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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.

This conceptual paper maps our methods used for content analysis in marketing and consumer research.

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.

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 purpose of this paper is to explain the technological phenomenon artificial intelligence (AI) and how it can contribute to knowledge-based marketing in B2B. Specifically, this paper describes the foundational building blocks of any artificial intelligence system and their interrelationships. This paper also discusses the implications of the different building blocks with respect to market knowledge in B2B marketing and outlines avenues for future research.

The paper is conceptual and proposes a framework to explicate the phenomenon AI and its building blocks. It further provides a structured discussion of how AI can contribute to different types of market knowledge critical for B2B marketing: customer knowledge, user knowledge and external market knowledge.

The paper explains AI from an input–processes–output lens and explicates the six foundational building blocks of any AI system. It also discussed how the combination of the building blocks transforms data into information and knowledge.

Aimed at general marketing executives, rather than AI specialists, this paper explains the phenomenon artificial intelligence, how it works and its relevance for the knowledge-based marketing in B2B firms. The paper highlights illustrative use cases to show how AI can impact B2B marketing functions.

The study conceptualizes the technological phenomenon artificial intelligence from a knowledge management perspective and contributes to the literature on knowledge management in the era of big data. It addresses calls for more scholarly research on AI and B2B marketing.

The purpose of this paper is to develop an instrument to measure employer branding in the information age. Firms increasingly migrate from matter-intensive business models to information-intensive models, where value lies in information rather than the physical objects. This shift has, in turn, led to a change in employee work skills. This is particularly true in the information technology (IT) sector, where firms rely on a limited supply of skilled labor. Employer branding, a firm’s reputation as a place to work, is an important strategy to attract and retain employees.

From the literature, the authors developed and refined an instrument to measure key value propositions of employer brands. The potential IT employees surveyed in the study were students enrolled in the disciplines of computer science and information systems at a comprehensive university in North America. The study went through three stages resulting in an instrument for psychometric properties.

This research revealed eight employer branding value propositions that future IT employees care about. These dimensions are important for both IT firms and industries competing for skilled IT labor to understand and manage.

This paper extends the work of Berthon et al. (2005) on employer branding to the information intensive age and particularly the IT sector. It allows executives to manage and measure their employer brand so as to maximize competitive advantage in attracting and retaining skilled employees.

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.

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.

Relative to manual and computer-aided approaches, AI-enabled content analysis provides clear advantages with high reliability, high validity and moderate efficiency.

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.

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.

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.

This paper aims to examine if (and how), enterprise social media (ESM) can be understood as a strategic knowledge management phenomenon to improve organizational performance.

This paper uses intellectual capital theory and its functional building blocks to organize different types of the ESM platforms, based on secondary data. It then connects these findings to the underling intellectual capital tenets to introduce a conceptual model that explicates how ESM impacts strategic knowledge management, and vice versa.

This paper concludes that ESM provides a unique complement to traditional strategic knowledge management. The authors argue that ESM differs substantially from other contexts in which intellectual capital has been applied, and extend intellectual capital with three appropriate dimensions (human, social and structural capital). Given the potentially disruptive nature of ESM, this framework helps firms understand the nature of the changes that are needed.

The paper provides the first review of the business needs that are served by the software functions and management processes under the ESM banner. This original contribution takes the intellectual capital and strategic knowledge management discussions from their usual high levels of abstraction and relates them to the real world of ESM, focusing on outcomes. Its unique “Intellectual Capital Framework for the Socially Oriented Enterprise” includes distinct, testable propositions that provide a practical approach to strategically planning, implementing and optimizing ESM.

Online brand communities provide a wealth of insights about how consumers perceive and talk about a brand, rather than what the firm communicates about the brand. The purpose of this paper is to understand whether the brand personality of an online brand community, rather than of the brand itself, can be deduced from the online communication within that brand community.

The paper is empirical in nature. The authors use community-generated content from eight online brand communities and perform content analysis using the text analysis software Diction. The authors employ the five brand personality dictionaries (competence, excitement, ruggedness, sincerity and sophistication) from the Pitt et al. (2007) dictionary source as the basis for the authors’ analysis.

The paper offers two main contributions. First, it identifies two types of communities: those focusing on solving functional problems that consumers might encounter with a firm’s offering and those focusing on broader engagement with the brand. Second, the study serves as a blueprint that marketers can adopt to analyze online brand communities using a computerized approach. Such a blueprint is beneficial not only to analyze a firm’s own online brand community but also that of competitors, thus providing insights into how their brand stacks up against competitor brands.

This is the first paper examining the nature of online brand communities by means of computerized content analysis. The authors outline a number of areas that marketing scholars could explore further based on the authors analysis. The paper also highlights implications for marketers when establishing, managing, monitoring and analyzing online brand communities.

Fake news is presently one of the most discussed phenomena in politics, social life and the world of business. This paper aims to report the aggregated opinions of 42 brand management academics on the level of threat to, the involvement of, and the available actions of brand managers resulting from fake news.

A Delphi study of 42 academics with peer-reviewed publications in the brand management domain.

The study found that on some dimensions (e.g. the culpability of brand managers for incentivizing fake news by sponsoring its sources), expert opinion varied greatly. Other dimensions (e.g. whether the impact of fake news on brand management is increasing) reached a high level of consensus. The general findings indicate that fake news is an increasing phenomenon. Service brands are most at risk, but brand management generally is need of improving or implementing, fake news mitigation strategies.

Widely diverse opinions revealed the need for conclusive research on the questions of: whether brands suffer damage from sponsoring fake news, whether fake news production is supported by advertising and whether more extensive use of internet facilitated direct interactions with the public through crowdsourcing increased vulnerability.

Experts agreed that practitioners must become more adept with contemporary tools such as fake news site blacklists, and much more aware of identifying and mitigating the brand vulnerabilities to fake news.

A noteworthy breadth of expert opinion was revealed as to whether embellished or fabricated brand narratives can be read as fake news, inviting the question as to whether brands now be held to higher standards of communication integrity.

This paper provides a broad-shallow exploratory overview of the professional opinions of a large international panel of brand management academics on how the recent arrival of industrial fake news does, and will, impact this field.

Sometimes consumers express their enthusiasm for a brand by creating brand extensions, products or services in new categories that are closely affiliated with the “parent” brand. This paper aims to examine the positive or negative impact that consumer-generated brand extensions (CGBEs) can have on brand image and revenue, and the options that companies have available to deal with them.

The paper presents a case study of the collectible strategy card game – Magic: The Gathering – and discusses how the company responded to five different brand extensions that were created by players. This case study was used to develop a framework that allows managers to evaluate CGBEs based on their benefits and risks and to select an appropriate response.

Four possible responses were identified: challenge, criticize, commend and catalyze. Which of these responses is appropriate for companies depends on whether the CGBE has a positive or negative impact on the brand image and revenue and whether this impact is large enough to merit an active response.

This study shows that it is essential for managers to understand how to evaluate CGBEs. Managed properly, they can improve product usage, help generate new customers and have a positive impact on revenue and brand image. However, CGBEs can also have a negative effect, in particular if they are substitutes for the original product.