Search results

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 investigate empirically the common alternative methods of measuring annual report narratives. Five alternative methods are employed, a weighted and un-weighted disclosure index and three textual coding systems, measuring the amount of space devoted to relevant disclosures.

The authors investigate the forward-looking voluntary disclosures of 30 UK non-financial companies. They employ descriptive analysis, correlation matrix, mean comparison t-test, rankings and multiple regression analysis of disclosure measures against determinants of corporate voluntary reporting.

The results reveal that while the alternative methods of forward-looking voluntary disclosure are highly correlated, important significant differences do nevertheless emerge. In particular, it appears important to measure volume rather than simply the existence or non-existence of each type of disclosure. Overall, we detect that the optimal method is content analysis by text-unit rather than by sentence.

This paper contributes to the extant literature in forward-looking disclosure by reporting important differences among alternative content analyses. However, the decision regarding whether this should be a computerised or a manual content analysis appears not to be driven by differences in the resulting measures. Rather, the choice is the outcome of a trade-off between the time involved in setting up coding rules for computerised analysis versus the time saved undertaking the analysis itself.

Covid-19 pandemic, war, climate emergency and other recent challenges are inflicting tremendous stress to youth. However, death and tragedy are nowadays considered taboo, as there is generally no standardized nor naturalized discussion on the subject, especially with young people. The current multi-crisis scenario is intensifying the need to incorporate an education on tragedy and resilience in our learning systems. In this context, it is necessary to find suitable teaching resources for this educational challenge that are attractive, entertaining and suitable for children and youth. A resource that meets all these requirements are children’s folk songs (CFSs). Apart from the intrinsic educational potential of music, folk songs have a simplicity and musicality that make them an ideal teaching resource. Considering their oral historical transmission, their survival confirms the attraction that this type of composition causes on children. However, to consider CFSs as an adequate resource to carry out an education for death and tragedy, it is necessary to study whether they present a non-negligible proportion of tragic passages and with enough variety of themes. This paper aims to address the study of the presence of explicit tragic content in Spanish CFSs and thus could be considered a cultural resource with transformative educational potential to develop resilience capabilities on the face of tragedy.

An analysis of lyrics of 2,558 Spanish CFSs is presented, using a manual content analysis as well as a computerized content analysis with the aim of identifying the tragic component of these songs and, thereby, assessing their pedagogical potential as a transformative educational resource.

The results obtained show a considerable presence of death and tragedy (19.78%) and a variety of tragedy dimensions. CFSs have been transmitted orally not only as a ludic resource, but also to prepare children for life (and death). The results show the complementarity of both analyses to avoid subjectivity while considering the underlying meanings of the songs.

This task had previously not been approached in an automated manner in the literature, nor there had been a similar study with a sample of this magnitude. The outcomes obtained show the considerable presence of tragedy in Spanish CFSs and emphasize the interest of this currently undervalued didactic resource.

This paper provides a wide-ranging and up-to-date (1997–2016) review of the archival empirical risk-reporting literature. The reviewed papers are classified into two principal themes: the incentives for and/or informativeness of risk reporting. Our review demonstrates areas of significant divergence in the literature specifically: mandatory versus voluntary risk reporting, manual versus automated content analysis, within-country versus cross-country variations in risk reporting, and risk reporting in financial versus non-financial firms. Our paper identifies a number of issues which require further research. In particular we draw attention to two: first, a lack of clarity and consistency around the conceptualization of risk; and second, the potential costs and benefits of standard-setters’ involvement.

A growing body of research outlines how to best facilitate and ensure methodological rigor when using dictionary-based computerized text analyses (DBCTA) in organizational research. However, these best practices are currently scattered across several methodological and empirical manuscripts, making it difficult for scholars new to the technique to implement DBCTA in their own research. To better equip researchers looking to leverage this technique, this methodological report consolidates current best practices for applying DBCTA into a single, practical guide. In doing so, we provide direction regarding how to make key design decisions and identify valuable resources to help researchers from the beginning of the research process through final publication. Consequently, we advance DBCTA methods research by providing a one-stop reference for novices and experts alike concerning current best practices and available resources.

This study aims to contribute to the relatively limited historic literature on social and environmental accounting/accountability. More specifically, the study explores accounting and accountability for fisheries over time and determines potential legitimacy relations as conveyed through reporting.

A content analysis method is used to analyse a fisheries-related section of an annual report of a state-owned electricity firm for 56 years (1935/36–1993). The time frame analysed is a period when environmental or social reporting was, in general, informal and not mandated. However, accountability was established for the company under study, through the legally mandated provision of (unspecific/discretional) information about fisheries activities. A lens evoking legitimacy relationships as a dyad is utilised.

The fisheries reporting within the case organisation is an early example of recognition of the important effects of business activities on the environment and biodiversity. The findings of the analyses suggest the content aligns with what may be anticipated in a contemporary setting. Drawing on trends noted from the content analysis, three potential legitimacy relationships are identified around the fisheries reporting. Only one is determined as a complete legitimacy relationship.

The research is limited in that it is an analysis of one case in a single context. Also, the content analysis methods used were developed specifically for the study, which may limit their application. Finally, the data source used, and the historic nature of the study, to some extent limits the ability to determine some legitimacy relationships.

This study offers some insights on the historic nature of environmental reporting from a fisheries perspective in the Northern Hemisphere. The longitudinal nature of the analysis also offers insights into how the content of the reporting changed over time. Additionally, the use of a relatively new approach to operationalising legitimacy may prove useful for future researchers in the accounting discipline, especially given recent concerns on how the concept of legitimacy has been utilised in such research.

This study aims to investigate motivations and human values of everyday consumers who participate in the annual day of consumption restraint known as Buy Nothing Day (BND). In addition, this study demonstrates a hybrid content analysis method in which artificial intelligence and human contributions are used in the data analysis.

This research uses a hybrid method of content analysis of a large Twitter data set spanning three years.

Consumer motivations are categorized as relating to consumerism, personal welfare, wastefulness, environment, inequality, anti-capitalism, financial responsibility, financial necessity, health, ethics and resistance to American culture. Of these, consumerism and personal welfare are the most common. Moreover, human values related to “openness to change” and “self-transcendence” were prominent in the BND tweets.

This research demonstrates the effectiveness of a hybrid content analysis methodology and uncovers the motivations and human values that average consumers (as opposed to consumer activists) have to restrain their consumption. This research also provides insight for firms wishing to better understand and respond to consumption restraint.

This research provides insight for firms wishing to better understand and respond to consumption restraint.

The question of why everyday consumers engage in consumption restraint has received little attention in the scholarly discourse; this research provides insight into “everyday” consumer motivations for engaging in restraint using a hybrid content analysis of a large data set spanning over three years.

This study aims to focus on automated text analyses (ATAs) of sustainability and integrated reporting as a recent approach in empirical–quantitative research.

Based on legitimacy theory, the author conducts a structured literature review and includes 38 quantitative peer-reviewed empirical (archival) studies on specific determinants and consequences of sustainability and integrated reporting. The paper makes a clear distinction between analyses of reports due to readability, tone, similarity and specific topics. In line with prior studies, it is assumed that more readable reports with less tone and similarity relate to increased reporting quality.

In line with legitimacy theory, there are empirical indications that specific corporate governance variables, other firm characteristics and regulatory issues have a main impact on the quality of sustainability and integrated reporting. Furthermore, increased reporting quality leads to positive market reactions in line with the business case argument.

The author deduces useful recommendations for future research to motivate researchers to include ATA of sustainability and integrated reports. Among others, future research should recognize sustainable and behavioral corporate governance determinants and analyze other stakeholders’ reactions.

As both stakeholders’ demands on sustainability and integrated reporting have increased since the financial crisis of 2008–2009, firms should increase the quality of reporting processes.

This analysis makes major contributions to prior research by including both sustainability and integrated reporting, based on ATA. ATAs play a prominent role in recent empirical research to evaluate possible drivers and consequences of sustainability and integrated reports. ATA may contribute to increased validity of empirical–quantitative research in comparison to classical manual content analyses, especially due to future CSR washing analyses.

This paper aims to develop a holistic measure for analysing impression management and for detecting bias introduced into corporate narratives as a result of impression management.

Prior research on the seven impression management methods in the literature is summarised. Four of the less‐researched methods are described in detail, and are illustrated with examples from UK annual results' press releases (ARPRs). A method of computing a holistic composite impression management score based on these four impression management methods is developed, based on both quantitative and qualitative data in corporate narrative disclosures. An impression management bias score is devised to capture the extent to which impression management introduces bias into corporate narratives. An example of the application of the composite impression management score and impression management bias score methodology is provided.

While not amounting to systematic evidence, the 21 illustrative examples suggest that impression management is pervasive in corporate financial communications using multiple impression management methods, such that positive information is exaggerated, while negative information is either ignored or is underplayed.

Four impression management methods are described in detail, illustrated by 21 examples. These four methods are examined together. New impression management methods are studied in this paper for the first time. This paper extends prior impression management measures in two ways. First, a composite impression management score based on four impression management techniques is articulated. Second, the composite impression management score methodology is extended to capture a measure for bias, in the form of an impression management bias score. This is the first time outside the USA that narrative disclosures in press releases have been studied.

This study aims to investigate three common approaches – quantitative blog features analysis, content analysis, and community identification – to detect influence in the blogosphere (i.e. among blog posts).

Quantitative analysis of blog features, together with manual sentiment and agreement analysis and community identification, were performed on blog postings and their content. Correlation studies of the selected influential variables were conducted to determine the effectiveness of each variable.

Agreement expressed by the linking blogger with the linked blogger, similar sentiments expressed by both bloggers on common topics, and community identity are statistically significant features for detecting influence in the linked blogs.

A small data set of 196 blog posting pairs was used for the study as the blog features and content are analysed manually. Nonetheless statistical analysis on the data set identified significant features that could be used in future studies to automate the influence detection process.

Knowing the effects of blog features and content analysis in detecting influence among blog posts allows a better influence detection method to determine the main chain of information propagation within the blogosphere and the identities of influential bloggers.

The approach of using blog features, content analysis, and community identity provides a comprehensive evaluation of influence in the blogosphere. Unlike previous content analysis approaches that measure document similarity (i.e. common terms) between linked blog posts, our study applies sentiment and agreement analysis to consider the context of the whole blog post content.