions of service quality, but after testing and retesting, some of the dimensions were found to be autocorrelated and the total number of dimensions was reduced to five, namely - reliability, assurance, tangibles, empathy and responsiveness. These five dimensions are thought to represent

4002-490: The print or web survey forms. The instrument which was developed over a five-year period; was tested, pre-tested and refined before appearing in its final form. The instrument's developers, Parasuraman, Zeithaml and Berry, claim that it is a highly reliable and valid instrument. Certainly, it has been widely used and adapted in service quality research for numerous industries and various geographical regions. In application, many researchers are forced to make minor modifications to

4071-432: The problem. The model of service quality is built on the expectancy–confirmation paradigm which suggests that consumers perceive quality in terms of their perceptions of how well a given service delivery meets their expectations of that delivery. Thus, service quality can be conceptualized as a simple equation: SQ = P − E When customer expectations are greater than their perceptions of received delivery, service quality

4140-414: The right will be a diagonal matrix with terms less than unity. The first term on the right is the "reduced correlation matrix" and will be equal to the correlation matrix except for its diagonal values which will be less than unity. These diagonal elements of the reduced correlation matrix are called "communalities" (which represent the fraction of the variance in the observed variable that is accounted for by

4209-444: The sample mean is: and the sample variance is given by: The factor analysis model for this particular sample is then: or, more succinctly: where In matrix notation, we have Observe that by doubling the scale on which "verbal intelligence"—the first component in each column of F {\displaystyle F} —is measured, and simultaneously halving the factor loadings for verbal intelligence makes no difference to

SECTION 60

#1732773366997

4278-447: The set of variables in a dataset. Factor analysis is commonly used in psychometrics , personality psychology, biology, marketing , product management , operations research , finance , and machine learning . It may help to deal with data sets where there are large numbers of observed variables that are thought to reflect a smaller number of underlying/latent variables. It is one of the most commonly used inter-dependency techniques and

4347-406: The ten initial dimensions were reduced and the labels amended to accurately reflect the revised dimensions. By the early 1990s, the authors had refined the model to five factors which in testing, appear to be relatively stable and robust. These are the five dimensions of service quality that form the basis of the individual items in the SERVQUAL research instrument (questionnaire). The acronym RATER,

4416-434: The two factor vectors. The projection of the data vectors onto the hyperplane is given by and the errors are vectors from that projected point to the data point and are perpendicular to the hyperplane. The goal of factor analysis is to find a hyperplane which is a "best fit" to the data in some sense, so it doesn't matter how the factor vectors which define this hyperplane are chosen, as long as they are independent and lie in

4485-465: The two kinds of intelligence of each student must be inferred from the data. In the following, matrices will be indicated by indexed variables. "Subject" indices will be indicated using letters a {\displaystyle a} , b {\displaystyle b} and c {\displaystyle c} , with values running from 1 {\displaystyle 1} to p {\displaystyle p} which

4554-457: The two types of intelligence are uncorrelated, then we cannot interpret the two factors as the two different types of intelligence. Even if they are uncorrelated, we cannot tell which factor corresponds to verbal intelligence and which corresponds to mathematical intelligence without an outside argument. The values of the loadings L {\displaystyle L} , the averages μ {\displaystyle \mu } , and

4623-453: The validation of the measuring tool”. SERVQUAL is not only the subject of academic papers, but it is also widely used by industry practitioners. Valarie Zeithaml Valarie A. Zeithaml is a marketing professor and author. She is the David S. Van Pelt Family Distinguished Professor of Marketing at Kenan-Flagler Business School , University of North Carolina at Chapel Hill . Zeithaml

4692-463: Was developed as part of a broader conceptualization of how customers understand service quality. This conceptualization is known as the model of service quality or more popularly as the gaps model. The model of service quality, popularly known as the gaps model , was developed by a group of American authors, A. Parasuraman , Valarie A. Zeithaml and Len Berry , in a systematic research program carried out between 1983 and 1988. The model identifies

4761-607: Was named a Thomson Reuters Highly Cited Researcher in the report on “The World’s Most Influential Scientific Minds.” Zeithaml held positions at the University of North Carolina ’s Kenan-Flagler Business School, including Associate Dean of the MBA Program, Senior Associate Dean for Academic Affairs, and Chair of the Marketing Area. She was also a Trustee of the Marketing Science Institute and

#996003