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This paper aims to illustrate the scope and challenges of using computer-aided content analysis in international marketing with the aim to capture consumer sentiments about COVID-19 from multi-lingual tweets.

The study is based on some 35 million original COVID-19-related tweets. The study methodology illustrates the use of supervised machine learning and artificial neural network techniques to conduct extensive information extraction.

The authors identified more than two million tweets from six countries and categorized them into PESTEL (i.e. Political, Economic, Social, Technological, Environmental and Legal) dimensions. The extracted consumer sentiments and associated emotions show substantial differences across countries. Our analyses highlight opportunities and challenges inherent in using multi-lingual online sentiment analysis in international marketing. Based on these insights, several future research directions are proposed.

First, the authors contribute to methodology development in international marketing by providing a “use-case” for computer-aided text mining in a multi-lingual context. Second, the authors add to the knowledge on differences in COVID-19-related consumer sentiments in different countries. Third, the authors provide avenues for future research on the analysis of unstructured multi-media posts.

This study aims to demonstrate the application of computer-aided text analysis (CATA) software in identifying primary associations and impressions of a specified tourist destination.

The Leximancer software is applied on primary information to analyze the concepts evoked by a destination. Because no specific planning has been done for destination image marketing strategies for rural tourism in Andalusia, this study visualizes and determines clusters of the main attributes associated with this destination.

The analysis identifies the main clusters among associations and impressions of the destination that can be useful in developing strategies.

Only a target segment is studied, with a relatively small sample size.

Leximancer can not only be applied to online user-generated content, but primary information can also be mapped to generate a holistic destination image. Furthermore, identification of the relevant attributes and impressions can serve to identify unique assets to help tourism organizations develop a destination.

Several implications concerning destination marketing are outlined.

Although previous studies have applied Leximancer and other CATA software, the present research uses a new approach. Deriving the primary information on destination image using an unstructured methodology, the concepts evoked by a destination are mapped. Because there is a lack of research on rural tourism in Andalusia and its destination image, its associated attributes are studied.

本论文展示如何使用CATA分析软件来确定一个具体旅游目的地的主要关系和印象。

Leximancer软件主要用于分析一个目的地引发的相关概念。因为安达卢西亚至今未有确定的乡村旅游目的地形象营销策略, 本论文视觉化和决定与此目的地相关的主要因素群。

研究结果指出了此安达卢西亚目的地的主要相关群和印象群, 这些对于指定战略计划有很大帮助。

本论文只研究了单一市场群, 分析样本量较小。

Leximancer不仅可以用来分析用户生成内容, 还可以分析主要信息, 以展示整体旅游目的地形象。此外, 分析指出的相关因素和印象群可以用来确立独特的资源组合, 帮助旅游机构开发旅游目的地。

本论文结构指出了多个旅游目的地营销的相关启示

尽管文献中有关Leximancer和其他CATA软件的使用文章, 本论文创立了新的使用方法。基于对旅游目的地形象的主要信息进行非结构性研究, 本论文对于旅游目的地的概念进行展开论述。由于至今未有针对安达卢西亚的乡村旅游研究以及旅游目的地形象研究, 本研究论述了其相关因素。

关键词 安达卢西亚、旅游目的地形象、旅游目的地因素、CATA软件、Leximancer

This study aims to provide insight into the current incorporation of corporate culture in national corporate governance codes. The authors identify three levels of incorporation for each of the following three dimensions: layers of corporate culture (the “what”), the alignment of corporate culture in the organization (the “for whom”) and the board’s roles regarding corporate culture (the “how”).

To assess the extent to which national codes have incorporated corporate culture, the authors used a sample of 88 national corporate governance codes. The authors performed a content analysis of these codes using a computer-aided text analysis program. The first step involved the identification of dimensions of corporate culture per national code. These dimensions were then assessed based on three levels of incorporation. Finally, the authors ranked national codes with similar levels of incorporation per dimension and aggregated the dimensions.

The data show that five of the 88 national corporate governance codes that the authors analysed scored the highest level in all three dimensions of corporate culture.

This is the first study to provide an overview of what national corporate governance codes say about corporate culture. The authors address two gaps in the existing literature. First, the authors develop and use a richer conceptualization of how corporate culture can be addressed in national corporate governance codes. Second, the authors analyse these corporate governance codes worldwide.

Stakeholders' uncertainty about firms' value drives their urge to get information, as well as managerial disclosure choices. In this study, the authors examine whether and how an important source of uncertainty – the recent COVID-19 pandemic's effect on corporate social responsibility (CSR) disclosure – is beyond managerial and stakeholders' control.

The authors develop a novel construct for daily CSR disclosure by employing computer-aided text analysis (CATA) on the press releases issued by 125 New Zealand Stock Exchange (NZX) listed from 28 February 2020 to 31 December 2020. To capture COVID-19 intensity, the authors use the growth rate of the population-adjusted cumulative sum of confirmed cases in New Zealand on a specific day. To examine the association between the COVID-19 outbreak and companies' CSR disclosure, the authors employed ordinary least squares (OLS) regression by clustering standard error at the firm level.

The authors find a one standard deviation increase in the COVID-19 outbreak leads to a 28% increase in such disclosures. These results remained robust to a series of sensitivity tests and continue to hold after accounting for potential endogeneity concerns. In the channel analysis, the study demonstrates that the positive relationship between COVID-19 and CSR disclosure is more pronounced in the presence of a well-structured board (i.e. a large, more independent board and with a higher proportion of women on it). In further analysis, the authors find the documented relationship varies over the pandemic's life cycle and is moderated by government stringency response, peer CSR pressure and media coverage.

This paper is the first study that contributes to the scant literature examining the impact of the COVID-19 outbreak on CSR disclosure. Prior research either investigates the relationship of the CSR-stock return during the COVID-19 market crisis or examines the relationship between corporate characteristics including the quality of financial information and the reactions of stock returns during COVID-19. The authors extend such studies by providing empirical evidence that managers respond to COVID-19 by increasing CSR disclosure.

This study presents evidence concerning the effects of affective and cognitive rhetoric on the underpricing of firms at the time of their initial public offering. It is suggested that firms that use less affective, and more cognitively oriented discourse in their IPO prospectus will experience better underpricing outcomes. We examine these assertions using a sample of young high-tech IPO firms where investors rely on prospectuses as accurate and informative firm communications. Results from a robust five-year time span observe initial support for the hypothesized effects. Moreover, the signaling of a higher degree of entrepreneurial orientation in the firm prospectus is found to worsen the negative effects of affective discourse

This methodological paper demonstrates how service firms can use digital technologies to quantify and predict customer evaluations of their interactions with the firm using unstructured, qualitative data. To harness the power of unstructured data and enhance the customer-firm relationship, the use of computerized text analysis is proposed.

Three empirical studies were conducted to exemplify the use of the computerized text analysis tool. A secondary data analysis of online customer reviews (n = 2,878) in a service industry was used. LIWC was used to conduct the text analysis, and thereafter SPSS was used to examine the predictive capability of the model for the evaluation of customer-firm interactions.

A lexical analysis of online customer reviews was able to predict evaluations of customer-firm interactions across the three empirical studies. The authenticity and emotional tone present in the reviews served as the best predictors of customer evaluations of their service interactions with the firm.

Computerized text analysis is an inexpensive digital tool which, to date, has been sparsely used to analyze customer-firm interactions based on customers' online reviews. From a methodological perspective, the use of this tool to gain insights from unstructured data provides the ability to gain an understanding of customers' real-time evaluations of their service interactions with a firm without collecting primary data.

This research contributes to the growing body of knowledge regarding the use of computerized lexical analysis to assess unstructured, online customer reviews to predict customers' evaluations of a service interaction. The results offer service firms an inexpensive and user-friendly methodology to assess real-time, readily available reviews, complementing traditional customer research. A tool has been used to transform unstructured data into a numerical format, quantifying customer evaluations of service interactions.