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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.

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.

Content analysis is a family of systematic, rule-guided techniques used to analyze the informational contents of textual data (Mayring, 2000). It is used frequently in nursing research, and is rapidly becoming more prominent in the medical and bioethics literature. There are several types of content analysis including quantitative and qualitative methods all sharing the central feature of systematically categorizing textual data in order to make sense of it (Miles & Huberman, 1994). They differ, however, in the ways they generate categories and apply them to the data, and how they analyze the resulting data. In this chapter, we describe a type of qualitative content analysis in which categories are largely derived from the data, applied to the data through close reading, and analyzed solely qualitatively. The generation and application of categories that we describe can also be used in studies that include quantitative analysis.

In a context where human–machine interaction is growing, understanding the limits between automated and human-based methods may leverage qualitative research. This paper aims to compare human and machine analyses, highlighting the challenges and opportunities of both approaches.

This study applied qualitative secondary analysis (QSA) with machine learning-based text mining on qualitative data from 25 interviews previously analyzed with traditional qualitative content analysis.

By analyzing both techniques' strengths and weaknesses, this study complements the results from the original research work. The previous human model failed to point to a particular aspect of the case, while the machine analysis did not recognize the sequence of time in the interviewee's discourse.

This study demonstrates that combining content analysis with text mining techniques improves the quality of the research output. Researchers may, therefore, better handle biases from humans and machines in traditional qualitative and quantitative research.

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 purpose of this paper is to propose a conceptual framework of experience co-creation that captures the multi-dimensionality of this construct, as well as a research process for defining of the antecedents of experience co-creation.

The framework of experience co-creation was conceptualized by means of a literature review. Subsequently, this framework was used as the conceptual basis for a qualitative content analysis of 66 empirical papers investigating alternative consumption models (ACMs), such as renting, remanufacturing, and second-hand models.

The qualitative content analysis resulted in 12 categories related to the consumer and 9 related to the ACM offerings that represent the antecedents of experience co-creation. These categories provide evidence that, to a large extent, the developed conceptual framework allows one to capture the multi-dimensionality of the experience co-creation construct.

This study underscores the understanding of experience co-creation as a function of the characteristics of the offering – which are, in turn, a function of the consumers’ motives as determined by their lifeworlds – as well as to service design as an iterative approach to finding, creating and refining service offerings.

The investigation of the antecedents of experience co-creation can enable service providers to determine significant consumer market conditions for forecasting the suitability and viability of their offerings and to adjust their service designs accordingly.

This paper provides a step toward the operationalization of the dimension-related experience co-creation construct and presents an approach to defining the antecedents of experience co-creation by considering different research perspectives that can enhance service design research.

Inconsistent research output makes critical literature reviews crucial tools for assessing and developing the knowledge base within a research field. Literature reviews in the field of supply chain management (SCM) are often considerably less stringently presented than other empirical research. Replicability of the research and traceability of the arguments and conclusions call for more transparent and systematic procedures. The purpose of this paper is to elaborate on the importance of literature reviews in SCM.

Literature reviews are defined as primarily qualitative synthesis. Content analysis is introduced and applied for reviewing 22 literature reviews of seven sub‐fields of SCM, published in English‐speaking peer‐reviewed journals between 2000 and 2009. A descriptive evaluation of the literature body is followed by a content analysis on the basis of a specific pattern of analytic categories derived from a typical research process.

Each paper was assessed for the aim of research, the method of data gathering, the method of data analysis, and quality measures. While some papers provide information on all of these categories, many fail to provide all the information. This questions the quality of the literature review process and the findings presented in respective papers.

While 22 literature reviews are taken into account in this paper as the basis of the empirical analysis, this allows for assessing the range of procedures applied in previous literature reviews and for pointing to their strengths and shortcomings.

The findings and subsequent methodological discussions aim at providing practical guidance for SCM researchers on how to use content analysis for conducting literature reviews.