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The purpose of this study is to extend Landry et al.’s (2016) work and examines the possible association between corporations having three or more female directors and these companies being features on corporate recognition lists.

This study examines a sample of 335 corporations ranked as Fortune 500 corporations in the period 2013–2019. The authors test for the association between the percent of corporations that had three or more female directors and the percent of these corporations on external recognition lists.

The data indicate that the percent of corporations with three or more female directors more than doubled between 2013 and 2019; this change was accompanied by an increase in the percent of presence of these companies in corporate recognition lists. The percent of corporations that had three or more female directors was significantly associated with the percent of these corporations on external recognition lists.

The first is the sample selection process; this study used only publicly traded corporations that were included in the Fortune 500 between 2013 through 2019. The second limitation is that this study did not include data on board members considered minorities.

The findings imply that there is a strong link between gender diversity on boards and being featured on corporate recognition lists, which means that firms who care about corporate social responsibility-related works, and more instrumentally, care about being on such lists should reconsider the gender balance on their boards.

This study extends this work for a time period in which the number of corporations with three or more female directors has significantly increased.

The purpose of this paper is to develop a better understanding of the presence of “green‐collar workers” in organizations, including whether their perception of the organization with regard to environmental activities would affect their willingness to recommend the employer to others. It also aims to analyse generational differences with regard to this phenomenon.

The study used a survey developed from other research on green‐collar workers. It was distributed electronically and the data analysed using primarily χ² and analysis of variance (ANOVA).

There were differences in knowledge levels regarding environmental topics such as the Kyoto treaty and the Green‐Collar Jobs Act. Significant correlations were also found among the variables of generation, willingness to recommend employer, and importance of school/workplace being environmentally friendly.

The use of an online survey was a limitation due to the need for technology access to respond. Despite this limitation, subjects included sufficient members of all four generations to perform the analyzes.

Organizations that are trying to “go green” may well benefit from improved employee relations as a result. Employees who are interested in environmental issues will more likely recommend their companies to others when they feel the organization reflects their interest.

Other studies have not included gender or generational aspects of the issue of environmentalism in their work. This empirical study also investigates the relationship between organizations’ environmental activities, employee perceptions of the organization, and their willingness to recommend their company to others.

Aim of the present monograph is the economic analysis of the role of MNEs regarding globalisation and digital economy and in parallel there is a reference and examination of some legal aspects concerning MNEs, cyberspace and e‐commerce as the means of expression of the digital economy. The whole effort of the author is focused on the examination of various aspects of MNEs and their impact upon globalisation and vice versa and how and if we are moving towards a global digital economy.

In this age of extremely well-developed social media, it is necessary to detect any change in the corporate image of an enterprise immediately so as to take quick action to avoid the wide spread of a negative image. However, existing survey-based corporate image evaluation methods are costly, slow and static, and the results may quickly become outdated. User comments, news reports and we-media articles on the internet offer varied channels for enterprises to obtain public evaluations and feedback. The purpose of this study is to effectively use online information to timely and accurately measure enterprises’ corporate images.

A new corporate image evaluation method was built by first using a literature review to establish a corporate image evaluation index system. Next, an automatic text analysis of online public information was performed through a topic classification and sentiment analysis algorithm based on the dictionary. The accuracy of the topic classification and sentiment analysis algorithm is then calculated. Finally, three internet enterprises were chosen as cases, and their corporate image was evaluated.

The results show that the author’s corporate image evaluation method is effective.

First, in this study, a new corporate image evaluation index system is constructed. Second, a new corporate image evaluation method based on text mining is proposed that can support data-driven decision-making for managers with real-time corporate image evaluation results. Finally, this study improves the understanding of corporate image by generating business intelligence through online information. The findings provide researchers with specific and detailed suggestions that focus on the corporate image management of emerging internet enterprises.

With the rapid increase in internet use, most people tend to purchase books through online stores. Several such stores also provide book recommendations for buyer convenience, and both collaborative and content-based filtering approaches have been widely used for building these recommendation systems. However, both approaches have significant limitations, including cold start and data sparsity. To overcome these limitations, this study aims to investigate whether user satisfaction can be predicted based on easily accessible book descriptions.

The authors collected a large-scale Kindle Books data set containing book descriptions and ratings, and calculated whether a specific book will receive a high rating. For this purpose, several feature representation methods (bag-of-words, term frequency–inverse document frequency [TF-IDF] and Word2vec) and machine learning classifiers (logistic regression, random forest, naive Bayes and support vector machine) were used.

The used classifiers show substantial accuracy in predicting reader satisfaction. Among them, the random forest classifier combined with the TF-IDF feature representation method exhibited the highest accuracy at 96.09%.

This study revealed that user satisfaction can be predicted based on book descriptions and shed light on the limitations of existing recommendation systems. Further, both practical and theoretical implications have been discussed.

Clinical notes typically contain medical jargons and specialized words and phrases that are complicated and technical to most people, which is one of the most challenging obstacles in health information dissemination to consumers by healthcare providers. The authors aim to investigate how to leverage machine learning techniques to transform clinical notes of interest into understandable expressions.

The authors propose a natural language processing pipeline that is capable of extracting relevant information from long unstructured clinical notes and simplifying lexicons by replacing medical jargons and technical terms. Particularly, the authors develop an unsupervised keywords matching method to extract relevant information from clinical notes. To automatically evaluate completeness of the extracted information, the authors perform a multi-label classification task on the relevant texts. To simplify lexicons in the relevant text, the authors identify complex words using a sequence labeler and leverage transformer models to generate candidate words for substitution. The authors validate the proposed pipeline using 58,167 discharge summaries from critical care services.

The results show that the proposed pipeline can identify relevant information with high completeness and simplify complex expressions in clinical notes so that the converted notes have a high level of readability but a low degree of meaning change.

The proposed pipeline can help healthcare consumers well understand their medical information and therefore strengthen communications between healthcare providers and consumers for better care.

An innovative pipeline approach is developed to address the health literacy problem confronted by healthcare providers and consumers in the ongoing digital transformation process in the healthcare industry.