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The authors wanted to provide an enriched understanding of how lawyers keep up-to-date with legal developments. Maintaining awareness of developments in an area (known as “monitoring”) is an important aspect of professional’s information work. This is particularly true for lawyers, who are expected to keep up-to-date with legal developments on an ongoing basis.

The authors conducted semi-structured interviews with a group of lawyers who authored and published current awareness content for LexisNexis – a large publishing organisation. The interviews focused on identifying the types of electronic, printed and people-based current awareness resources the lawyers used to keep up-to-date with legal developments and the reasons for their choices.

The lawyers mostly used electronic resources (particularly e-mail alerts and an electronic tool that alerted them to changes in website content), alongside interpersonal sources, such as colleagues, customers and professional contacts. Printed media, such as journals and newspapers, were used more rarely and usually to complement electronic and person-based resources. A number of factors were found to influence choice. These included situational relevance, presentation, utility and trustworthiness, the speed of content acquisition and interpretation facilitated by the resource.

The authors' findings enrich their understanding of lawyers’ monitoring behaviour, which has so far received little direct research attention. Their design suggestions have the potential to feed into the design of new and improvement of existing digital current awareness resources. Their findings have the potential to act as “success criteria” by which these resources can be evaluated from a user-centred perspective.

The purpose of this paper is to introduce, apply and compare how artificial intelligence (AI), and specifically the IBM Watson system, can be used for content analysis in marketing research relative to manual and computer-aided (non-AI) approaches to content analysis.

To illustrate the use of AI-enabled content analysis, this paper examines the text of leadership speeches, content related to organizational brand. The process and results of using AI are compared to manual and computer-aided approaches by using three performance factors for content analysis: reliability, validity and efficiency.

Relative to manual and computer-aided approaches, AI-enabled content analysis provides clear advantages with high reliability, high validity and moderate efficiency.

This paper offers three contributions. First, it highlights the continued importance of the content analysis research method, particularly with the explosive growth of natural language-based user-generated content. Second, it provides a road map of how to use AI-enabled content analysis. Third, it applies and compares AI-enabled content analysis to manual and computer-aided, using leadership speeches.

For each of the three approaches, nine steps are outlined and described to allow for replicability of this study. The advantages and disadvantages of using AI for content analysis are discussed. Together these are intended to motivate and guide researchers to apply and develop AI-enabled content analysis for research in marketing and other disciplines.

To the best of the authors’ knowledge, this paper is among the first to introduce, apply and compare how AI can be used for content analysis.

In this paper, the authors analyze the use of content analysis in disclosing voluntarily information on intangible assets, the intangible assets disclosures (IAD). The purpose of this paper is to conduct a structured literature review (SLR) that assesses the possibilities and limitations of content analysis.

To that end, the authors analyze the existing literature on the topic in the main international databases. In all, 74 empirical articles utilizing content analysis as a research methodology for IAD were reviewed. Regarding the selection of sources, the authors should indicate that the SLR performed includes academic studies published in journals or presented at conferences and that are always subject to a double process of anonymous review.

The obtained results indicate that despite the frequent use of content analysis in studies on IAD, its use does not meet all expectations.

The study synthesizes the research on content analysis for the case of information on intangible assets, offering an updated and global framework for future researchers through the SLR.

Among other problems, the authors found its excessive emphasis on the amount disclosed in the annual report, ignoring other reports in which more information regarding intangible assets is available, such as in the case of the sustainability reports. Furthermore, the use of very different coding systems and its exclusive use without being combined with other methodologies are detected. These aspects affect the quality problems of the sources used, which directly results in the utility of the evidenced findings.

These conclusions allow the authors to conclude on the need to open different lines of study that review the use of content analysis in this topic.

The work focuses on the quality of disclosures more so than on the quantity, offering a critical view that summarizes the utility of the employment of content analysis for this type of disclosure and its implications for future research on this topic. Despite previous studies, the authors highlight the new insights revealed from IAD research, especially since the seminal paper of Dumay and Cai (2014).

For the case of many content features, This paper aims to investigate which content features in video and text ads more contribute to accurately predicting the success of crowdfunding by comparing prediction models.

With 1,368 features extracted from 15,195 Kickstarter campaigns in the USA, the authors compare base models such as logistic regression (LR) with tree-based homogeneous ensembles such as eXtreme gradient boosting (XGBoost) and heterogeneous ensembles such as XGBoost + LR.

XGBoost shows higher prediction accuracy than LR (82% vs 69%), in contrast to the findings of a previous relevant study. Regarding important content features, humans (e.g. founders) are more important than visual objects (e.g. products). In both spoken and written language, words related to experience (e.g. eat) or perception (e.g. hear) are more important than cognitive (e.g. causation) words. In addition, a focus on the future is more important than a present or past time orientation. Speech aids (see and compare) to complement visual content are also effective and positive tone matters in speech.

This research makes theoretical contributions by finding more important visuals (human) and language features (experience, perception and future time). Also, in a multimodal context, complementary cues (e.g. speech aids) across different modalities help. Furthermore, the noncontent parts of speech such as positive “tone” or pace of speech are important.

Founders are encouraged to assess and revise the content of their video or text ads as well as their basic campaign features (e.g. goal, duration and reward) before they launch their campaigns. Next, overly complex ensembles may suffer from overfitting problems. In practice, model validation using unseen data is recommended.

Rather than reducing the number of content feature dimensions (Kaminski and Hopp, 2020), by enabling advanced prediction models to accommodate many contents features, prediction accuracy rises substantially.

The purpose of this paper is to examine the factors that significantly affect the prediction of fake news from the virality theory perspective. The paper looks at a mix of emotion-driven content, sentimental resonance, topic modeling and linguistic features of news articles to predict the probability of fake news.

A data set of over 12,000 articles was chosen to develop a model for fake news detection. Machine learning algorithms and natural language processing techniques were used to handle big data with efficiency. Lexicon-based emotion analysis provided eight kinds of emotions used in the article text. The cluster of topics was extracted using topic modeling (five topics), while sentiment analysis provided the resonance between the title and the text. Linguistic features were added to the coding outcomes to develop a logistic regression predictive model for testing the significant variables. Other machine learning algorithms were also executed and compared.

The results revealed that positive emotions in a text lower the probability of news being fake. It was also found that sensational content like illegal activities and crime-related content were associated with fake news. The news title and the text exhibiting similar sentiments were found to be having lower chances of being fake. News titles with more words and content with fewer words were found to impact fake news detection significantly.

Several systems and social media platforms today are trying to implement fake news detection methods to filter the content. This research provides exciting parameters from a viral theory perspective that could help develop automated fake news detectors.

While several studies have explored fake news detection, this study uses a new perspective on viral theory. It also introduces new parameters like sentimental resonance that could help predict fake news. This study deals with an extensive data set and uses advanced natural language processing to automate the coding techniques in developing the prediction model.

The purpose of this paper is to describe an online faculty development pilot course on how to engage students online. A framework was used, referred to as the Trifecta of Student Engagement, for the design of the course. The Trifecta of Student Engagement proposes that students, in order to be fully engaged in a course, need to be engaged with their course content, with their peers and with their instructor. The course has three units of content that each correspond to the Trifecta of Student Engagement. This course has gone through one pilot with faculty and has impacted students and faculty positively.

An online faculty development course was piloted with eight faculty members across a range of disciplines who participated in the program. After taking the course, they had to apply the Trifecta of Student Engagement framework to a course they taught and share what they did via written report, webinar, or web presentation. This study summarized the faculty participants’ written reports and presentations as well as provided a qualitative evaluation on the impact this course had on students and faculty.

After faculty applied the Trifecta of Student Engagement framework to courses taught, faculty saw an improvement in student engagement, satisfaction, learning and achievement. Three faculty surveyed students to determine their engagement and satisfaction and found students to respond positively to the use of tools and activities for student-to-content engagement, student-to-student engagement and student-to-instructor engagement. Two faculty examined student grades to determine if there were changes in student outcomes. One professor saw average grades increase by 11 percent. Another professor saw grades improve by 8 percent. She also found that student assessment of learning increased by 0.57. Both faculty attributed the improvement to the effectiveness of the teaching strategies employed.

This research is limited to the eight faculty who participated in the pilot. Some faculty used methods to attempt to measure the impacts of their teaching practices by surveying students and looking at student performance data. A second pilot is needed for additional faculty to take the course and apply the Trifecta of Engagement framework to generate more data for impact.

Institutions looking to create an online teaching professional development course for faculty can utilize the Trifecta of Student Engagement framework for their course design. Additionally, faculty can read about tools and strategies that they can immediately apply to create more student-to-content engagement, student-to-student engagement and student-to-instructor engagement.

Faculty can be more intentional in how they engage students in their online course experience.

This paper adds to the literature on faculty development regarding student-centered teaching practices. Other institutions looking to create a faculty development course or program that utilizes a student-centered framework may find aspects of this paper useful for their own online teaching professional development initiatives.

People can easily track and understand their usage pattern for any content (e.g. movies, games) or service (e.g. card payment, cell phone usage) by using technologies such as the internet and smart phones. When consumers evaluate their past consumption patterns, they may experience two different kinds of regret: content-based or monetary-based. The purpose of this paper is to propose that perceived self-control, defined as the extent to which people believe they can control their usage, plays a moderating role in the tariff-choice process (flatrate vs pay-per-use) for two types of content: vice-based and virtue-based.

Two laboratory experiments were designed to test the hypotheses. There were a total of 200 participants (86 for Experiment 1 and 114 for Experiment 2) who completed the entire experimental process (i.e. stimulus exposure, questionnaire reporting, dependent variable measurement, manipulation of the independent variables, and control checks).

The results of this research provide evidence supporting the role of perceived self-control in tariff preference by showing that preference varies between flat-rate and pay-per-use tariff options. Specifically, virtue-based content users were more likely to prefer the pay-per-use tariff when their perceived self-control was low vs when it was high. In contrast, vice-based content users were more likely to prefer the flat-rate tariff when their perceived self-control was low vs when it was high.

There are three contributions of the present research. First, the authors investigated the effect of content type on tariff preference. Second, the authors suggest that there is a moderating effect of perceived self-control on tariff preference. Third, this study revealed the factors affecting consumers’ perceived self-control.

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 investigate the use of television (TV) content for scholarly purposes. It focuses on: profile of scholars using TV content; the structure of their need for TV content; the situations in which scholars need TV content; and their patterns of use of TV content in each research stage.

Taylor’s four components of the information use environment has contributed to the development of a conceptual framework. The data from the use of TV content by 668 scholars were profiled using correspondence analysis and co-word analysis. Additionally, the data from 15 interviews and content from 240 journal articles were analysed.

The authors determined that the environment of the scholarly use of TV content is unique in terms of the scholars’ academic domains, research topics, motivation, and patterns of use. Six academic domains were identified as having used TV content to a meaningful degree, and their knowledge structure was presented as a map depicting the scholars’ needs for TV content. Scholars are likely to use TV content when they deal with timely social and cultural topics, or human behaviour. The scholars also showed different patterns of use of TV content at each stage of research.

In this study, TV content was newly examined from the perspective of an information source for scholarly purposes, and it was found to be a meaningful source in several domains. This result extends the knowledge of information sources in scholarly communication and information services.