bluuebunny/crossref_metadata_2025_split huggingface dataset:
Sales Force Strategy
Authors: Cron William L. , Cravens David W. 
Published: December 2010

Abstract
Sales force strategy is defined as the set of strategic decisions that determine where the sales force will focus its attention and the role of the sales force in creating customer value that is consistent with the overall strategy of the firm. The sales force's ability to create superior customer value is based on its ability to perform a set of fundamental customer‐focused capabilities: new customer acquisition, customer relationship development, customer trust building, and customer retention. To create a competitive advantage, the nature of these capabilities will depend on the types of customer relationships a firm has with its customer base, for example, transactional, consultative, and enterprise customer relationships. Likewise, sales force capabilities performance will be influenced by a firm's sales force support system which includes its sales force structure, management control processes, performance metrics, and motivation and talent development systems.
Marketing Warfare Strategies
Authors: Laufer Daniel 
Published: December 2010

Abstract
Marketing warfare strategies are competitor‐centered strategies borrowed from the field of military science and applied in a marketing context. According to Rindfleisch, advocates of this approach suggest that both marketing and the military are very similar, and both fields share “(i) two or more opposing forces; (ii) competition for a limited and valuable resource; (iii) a zero‐sum orientation in which gains come at the expense of competitor losses; and (iv) defined rules of engagement in which one competitor attacks while the opposing defends.” This approach was popular in the 1980s, whereas in the current era of relationship marketing this approach has become less relevant. Also, an increased awareness of differences between business and military organizational cultures raises questions of the applicability of military strategies in a business context.
Descriptive Research
Authors: Mittal Vikas 
Published: December 2010

Abstract

Descriptive research
is a term used to describe many types of survey research that are undertaken by marketing researchers to understand customer perceptions, judgments, and intentions. Common types of descriptive research studies include buyer‐behavior studies, brand perception studies, customer segmentation studies, customer satisfaction studies, lost‐customer studies, advertising evaluation studies, and employee attitude and satisfaction studies. We describe the key considerations that guide the design of a descriptive study such as survey method (e.g., phone versus mail versus email), cost, and data analysis technique. We also provide a brief overview of key analyses that can be undertaken to gain insights from survey research.
Ethics in Marketing Research
Authors: Schmidt Marcus J. 
Published: December 2010

Abstract
The article discusses ethical issues and dilemmas that may arise during the marketing research process. Topics discussed involve deceitful interviewers, use of leading questions, employing a sampling frame that overrepresents subjects with supportive responses, and deliberately misinterpreting results such that a company's product, an organization's mission, and so on, appear more favorable than an objective analysis of the results would justify. In many cases, such behavior must be regarded unethical. Several forms of unethical behavior are well known: The market research agency may disguise errors in the survey or in statistical computations. Sometimes, clients have a hidden agenda and try pressurizing agencies to make them come up with certain prespecified findings. Clients may misuse a methodological framework developed by an agency and agencies may inappropriately reuse a research design worked out exclusively for a specific client. A client may be persuaded by an agency to purchase a market research study that is too expensive compared to what the client really needs. Relations between agencies and clients may be so close that the agency's competitors are not even asked to give a bid. The article discusses these and other ethical dilemmas by way of case examples. Finally, some minimum standards are suggested and the ethical codes provided by three market research associations are presented.
Marketing Research Process
Authors: Bhalla Gaurav 
Published: December 2010

Abstract
Marketing research (MR) is widely used by a large number of companies to provide relevant and timely information to business and marketing decision‐makers. To conduct a quality MR project, the individual or the agency conducting the research needs to make several coordinated decisions that build on each other and fit with each other. The marketing research process (MRP) represents the sequence and nature of all major decisions and activities that need to be undertaken to plan, design, and implement an MR project. This article discusses the three macrophases of the MRP and the key decisions within each phase. Since companies do not always function as linear, sequential, and rational entities, the article also discusses factors that cause marketing researchers to compress, modify, and adapt the MRP. No discussion of the MRP would be complete without looking ahead and taking into account the changing information and decision‐making landscape of modern organizations. In that spirit, the article also proposes a forward‐looking information system for influencing business outcomes; and it also suggests that the traditional MRP is one specific example of this forward‐looking comprehensive information system.
Motivation Research
Authors: Kozinets Robert V. 
Published: December 2010

Abstract

Motivation research
is a term used to refer to a selection of qualitative research methods that were designed to probe consumers' minds in order to discover the subconscious or latent reasons and goals underlying everyday consumption and purchasing behaviors. Motivation research was derived from an application of Sigmund Freud's psychoanalytic personality theories and became the premier consumer research method in the 1950s. It has had a lasting influence on the areas of advertising and consumer research, as well as advertising practice. Dr. Ernest Dichter, a trained psychoanalyst, was the first and foremost practitioner of motivation research. Critics of motivation research disliked its small sample sizes, its subjective interpretations, its basis in clinical methods, and its exotic explanations. Despite its controversial past, motivation research is still regarded as an important technique by marketers who want to gain a deeper understanding into consumer motivations. Current techniques in wide use derive directly from motivation research. These methods include projective tests, metaphor analysis, storytelling, word‐association tasks, sentence‐completion tasks, picture generation, and also photo sorts. Some of the most prominent contemporary marketing researchers such as Gerald Zaltman and Clotaire Rapaille use techniques that are recognizably affiliated with motivation research.
Research Reliability and Validity
Authors: Du Rex Yuxing 
Published: December 2010

AbstractThis article discusses two important and interrelated concepts in marketing research: reliability and validity. A reliable research instrument should demonstrate consistency, that is, leading to the same observation/conclusion each time it is used with the same subjects under the same condition. There are many operational approaches to assess reliability, including internal consistency (e.g., average interitem correlation, average item‐total correlation, split‐half, Cronbach's alpha), parallel‐forms, test–retest, and interrater, all of which involve some sort of repetition. A valid research instrument should capture what it is supposed to examine. Major types of validity include face, content, criterion (concurrent and predictive), and construct (convergent, discriminant and nomological). Assessing reliability is essentially about gauging the size of random error. Assessing validity is essentially about gauging the existence of systematic error. Perfect reliability is achieved when there is no random error. Perfect validity is achieved when there is neither random nor systematic error. In practice, it is often easier to establish reliability than validity. However, it is important to keep in mind that reliability is a necessary, but not sufficient, condition for validity.
Itemized Rating Scales (Likert, Semantic Differential, and Stapel)
Authors: Russell Gary J. 
Published: December 2010

Abstract
Itemized rating scales are ordinal scales used to measure consumer attitudes such as attribute perceptions, product preferences, purchase intentions, and service satisfaction. Three types of scales are commonly used in marketing research: Likert, semantic differential, and Stapel. Likert scales ask consumers to agree or disagree to declarative statements. Semantic differential and Stapel scales ask consumers to rate entities along an ordered continuum. Attitude scales in marketing research are usually created as multiitem scales, which are analyzed using regression and factor analytic methodologies to examine reliability and validity. Newer analysis methods based upon latent variable models assume that itemized rating scales only have ordinal measurement properties, but allow the development of continuous measures of consumer attitude.
Observation Methods
Authors: Kozinets Robert V. 
Published: December 2010

Abstract
Observation methods are qualitative marketing research methods in which researchers view, record, and then analyze the manifest actions of consumers as they engage in some market‐related activity. Observation methods include mass observations and measurements of large groups of anonymous people, placement of cameras, and direct observations that researchers conduct in consumers' homes. Observation methods originated in the anthropological techniques of ethnography. A key characteristic of observation methods is that they reveal what consumers actually do, rather than what they say or remember that they have done. Videographic techniques, involving the use of video cameras to record naturalistic observations for later analysis and presentation, are increasing in popularity. Observation methods have been found to be particularly useful for strengthening brand differentiation, for identifying areas of untapped opportunity for new‐product development, and for revealing opportunities for improving consumers' retail experiences. Technological developments in this area have included new recording devices such as TiVo, methods of technologically mediated trend spotting, online behavior measurement and monitoring, as well as netnography, the practice of online ethnography. The limitations of observation methods tend to arise from their small sample sizes, their time‐intensive nature, their intrusiveness, and the significant researcher skill that is required.
Statistical Approaches to Determining Sample Sizes
Authors: Kamakura Wagner A. 
Published: December 2010

Abstract
Researchers who plan to draw inferences about the population from a random sample, must first determine how many members of the population should be sampled. To determine this, the researcher must specify the desired precision of the sample estimate, and confidence level associated with this estimate. If the purpose of the random sample is to test a hypothesis, the researcher must specify the desired statistical power for the test, as well as the acceptable risk of rejecting the null hypothesis when it is true (type I error). This essay explains how to determine the sample size for these two types of study.
Analysis of Variance and Covariance
Authors: Moon Sangkil 
Published: December 2010

Abstract

This article is aimed at providing some basics of analysis of variance (ANOVA) for MBA and undergraduate students, particularly those interested in marketing research. ANOVA is used to determine the probability that differences in means across multiple categories are due solely to sampling error. ANOVA must be composed of at least one metric‐dependent variable and one or more nonmetric independent variables. In this article, ANOVA is introduced with the experimental design. Then, several types of ANOVA are discussed such as one‐way ANOVA,
N
‐way ANOVA, analysis of covariance (ANCOVA), and multivariate analysis of variance (MANOVA). Furthermore, ANOVA is compared with multiple regression, another common statistical technique in marketing research. The last section shows some ANOVA examples applied in marketing research.
Cluster Analysis
Authors: Wedel Michel , Shi (Savannah) Wei 
Published: December 2010

Abstract

Cluster analysis is used to categorize consumers into clusters that are homogeneous along a range of variables. In marketing, it is most often applied for purposes of market segmentation, product perceptual mapping, and data mining. We discuss two important clustering methods here: hierarchical clustering as a “bottom‐up” procedure, and nonhierarchical clustering as a “top‐down” procedure. Hierarchical clustering begins with each consumer in a cluster by itself. Then, with a (dis)similarity metric, subjects that are similar are taken into the same cluster. We provide an introduction to popular (dis)similarity metrics for continuous and discrete variables, as well as main hierarchical clustering algorithms. The result of hierarchical classification is a
dendrogram
: a tree structure that represents the hierarchical relations among all subjects being clustered. The nonhierarchical clustering methods, instead, partition the data into a predetermined number of segments and try to minimize some criterion of interest. K‐means clustering, partitioning around mediods (PAM), and fuzzy clustering are some of the most popular nonhierarchical algorithms. We also touch upon the issue regarding performance of different clustering algorithms, decision on the number of clusters, clustering validation, and software available for the clustering algorithms.
Exploratory Factor Analysis
Authors: Wedel Michel , Shi (Savannah) Wei 
Published: December 2010

Abstract
Factor analysis is a group of methods that enjoys high popularity in marketing research. We either use it as a data‐reduction tool in the domain of data mining, or as a latent structures detection tool based on subjective judgments, and in perceptual mapping. In this article, we introduce two basic factor analysis methods: principle component analysis (PCA) and factor analysis in the strict sense, using satisfaction survey data of an administrative computer system as an example. Specifically, PCA is a formative approach that “forms” the components from the observed variable. It is a data analysis or data‐mining tool that reduces the number of variables and explores meaningful patterns. We discuss “loadings,” “eigenvalues,” “communality,” “component score,” and “biplot” in the output of PCA, and also provide some guidelines to determine the number of principal components. Factor analysis, as a reflective approach, assumes that the unobserved factors are reflected in the observed variables. It differs from PCA in that it is not a data‐reduction tool, but a statistical model. It identifies latent factors under observed variables, and specifies a relationship between them with error terms. We briefly introduce the estimation and rotation methods of factor analysis. Software available for PCA and factor analysis is also discussed at the end of this article.
Discriminant Analysis for Marketing Research Applications
Authors: Feinberg Fred M. 
Published: December 2010

Abstract

Among marketers' main tasks is
segmentation
: breaking consumers, products, and firms into meaningful groupings. Marketing data often appear in discrete buckets, like “light,” “medium,” and “heavy” users, and marketers need to understand and predict which consumers (or products, or firms, etc.) fit into which group. That is, they would like to use available information (predictor variables) help explain how, and perhaps why, these groupings come out the way they do. Common examples include distinguishing new versus returning customers, explaining which stores different consumers chose to shop at, using demographics to predict various kinds of shopping behavior, or how to entice “loyal” versus “switcher” customer groups. Multiple discriminant analysis (MDA) allows marketers to do several important things:
distinguish
among two or more known groups, using available predictor variables;
classify
new items into those known groups;
verify
whether there actually are significant differences across the groups; and
test
for which specific predictor variables best account for between‐group differences. We illustrate the meaning and use of the
linear discriminant function
in marketing applications, as well as how managers can interpret DA output to make better segmentation decisions. Connections to other methods included in this volume are highlighted throughout.
Multiple Regression
Authors: Moon Sangkil 
Published: December 2010

Abstract
The objective of multiple regression is to use the independent variables to explain or predict the dependent variable. From a marketing practitioner's perspective, it would be much more effective to develop actionable marketing strategies based on an understanding of the relationships identified from this analysis than to simply examine multiple individual variables separately. This article first introduces the basics of the technique, following which it specifically discusses such issues as interpreting parameter estimates, dummy variables, and choosing relevant independent variables. It also gives several application examples of multiple regression in marketing research. Lastly, several common caveats in using the technique are discussed.
Univariate Techniques
Authors: Du Rex Yuxing 
Published: December 2010

AbstractUnivariate techniques are used for analyzing data when there is a single measurement concerning each element in the sample or when there are several measurements on each element but each measurement is analyzed independently or in isolation. This article provides a survey of a few of these techniques that are most commonly encountered in marketing research. Depending on the type of the focal variable (interval, ratio, nominal, or ordinal), different techniques are needed. When the focal variable is interval or ratio,t‐tests are often used to compare the sample mean against a known or a given standard, or compare one sample against another, which can be independent or paired. When the focal variable is nominal, chi‐square tests can be applied. When the focal variable is an ordinal, Kolmogorov–Smirnov (K–S) tests are preferred. All these techniques are concerned with the levels and distributions of each individual variable, and they all involve first calculating some sort of test statistics, and then comparing the test statistics against some critical values in determining whether the null hypothesis should or should not be rejected with a certain level of confidence.
Criteria for Evaluating Secondary Data
Authors: Mulhern Frank J. 
Published: December 2010

Abstract
Secondary data are market research data collected for a purpose other than the one on hand. The data have the advantage of being much less expensive and more quickly available than primary data. However, because secondary data are collected for a different purpose, one must carefully evaluate their appropriateness for a given market research situation. Secondary data should be evaluated with respect to several important criteria. The data should be accurate, that is, without errors. The data should be relevant to the particular research need on hand. Relevance is a function of the level of aggregation of the data, as well as the units and time increments in which the data are reported. The data should also come in a format that can be utilized easily. In addition, secondary data should not be out of date. Consideration should also be given to the format of the data and any restrictions on their use. When secondary data are evaluated carefully with these criteria, they can be an excellent source of inexpensive yet valuable information.
Ethnograpic Research
Authors: Price Linda L. 
Published: December 2010

Abstract
Ethnography is a practice that allows researchers to directly interact, participate, and become intimately involved in their individual subjects of study, particularly in the study of culture and social behavior. Ethnography here refers both to fieldwork itself and its representation, and draws from a broad range of source data. This includes participant and nonparticipant observation, interviews, conversations, and informant diaries, which are the most common sources of ethnographic data. Ethnography is useful for a wide range of problems including understanding disjunctures between what people say and do, identifying “taken for granted” practices and linking them to shared cultural templates and meanings, and uncovering complex interplays of marketing phenomena and people in social systems. Ethnographic data collection is largely unstructured, and gaining access to data can be problematic since researchers often operate in settings where they have little power or control. New tools such as “netnography” and consumer‐generated videos have helped expand, access, and correct this issue. Data analysis is open‐ended, flexible, adaptive, and involves interpreting meanings, functions, and consequences of actions and practices of people and institutions.
Web Surveys
Authors: Wang Tuo 
Published: December 2010

Abstract
Web survey is a method of collecting survey data electronically using the Internet. As the percentage of households having access to the Internet rapidly increased, web surveys are becoming a popular method of collecting survey data. Not only is it the cheapest method of collecting survey data, it is also the fastest in response time. Unlike other methods such as mail or telephone surveys, it also allows the incorporation of audiovisual stimuli in the survey. The presence of many commercial companies providing easy‐to‐use software to design the survey and manage data collection has further increased popularity of web survey. A significant advantage of using the web survey is that the technology allows the generation of tables of data without going through the task of coding and typing responses from each survey. There are, however, still concerns regarding representativeness of sample since certain segments of the population may not have access to the Internet. Another concern is the potential to compromise security. Nonresponse bias is also an issue due to a low response rate resulting from misuse of the survey.
Personal Observation
Authors: Price Linda L. 
Published: December 2010

Abstract

Observation methods include all data collection in which participants are monitored, rather than being asked to give experimenters data directly. Observation can be quantitative (e.g., head counts) or qualitative (e.g., netnography). Personal observation is a subset category, in which the researcher is the primary instrument for monitoring and data collection, and is widely used with ethnography. Collection can be covert or overt, participatory or not, and can vary in degree of invasiveness to the participants' lives.
Participant observation
is a systemic recording and analysis of information gained while engaged in and observing activities, rituals, interactions, and events in order to uncover explicit and tacit aspects of the field of study. Personal observation raises many important ethical issues for researchers, including establishing limits to participation, finding the proper mix of observation and participation, and maintaining the anonymity of informants' studied.