Search results

This chapter discusses the use of qualitative content analysis in the field of hospitality and tourism. The primary objective of this chapter is to draw attention to the use of a content analysis approach for the treatment of data. As a further objective, the chapter contributes to current knowledge by underscoring a qualitative content analysis approach that would be of benefit to hospitality and tourism scholars. Overall, the chapter serves to inform hospitality and tourism scholars of how to increase the trustworthiness of qualitative content analysis approaches, which is seen as one of the most crucial impediments to its use.

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.

Access to high-quality data is a challenge for humanitarian logistics researchers. However, humanitarian organizations publish large quantities of documents for various stakeholders. Researchers can use these as secondary data, but interpreting big volumes of text is time consuming. The purpose of this paper is to present an automated quantitative content analysis (AQCA) approach that allows researchers to analyze such documents quickly and reliably.

Content analysis is a method to facilitate a systematic description of documents. This paper builds on an existing content analysis method, to which it adds automated steps for processing large quantities of documents. It also presents different measures for quantifying the content of documents.

The AQCA approach has been applied successfully in four papers. For example, it can identify the main theme in a document, categorize documents along different dimensions, or compare the use of a theme in different documents. This paper also identifies several limitations of content analysis in the field of humanitarian logistics research and suggests ways to mitigate them.

The AQCA approach does not provide an exhaustive qualitative analysis of documents. Instead, it aims to analyze documents quickly and reliably to extract the contents’ quantifiable aspects.

Although content analysis has been used in humanitarian logistics research before, no paper has yet proposed an automated, step-by-step approach that researchers can use. It also is the first study to discuss specific limitations of content analysis in the context of humanitarian logistics.

The reliance on teams in today’s work environment underscores the importance of understanding how teams function. To better understand teams, one must be able to measure team dynamics or interaction. The purpose of this chapter is to outline an unobtrusive approach to measuring team dynamics from verbal communications.

The basic premise of this approach is that the words we use provide insight into how we feel and think at any given time. The methodology described in this chapter employs a lexical analytic approach to examining team dynamics. To best accomplish this, we first identify the principal features or dimensions of teamwork and then we propose lexical measures that may map to these processes.

This approach can be employed to track team functioning over time “at a distance” without interrupting task performance.

This chapter describes an approach to measuring relevant teamwork dimensions through verbal content. This approach has the potential to give us direct, unobtrusive insight into the emotional and cognitive states of teams. It is original in its examination of how team dynamics can be indexed in speech.

Based on a framework developed by Kovács and Spens, this paper seeks to assess the use of the three different research approaches in logistics research; discuss the use of different research methods within the three research approaches; find and discuss applications of the abductive research approach to logistics problems.

Content analysis is used in order to categorize the different research approaches. While content analysis commonly uses smaller units such as paragraphs, sentences, words or characters, this study used entire articles as the unit of analysis. The scope of the review encompassed five years (1998‐2002) of articles in IJLM, IJPDLM and JBL. A total of 378 articles was reviewed and categorized.

The findings of the study corroborate earlier studies regarding the main research approach used in logistics. Published logistics research is hypothetico‐deductive, with a strong emphasis on using survey methods. Nevertheless, inductive as well as abductive research is gaining importance. However, most logistics articles do not explicitly discuss the research process, nor the approach used. Therefore, a call for more explicit statements of the research approach is suggested.

The review of the articles is limited to three main journals in the field. A more comprehensive view of research approaches could be obtained by broadening the review to include also other types of research.

The paper provides a framework and guidelines to researchers for explicitly discussing the research approach used in logistics articles.

The paper provides an overview of the research approaches used in logistics research.

The purpose of this paper is to present an approach to identify the knowledge, skills, and abilities required by a job market by systematically analyzing classified advertisements of position openings. It suggests appropriate methodological techniques to conduct such a study and compares these to traditional methods.

While this paper discusses several methods used to assess the skill, knowledge, and ability (SKA) needs in the marketing research industry, it focuses on a new approach: content analysis. Content analysis is a systematic and an objective qualitative research technique used to identify the needs of hiring firms through the analysis of classified advertisements of position openings in marketing research. These openings (listings available online and through print classified advertisements) can be examined for the general and specific marketing research skills and knowledge areas identified as critical workplace competencies for employers.

Content analysis is an alternative, or supplemental method, not meant to replace, but rather to enhance other methods of assessing the industry's SKA needs.

Human resource managers and personnel responsible for developing professional training and development programs will also benefit from these findings. Assessment results can also be useful in designing marketing research programs, adjusting existing marketing research curricula, and enhancing marketing research career service development efforts.

While content analysis has previously been used to assess advertisements, this is a unique application of the method which adds objectivity to marketplace assessment.

The purpose of this paper is to review the use of content analysis in social and environmental reporting (SER) research. It explores how the relevant literature has evolved over time and particularly how recent developments have affected the validity and reliability challenges that researchers face when executing the method.

The paper combines a quasi-systematic review of the literature employing content analysis (examining a sample of 251 studies published over the last 40 years in a wide array of journals with interest in the field), with a largely interpretive meta-analysis, using an index, considering the research questions asked and frameworks used as well as the specific content analysis decisions.

A number of issues of concern in the use of the method are identified, mainly over comparability and reliability of coding schemes. Potential explanations are developed and methodological refinements that could enhance the usefulness of content analysis methods in SER research are subsequently proposed.

It should be acknowledged that, as 251 SER studies have been reviewed, there is always the possibility that some unique studies that could have contributed in the discussion have been ignored.

By reviewing the use of the method in a comprehensive sample of 251 SER studies published over the last 40 years in a wide array of journals with interest in the field, the paper also offers a guide for researchers (particularly in the SER field) wishing to employ content analysis in the future.

The paper contributes to the literature by offering a critical and comprehensive review of the method’s theoretical underpinnings and application in SER research, and by describing changing patterns in content analysis, in order to help build a more secure foundation for future work.

Considering the significant increase of studies in the hospitality and tourism field that use content analysis as a research method, this chapter aims to describe the research process when the methodology of qualitative content analysis is utilized. Particular attention is placed on the operational procedures of this method—from the initial planning and preparation to presentation of findings and evaluation of the process, as this is often omitted by hospitality and tourism researchers. Four distinct stages are described in this chapter: preparation, data collection and analysis, reporting findings, and evaluation of the process. The discussion in this chapter helps to clarify how qualitative content analysis should be undertaken in a systematic manner, which would be of particular benefit to hospitality and tourism researchers. Advantages and disadvantages of the qualitative content analysis and its contribution to hospitality and tourism studies are also discussed.

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.

The purpose of this paper is to respond to the call by Dumay and Cai (2014) for new ideas to enhance intellectual capital (IC) research. One possibility is to draw conclusions on comparability across the results of prior studies. This study investigates whether the results of prior IC content analyses are comparable despite differences in their IC research frameworks.

A content analysis of 428 German management reports is conducted, capturing the IC reporting scores for individual IC items to investigate the role of certain widely used IC items. The relationships of IC scores for different combinations of widely used IC items are further examined in a correlation analysis to indicate comparability of prior results.

The findings show that widely used IC items capture the majority of IC reporting and that the IC scores for different combinations of these IC items are highly correlated. These findings indicate that the results of prior IC content analyses are comparable as long as most of the widely used IC items are included in prior IC research frameworks.

The study contributes to IC reporting research as it shows that conclusions can be drawn across prior studies in meta-analyses because the results of prior studies are comparable in rankings and key findings.

Although content analyses of IC reporting have been previously criticised, this study seminally questions the comparability of the results of prior studies due to differences in the IC research frameworks.