George P. Moschis

Bio: George P. Moschis is an academic researcher from Georgia State University. The author has contributed to research in topics: Consumer behaviour & Life course approach. The author has an hindex of 46, co-authored 168 publications receiving 8616 citations. Previous affiliations of George P. Moschis include University of Maryland, College Park & College of Business Administration.

Consumer Socialization: A Theoretical and Empirical Analysis:

Abstract: The results of a large-scale study of adolescent consumer socialization are presented. A general conceptual framework of socialization is outlined to serve as a blueprint for discussing variables a...

The Role of Family Communication in Consumer Socialization of Children and Adolescents

Abstract: While studies of communication effects on consumer behavior of the young have focused mainly on the effects of mass media (advertising in particular), little research has examined the effects of interpersonal communication. One finds relatively little theoretical and empirical work regarding the role of interpersonal communication in the development of consumer behavior of young people. This article deals with one important type of interpersonal communication—family communication. It conceptualizes the family communication processes and effects, reviews literature regarding the role of family communication in consumer learning of children and adolescents, develops a set of propositions on the basis of theory research, and suggests directions for future research.

Television and Interpersonal Influences on Adolescent Consumer Learning

Abstract: A model of consumer socialization is developed and tested. The development of the model is guided by theoretical notions and empirical findings drawn from various disciplinary areas, and the model is tested using two-stage least squares. The empirical results presented contribute to the understanding of the influence of television, family, and peers on adolescent consumer learning.

Decision Making Among the Young: A Socialization Perspective

Abstract: This article examines decision-making patterns among teen-age consumers. Variables associated with several stages in the decision-making process (information seeking, product evaluation, and purchase), as well as “anticipatory” cognitions regarding family decision making are examined within the context of general theories of socialization.

Marketing for management.

Abstract: MKT 6009 Marketing Internship (0 semester credit hours) Student gains experience and improves skills through appropriate developmental work assignments in a real business environment. Student must identify and submit specific business learning objectives at the beginning of the semester. The student must demonstrate exposure to the managerial perspective via involvement or observation. At semester end, student prepares an oral or poster presentation, or a written paper reflecting on the work experience. Student performance is evaluated by the work supervisor. Pass/Fail only. Prerequisites: (MAS 6102 or MBA major) and department consent required. (0-0) S MKT 6244 Digital Marketing Strategy (2 semester credit hours) Executive Education Course. The course explores three distinct areas within marketing and sales namely, digital marketing, traditional sales prospecting, and executive sales organization and strategy. The continuing convergence of the digital marketing and sales funnels has created a strategic continuum from digital lead generation to digital sales. The course identifies the current composition of this digital continuum while providing opportunities to evaluate sales and marketing digital strategies. Prerequisites: MKT 6301 and instructor consent required. (2-0) Y MKT 6301 (SYSM 6318) Marketing Management (3 semester credit hours) Overview of marketing management methods, principles and concepts including product, pricing, promotion and distribution decisions as well as segmentation, targeting and positioning. (3-0) S MKT 6309 Marketing Data Analysis and Research (3 semester credit hours) Methods employed in market research and data analysis to understand consumer behavior, customer journeys, and markets so as to enable better decision-making. Topics include understanding different sources of data, survey design, experiments, and sampling plans. The course will cover the techniques used for market sizing estimation and forecasting. In addition, the course will cover the foundational concepts and techniques used in data visualization and \"story-telling\" for clients and management. Corequisites: MKT 6301 and OPRE 6301. (3-0) Y MKT 6310 Consumer Behavior (3 semester credit hours) An exposition of the theoretical perspectives of consumer behavior along with practical marketing implication. Study of psychological, sociological and behavioral findings and frameworks with reference to consumer decision-making. Topics will include the consumer decision-making model, individual determinants of consumer behavior and environmental influences on consumer behavior and their impact on marketing. Prerequisite: MKT 6301. (3-0) Y MKT 6321 Interactive and Digital Marketing (3 semester credit hours) Introduction to the theory and practice of interactive and digital marketing. Topics covered include: online-market research, consumer behavior, conversion metrics, and segmentation considerations; ecommerce, search and display advertising, audiences, search engine marketing, email, mobile, video, social networks, and the Internet of Things. (3-0) T MKT 6322 Internet Business Models (3 semester credit hours) Topics to be covered are: consumer behavior on the Internet, advertising on the Internet, competitive strategies, market research using the Internet, brand management, managing distribution and supply chains, pricing strategies, electronic payment systems, and developing virtual organizations. Further, students learn auction theory, web content design, and clickstream analysis. Prerequisite: MKT 6301. (3-0) Y MKT 6323 Database Marketing (3 semester credit hours) Techniques to analyze, interpret, and utilize marketing databases of customers to identify a firm's best customers, understanding their needs, and targeting communications and promotions to retain such customers. Topics

Introduction to Machine Learning

Abstract: The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. In order to present a unified treatment of machine learning problems and solutions, it discusses many methods from different fields, including statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining. All learning algorithms are explained so that the student can easily move from the equations in the book to a computer program. The text covers such topics as supervised learning, Bayesian decision theory, parametric methods, multivariate methods, multilayer perceptrons, local models, hidden Markov models, assessing and comparing classification algorithms, and reinforcement learning. New to the second edition are chapters on kernel machines, graphical models, and Bayesian estimation; expanded coverage of statistical tests in a chapter on design and analysis of machine learning experiments; case studies available on the Web (with downloadable results for instructors); and many additional exercises. All chapters have been revised and updated. Introduction to Machine Learning can be used by advanced undergraduates and graduate students who have completed courses in computer programming, probability, calculus, and linear algebra. It will also be of interest to engineers in the field who are concerned with the application of machine learning methods. Adaptive Computation and Machine Learning series

The Persuasion Knowledge Model: How People Cope with Persuasion Attempts

Abstract: In theories and studies of persuasion, people's personal knowledge about persuasion agents' goals and tactics, and about how to skillfully cope with these, has been ignored. We present a model of how people develop and use persuasion knowledge to cope with persuasion attempts. We discuss what the model implies about how consumers use marketers' advertising and selling attempts to refine their product attitudes and attitudes toward the marketers themselves. We also explain how this model relates to prior research on consumer behavior and persuasion and what it suggests about the future conduct of consumer research.

A Consumer Values Orientation for Materialism and Its Measurement: Scale Development and Validation

Abstract: This article reviews the construct and measurement of materialism and concludes that materialism is appropriately conceptualized as a consumer value. The development of a values-oriented materialism scale with three components—acquisition centrality, acquisition as the pursuit of happiness, and possession-defined success—is described. In validation tests high scorers (compared with low scorers) desired a higher level of income, placed greater emphasis on financial security and less on interpersonal relationships, preferred to spend more on themselves and less on others, engaged in fewer voluntary simplicity behaviors, and were less satisfied with their lives.