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Most new media research focuses on behavior as a measure of engagement, while the psychological state of being occupied with its content has received little attention. This study examined news engagement beyond pure action observation by exploring young people’s psychological experiences with the news.

The study carried out a digital native’s survey on 212 people (18–28 years). The focus of the survey was on understanding individuals’ engagement with online news using affective and cognitive components. The authors compared the influence of each type of engagement on youth consumption of and attitudes toward online news.

The results of the hierarchical regression analysis showed that affective engagement can be a stronger predictor of online news consumption than cognitive engagement. While affective engagement significantly predicts positive attitudes toward online news, cognitive engagement had no significant effect.

These findings suggest that “engaging the heart” is more influential than “engaging the mind” in drawing young people to the news in today’s information environment. The study thus contributes to the understanding of the cognitive and emotional focus on news content and their importance in shaping young people’s expectations of online news. The findings from this study could have broader implications for future trends in online news consumption.

News research scholars define immediacy as constant news updating, whereas scholars in other fields conceptualize it more broadly as meaning closeness. The present study explicates the concept of immediacy and proposes a multidimensional notion of news immediacy that reflects physical and psychological closeness to the news.

A scale for measuring multifaceted immediacy was developed and tested in a between-subjects design experiment. Four dimensions were extracted from the analysis: transportation, involvement, vividness and timeliness.

The results reveal greater immediacy in online than print news contexts. Involvement is key to the experience of immediacy in both contexts; yet the feeling of being transported to the places of the news events was stronger among online than print news users. The latter relied more on vividness of the news presentation to attain closeness to the news.

Implications of the study were discussed.

Online news websites provide diverse article topics, such as fashion news, entertainment and movie information, to attract more users and create more benefits. Recommending movie information to users reading news online can enhance the impression of diverse information and may consequently improve benefits. Accordingly, providing online movie recommendations can improve users’ satisfactions with the website, and thus is an important trend for online news websites. This study aims to propose a novel online recommendation method for recommending movie information to users when they are browsing news articles.

Association rule mining is applied to users’ news and movie browsing to find latent associations between news and movies. A novel online recommendation approach is proposed based on latent Dirichlet allocation (LDA), enhanced collaborative topic modeling (ECTM) and the diversity of recommendations. The performance of proposed approach is evaluated via an online evaluation on a real news website.

The online evaluation results show that the click-through rate can be improved by the proposed hybrid method integrating recommendation diversity, LDA, ECTM and users’ online interests, which are adapted to the current browsing news. The experiment results also show that considering recommendation diversity can achieve better performance.

Existing studies had not investigated the problem of recommending movie information to users while they are reading news online. To address this problem, a novel hybrid recommendation method is proposed for dealing with cross-type recommendation tasks and the cold-start issue. Moreover, the proposed method is implemented and evaluated online in a real world news website, while such online evaluation is rarely conducted in related research. This work contributes to deriving user’s online preferences for cross-type recommendations by integrating recommendation diversity, LDA, ECTM and adaptive online interests. The research findings also contribute to increasing the commercial value of the online news websites.

Few studies on hospitality firm survival consider the impact of online media exposure. This paper aims to investigate how the online news coverage of restaurants, characterized in terms of the number of articles, channel (Web page or mobile app), topic (operations or products) and consistency (mix of news tones), influences their survival.

A yearly panel data set covering 682 news-reported restaurants in Shanghai, China, over the period 2011–2019 is analyzed using a Cox model, and an extended cross-sectional data set containing 9,488 restaurants is used for robustness checks.

A larger number of online news articles, regardless of channel or topic, significantly improves restaurants’ chances of survival, and this positive impact of online exposure is greater if that news is published by mobile apps (rather than on Web pages) or reports topics related to operations (rather than products). Although, generally, news inconsistency is not good for restaurant survival, when the number of online news items is eight or more, inconsistency becomes good for survival.

This research guides restaurant operators to use news exposure in an online marketing environment to increase the firm’s chances of long-term survival.

Online media exposure has hitherto been ignored in the literature on the survival of hospitality firms. This paper provides a new perspective on hospitality firm survival and also contributes to the literature on media exposure by conceptualizing a unique factor, namely, the consistent online exposure.

The purpose of this study is to examine, in the context of online news use, the predictive values of two factors: perceived bias in traditional media and preference for partisan news.

This study used data collected as part of the Pew Internet and American Life Project between December 28, 2009, and January 19, 2010. The data were analyzed using linear regression analysis.

The findings provide evidence of the values of two potentially significant predictors of online news use: a perception of bias in traditional media and preference for partisan news. In addition, higher levels of political partisanship were shown to intensify the positive effect of perceived bias in traditional media on online news use in new media outlets, reinforcing the impact of preference for partisan news on participatory online news use.

Depending on individual decisions, the internet can either help to empower deliberative democracy (where diverse and different voices coexist) or lead to an extremely polarized society.

With the explosive growth of the internet as a news source, media scholars have explored the factors that encourage people to rely on the internet for news and information. Nevertheless, certain attributes of online news consumption originating from individual attitudes about and perceptions of the media environment remain underspecified. This research helps advance an understanding of the types of people who seek news online and how they use various sources.

Coronavirus disease (COVID-19) pandemic has given rise to different dimensions of uncommon human behavior, and panic buying is one of them. Interestingly, panic buying research has not been given much attention. The purpose of this paper is threefold. Firstly, it examines the influences of the theory of planned behavior (TPB) elements (subjective norm, attitude and perceived behavior control (PBC)) on panic buying. Secondly, it investigates online news and the perceived likelihood of being affected (PLA) as antecedents to the TPB constructs. Finally, to examine online news verification as a moderator on the relationship between the TPB constructs and panic buying.

Data were collected from 371 respondents and analyzed using the partial least squares method structural equation modeling (PLS-SEM). PLS predict was applied to determine the predictive power of the model further.

This study found that subjective norms and attitude influence panic buying. The results further revealed that online news has a direct influence on the PLA and attitude. However, PBC has no such effect on panic buying. Surprisingly, online news verification also has no moderating effects on the relationships between the TPB elements and panic buying.

This research helps to understand consumer panic buying behavior, especially during shock events such as the COVID-19 pandemic. This study is the first that extends the TPB incorporating both online news and PLA as antecedents to panic buying in the same model. Furthermore, the study serves as an initial attempt to investigate online news verification as a moderator between the link of three constructs of TPB and panic buying, contributing to existing literature. Lastly, it advances the body of knowledge on consumer behavior and contributes methodologically by introducing the PLS approach.

Providing online news recommendations to users has become an important trend for online media platforms, enabling them to attract more users. The purpose of this paper is to propose an online news recommendation system for recommending news articles to users when browsing news on online media platforms.

A Collaborative Semantic Topic Modeling (CSTM) method and an ensemble model (EM) are proposed to predict user preferences based on the combination of matrix factorization with articles’ semantic latent topics derived from word embedding and latent topic modeling. The proposed EM further integrates an online interest adjustment (OIA) mechanism to adjust users’ online recommendation lists based on their current news browsing.

This study evaluated the proposed approach using offline experiments, as well as an online evaluation on an existing online media platform. The evaluation shows that the proposed method can improve the recommendation quality and achieve better performance than other recommendation methods can. The online evaluation also shows that integrating the proposed method with OIA can improve the click-through rate for online news recommendation.

The novel CSTM and EM combined with OIA are proposed for news recommendation. The proposed novel recommendation system can improve the click-through rate of online news recommendations, thus increasing online media platforms’ commercial value.

To provide an empirically based analysis and evaluation of the existing and possible future evolution of Internet business models within the digital content market, focusing particularly on the possibilities for cooperation and coopetition within this market‐space.

The paper is based on a three‐year study of the European online news and online music sectors, comprising a set of preliminary, scene‐setting case studies of a number of major players within the European online news and music sectors; a detailed, two‐stage survey made up of online questionnaires and face‐to‐face interviews; and a small number of in‐depth case studies.

Provides a discussion of the changes taking place in the online news and music sectors, the evolution of the business models within them, the driving forces we have identified, and finally some predictions about what the future may hold for both these sectors.

The research is indicative, rather than general – being centred on European participants in two sectors of the digital content market‐space in the period between May 2003 and August 2004.

A rich evaluation of these two fast‐moving digital content sectors, providing empirically based insights into the ways in which they are evolving and changing and into parallels with other, similar sectors of the digital content market.

This paper is the first major empirical evaluation of the digital content market‐space and offers practical assistance, as well as new theoretical insights on e‐business model evolution in this area.

Developments in the technology of electronic transmission of information are rapidly transforming the tools available to the general public for gathering information about current events. Direct access to news and retrieval of bibliographic information pertaining to traditional printed news sources are a daily reality for a growing number of individuals, on the job, at home, through personal computer terminals or at libraries. The purpose of this paper is to examine the characteristics of electronic databases providing access to news, as well as applications of these tools to various library settings.

The purpose of the study is to use web serer logs in analyzing the changes of user behavior in reading online news, in terms of desktop and mobile users. Advances in mobile technology and social media have paved the way for online news consumption to evolve. There is an absence of research into the changes of user behavior in terms of desktop versus mobile users, particularly by analyzing the server logs.

In this paper, the authors investigate the evolution of user behavior using logs from the Malaysian newspaper Berita Harian Online in April 2012 and April 2017. Web usage mining techniques were used for pre-processing the logs and identifying user sessions. A Markov model is used to analyze navigation flows, and association rule mining is used to analyze user behavior within sessions.

It was found that page accesses have increased tremendously, particularly from Android phones, and about half of the requests in 2017 are referred from Facebook. Navigation flow between the main page, articles and section pages has changed from 2012 to 2017; while most users started navigation with the main page in 2012, readers often started with an article in 2017. Based on association rules, National and Sports are the most frequent section pages in 2012 and 2017 for desktop and mobile. However, based on the lift and conviction, these two sections are not read together in the same session as frequently as might be expected. Other less popular items have higher probability of being read together in a session.

The localized data set is from Berita Harian Online; although unique to this particular newspaper, the findings and the methodology for investigating user behavior can be applied to other online news. On another note, the data set could be extended to be more than a month. Although initially data for the year 2012 was collected, unfortunately only the data for April 2012 is complete. Other months have missing days. Therefore, to make an impartial comparison for the evolution of user behavior in five years, the Web server logs for April 2017 were used.

The user behavior in 2012 and 2017 was compared using association rules and Markov flow. Different from existing studies analyzing online newspaper Web server logs, this paper uniquely investigates changes in user behavior as a result of mobile phones becoming a mainstream technology for accessing the Web.