Purpose: This paper aims to investigate emerging trends in data analytics and knowledge management (KM) job market by using the knowledge, skills and abilities (KSA) framework. The findings from the study provide insights into curriculum development and academic program design. Design/methodology/approach: This study traced and retrieved job ads on LinkedIn to understand how data analytics and KM interplay in terms of job functions, knowledge, skills and abilities required for jobs, as well as career progression. Conducting content analysis using text analytics and multiple correspondence analysis, this paper extends the framework of KSA proposed by Cegielski and Jones‐Farmer to the field of data analytics and KM. Findings: Using content analysis, the study analyzes the requisite KSA that connect analytics to KM from the job demand perspective. While Kruskal–Wallis tests assist in examining the relationships between different types of KSA and company’s characteristics, multiple correspondence analysis (MCA) aids in reducing dimensions and representing the KSA data points in two-dimensional space to identify potential associations between levels of categorical variables. The results from the Kruskal–Wallis tests indicate a significant relationship between job experience levels and KSA. The MCA diagrams illustrate key distinctions between hard and soft skills in data across different experience levels. Practical implications: The practical implications of the study are two-fold. First, the extended KSA framework can guide KM professionals with their career planning toward data analytics. Second, the findings can inform academic institutions with regard to broadening and refining their data analytics or KM curricula. Originality/value: This paper is one of the first studies to investigate the connection between data analytics and KM from the job demand perspective. It contributes to the ongoing discussion and provides insights into curriculum development and academic program design.

The Twitter hashtag is a unique tagging format linking Tweets to user-defined concepts. The aim of the paper is to describe various applications of Twitter hashtags and to determine the functional characteristics of each application. Twitter hashtags can assist in archiving twitter content, provide different visual representations of tweets, and permit grouping by categories and facets. This study seeks to examine the trends in Twitter hashtag features and how these may be applied as enhancements for next-generation library catalogues. For this purpose, Taylor's value-added model is used as an analytical framework. The morphological box developed by Zwicky is used to synthesize functionalities of Twitter hashtag applications. And finally, included are recommendations for the design of hashtag-based value-added dimensions for future library catalogues.

Mobile advertising accounted for 137 billion dollars or 55% of 2021 U.S. total digital advertising expenditures. Delivery time and tone of language in advertisements (ads) are two little explored characteristics in mobile advertising. This paper examines how morning delivery and the use of a friendly language tone in ads are related to customer purchase intention. Based on survey data from the U.S. population, morning ads with a friendly tone are less irritating and positively influence customer attitude toward mobile learning from the ads, which result in increasing consumer purchase intention. In other words, morning and friendly-toned ads have a positive indirect impact on purchase intention. This research contributes to the body of literature on mobile marketing by providing empirical evidence on the value of delivery time and language tone of mobile communication because such delivery avoids irritating users and increases purchase intention. The developed research model is supported by the appraisal theory of emotions.