Search results

Content analysis is a methodology that has been used in many academic disciplines as a means to extract quantitative measures from textual information. The purpose of this paper is to document the use of content analysis in the supply chain literature. The authors also discuss opportunities for future research.

The authors conduct a literature review of 13 leading supply chain journals to assess the state of the content analysis-based literature and identify opportunities for future research. Additionally, the authors provide a general schema for and illustration of the use of content analysis.

The findings suggest that content analysis for quantitative studies and hypothesis testing purposes has rarely been used in the supply chain discipline. The research also suggests that in order to fully realize the potential of content analysis, future content analysis research should conduct more hypothesis testing, employ diverse data sets, utilize state-of-the-art content analysis software programs, and leverage multi-method research designs.

The current research synthesizes the use of content analysis methods in the supply chain domain and promotes the need to capitalize on the advantages offered by this research methodology. The paper also presents several topics for future research that can benefit from the content analysis method.

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 aim of this paper is to review the use of content analysis as a research method in understanding social and environmental accounting (SEA) and to examine current contemporary foci of this research tradition. Further, seeks to examine several research method issues relating to the use of content analysis are examined.

Contemporary focus and research issues are analyzed to provide some future directions for scholars in the field of SEA, by categorizing work in the SEA, social environmental reporting (SER) and intellectual capital reporting (ICR) literature, according to the following: normative literature/theory/commentaries; focus of empirical investigation; quality SER research; combined research methodologies; content analysis method issues.

Literature indicates that few attempts have been made to combine other research methodologies with content analysis, although it has proven fruitful with the limited investigation undertaken to examine aspects of SER. Further extending the performance reporting by combining SER with ICR may provide useful information.

Increasingly, researchers in the field of SEA need to be able to justify the specific research methods they use when collecting the empirical data that they examine in order to support and test opinions regarding the merit of different approaches to managing, measuring and reporting of SEA.

Traditionally, the focus of content analysis has been narrow but this paper breaks new ground in proposing to broaden the focus to include SEA and to combine content analysis with other methods of data collection.

Intellectual capital (IC) is believed to be more important resources to add the value of a company rather than physical assets. This gives rise to the increasing practice of reporting IC information in corporate annual report. Over the past fifteen years, considerable numbers of studies have employed content analysis to examine the extent and nature of IC information in several countries, but they presented different results. These results might partly contribute to different methods in counting information. In fact, the previous studies have been critised for not explicitly clarifying how information was recoded and counted which led to incomparable findings. Therefore, this paper firstly seeks to discuss an illustrative example of ‘sense-making‘ process in identifying, categorizing, and counting of IC information in annual reports of pilot sample company. Secondly, the method refined in the pilot study was applied over the final samples of six large companies in the UK from 1974 to 2008 The contribution of this paper is to primarily refine the previous method in recoding information, to send a message that transparency is crucial in content analysis and to facilitate method replication for future studies. Overall, this study demonstrates a marked increase in IC information disclosure was identified over the 35 years. The relational capital information disclosure was relatively more prominent over time, followed by human capital and structural capital.

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.

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.

This paper offers a “content analysis of metadata, titles, and abstracts” (CAMTA) method underpinned by a newly evolved metadata, title, abstract, introduction, methodology, results, analysis, and discussion (M-TAIMRAD) Framework.

Draws on innovations of content analysis from the field of health- care to offer a pragmatic and transparent method for conducting rigorous and valid research within the field of business and management.

Replicable and valid guidelines for conducting the CAMTA method are offered, including an illustration. This is followed by a critical examination of the potential applications and benefits of the method to the field of business and management research.

The CAMTA method enables researchers to assimilate and synthesise metadata, titles and abstracts as a means of identifying grounds for future research and theory development. This will help to advance the field and subsequently benefit the wider readership including fellow academics, practitioners and policymakers. The flexibility of the CAMTA method means that it can be used as a stand-alone method or combined as part of a mixed-methods approach.

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.

Dynamic planning and scheduling forms a widely adopted smart strategy for solving real-world problems in diverse business systems. This paper uses deductive content analysis to explore secondary data from previous studies in dynamic planning and scheduling to draw conclusions on its current status, forward action and research needs in construction management.

The authors searched academic databases using planning and scheduling keywords without a periodic setting. This research collected secondary data from the database to draw an objective comparison of categories and conclusions about how the data relates to planning and scheduling to avoid the subjective responses from questionnaires and interviews. Then, applying inclusion and exclusion criteria, we selected one hundred and four articles. Finally, the study used a seven-step deductive content analysis to develop the categorisation matrix and sub-themes for describing the dynamic planning and scheduling categories. The authors used deductive analysis because of the secondary data and categories comparison. Using the event types represented in a quadrant mapping, authors delve into where, when, application and benefits of the classes.

The content analysis showed that all the accounts and descriptions of dynamic planning and scheduling are identifiable in an extensive research database. The content analysis reveals the need for multi-hybrid (4D BIM-Agent based-discrete event-discrete rate-system dynamics) simulation modelling and optimisation method for proffering solutions to scheduling and planning problems, its current status, tools and obstacles.

This research reveals the deductive content analysis talent in construction research. It also draws direction, focuses and raises a question on dynamic planning and scheduling research concerning the five-integrated model, an opportunity for their integration, models combined attributes and insight into its solution viability in construction.

The purpose of this paper is to describe a methodology that enables the generation of valid and reliable inferences on what and how intellectual capital (IC) information is communicated by sell‐side analysts in their research reports.

The method described in this paper involves content‐analysing initiating coverage analyst reports using a four‐dimensional IC coding framework and a detailed coding instrument, which is founded in the literature and indigenous to analyst reports. The paper explicates methodological decisions associated with content analysis: selecting the appropriate sampling unit; recording unit and measurement unit; developing the categorisation scheme and coding instrument; the need for test coding; the approach to data collection; and assessment of reliability and validity.

The methodology described is applied to a sample of analyst reports to illustrate inferences that can be drawn on what and how IC information is communicated in analyst reports.

Various practical issues arising in the application of content analysis method are discussed and a methodology for investigating IC communications by sell‐side analysts is described in this paper. This knowledge can be useful to future researchers conducting content‐analytic studies involving analyst reports in general, and IC communications in analyst reports in particular.

This paper extends the methodology developed previously to examine IC information in analyst reports. Although inspired and heavily influenced by these works, the methodology presented in this paper differs from theirs on several fronts. The paper introduces an alternative methodological paradigm to the study of analyst reports by emphasising them as a communication medium through which sell‐side analysts may pursue an agenda of their own. This is contrasted with the view held by several prior researchers that analyst reports just provide a record of analysts' thought processes.