Purpose Visual analytics is increasingly becoming a prominent technology for organizations seeking to gain knowledge and actionable insights from heterogeneous and big data to support decision-making. Whilst a broad range of visual analytics platforms exists, limited research has been conducted to explore the specific factors that influence their adoption in organizations. The purpose of this paper is to develop a framework for visual analytics adoption that synthesizes the factors related to the specific nature and characteristics of visual analytics technology. Design/methodology/approach This study applies a directed content analysis approach to online evaluation reviews of visual analytics platforms to identify the salient determinants of visual analytics adoption in organizations from the standpoint of practitioners. The online reviews were gathered from Gartner.com, and included a sample of 1,320 reviews for six widely adopted visual analytics platforms. Findings Based on the content analysis of online reviews, 34 factors emerged as key predictors of visual analytics adoption in organizations. These factors were synthesized into a conceptual framework of visual analytics adoption based on the diffusion of innovations theory and technology–organization–environment framework. The findings of this study demonstrated that the decision to adopt visual analytics technologies is not merely based on the technological factors. Various organizational and environmental factors have also significant influences on visual analytics adoption in organizations. Research limitations/implications This study extends the previous work on technology adoption by developing an adoption framework that is aligned with the specific nature and characteristics of visual analytics technology and the factors involved to increase the utilization and business value of visual analytics in organizations. Practical implications This study highlights several factors that organizations should consider to facilitate the broad adoption of visual analytics technologies among IT and business professionals. Originality/value This study is among the first to use the online evaluation reviews to systematically explore the main factors involved in the acceptance and adoption of visual analytics technologies in organizations. Thus, it has potential to provide theoretical foundations for further research in this important and emerging field. The development of an integrative model synthesizing the salient determinants of visual analytics adoption in enterprises should ultimately allow both information systems researchers and practitioners to better understand how and why users form perceptions to accept and engage in the adoption of visual analytics tools and applications.

This study presents a data analytics framework that aims to analyze topics and sentiments associated with COVID-19 vaccine misinformation in social media. A total of 40,359 tweets related to COVID-19 vaccination were collected between January 2021 and March 2021. Misinformation was detected using multiple predictive machine learning models. Latent Dirichlet Allocation (LDA) topic model was used to identify dominant topics in COVID-19 vaccine misinformation. Sentiment orientation of misinformation was analyzed using a lexicon-based approach. An independent-samples t-test was performed to compare the number of replies, retweets, and likes of misinformation with different sentiment orientations. Based on the data sample, the results show that COVID-19 vaccine misinformation included 21 major topics. Across all misinformation topics, the average number of replies, retweets, and likes of tweets with negative sentiment was 2.26, 2.68, and 3.29 times higher, respectively, than those with positive sentiment.

This study presents a systematic approach that integrates the information adoption model (IAM) with topic modeling to analyze the digital voice of users in online open innovation communities (OOICs) and empirically examines the usefulness of UGC with large amounts of redundant information and varying content quality across two dimensions: information quality and information source credibility. A total of 61,227 bug comments were collected from the OOIC of Huawei EMUI and analyzed using binary logistic regression. The results show that information timeliness and completeness have a positive effect on the usefulness of UGC in OOICs; conversely, information semantics have a negative effect on the usefulness of UGC. Prior user experience has no influence on the usefulness of UGC in OOICs, while active user contribution has a positive effect on the usefulness of UGC. The results of this study offer several implications to researchers and practitioners, and thus could serve as a pivotal reference source for further investigation of potential determinants of UGC usefulness in OOICs.

With the huge proliferation of Big Data, combined with the increasing demand for analytics-driven decision-making, the data analytics and visualization (DAV) ecosystem is increasingly becoming a trending practice that many enterprises are adopting to gain actionable insights from corporate data for effective decision-making. Although DAV platforms have tremendous benefits, extant research has paid insufficient attention to the investigation of the critical success factors (CSFs) underpinning their successful implementation in enterprises. In order to bridge this knowledge gap, this study presents an integrative framework synthesizing a set of CSFs for implementing DAV platforms in enterprises. A qualitative research methodology, comprising semi-structured interviews with IT and business analysts, was conducted to collect and analyze the interview data. Analysis of results revealed that the CSFs of DAV implementation exist in various dimensions composed of organizational, technological, process, and people perspectives. This study provides several theoretical and practical implications.

With the proliferation of big data and business analytics practices, data storytelling has gained increasing importance as an effective means for communicating analytical insights to the target audience to support decision-making and improve business performance. However, there is a limited empirical understanding of the relationship between data storytelling competency, decision-making quality, and business performance. Drawing on the resource-based view (RBV), this study develops and validates the concept of data storytelling competency as a multidimensional construct consisting of data quality, story quality, storytelling tool quality, storyteller skills, and storyteller domain knowledge. It also develops a mediation model to examine the relationship between data storytelling competency and business performance, and whether this relationship is mediated by decision-making quality. Based on an empirical analysis of data collected from business analytics practitioners, the results of this study reveal that the data storytelling competency is positively linked to business performance, which is partially mediated by decision-making quality. These results provide a theoretical basis for further investigation of possible antecedents and consequences of data storytelling competency. They also offer guidance for practitioners on how to leverage data storytelling capabilities in business analytics practices to improve decision-making and business performance.

The increasing popularity of self-service analytics (SSA) is empowering business users to analyze data and generate actionable insights autonomously. While there are many benefits to SSA tools, there is a scarcity of research on the factors influencing their adoption in business organizations. This article presents an extended technology acceptance model (TAM) that incorporates the task-technology fit (TTF), compatibility, and user empowerment as critical antecedents of users' intention to adopt SSA tools for reporting and analytics tasks. To test the proposed model, data were collected through a questionnaire survey of 211 business users working in different industries in Jordan. The collected data were analysed using structural equation modeling (SEM). The results of this study demonstrate that the task-technology fit, compatibility, and user empowerment are significant predictors of users' perceptions of usefulness and ease of use of SSA tools. Both of perceived usefulness and perceived ease of use have a positive effect on users' intention to adopt SSA tools. Collectively, all these factors account for 51.6 percent of the variance in the behavioral intention. The findings of this study provide several key implications for research and practice, and thus should contribute to the design and adoption of more user-accepted SSA tools and applications.

Visual analytics is increasingly being recognized as a source of competitive advantage. Yet, limited research has examined the factors deriving it organizational adoption. By integrating the technology acceptance model (TAM) with the task-technology fit (TTF) model, this research developed a model for visual analytics adoption in business enterprises. To test the research model, data was collected through a questionnaire survey distributed to 400 business professionals working in a variety of industries in Jordan. Collected data were tested and analyzed using structural equation modeling (SEM) technique. Findings of this research confirmed the applicability of the integrated TAM/TTF model to explain the key factors that affect the adoption of visual analytics systems for work-related tasks. Specifically, the results of this research demonstrated that the task, technology, and user characteristics are fundamental and influential antecedents of TTF, which in turn has a significant positive effect on the perceived usefulness and perceived ease of use of visual analytics systems. Additionally, there are significant positive effects from perceived usefulness and perceived ease of use toward users' intention to adopt visual analytics systems, and a firm relationship between perceived ease of use and perceived usefulness of visual analytics systems. Together all these constructs explain 59.9% of the variance in user's intention to adopt visual analytics systems at the workplace. Findings of this research provide several important implications for research and practice, and thus should help in the design and development of more user-accepted visual analytics systems and applications.

This study aims to examine and compare the mechanisms through which social learning processes influence the knowledge contribution behavior of lurkers and contributors in open innovation communities (OICs). Based on social learning theory and stimulus–organism–response (SOR) framework, this study developed a model of knowledge contribution formation mechanism from environmental stimuli (observational learning, reinforcement learning), organism cognition (self-efficacy, outcome expectancy) to behavioral response (initial contribution, continuous contribution). The model was tested using structural equation modeling based on a dataset collected through a questionnaire from an OIC of business intelligence and analytics software. The empirical results showed that, at the initial participation stage, observational learning had a significant effect on the organism’s cognition of the lurkers, and indirectly influenced the initial knowledge contribution behavior through self-efficacy and outcome expectancy. At the continuous participation stage, observational learning had a significantly lower impact on the organism’s cognition of contributors and only indirectly influenced continuous knowledge contribution behavior through outcome expectancy. In contrast, reinforcement learning influenced the organism’s cognition of contributors and partially influenced their continuous knowledge contribution behavior through the mediating effects of self-efficacy and outcome expectancy. However, self-efficacy had a more pronounced effect on contributors’ continuous knowledge contribution behavior than outcome expectancy. These findings provide practical guidance for the management of OICs to reduce knowledge contributor attrition and induce lurkers to evolve into knowledge contributors for sustainable community development.