bluuebunny/crossref_metadata_2025_split huggingface dataset:
Pretesting a Questionnaire
Authors: Grimm Pamela 
Published: December 2010

Abstract
Pretesting is a very important step in survey research. It is an absolutely necessary step to ensure all kinds of errors that are associated with survey research are reduced. It helps to improve the quality of data significantly. Pretesting is done on a small sample of respondents from the target population. After the pilot test, both the interviewer(s) and respondents are asked a series of questions regarding the survey as well as the process of data collection during the debriefing session. Such debriefing sessions can help detect any problem with the questionnaire design leading to ambiguity of words, misinterpretation of questions, inability to answer a question, sensitive questions, and many other problems associated with the questionnaire as well as the process of administering the survey. It also provides an opportunity to give feedback to the interviewer to ensure that she/he follows the proper protocol of data collection procedures to ensure objectivity in data collection.
Primary Scales of Measurement
Authors: Winter Frederick 
Published: December 2010

Abstract
Knowing scales and their capabilities is important in marketing research since they dictate the measures of centrality (e.g., mode vs. median vs. mean) or subsequent analyses that can be used. The simplest scale is a nominal scale and it indicates a name or a label. The next level of scale is ordinal and some form of rank order is generally associated with this type of scale. At the high end, interval and ratio scales are such that the differences in value apply no matter where they are on the overall scale. Clearly, some scales contain more data – for example, interval scales contain more data than nominal scales – but some scales are naturally suited to measure certain attributes. When the researcher chooses a scale to employ, he/she is not only choosing a scale that best reflects what he/she is measuring but also preselecting the possible analyses that can be used later at the time of data analysis.
Probability Sampling
Authors: Fox Richard J. 
Published: December 2010

Abstract

Sampling involves the selection of a subset from a population, and making inferences pertaining to the population based on information from the sample. Probability sampling refers to sampling methods in which chance or randomness is involved in the sample selection process. Each element in the population from which the sample is to be selected has a positive probability of being included in the sample. Probability sampling is the basis for the statistical calculation of the potential error associated with an estimate of a population parameter developed from sample data. Simple random sampling is the most basic form of probability sampling. A simple random sample of size
n
is selected in such a way that all subsets of size
n
are equally likely to be the sample selected. The probability that any single item is included in the sample when simple random sampling is used is the ratio of the sample size to the population size. This is the type of sampling that is typically assumed when making basic statistical calculations regarding the precision of sample estimates. For example, the typical 95% confidence interval associated with an estimate is based on the assumption that simple random sampling was used to select the sample. There are more sophisticated forms of probability sampling than simple random sampling, and different statistical calculations are required to determine the precision of estimates when these alternative forms of probability sampling are used. It is obviously important in practice to make sure that the statistical precision calculations are appropriate for the type of probability sampling used.
Projective Techniques
Authors: Price Linda L. 
Published: December 2010

Abstract

Projective techniques use stimuli to allow participants to articulate repressed or withheld feelings by projecting these onto another character or object. However, many researchers also use group enabling techniques in conjunction with projective ones. Enabling techniques are devices that allow the participant to find means of expressing feelings and thoughts they find hard to articulate, and in marketing the difference between the two techniques are blurred. Despite high variance, both techniques show distinct similarity in terms of the research stimuli, and the types of data elicited. Projective techniques rely on
indirect questioning
, and
ambiguous stimuli
, allowing for variations in informant responses, and can uncover
primary motives
as well as
latent motives
. Both techniques elicit data that is more
sensory
,
emotional
,
symbolic
,
metaphorical
, and
imaginative
, and both techniques are relatively
free of social desirability bias
: indirect questioning and ambiguous stimuli allow informants to express their true, deeper level concerns. Projective and enabling techniques differ both in the quantity of data they elicit and in their response modality, and the skill and experience of the researcher is paramount in constructing a trustworthy account. In deciding whether the use of these techniques will add value, researchers should consider consumer memory, communication, and self‐disclosure concerns.
Validity and Reliability
Authors: Winter Frederick 
Published: December 2010

Abstract
The reliability and validity of the measurements that we use in marketing research are crucial if we are to have meaningful results. Reliability is whether the individual measure taken is close to the true actual measure (precise) or if there is so much noise, variance, or deviation from the true measure that the measure that we take has no precision. Alternative ways to assess reliability are test–retest reliability where we correlate a second measure (retest) with an original measure. Split half reliability splits the sample into two subsamples and compares the correlation. Validity is concerned with whether the measure one takes really is measuring what one wants it to measure. There are alternative types of validity – face validity, construct validity, and criterion validity. Assessment of validity is a bit of an art, but researchers need to make a good effort to insure that their results have meaning. The absence of either reliability or validity makes marketing research either meaningless (in the case of validity problems) or not very precise and therefore unable to show differences the research might be trying to detect.
Social Desirability Bias
Authors: Grimm Pamela 
Published: December 2010

Abstract
Social desirability bias refers to the tendency of research subjects to give socially desirable responses instead of choosing responses that are reflective of their true feelings. The bias in responses due to this personality trait becomes a major issue when the scope of the study involves socially sensitive issues such as politics, religion, and environment, or personal issues such as drug use, cheating, and smoking. Whenever possible, it is desirable to measure the extent of the bias present in responses to a survey by incorporating a socially desirable scale in the survey. A number of methods to address this issue are suggested in the literature. Use of a well‐trained interviewer or collection of data through methods that do not require presence involvement of an interviewer can help avoid this bias to some extent. Properly identified options to questions vulnerable to social desirability effect is another means of tackling this issue.
Frequency Distribution
Authors: Dass Mayukh 
Published: December 2010

Abstract

In a frequency distribution, each variable in the study is considered separately and a response count along with their percentages and cumulative percentages are computed for all different values of the categorical variable. This information is presented in a table called as
frequency table
. A frequency table is not only effective to determine how the respondents have answered the questions but it is also useful in identifying cases of nonresponses or illegitimate responses in the dataset. Furthermore, frequency distribution is also useful in finding ways to recode the data for better analysis.
Nonparametric Test
Authors: Dass Mayukh 
Published: December 2010

Abstract

There are many instances where the scales used in a study are either nominal or ordinal in nature with measures having no clear numeric interpretation. For example, in a case of gender classification of customers, “male” or “female” (nominal scale) are just used as a labeling scheme. Similarly, in a different study, when customers provide a preference ranking of different brands of breakfast cereal, their responses (ordinal scale) include an order in which the brands are preferred, but not the difference in preference between them. In such situations, it is inappropriate to compute means, variances, and standard deviations of the variables as they do not provide any meaningful insights. Moreover, the assumption that the data are drawn from a particular distribution is also not valid. In such cases, a different set of analyses called
nonparametric analyses
can only be used to analyze the data. The tests used for hypotheses testing are called
nonparametric tests
. Like parametric tests, nonparametric tests are categorized into one‐ and two‐sample tests. Two‐sample tests are further classified into independent and paired‐samples tests.
Product Moment Correlation
Authors: Franke George R. 
Published: December 2010

Abstract

The Pearson product moment correlation coefficient is an index of the degree of linear relationship between two variables. For two variables
X
and
Y
the correlation
r
is computed where
r
can be interpreted as the predicted standard deviation difference in
Y
for two observations that are different by one standard deviation in
X
. Rules of thumb for interpreting
r
are that population values of
r
= 0.1, 0.3, and 0.5 (positive or negative) can be viewed as small, medium, and large effects, respectively. The significance of
r
in a sample of
n
observations can be tested using values from Student's
t
distribution with
n
–2 degrees of freedom.

In general,
r
can range from −1 to +1. However, many factors can influence the magnitude of
r
, including nonlinear relationships, restrictions on the range of variables, dichotomization or unequal distributions of variables, outlying observations, and measurement error. Thus, while
r
is a useful summary of linear relationships between two variables, it may sometimes conceal more than it reveals. Creating a scatter plot of
X
and
Y
values is a useful tool to avoid misleading inferences from estimated correlation coefficients.
Stepwise Regression
Authors: Franke George R. 
Published: December 2010

Abstract

Stepwise regression
is both a general term and a specific method for choosing predictor variables from a larger pool of possible predictors in multiple regression. Related approaches include forward selection and backward elimination. All three procedures may identify different and suboptimal sets of predictors for a given sample of data. Some regression packages can identify the best possible one‐predictor model, two‐predictor model, and so on, according to the criteria specified by the user. This approach is computer intensive but is feasible with sets of at least 50 or 60 predictors. However they are selected, the best set of predictors in one sample will often not be the best in another sample or in the underlying population. Significance tests and
R

2

values may be far more liberal than their nominal values. Some of the estimated coefficients are also likely to be substantially overstated relative to their population values. Researchers using stepwise regression procedures will generally get better results when they have large samples of observations and relatively small sets of potential predictors; when they consider multiple procedures and criteria for selecting predictors; when they cross‐validate findings with new data or with random subsamples from the available data; and when they avoid self‐deception in interpreting the findings.
Logit Model
Authors: Arora Neeraj 
Published: December 2010

Abstract

Consumer choice is arguably the most important variable in marketing. Examples include brand choice in a grocery store purchase, selection of a kitchen appliance at a retailer, or choosing a health club from the available alternatives. Choice is a discrete variable (taking a 0–1 value) and therefore does not lend itself to a linear regression model, where the dependent variable is continuous. The logit model is of great value because it allows a manager to understand the link between marketing actions and consumer behavior. For example, the model can be used to conduct a “what if” analysis for a price change, an in‐store display, or a coupon drop. Two popular application areas for the logit model in marketing include household level panel data and choice‐based conjoint analysis. While enormously popular, the logit model has limitations. First, the distribution assumption for the error term is fairly ad hoc and results in an undesirable property called the
IIA
or
independence of irrelevant alternatives property
. A second limitation is the implicit assumption that consumers use a compensatory evaluation process in selecting an alternative. It is certainly plausible that consumers, in certain contexts, may use a noncompensatory evaluation process. Several solutions to both these problems have been proposed.
Consumer Memory Processes
Authors: Mercurio Kathryn R. , Forehand Mark R. 
Published: December 2010

Abstract
Consumer memory refers to the processes that allow us to record and later retrieve experiences and information related to products, services, and consumption experiences. Consumer memory can be represented as an associative network or a massive network of associated ideas and concepts. When learning about brands, the strength of association between the brand node and other nodes will depend on how often the brand node is linked to the category with which it holds membership. Retrieval in associative networks occurs through a spreading activation process where the activation of a single node spreads to the nearby nodes. Retrieval cues are thought to improve target item recollection if the cues activate network nodes that are linked to the desired information. We examine a variety of marketing controlled factors or categories at the time of encoding and retrieval that influence memory performance.
Online Consumption
Authors: Mazursky David , Vinitzky Gideon 
Published: December 2010

Abstract
On‐line shopping process possesses several unique characteristics that distinguish it from other forms of transactions. This article describes the research focusing on the effects of on‐line capabilities on consumers' experience, motivation to engage in the process, the unique process dynamics, and postconsumption behavior. The research on the unique characteristics originated from the improved interactivity and vividness associated with on‐line shopping. Some of these characteristics, such as telepresence and word‐of‐mouth were assessed mainly for the new opportunities and economic potential that they offer. In contrast, others, such as trust and immediate negative feedback, triggered research concerned more with the precautions needed in the emergence of on‐line shopping.
Impulsive and Compulsive Buying
Authors: Faber Ronald J. 
Published: December 2010

AbstractImpulsive and compulsive buying are terms that are frequently confused for each other, but represent behaviors that differ greatly in their frequency, cause, outcome and severity. Impulsive buying is a more common and ordinary behavior. Almost everyone makes a purchase on impulse (without much deliberation) from time to time.Impulse buying is defined as a sudden and powerful urge in the consumer to buy immediately. It occurs when desire for a product or brand outweighs one's willpower to resist. Research on impulse buying focuses on characteristic of individuals that make them more or less likely to engage in impulse buying. These include mood states, personality characteristics, and situational factors such as proximity and depletion in resources needed for self‐control.Compulsive buying, on the other hand, is a psychological disorder where one experiences an uncontrollable urge to buy. Failing to act on this urge creates increasing tension that can only dissipate with buying. Frequently, this urge is triggered by negative events or feelings. Ultimately, this behavior leads to extreme negative consequences for the individual. Many compulsive buyers never use the items they purchase. Thus, compulsive buying appears to be more about obtaining short‐term relief from negative feelings than about a desire for specific goods.
Opinion Leadership and Market Mavens
Authors: Goldsmith Ronald Earl 
Published: December 2010

Abstract
Opinion leadership refers to the role some consumers play by which they provide information and advice to other consumers. Opinion leaders can have a powerful influence on the sales of goods and services and so marketers are eager to encourage opinion leaders to spread positive word of mouth about their brands. Market mavens are a minority of consumers who are highly involved or engaged in the marketplace. They like to shop and buy, they are knowledgeable about brands and stores, and they actively engage with other consumers regarding their enthusiasms. Market mavens thus often play the role of opinion leaders for other consumers.
Hedonism: Gratification is Value
Authors: Babin Barry J. , Griffin Mitch 
Published: December 2010

Abstract
Marketers use many ways to sum up the reasons why people buy and consume. A very simple approach contrasts hedonic value and utilitarian value. Hedonism reflects the immediate gratification derived from consumption. In this sense, hedonism may be thought of as an additional effect. However, hedonic value is by no means sufficient to understand how marketers create successful outcomes for customers.
How Consumers Respond to Price Information
Authors: Monroe Kent B. 
Published: December 2010

Abstract
An important factor for understanding consumer behavior is learning how consumers use price information in their purchase decisions. In this article, we summarize knowledge that provides insights for understanding how consumers respond to price information. One of the prerequisites for knowing how consumers respond to price information is to understand how people perceive prices. An important extension of this knowledge concerns how customers form value judgments, given their price perceptions. It is these value judgments that determine the willingness of consumers to pay particular prices for the products and services they desire.
The traditional model of consumer behavior assumes that the decisions of what to buy and how much to buy depend on (i) the prices of all goods; (ii) the level of income or amount of purchasing power; and (iii) the tastes and preferences of the consumer. Assuming perfect information about prices, a fixed level of income, and knowledge about tastes and preferences, the consumer maximizes satisfaction by minimizing the price paid for each purchase. This assumption of “rational behavior” implies that consumers (i) have perfect information about prices; (ii) are capable of perfectly processing this information; (iii) are not affected subjectively by price information; and (iv) are perfectly aware of their tastes and preferences.
Assumptions (i), (ii), and (iv) are clearly not valid. Further, it would be expected that consumers' preferences or choices depend on how they evaluate the quality or benefits they receive from a product relative to the cost or sacrifice inherent in the price. Thus, consumers' perceptions of value represent a mental trade‐off between the quality and benefits they perceive in the product relative to the sacrifice they perceive by paying the price. Perceived value is positive when perceptions of quality and benefits are greater than perceptions of sacrifice. Willingness to buy is positively related to perceived value.
Consumer Behavior Analysis
Authors: Foxall Gordon R. 
Published: December 2010

AbstractConsumer psychology is usually cognitive in orientation. Yet, there is a large volume of evidence to show that economic behavior is reliably sensitive to environmental contingencies. Specifically, the subdiscipline ofbehavioral economicsis concerned with the combination of experimental economics and operant psychology to elucidate economic relationships.Consumer behavior analysisis the application of behavioral economics to the sphere of human consumer choice, particularly in the context of advanced marketing‐oriented economies. The inclusion of marketing brings a further level of complexity to what is already a multidisciplinary exercise. There are considerations stemming from human marketing activity that are never encountered in the laboratory regardless of whether the subjects are human or nonhuman. The very concept of exchange, for instance, is either nonexistent in the nonmarketing environment or very much less complicated. Considerations of price, which are easily replicable in experiments, are compounded by those of advertising, distribution, and subtleties of product characteristics, as well as by issues of status consumption and interpersonal effects that are absent from behavioral economics work in nonmarketing environments. It is the aim of consumer behavior analysis to comprehend marketing‐led consumer choice in terms of behavior analysis: to understand how far the methods of behavioral economics can be transferred from their original contexts to the marketing sphere, to recognize differences in human marketing behavior that require behavioral economics to be modified, to arrive at the boundaries of a behavior analytical interpretation of complex human behavior.
Consumer Decision Making
Authors: Yang Haiyang , Carmon Ziv 
Published: December 2010

AbstractIn a selective review of what is known about consumer decision making, we highlight ways in which consumers deviate from the classical economic model ofhomo economicus. We first consider how consumer preferences are influenced by characteristics of the decision task and context, such as choice set size, attribute quantity, option similarity and justifiability, presentation format, and response mode. We then review how the interplay of affect and cognitions affects consumer decision making, and how consumers misforecast their consumption experiences, cope with self‐control difficulties, and make poorer decisions as they try to choose well. Finally, we advocate using insights on consumer decision making to design choice settings helping consumers make better decisions.
Social Influence
Authors: Griskevicius Vladas , Cialdini Robert B. 
Published: December 2010

Abstract
Social influence involves the changing of a person's attitude or behavior through the doings of another person. Here we focus on the realm of consumer behavior and on the factors that cause one individual to comply with another's request. The starting point in ascertaining what are the most successful influence strategies was an investigation of the techniques that are most successful in professions dependent on their ability to induce compliance (e.g., salespeople, fund‐raisers, advertisers, political lobbyists, cult recruiters, negotiators, and con artists). What emerged from this period of systematic observation was a list of six principles on which compliance professionals base most of their successful influence attempts: (i) reciprocity – repaying a gift, favor, or service; (ii) consistency – acting consistently with prior commitments; (iii) social validation – following the lead of similar others; (iv) liking – accommodating the requests of those we know and like, (v) authority – conforming to the directives of legitimate authorities, and (vi) scarcity – seizing opportunities that are scarce or dwindling in availability. We provide a summary of the principles and the scientific evidence regarding how each principle functions to influence consumer behavior.