Automated Text and Content Analysis
In the B2B Setting – Insights for Scholars and Practitioners, Special issue of Industrial Marketing Management; Deadline 15 Jan 2022
Author: Selma Kadić-Maglajlić
INDUSTRIAL MARKETING MANAGEMENT
Call for Papers
Automated Text and Content Analysis in the B2B Setting – Insights for Scholars and Practitioners
Deadline for submission: 15th January, 2022
Overview and purpose of the special issue
Business-to-business (B2B) marketing scholars and practitioners alike would acknowledge that it has become increasingly difficult to gather primary data from organizations and individuals. Twenty or thirty years ago, managers in B2B environments would respond to paper surveys, and requests of depth interviews, at rates acceptable enough for researchers to use. They would have been more willing to answer mainline telephone calls, and provide answers to telephone surveys and interviews, if these were reasonably brief. Nowadays, it is very easy for busy executives to ignore and delete email surveys, and mailed paper surveys are a thing of the past. Caller ID on mobile phones enables recipients to ignore or block numbers they don’t recognize. Yet the online environment of the 21st century provides B2B marketing researchers and executives with masses of rich data in a vast array of other formats, including text, images, sound and video. Text is generated on various forms of social media, including those more apposite to B2B such as Glassdoor and LinkedIn. There are countless blogs, podcasts, product reviews, interviews with executives, corporate reports of all kinds, and of course the websites of B2B firms themselves. There are thousands of images including photographs, graphics and cartoons on websites and materials published electronically by firms and individuals. There are many millions of hours of video hosted on sites such as YouTube, Vimeo and other social media as well as corporate websites and the blogs and websites of individuals. The greatest challenge is to make sense of this mass of data. In most cases attempting to do this manually simply doesn’t work.
In marketing, Humphreys & Wang (2018, p.1274) have recently noted, “Researchers, consumers, and marketers swim in a sea of language, and more and more of that language is recorded in the form of text.” The sheer volume, variability, veracity and velocity with which online text is generated today, and as a result is available to B2B marketing researchers, makes manual content analysis futile at best, and mostly, impossible to conduct. Not only are human coders very slow, they tire quickly and get bored from the essentially mundane task of counting and allocating words to categories and dictionaries, images to classifications, and video and sound to groupings. They are also prone to making errors. Computers, and automated text analysis software, are very fast when it comes to coding and categorizing content, they don’t suffer from fatigue or tedium, and they don’t make mistakes.
The recent past has not only seen a significant rise in the amount of unstructured textual data available to researchers, but also a noteworthy increase in the number and sophistication of tools available to perform automated text analysis. Dictionary-based tools rely on allocating the words in a text corpus to pre-constructed dictionaries that capture constructs of interest, and then score the text on those constructs. Their use has received some attention in the recent B2B marketing literature. This work has utilized tools such as DICTION (e.g. Pitt, et al., 2019; Robertson, et al., 2019), and LIWC (Winkler, Rieger, & Engelen, 2020; Duncan, Chohan & Ferreira, 2019), as well as packages that produce graphic output that users then need to interpret, such as Leximancer (e.g., Purchase, Rosa & Schepis, 2016). Other recent research has used more customized automated text analysis to identify themes in vast amounts of text to predict various business outcomes in the B2B context (e.g., Mishra, Ewing & Pitt, 2020). Perhaps the most exciting and promising tools for automated text analysis are in the domain of artificial intelligence. Documents and even whole websites can be analyzed using tools such as Google’s Brain, Microsoft’s Azure, and IBM’s Watson. While the use of these tools has been reported in the consumer marketing context (e.g. Pitt, Mulvey & Kietzmann, 2018), there has been little or no attention given them to date in the B2B marketing literature.
Marketing scholars such as Humphreys & Wang (2018), Berger et al. (2020) and Chapman (2020) contend that while automated text analysis cannot be used to explore all marketing phenomena, it is a useful tool for examining patterns in text that researchers could not be able to uncover without access to computers and powerful software. The wide range of software tools available to marketing scholars and practitioners today enables them to discover and work with a spectrum of psychological and sociological constructs produced by organizations and players in business-to-business markets.
While work that uses automated text analysis to explore marketing issues has begun to appear sporadically in scholarly B2B journals, there is a need for a concerted concentration of work that will alert the broader academic B2B marketing community to the potential of automated text analysis. This special issue of IMM seeks to publish excellent recent work on automated text analysis in B2B marketing from both theoretical/conceptual and empirical perspectives.
Text is also not the only type of content that lends itself to computerized analysis. The internet in general, and social media in particular, have also seen a profusion of other media content, including various kinds of graphics, photographs and video. Marketing- and other scholars have recently given attention to understanding and interpreting the use of photographs (e.g. Marder et al., 2020; Sparks & Wang, 2014; Yazdani & Manovich, 2015) in marketing communication. Similarly, scholars have also turned to the automated content analysis of online video (Knoblauch, et al., 2006; Hsieh, Hsieh & Tang. 2012). Wiid and her colleagues (2016) have studied the interpretation of cartoons as gauges of public sentiment in the specific context of selling. More recently Kietzmann (Kietzmann, Mills & Plangger, 2020; Kietzmann et al., 2020) has led research initiatives into the sometimes amusing, but potentially devastating effect of deep fakes in marketing. Again, research into these issues has not yet been fully embraced by the community of B2B marketing scholars, and these domains offer intriguing avenues for future investigation.
It should be noted that this is not a call for papers that report the results of qualitative content analysis of depth interviews or case studies per se, or where simple computerized tools such as NUDIST of NVIVO are used to identify themes. While the importance and value of these tools is recognized, the focus of this special issue is on work in which computers and software plays a more significant role in processing and facilitating the interpretation of content. Stated simply, this special issue focuses more on the how than on the what.
Potential contributions may address, but are not limited to the following topics:
Theorizing the value of automated text analysis in B2B marketing research and practice
- Is there a “theory” of automated text analysis and/or content analysis, and if so how might it apply in B2B markets?
- Are there conceptual- or theoretical frameworks that can guide the research of B2B marketing scholars with regard to automated content analysis? What might these look like? How could they be implemented in research?
- Transplanting theories from other academic domains to the analysis and interpretation of content in B2B marketing. For example, Media Richness Theory (Daft & Lengel, 1986) from information science, and Greenberg’s (2002) and Caswell’s (2004) theories on the impact of cartoons from sociology and journalism.
Comparing various approaches to automated content analysis in B2B marketing
- How do the various tools available for automated text analysis stack up against each other in the B2B context? For example, what are the relative advantages and suitable applications for dictionary-based automated text analysis tools such as LIWC and DICTION, versus more thematic/interpretive tools such as Leximancer?
- What are the advantages and limitations of artificial intelligence-based tools such as IBM’s Watson?
- What new tools, especially those developed by potential authors, are available for automated text analysis, and what novel insights do they bring to B2B marketing scholars
- What tools are available to B2B marketing scholars with which to study content such as video, sound, graphics and photographs? What results do they produce? What are the advantages and limitations of these tools?
How can the tools of automated content analysis be used to explore the behavior of B2B stakeholders?
- How can automated content analysis techniques be used to understand behavioral issues within the B2B environment? What insights can they give researchers and practitioners into issues such as stakeholder emotions, sentiment, personalities, values and preferences?
- How can insights from behavioral issues as uncovered by automated content analysis be used to group stakeholders into more homogenous units, for example, for purposes of market segmentation. What statistical techniques (e.g. clustering procedures, correspondence analysis) are best suited to analyze this type of data?
- What relationships exist between the behavioral issues uncovered by automated content analysis in B2B markets and other actions by stakeholders, such as purchases, brand engagement, and online activities such as liking or following?
What can the tools of automated context analysis tell us about B2B websites
- How do the textual characteristics of B2B websites (e.g., sentiment, tone, underlying emotions, brand personality) link to aspects of their performance (e.g., leads, hits, search prominence, sales, etc.)?
- How do aspects of B2B websites such as graphics, images and video link to aspects of their performance (e.g., leads, hits, search prominence, sales, etc.)?
- How can websites within a B2B category (e.g. heavy machinery, mining, chemicals, etc.) be compared and positioned based on aspects of their content, gleaned from the results of automated content analysis
How can we predict B2B social media engagement statistics based on textual and other data from sources such as public relations, CEO posts/interviews, or financial reports?
- How does B2B content as analyzed by automated content analysis tools predict social media engagement as measured by outcomes such as likes, number of followers, comments, etc?
- Are there particular social media planning and implementation actions in the use of social media by B2B firms that impact stakeholder engagement, and can automated content analysis tools be used to understand this more effectively?
- Are there particular mixes of text, and other communication devices such as graphics video and images that impact B2B social media engagement and how are these best assessed using automated content analysis tools?
Is there a dark side to online communication by B2B firms, and can automated text analysis tools be used to detect it?
- How can B2B scandals (e.g., accounting irregularities, greenwashing, ethical issues) be understood and even predicted using independent or organizational text data or other content?
- What is the nature of fake news online in the B2B setting and how can this be determined and understood using tools of automated content analysis? How might AI/Machine Learning tools such as GPT2 and GPT3 create content that might be detrimental to B2B firms?
- To what extent are “deepfakes” an issue in B2B communication, and is it possible to detect and predict directions of trends in this regard using the tools of automated content analysis?
What are the measurement issues when conducting automated text analysis in B2B settings?
- What sampling issues arise when conducting automated content analysis in B2B environments?
- How can B2B marketing scholars assure reliability and validity of results when conducting automated content analysis in B2B settings?
Are there ethical research issues involved in automated content analysis research in B2B marketing?
- Is it acceptable to gather, download or scrape what is perceived by many to be “free” data in the form of personal blogs, product and service reviews, and posts on social media in B2B settings?
- To what extent is it acceptable to name stakeholders (firms, individuals, organizations) in reporting the results of automated content analysis in B2B research?
Preparation and submission of paper and review process
Papers submitted must not have been published, accepted for publication, or presently be under consideration for publication elsewhere. Submissions should be about 6,000-8,000 words in length. Copies should be uploaded on Industrial Marketing Management’s homepage through the Editorial management system. You need to upload your paper using the dropdown box for the special issue on VSI: B2B AUTO CONTENT ANALYSIS. For guidelines, visit
Papers not complying with the notes for contributors (cf. homepage) or poorly written will be desk rejected. Suitable papers will be subjected to a double-blind review; hence, authors must not identify themselves in the body of their paper. Please do not submit a Word file with “track changes” active or a PDF file. Manuscripts falling within the scope of the special issue (as described above) and deemed to have a reasonable chance of conditional acceptance after no more than two rounds of revisions will enter the review process.
The special issue editors will also assume that all submitting authors to the special issue will also be willing to serve as reviewers for submissions if required.
- Submission opens: 1 November 2021
- Deadline for submission: 15 January 2022
Dr. Leyland Pitt (email@example.com), Dennis F. Culver EMBA Alumni Chair of Business, Simon Fraser University, Vancouver, Canada.
Dr. Kirk Plangger (firstname.lastname@example.org), Senior Lecturer in Marketing, King’s Business School, King’s College, London, UK.
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