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.

scholarly journals Second Life as a research environment: avatar-based focus groups (AFG)

2016 ◽  
Vol 19 (1) ◽  
pp. 101-114 ◽  
Author(s):  
Eman Gadalla ◽  
Ibrahim Abosag ◽  
Kathy Keeling
Keyword(s):  
Focus Groups ◽  
Data Quality ◽  
Virtual Worlds ◽  
Second Life ◽  
Three Dimensional ◽  
Marketing Research ◽  
Two Phase ◽  
Content Type ◽  
Face To Face ◽  
Advantages And Disadvantages

Purpose – This study aims to examine the nature and the potential use of avatar-based focus groups (AFGs) (i.e. focus groups conducted in three-dimensional [3D] virtual worlds [VWs]) as compared to face-to-face and online focus groups (OFGs), motivated by the ability of VWs to stimulate the realism of physical places. Over the past decade, there has been a rapid increase in using 3D VWs as a research tool. Design/methodology/approach – Using a two-phase reflective approach, data were collected first by using traditional face-to-face focus groups, followed by AFGs. In Phase 2, an online, semi-structured survey provided comparison data and experiences in AFGs, two-dimensional OFGs and traditional face-to-face focus groups. Findings – The findings identify the advantages and disadvantages of AFGs for marketing research. There is no evident difference in data quality between the results of AFGs and face-to-face focus groups. AFG compensates for some of the serious limitations associated with OFGs. Practical implications – The paper reflects on three issues, data quality, conduct of AFGs (including the moderator reflection) and participant experience, that together inform one’s understanding of the characteristics, advantages and limitations of AFG. Originality/value – This is the first paper to compare between AFGs, traditional face-to-face focus groups and OFGs. AFG holds many advantages over OFGs and even, sometimes, over face-to-face focus groups, providing a suitable environment for researchers to collect data.

scholarly journals Modern technologies of marketing research and market analysis

2021 ◽  
Vol 2021 (2) ◽  
pp. 29-39
Author(s):  
Oleh Duma ◽  
◽  
M. Melnyk ◽  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Human Life ◽  
Marketing Research ◽  
Research Process ◽  
Target Market ◽  
Advantages And Disadvantages ◽  
Marketing Activities ◽  
Modern Technologies

Nowadays, marketing research is increasingly important for the success of enterprises. Conducting marketing research reduces the risk of making wrong decisions in the analysis and development of marketing strategies, planning and control of marketing activities. The article provides an overview of the emergence of marketing research, explores the latest methods of marketing research, their advantages and disadvantages, the possibility of its application at different stages of marketing activities. Scientific approaches to the interpretation of the concepts "marketing research", "methods of marketing research" are systematized. The latest methods of marketing research that widely use AI, Big Data, ML, TRI * M, have been studied. The technologies of mobile advertising, areas of use of artificial intelligence, the essence and features of the formation of Big Data and machine learning were researched in the article. The benefits of using artificial intelligence, big data and machine learning to conduct marketing research were researched in the article. Analytical materials are confirmed by cases from the practice of marketing research. All research outcomes were proved by cases of Independent Media, TNS Ukraine, British Council, Chat fuel and Coca - Cola. The scheme of the marketing research process is supplemented by the possibilities of applying the latest technologies, which are grouped by stages. Any marketing research is a sequence of steps. Each of them uses a set of tools that provide collection, processing and analysis of data about the target market, customers, or economic processes. Each of these stages can be implemented using the modern technologies that are widely used in various spheres of human life. The directions of application the artificial intelligence, Big data, machine learning for carrying out office researches, field researches, pilot researches and a method of focus groups are offered. The analysis of realization of methods of marketing researches on the basis of Big Data, AI, ML is carried out.

Additive manufacturing technology: mapping social impacts

2019 ◽  
Vol 30 (1) ◽  
pp. 70-97 ◽  
Author(s):  
Florinda Matos ◽  
Celeste Jacinto
Keyword(s):  
Content Analysis ◽  
Additive Manufacturing ◽  
Business Models ◽  
Methodological Approach ◽  
Academic Research ◽  
Social Impacts ◽  
Content Type ◽  
Social Challenges ◽  
Computer Aided ◽  
Recent Developments

Purpose Recent developments in additive manufacturing (AM) technology have emphasized the issue of social impacts. However, such effects are still to be determined. So, the purpose of this paper is to map the social impacts of AM technology. Design/methodology/approach The methodological approach applied in this study combines a literature review with computer-aided content analysis to search for keywords related to social impacts. The content analysis technique was used to identify and count the relevant keywords in academic documents associated with AM social impacts. Findings The study found that AM technology social impacts are still in an exploratory phase. Evidence was found that several social challenges of AM technology will have an influence on the society. The topics associated with fabrication, customization, sustainability, business models and work emerged as the most relevant terms that can act as “pointers” to social impacts. Research limitations/implications The research on this subject is strongly conditioned by the scarcity of empirical experience and, consequently, by the scarcity of data and publications on the topic. Originality/value This study gives an up-to-date contribution to the topic of AM social impacts, which is still little explored in the literature. Moreover, the methodological approach used in this work combines bibliometrics with computer-aided content analysis, which also constitutes a contribution to support future literature reviews in any field. Overall, the results can be used to improve academic research in the topic and promote discussion among the different social actors.

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.

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.

Establishment of a maturity model to assess the development of industrial AI in smart manufacturing

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Wenting Chen ◽  
Caihua Liu ◽  
Fei Xing ◽  
Guochao Peng ◽  
Xi Yang
Keyword(s):  
Artificial Intelligence ◽  
Manufacturing Firms ◽  
Smart Manufacturing ◽  
Maturity Model ◽  
Structured Interviews ◽  
Maturity Models ◽  
Content Type ◽  
Domain Experts ◽  
Road Map ◽  
Industrial Context

PurposeThe benefits of artificial intelligence (AI) related technologies for manufacturing firms are well recognized, however, there is a lack of industrial AI (I-AI) maturity models to enable companies to understand where they are and plan where they should go. The purpose of this study is to propose a comprehensive maturity model in order to help manufacturing firms assess their performance in the I-AI journey, shed lights on future improvement, and eventually realize their smart manufacturing visions.Design/methodology/approachThis study is based on (1) a systematic review of literature on assessing I-AI-related technologies to identify relevant measured indicators in the maturity model, and (2) semi-structured interviews with domain experts to determine maturity levels of the established model.FindingsThe I-AI maturity model developed in this study includes two main dimensions, namely “Industry” and “Artificial Intelligence”, together with 12 first-level indicators and 35 second-level indicators under these dimensions. The maturity levels are divided into five types: planning level, specification level, integration level, optimization level, and leading level.Originality/valueThe maturity model integrates indicators that can be used to assess AI-related technologies and extend the existing maturity models of smart manufacturing by adding specific technical and nontechnical capabilities of these technologies applied in the industrial context. The integration of the industry and artificial intelligence dimensions with the maturity levels shows a road map to improve the capability of applying AI-related technologies throughout the product lifecycle for achieving smart manufacturing.

Developing an Artificial Intelligence Project in your Radiology Department

2020 ◽  
Vol 2 ◽  
pp. 58-61 ◽  
Author(s):  
Syed Junaid ◽  
Asad Saeed ◽  
Zeili Yang ◽  
Thomas Micic ◽  
Rajesh Botchu
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Health Care ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Radiology Department ◽  
Short Article ◽  
Computing Power ◽  
Road Map ◽  
Key Concepts

The advances in deep learning algorithms, exponential computing power, and availability of digital patient data like never before have led to the wave of interest and investment in artificial intelligence in health care. No radiology conference is complete without a substantial dedication to AI. Many radiology departments are keen to get involved but are unsure of where and how to begin. This short article provides a simple road map to aid departments to get involved with the technology, demystify key concepts, and pique an interest in the field. We have broken down the journey into seven steps; problem, team, data, kit, neural network, validation, and governance.

Virtual Screening Meets Deep Learning

2018 ◽  
Vol 15 (1) ◽  
pp. 6-28 ◽  
Author(s):  
Javier Pérez-Sianes ◽  
Horacio Pérez-Sánchez ◽  
Fernando Díaz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Virtual Screening ◽  
Great Increase ◽  
New Drugs ◽  
Learning Approach ◽  
Screening Strategies ◽  
Computer Aided ◽  
Recent Developments

Background: Automated compound testing is currently the de facto standard method for drug screening, but it has not brought the great increase in the number of new drugs that was expected. Computer- aided compounds search, known as Virtual Screening, has shown the benefits to this field as a complement or even alternative to the robotic drug discovery. There are different methods and approaches to address this problem and most of them are often included in one of the main screening strategies. Machine learning, however, has established itself as a virtual screening methodology in its own right and it may grow in popularity with the new trends on artificial intelligence. Objective: This paper will attempt to provide a comprehensive and structured review that collects the most important proposals made so far in this area of research. Particular attention is given to some recent developments carried out in the machine learning field: the deep learning approach, which is pointed out as a future key player in the virtual screening landscape.

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