There are several key sensemaking models and theories that have attracted a lot of attention among researchers and practitioners in the last few decades. The adaptation and application of sensemaking has varied by field of study, organizational type, and industry. This study explored these sensemaking models and theories to better answer the following questions: what is sense-making/sensemaking? How is sensemaking practiced today compared to the original sensemaking frameworks, models, and theories? To answer these questions, the current study conducted a systematic literature review and content analysis of current research involving sensemaking methods, practices, and techniques. As a result, topic modeling and data analytic techniques were used to construct a multifaceted conceptual framework that has been contrasted and compared with previous sensemaking frameworks, models, and theories to show its coverage and coherence. The new multifaceted sensemaking (MSM) theory consists of nine stages with defining characteristics for each stage that were either derived from the data analysis or conceptualized by the researcher based on the literature review. The new theory presented demonstrates how previous sensemaking theories evolved and have influenced both practice and research today. The multifaceted sensemaking theory is influenced by previous sensemaking theories while also representing sensemaking in current practice. The multifaceted sensemaking theory contributes to the sensemaking field of study a new theory with nine stages and defining characteristics.

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.

Cloud computing (CC) represents a key digital transformation advancement, reshaping the ways in which businesses operate across diverse industries. The exponential growth in data production has created an unprecedented challenge in the ways in which data is created, processed, and managed. This study investigates the impact of cloud computing on the digital transformation of the enterprise, including issues concerning sustainability and long-term preservation and curation. While there has been a proliferation of studies concerning the adoption and implementation of cloud computing in the enterprise, there is still a gap in the literature concerning the use of cloud computing technology for long-term preservation, digital curation, and sustainability. The study employed a mixed-methods approach that utilized a systematic review of the literature and an Internet-based survey. The combination of the systematic review and survey was intended to provide insights into the key strategic factors impacting the use of cloud computing for long-term preservation and sustainability. The results of the study show that, despite the growing recognition of the benefits of cloud computing, most organizations are still concerned about issues such as security, privacy, accessibility, and cost. Concerns regarding the long-term preservation and sustainability of enterprise information are closely tied to the extent to which cloud computing services are deemed reliable and trustworthy. This study underscores the varying levels of satisfaction among users, with businesses acknowledging both the advantages and disadvantages of the current cloud solutions.

Social media has become a platform for information diffusion, voicing concerns of existing inequalities and raising public awareness of various social and societal issues. Despite the social good, social media has become a fertile ground for spreading misinformation, hate speech and conspiracy theories. The death of George Floyd in May 2020 triggered a series of protests worldwide in support of the Black Lives Matter (BLM) movement and triggered a debate about equity, inclusion and social justice. The purpose of this study is to examine the impact of misinformation and social noise on the original intended message of BLM using data from the Twitter hashtag ‘BLM’. Results from topic modelling have shown the strong presence of misinformation and social noise. Such information was most probably intended to influence, mislead and dilute the original intended message. However, despite the effort to distort the original message of BLM, results from sentiment analysis show that users’ opinions of the BLM movement remained positive.

Relevance feedback techniques have been success fully applied to document retrieval systems (DRS) to refine queries and document representations. This paper represents a first attempt to apply and optimise relevance feedback techniques to improve retrieval in a text-based image archival system. The design exploits the rapid assessment possible with image data to facilitate query refinement and collect large amounts of relevance feedback data This data can then be used to extend incomplete image descriptions, thus ameliorat ing problems associated with text annotation of images. Visu ally oriented relevance feedback and query modification is implemented using direct mampulation of icons. An algorithm designed for dynamic modification of image descriptions based on relevance feedback is proposed, implemented, and experi mentally tested. Initial experiments show significant improve ments and demonstrate the potential of using these tech niques for image retrieval applications.