Drawing upon the heuristic–systematic model (HSM) and considering the readers’ perspective, this study predicts that readers’ involvement and homophily between the reader and the review author (source) moderate the relationships between the credibility perception of online reviews and its antecedent factors. To test our hypotheses, we performed a user study on the Amazon Mechanical Turk platform. The results show that reader’s involvement moderates source credibility, internal consistency, review objectivity, and review sidedness on review credibility. In addition, homophily between the reader and the source also moderates the relationship between review credibility and its source. Our study contributes to information processing literature, especially in the context of online reviews, and suggests a better classification of the attributes related to online reviews using the HSM. Besides, it helps e-commerce platforms to customize online reviews for each reader to satisfy their information need and help them to make a better purchasing decision.

Purpose Crypto-currencies, decentralized electronic currencies systems, denote a radical change in financial exchange and economy environment. Consequently, it would be attractive for designers and policy-makers in this area to make out what social media users think about them on Twitter. The purpose of this study is to investigate the social opinions about different kinds of crypto-currencies and tune the best-customized classification technique to categorize the tweets based on sentiments. Design/methodology/approach This paper utilized a lexicon-based approach for analyzing the reviews on a wide range of crypto-currencies over Twitter data to measure positive, negative or neutral sentiments; in addition, the end result of sentiments played a training role to train a supervised technique, which can predict the sentiment loading of tweets about the main crypto-currencies. Findings The findings further prove that more than 50 per cent of people have positive beliefs about crypto-currencies. Furthermore, this paper confirms that marketers can predict the sentiment of tweets about these crypto-currencies with high accuracy if they use appropriate classification techniques like support vector machine (SVM). Practical implications Considering the growing interest in crypto-currencies (Bitcoin, Cardano, Ethereum, Litcoin and Ripple), the findings of this paper have a remarkable value for enterprises in the financial area to obtain the promised benefits of social media analysis at work. In addition, this paper helps crypto-currencies vendors analyze public opinion in social media platforms. In this sense, the current paper strengthens our understanding of what happens in social media for crypto-currencies. Originality/value For managers and decision-makers, this paper suggests that the news and campaign for their crypto in Twitter would affect people’s perspectives in a good manner. Because of this fact, the firms, investing in these crypto-currencies, could apply the social media as a magnifier for their promotional activities. The findings steer the market managers to see social media as a predictor tool, which can analyze the market through understanding the opinions of users of Twitter.

Human resources management has seen a significant change by the emergence of information systems from a traditional or popularly called personnel management to the modern one. The purpose of this article is to study the trends of information systems in the field of human resources management in combination with information systems through text mining approaches on a broad exploration of internationally published papers. Among text analytics methods for extracting trends, text clustering has been applied to the dataset of highly-ranked information systems journals. The data set was obtained from Scopus database for the period of 2013 to 2017. Afterwards, text clustering algorithms were applied and validated on the titles, abstracts and keywords. The results present practical and intuitive information which can help practitioners and scholars to grasp a useful overview and provides them with the opportunity to focus on trends in information systems in the field human resources management.

This paper aims to analyze the content of validated journal articles related to Knowledge Management (KM) in more than 18,000 papers of the Web of Science (WoS) database and then provide the most recent specific trends in KM field using text mining and burst detection to help researchers invest in the most challenging and fruitful areas of KM research domain. The method for finding the recent trend of KM includes the following steps: Conducting searches and collecting the publication data from WoS; using a hybrid analysis through burst detection and text clustering; also enriching and analyzing the results in order to achieve an overall perspective about the KM position and the popularity among researchers. This study could be valuable for researchers and KM specialists as well as managers as they may study the history of a subject by getting the structure of its scientific productions, so as to purposefully plan and determine the research priorities in KM.

COVID-19 has ruptured routines and caused breakdowns in what had been conventional practice and custom: everything from going to work and school and shopping in the supermarket to socializing with friends and taking holidays. Nonetheless, COVID-19 does provide an opportunity to study how people make sense of radically changing circumstances over time. In this paper we demonstrate how Twitter affords this opportunity by providing data in real time, and over time. In the present research, we collect a large pool of COVID-19 related tweets posted by New Zealanders–citizens of a country successful in containing the coronavirus–from the moment COVID-19 became evident to the world in the last days of 2019 until 19 August 2020. We undertake topic modeling on the tweets to foster understanding and sensemaking of the COVID-19 tweet landscape in New Zealand and its temporal development and evolution over time. This information can be valuable for those interested in how people react to emergent events, including researchers, governments, and policy makers.