Congruity and Incongruity between Projected (DMO) and Perceived (UGC) Destination image – A Comparative Content Analysis

User-generated Content (UGC) in the digital world has gained credibility as a kind of word-of-mouth in recent years with the advent of new Information and communication technologies (ICT). Travelers rely on UGC information than DMO content while the travel decision-making process. No previous research has been conducted to correlate both UGC and DMO destination images of Kerala. The study analyzes the dominant attributes of the destination image of Kerala presented through DMO and UGC Visual Content. Also, find out whether there is any commonality in destination attributes between pictures posted by tourists (UGC) and DMO or vice versa. Images of Kerala were gathered for this study from DMO’s website (www.keralatourism.org), Instagram, and Facebook, two of the most popular social media platforms worldwide. By applying content analysis methodology to the collected DMO and UGC images of Kerala Tourism, the research objectives are attained. Data from the last five years were considered for the study to understand the latest trends and changing patterns. The results aid tourism stakeholders in planning effective social media marketing strategies to capture the imagination of tourists, hence creating a better destination image online

What Makes a (Ro)bot Smart? Examining the Antecedents of Perceived Intelligence in the Context of Using Physical Robots, Software Robots, and Chatbots at Work

In recent years, the acceptance and use of intelligent robots and other kinds of intelligent systems have begun to gain more and more attention also in information systems research. Here, many studies have found the perceived intelligence of robots to act as one critical antecedent for their acceptance and use, but few studies have focused on the antecedents of perceived intelligence itself. In this study, we aimed to address this gap in prior research by examining the effects of individual intelligence dimensions on the overall intelligence perception of robots in the work context. In addition, we also examined the potential differences in these effects as well as in the individual intelligence dimensions and overall intelligence perception themselves between three common types of robots: physical robots, software robots, and chatbots. These examinations were based on online survey data from 1,080 present or prior users of robots at work. In summary, we found that adaptability, personality, autonomy, and multifunctionality act as the most influential antecedents of perceived intelligence in the case of all three types of robots. In addition, we also found that software robots and chatbots perform better than physical robots in most individual intelligence dimensions and in overall intelligence perception