Automated Text Analysis of Online Content in Marketing
Dictionray-Based Methods and Artificial Intelligence
Time: Mon 2020-04-20 15.00
Subject area: Industrial Economics and Management
Doctoral student: Christine Pitt , Industriell ekonomi och organisation (Inst.)
Opponent: Professor Julie Tinson, University of Stirling
Supervisor: Professor Esmail Salehi-Sangari, Industriell ekonomi och organisation (Inst.)
Far more than products or services, words are the most fundamental element in the exchanges between sellers and buyers. Understanding the words that constitute the text that is created when sellers and buyers interact with each other is therefore critical for marketing decision makers. This has become especially relevant in the age of the internet, and particularly with the advent of social media. In the pre-computer age, the content analysis of text was a time-consuming, laborious and frequently error-prone tool for marketing scholars and practitioners to use. Now, powerful computers and software enable the content analysis of text to be performed rapidly, and with little human effort or error. Two fundamental types of tools that enable the automated analysis of text are those that are dictionary-based and those that are artificial intelligence-based. The former automated text analysis tools rely on pre-constructed dictionaries, and then scan a piece of text in order to count and match the words in it to obtain scores on the dimensions of interest. Artificial intelligence-based automated text analysis tools employ machine learning algorithms to recognize patterns in text. They compare text to other pre-classified texts, having been trained by human experts to recognize the desired dimensions of a construct, and can “learn” to do this more effectively the more they are used.
The service dominant logic perspective on marketing holds that value is co-created by both sellers and buyers. This enables the identification of two fundamental marketing focus activities. First, sellers and buyers engage in acts of creation; second, sellers and buyers engage in acts of experience. On a wide range of forums, both buyers and sellers create text about these marketing focus activities. This text lends itself to analysis by the two categories of automated text analysis tools. Therefore, the central question is: How can automated textual analysis tools enable marketing practitioners and scholars to gain insights from different types of textual data?
Marketing scholars have recently given more attention to the use of automated text analysis tools in marketing research. These efforts have included overviews of the approach, suggestions on choosing amongst methods, and considerations of the sampling and statistical issues unique to automated text analysis. Less emphasis has been placed on specifically examining the use of the two different types of automated text analysis tools (dictionary-based and artificial intelligence based) in exploring the text generated by sellers and buyers in the context of the focal marketing activities of creation and experience. The current research therefore explores the following four research questions:
- RQ1: What insights can an artificial intelligence-based automated text analysis tool deliver from depth interviews with respondents engaged in a creative focus activity?
- RQ2: What insights can an artificial intelligence-based automated text analysis tool deliver from online reviews by respondents engaged in an experience focus activity?
- RQ3: What insights can a dictionary-based automated text analysis tool deliver from online reviews by respondents engaged in an experience focus activity?
- RQ4: What insights can a dictionary-based automated text analysis tool deliver from online interviews by respondents engaged in a creation focus activity?
The empirical part of this research covered four papers, all of which involved analyzing textual data with the two categories of automated text analysis tools. Two of these papers used artificial intelligence-based automated text analysis tools in both the creation and experience settings, and the other two used dictionary-based automated text analysis tools, again, in these settings.
The overall contribution to the body of knowledge is to provide evidence of the applicability of both artificial intelligence-based- and dictionary-based automated text analysis tools in two fundamental marketing focus activities, namely, creation and experience. The individual papers also
further our understanding of the use of automated text analysis to study comparisons between groups, as well as correlation between traits and ways of speaking within samples of text.
The document is organised as an overall introduction to the research narrative of four related published papers. The document opens with a chapter providing an overview of automated text analysis in marketing, the statement of the overall research problem, and the identification of four
research sub-questions. This is followed by a chapter on the literature review. Next is a chapter on the methodology used in the studies. The fourth chapter considers the four papers in more detail, acknowledging their limitations, identifying the implications for marketing practice, and suggesting avenues for future research by marketing scholars. The four papers follow under Chapter 5 at the end. Three of these papers have either been published or accepted for publication; the other is in the second round of revision and resubmission.