Art Gallery website and content analysis on the elements of the leading marketing research

2009 ◽  
Vol 11 (1) ◽  
pp. 265-287
Author(s):  
Lee,Woo-Chae
Keyword(s):  
Content Analysis ◽  
Marketing Research ◽  
Art Gallery

scholarly journals Typology of Business-Related Fake News Online: A Literature Review

2020 ◽  
Vol 5 (4) ◽  
pp. 234-243
Author(s):  
Marko Selakovic ◽  
Anna Tarabasz ◽  
Monica Gallant
Keyword(s):  
Content Analysis ◽  
Literature Review ◽  
Academic Research ◽  
Marketing Research ◽  
Jel Classification ◽  
Future Research ◽  
Fake News ◽  
Recent Emergence ◽  
News Online

Objective – This review paper discusses the emergence of scholarly articles related to the typology and classification of fake news and offers solutions for identified gaps, such as unstandardized terminology and unstandardized typology in the field of fake news-related research. Typology of fake news is a critical topic nowadays: recently emerged fake news needs to be categorized and analyzed in a structured manner in order to respond appropriately. Methodology/Technique – Based on the systematic review of literature identified in scientific databases, different typologies of fake news have been identified and a new typology of business-related fake news online has been proposed. New typology of business-related fake news online is based on factors such as level of facticity, intention to deceive and financial motivation. Findings and novelty – Content analysis of 326 articles containing terms related to the typology of fake news and classification of fake news indicates that the term “typology of fake news” is predominantly used in management, marketing and communications research, while the term “classification of fake news” is predominantly used in the information technology research. The content analysis also indicates the recent emergence of the topic of typology and classification of fake news in academic research, revealing that all articles related to these topics have been published on or after 2016. In addition to the contribution by presenting comprehensive typology of business-related fake news online, this paper also provides recommendations for future research and improvements related to the typology of fake news, emphasizing business-related fake news and fake news spread in the digital space. Type of Paper: Review JEL Classification: M31, M39. Keywords: Fake News; Crisis Communications; Online Communications; Digital Marketing; Management Research; Marketing Research Reference to this paper should be made as follows: Selakovic, M; Tarabasz, A; Gallant, M. (2020). Typology of Business-Related Fake News Online: A Literature Review, J. Mgt. Mkt. Review 5(4) 234 – 243. https://doi.org/10.35609/jmmr.2020.5.4(5)

Reliability of Nominal Data Based on Qualitative Judgments

1989 ◽  
Vol 26 (2) ◽  
pp. 135-148 ◽  
Author(s):  
William D. Perreault ◽  
Laurence E. Leigh
Keyword(s):  
Content Analysis ◽  
Meta Analysis ◽  
Marketing Research ◽  
Reliability And Validity ◽  
Cognitive Response ◽  
Nominal Data ◽  
Consumer Responses ◽  
Nominal Scale ◽  
Scale Data

Most research related to the reliability and validity of marketing measures has focused on multi-item quantitative scales. In contrast, little attention has been given to the quality of nominal scale data developed from qualitative judgments. Judgment-based (“coded”) nominal scale data are important and frequently used in marketing research-for example, in analysis of consumer responses to open-ended survey questions, in cognitive response research, in meta-analysis, and in content analysis. The authors address opportunities and challenges involved in evaluating and improving the quality of judgment-based nominal scale data, with specific emphasis on the use of multiple judges. They review approaches commonly used in other disciplines, then develop a new index of reliability that is more appropriate for the type of interjudge data typically found in marketing studies. Data from a cognitive response experiment are used to illustrate the new index and compare it with other common measures. The authors conclude with suggestions on how to improve the design of studies that rely on judgment-coded data.

Computerized content analysis of online data – opportunities for marketing scholars and practitioners

2020 ◽  
Vol 54 (3) ◽  
pp. 473-477
Author(s):  
Jan Kietzmann ◽  
Leyland F. Pitt
Keyword(s):  
Content Analysis ◽  
Consumer Research ◽  
Marketing Research ◽  
Computer Assisted ◽  
Online Data ◽  
Content Type ◽  
Making Sense ◽  
Consumer Data ◽  
Computerized Content Analysis ◽  
The Many

Purpose The purpose of this paper is to summarize the main developments from the early days of manual content analysis to the adoption of computer-assisted content analysis and the emerging artificial intelligence (AI)-supported ways to analyze content (primarily text) in marketing and consumer research. A further aim is to outline the many opportunities these new methods offer to marketing scholars and practitioners facing new types of data. Design/methodology/approach This conceptual paper maps our methods used for content analysis in marketing and consumer research. Findings This paper concludes that many new and emerging forms of unstructured data provide a wealth of insight that is neglected by existing content analysis methods. The main findings of this paper support the fact that emerging methods of making sense of such consumer data will take us beyond text and eventually lead to the adoption of AI-supported tools for all types of content and media. Originality/value This paper provides a broad summary of nearly five decades of content analysis in consumer and marketing research. It concludes that, much like in the past, today’s research focuses on the producers of the words than the words themselves and urges researchers to use AI and machine learning to extract meaning and value from the oceans of text and other content generated by organizations and their customers.

scholarly journals Making sense of text: artificial intelligence-enabled content analysis

2020 ◽  
Vol 54 (3) ◽  
pp. 615-644 ◽  
Author(s):  
Linda W. Lee ◽  
Amir Dabirian ◽  
Ian P. McCarthy ◽  
Jan Kietzmann
Keyword(s):  
Artificial Intelligence ◽  
Content Analysis ◽  
High Reliability ◽  
Marketing Research ◽  
Content Type ◽  
Performance Factors ◽  
Advantages And Disadvantages ◽  
Road Map ◽  
Computer Aided ◽  
Ibm Watson

Purpose The purpose of this paper is to introduce, apply and compare how artificial intelligence (AI), and specifically the IBM Watson system, can be used for content analysis in marketing research relative to manual and computer-aided (non-AI) approaches to content analysis. Design/methodology/approach To illustrate the use of AI-enabled content analysis, this paper examines the text of leadership speeches, content related to organizational brand. The process and results of using AI are compared to manual and computer-aided approaches by using three performance factors for content analysis: reliability, validity and efficiency. Findings Relative to manual and computer-aided approaches, AI-enabled content analysis provides clear advantages with high reliability, high validity and moderate efficiency. Research limitations/implications This paper offers three contributions. First, it highlights the continued importance of the content analysis research method, particularly with the explosive growth of natural language-based user-generated content. Second, it provides a road map of how to use AI-enabled content analysis. Third, it applies and compares AI-enabled content analysis to manual and computer-aided, using leadership speeches. Practical implications For each of the three approaches, nine steps are outlined and described to allow for replicability of this study. The advantages and disadvantages of using AI for content analysis are discussed. Together these are intended to motivate and guide researchers to apply and develop AI-enabled content analysis for research in marketing and other disciplines. Originality/value To the best of the authors’ knowledge, this paper is among the first to introduce, apply and compare how AI can be used for content analysis.

The evolution of marketing research

2014 ◽  
Vol 26 (5) ◽  
pp. 706-726 ◽  
Author(s):  
Cristian Morosan ◽  
John T. Bowen ◽  
Morgan Atwood
Keyword(s):  
Content Analysis ◽  
Research Agenda ◽  
Design Methodology ◽  
Marketing Research ◽  
Content Type ◽  
Research Paradigms ◽  
The Past ◽  
Over Time ◽  
Comprehensive Study

Purpose – The purpose of this study is to provide a domain statement for hospitality marketing research. The objectives of the study are to analyze the evolution of hospitality marketing research over the past 25 years, determine how the research paradigms changed over time in hospitality marketing relative to mainstream marketing and provide scholars with suggestions for developing and managing a marketing research agenda. The findings of this study help not only scholars involved in marketing research but also hospitality scholars across all disciplines. Design/methodology/approach – A content analysis of > 1,700 marketing articles is provided, with articles published in three leading hospitality journals and one mainstream marketing journal over a 25-year period. Additionally, the authors consulted leading hospitality scholars to solicit their views and suggestions on hospitality marketing research. Findings – The results show the evolution of hospitality marketing over a 25-year period. This provides insights into how hospitality has unique aspects, which can lead to contributions in mainstream marketing. Originality/value – Due to its longitudinal nature and breadth (e.g., number of journals covered), this is the most comprehensive study of hospitality marketing research. The findings of the study provide direction for all hospitality scholars as well as those involved in hospitality marketing research.

Introduction to the Special Section: Measurement and Marketing Research—Is the Flirtation Going to Lead to a Romance?

1979 ◽  
Vol 16 (1) ◽  
pp. 1-5 ◽  
Author(s):  
Michael L. Ray ◽  
Gilbert A. Churchill
Keyword(s):  
Content Analysis ◽  
Special Section ◽  
Marketing Research ◽  
Reliability And Validity ◽  
The Past ◽  
Section Measurement ◽  
Practical Guidelines

The articles in this special section provide practical guidelines and examples for carrying out reliability and validity measurement testing, which is essential to the advancement of marketing research. A content analysis of JMR articles shows increasing concern with these issues in the past five years. A special AMA committee and a new JMR section are proposed to move the field beyond concern (flirtation) and into action (romance).

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