Disembodied conversational agents in the form of chatbots are increasingly becoming a reality on social media and messaging applications, and are a particularly pressing topic for service encounters with companies. Adopting an experimental design with actual chatbots powered with current technology, this study explores the extent to which human-like cues such as language style and name, and the framing used to introduce the chatbot to the consumer can influence perceptions about social presence as well as mindful and mindless anthropomorphism. Moreover, this study investigates the relevance of anthropomorphism and social presence to important company-related outcomes, such as attitudes, satisfaction and the emotional connection that consumers feel with the company after interacting with the chatbot.

Fueled by ever-growing amounts of (digital) data and advances in artificial intelligence, decision-making in contemporary societies is increasingly delegated to automated processes. Drawing from social science theories and from the emerging body of research about algorithmic appreciation and algorithmic perceptions, the current study explores the extent to which personal characteristics can be linked to perceptions of automated decision-making by AI, and the boundary conditions of these perceptions, namely the extent to which such perceptions differ across media, (public) health, and judicial contexts. Data from a scenario-based survey experiment with a national sample (N = 958) show that people are by and large concerned about risks and have mixed opinions about fairness and usefulness of automated decision-making at a societal level, with general attitudes influenced by individual characteristics. Interestingly, decisions taken automatically by AI were often evaluated on par or even better than human experts for specific decisions. Theoretical and societal implications about these findings are discussed.

How much time do you spend online? Understanding and improving the accuracy of self-reported measures of internet use Araujo, T.B.; Wonneberger, A.; Neijens, P.C.; de Vreese, C.H. : 10.1080/19312458.2017.1317337 Link to publication Published in: Communication Methods and Measures DOI Citation for published version (APA):Araujo, T., Wonneberger, A., Neijens, P., & de Vreese, C. (2017). How much time do you spend online? Understanding and improving the accuracy of self-reported measures of internet use. Communication Methods and Measures, 11(3), 173-190. DOI: 10.1080173-190. DOI: 10. /19312458.2017 General rights It is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons). Disclaimer/Complaints regulationsIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: http://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible. Given the importance of survey measures of online media use for communication research, it is crucial to assess and improve their quality, in particular because the increasingly fragmented and ubiquitous usage of internet complicates the accuracy of self-reported measures. This study contributes to the discussion regarding the accuracy of self-reported internet use by presenting relevant factors potentially affecting biases of self-reports and testing survey design strategies to improve accuracy. Combining automatic tracking data and survey data from the same participants (N = 690) confirmed low levels of accuracy and tendencies of over-reporting. The analysis revealed biases due to a range of factors associated with the intensity of (actual) internet usage, propensity to multitask, day of reference, and the usage of mobile devices. An anchoring technique could not be proved to reduce inaccuracies of reporting behavior. Several recommendations for research practice follow from these findings.

With digital communication increasingly shifting to mobile devices, communication research needs to explore ways to retrieve, process, and analyze digital trace data on people’s most personal devices. This study presents a new methodological approach, mobile data donations, in which smartphone usage data is collected unobtrusively with the help of mobile log data. The iOS Screen Time function is used as a test case for gathering log data with the help of screenshots. The study investigates the feasibility of the method, sample biases, and accuracy of smartphone usage self-reports on a general population sample of Dutch citizens ( n=404). Importantly, it explores how mobile data donations can be used as add-ons or substitutes for conventional media exposure measures. Results indicate that (a) users’ privacy concerns and technical skills are crucial factors for the willingness to donate mobile log data and (b) there is a strong tendency for underreporting of smartphone usage duration and frequency.

Using a sample of over 5300 tweets from top global brands, this study investigated how different types of users can influence brand content diffusion via retweets. Twitter users who influenced followers to retweet brand content were categorized as (1) influentials, because of their above average ability to influence others to retweet their tweets (in general), (2) information brokers, because of their position connecting groups of users or (3) having strong ties, because of their high percentage of friends in common and a mutual friendÀfollower relationship with the influenced follower. The results indicate that influentials and information brokers are associated with larger number of retweets for brand content. In addition, although information brokers have a larger overall influence on retweeting, they are more prone to do so when influentials are mentioned in the brand tweet, providing support for the strategy that aims to associate the brand with influential users.

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Purpose -This study focuses on how brands participate in social network sites (SNSs) and investigates both the different strategies they adopt and the factors that influence these strategies. Design/methodology/approach -The activities of top brands in SNSs were investigated through a content analysis of brand web sites in three countries, measuring two primary dimensions: presence in SNSs and level of engagement which the brand hoped to establish with stakeholders. The study then built logistic regression models to understand which factors drive brands to adopt and use SNSs, using insights from previous studies related to internet innovation adoption cycles. Findings -The study confirmed that SNS adoption follows a general path of Internet innovation adoption by brands. Industry markets exert influence, with technology and consumer intensive brands being more likely than all other industry segments to be present in SNSs and to participate at higher engagement levels, while brands targeting younger audiences also engage at higher levels than brands targeting generic audiences. The country in which the brand operates plays a significant role in a brand's likelihood of adopting SNSs: web sites in the USA are more likely to use SNSs than other countries, even after controlling for factors such as pre-existing web operations or the headquarters being in the same country. It was also found that top brands operate in a global world, as shown by their preference for SNSs with global coverage compared to local SNS, even when the penetration rates of these local SNSs were higher. Originality/value -The paper advances research on SNS adoption by brands from an organizational standpoint, at a time when the majority of the top global brands actively promote such services. IntroductionThe sharp increase in the popularity of social network sites (SNSs) has turned services such as Facebook and Twitter into attractive spaces for both consumers and brands. Used by over 70 percent of the global online population (comScore, 2010), SNSs are used not only for entertainment, but also as places where consumers discuss brand experiences and preferences (Needham, 2008;Jansen and Zhang, 2009;Acar and Polonsky, 2008;Dunne et al., 2010)[1]. SNSs are not, however, only used by consumers: Coca-Cola, for example, has over 36 million users on its Facebook page and actively promotes its activities on its brand web site [2]. This study focusses on how brands participate in SNSs and investigates the different strategies they adopt and the factors influencing these strategies.Recent trade studies propose that brands have several reasons to adopt SNSs (Bernoff and Li, 2008;Edelman and Salsberg, 2010;Kaplan and Haenlein, 2010;Zeisser, 2010). First, SNSs are popular web sites (second only to search engines): Facebook, the largest SNS, is used by 44 percent of the online population, with over 800 million users [3]. Second, establishing a presence on SNSs has little upfront cost: pages in Facebook or Twitter are free. Third, consumers trust brand pages ...

This article scrutinizes the method of automated content analysis to measure the tone of news coverage. We compare a range of off-the-shelf sentiment analysis tools to manually coded economic news as well as examine the agreement between these dictionary approaches themselves. We assess the performance of five off-the-shelf sentiment analysis tools and two tailor-made dictionary-based approaches. The analyses result in five conclusions. First, there is little overlap between the off-the-shelf tools; causing wide divergence in terms of tone measurement. Second, there is no stronger overlap with manual coding for short texts (i.e., headlines) than for long texts (i.e., full articles). Third, an approach that combines individual dictionaries achieves a comparably good performance. Fourth, precision may increase to acceptable levels at higher levels of granularity. Fifth, performance of dictionary approaches depends more on the number of relevant keywords in the dictionary than on the number of valenced words as such; a small tailor-made lexicon was not inferior to large established dictionaries. Altogether, we conclude that off-the-shelf sentiment analysis tools are mostly unreliable and unsuitable for research purposesat least in the context of Dutch economic newsand manual validation for the specific language, domain, and genre of the research project at hand is always warranted.