Search results

The purpose of this paper is to give a comprehensive review and synthesis of automated textual analysis of corporate disclosure to show how the accuracy of disclosure tone has been incremented with the evolution of developed automated methods that have been used to calculate tone in prior studies.

This study have conducted the survey on “automated textual analysis of corporate disclosure and its impact” by searching at Google Scholar and Scopus research database after the year 2000 to prepare the list of papers. After classifying the prior literature into a dictionary-based and machine learning-based approach, this study have again sub-classified those papers according to two other dimensions, namely, information sources of disclosure and the impact of tone on the market.

This study found literature on how value relevance of tone is varied with the use of different automated methods and using different information sources. This study also found literature on the impact of such tone on market. These are contributing to help investor’s decision-making and earnings and returns prediction by researchers. The literature survey shows that the research gap lies in the development of methodologies toward the calculation of tone more accurately. This study also mention how different information sources and methodologies can influence the change in disclosure tone for the same firm, which, in turn, may change market performance. The research gap also lies in finding the determinants of disclosure tone with large scale data.

After reviewing some papers based on automated textual analysis of corporate disclosure, this study shows how the accuracy of the result is incrementing according to the evolution of automated methodology. Apart from the methodological research gaps, this study also identify some other research gaps related to determinants (corporate governance, firm-level, macroeconomic factors, etc.) and transparency or credibility of disclosure which could stimulate new research agendas in the areas of automated textual analysis of corporate disclosure.

The general structure of geometrically‐based automatic finite element modelling systems is discussed. The development of a specific system employing the modified‐quadtree and modified‐octree mesh generators is presented. The application of this approach to metal forming analysis is then given.

While the motivation for collecting art has received considerable attention in the literature, less is known about the characteristics of the typical art collector. This paper aims to explore these characteristics to develop a typology of art consumers using a mixed method approach over several studies.

This is achieved by analyzing qualitative data, gathered via semi-structured interviews of art collectors, and quantitatively by means of natural language processing analysis and automated text analysis and using correspondence analysis to analyze and present the results.

The study’s findings reveal four distinct clusters of art collectors based on their “Big Five” personality traits, as well as uncovering insights into how these types talk about their possessions.

In addition to contributing to the arts marketing literature, the findings provide a more nuanced understanding of consumers that managers can use for market segmentation and target marketing decisions in other markets. The paper also offers a methodological contribution to the literature on correspondence analysis by demonstrating the “doubling” procedure to deal with percentile data.

In addition to contributing to the arts marketing literature, the findings provide a more nuanced understanding of art collectors that managers can use for market segmentation and target marketing decisions. The paper also offers a methodological contribution to the literature on correspondence analysis by demonstrating a non-traditional application of correspondence analysis using the “doubling” procedure. Buyer behavior in the fine art market is not exhaustively studied. By understanding the personality traits of consumers in the art market, sales forces can better provide assistance and product to consumers. Further, understanding the personalities of consumers is better for art retail spaces to better serve consumers.

This paper demonstrates a unique mixed methods approach to analyzing unstructured qualitative data. It shows how text data can be used to identify measurable market segments for which targeted strategies can be developed.

The authors have systematically reviewed 534 corporate social responsibility communication (CSRC) papers, updating the current debate about the ontological and epistemological paradigms that characterize the field, and providing evidence of the interactions between these paradigms and the related methodological choices. The purpose of this paper is to provide theoretical and methodological implications for future research in the CSRC research domain.

The authors used the Scopus database to search for titles, abstracts and related keywords with two queries sets relating to corporate social responsibility (e.g. corporate ethical, corporate environmental, social responsibility, corporate accountability) and CSRC (e.g. reporting, disclosure, dialogue, sensemaking). The authors identified 534 empirical papers (2000–2016), which the authors coded manually to identify the research methods and research designs (Creswell, 2013). The authors then developed an ad hoc dictionary whose keywords relate to the three primary CSRC approaches (instrumental, normative and constitutive). Using the software Linguistic Inquiry and Word Count, the authors undertook an automated content analysis in order to measure these approaches’ relative popularity and compare the methods employed in empirical research.

The authors found that the instrumental approach, which belongs to the functionalist paradigm, dominates the CSRC literature with its relative weight being constant over time. The normative approach also belongs to the functionalist paradigm, but plays a minor yet enduring role. The constitutive approach belongs to the interpretive paradigm and grew slightly over time, but still remains largely beyond the instrumental approach. In the instrumental approach, many papers report on descriptive empirical analyses. In the constitutive approach, theory-method relationships are in line with the various paradigmatic traits, while the normative approach presents critical issues. Regarding methodology, according to the findings, the literature review underlines three major limitations that characterize the existing empirical evidence and provides avenues for future research. While multi-paradigmatic research is promoted in the CRSC literature (Crane and Glozer, 2016; Morsing, 2017; Schoeneborn and Trittin, 2013), the authors found no empirical evidence.

This is the first paper to systematically review empirical research in the CSRC field and is also the first to address the relationship between research paradigms, theoretical approaches, and methods. Further, the authors suggest a novel way to develop systematic reviews (i.e. via quantitative, automated content analysis), which can now also be applied in other literature streams and in other contexts.

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.

Flexible fixturing is an important aspect of any flexible manufacturing system (FMS) and computer integrated manufacturing (CIM) environment. The production analysis for fixturing within an FMS environment is presented. Various approaches to flexible fixturing are briefly described. The reconfigurable fixturing is one of the most appropriate flexible fixturing techniques for CIM environment. Reconfigurable and/or automated modular fixturing employs a number of fixture modules that are set up, adjusted and changed to form different fixture layout. The requirements for locating and constraining workpieces are presented. In addition, computer‐aided planning and analysis of fixture set up are discussed.

The explosion of internet-generated content, coupled with methodologies such as sentiment analysis, present exciting opportunities for marketers to generate market intelligence on consumer attitudes and brand opinions. The purpose of this paper is to review the marketing literature on online sentiment analysis and examines the application of sentiment analysis from three main perspectives: the unit of analysis, sampling design and methods used in sentiment detection and statistical analysis.

The paper reviews the prior literature on the application of online sentiment analysis published in marketing journals over the period 2008-2016.

The findings highlight the uniqueness of online sentiment analysis in action-oriented marketing research and examine the technical, practical and ethical challenges faced by researchers.

The paper discusses the application of sentiment analysis in marketing research and offers recommendations to address the challenges researchers confront in using this technique.

This study provides academics and practitioners with a comprehensive review of the application of online sentiment analysis within the marketing discipline. The paper focuses attention on the limitations surrounding the utilization of this technique and provides suggestions for mitigating these challenges.

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.

The purpose of this research is to determine firms' sustainability efforts through triple bottom line reporting on the World Wide Web. Sustainability reporting can assist in brand differentiation to stakeholder groups and ultimately lead to a positive corporate reputation.

Automated web content analysis was used to determine and differentiate 39 oil and gas firms' reporting of economic, social and environmental disclosures across Europe, North America and Asia. Firms were benchmarked for their disclosures against key terms derived from the Global Reporting Initiative.

North American firms disclose the greatest amount of TBL information for both environmental and economic indicators. European firms are the most prevalent reporters of social indicators. Asian firms displayed the most positive bias to their sustainability reporting.

Future research would benefit from linking firms' TBL reporting with firm performance as well as including a greater range of countries and industries for comparative purposes.

Firms should demonstrate a greater completeness of information across the three TBL indicators to effectively manage their relationships with their key stakeholders. Information should be unbiased and honest for firms to successfully legitimacy.

This paper uses automated content analyse to differentiate disclosure levels of TBL indicators across three different geographical regions.

This paper aims to show that current Industry 4.0 maturity models primarily focus on manufacturing processes. Until now, research has been lacking with regard to outbound logistics, that is, the delivery process. This paper develops such a model.

Methodologically, this paper is grounded in design science research (DSR) and rigorously follows the model development guidelines presented by De Bruin et al. (2005). This work builds on current maturity models and original empirical research to populate and test the model.

The model appears to be applicable to describing the status quo of the digitization efforts in outbound logistics, developing a corporate vision for delivery logistics excellence and providing guidance on the development path.

Thus far, the model has been applied only for a development stakeholder. For further validation, the authors are currently working on additional case studies to demonstrate the model’s applicability.

The developed model provides guidance for the digitization of an important value-adding activity in supply chain management: the delivery process.

To the authors’ knowledge, the proposed model is the first to explicitly consider the delivery process; therefore, it complements available approaches that focus on the manufacturing process. Moreover, the results show that the widely used Supply Chain Operations Reference model can serve as the basis for additional process maturity models.