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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.

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.

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.

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.

This study highlights several factors that organizations should consider to facilitate the broad adoption of visual analytics technologies among IT and business professionals.

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.

The benefits of data analytics in the internal audit function (IAF) are clear; less is known about IAF adoption of analytics. The purpose of this study is to examine the factors driving IAF adoption of analytics.

The Common Body of Knowledge of Internal Auditing Database (IIA, 2015) provides auditor responses on key variables of analysis.

The results of this study indicate the most critical adoption factor is data-specific IT knowledge in the IAF. Critical thinking skills and business knowledge of chief audit executive (CAEs) also contribute to adoption. IAFs with fraud risk detection responsibly are more likely to adopt. IAFs in technologically advanced cultures are more likely to adopt analytics.

The results of this study document the critical factors driving adoption of audit analytics, benefitting both industry and research.

Despite a growing interest in business analytics (BA) from the business and academic communities, it is still unknown what truly motivates and hinders the adoption of BA. To have a clear picture of what will lead to the successful implementation of BA, this paper identifies contextual variables (e.g. user characteristics, organizational readiness and technology infrastructure/expertise) that significantly influence the BA adoption decision.

This paper conducted a series of classification, discriminant and logistics regressions analyses to analyze the differences in mail survey responses between adopters and nonadopters of BA and then determine what either motivate or inhibit the BA adoption.

Through a series of hypothesis testing, we discovered that large firms with a greater number of information technology (IT) staff and budget tended to adopt BA more than their smaller counterparts. Also, we found that BA skeptics, who did not fully recognize BA benefit potentials, were more concerned about BA implementation costs and experienced the greater organization resistance to BA adoption than the others did. Therefore, they were less likely to adopt BA.

In the era of knowledge-based economy, the firm's ability to derive actionable insights from big data can be a game changer. Such ability can be developed and nurtured by utilizing BA which is designed to help business executives and policymakers make well-thought and informed decisions. This paper is one of the first attempts to develop practical guidelines for the successful implementation of BA based on the exploratory study of BA practices among the Korean firms.

The analysis of data collected from user interactions with educational and information technology has attracted much attention as a promising approach to advancing our understanding of the learning process. This promise motivated the emergence of the field of learning analytics and supported the education sector in moving toward data-informed strategic decision making. Yet, progress to date in embedding such data-informed processes has been limited. The purpose of this paper is to address a commonly posed question asked by educators, managers, administrators and researchers seeking to implement learning analytics – how do we start institutional adoption of learning analytics?

A narrative review is performed to synthesize the existing literature on learning analytics adoption in higher education. The synthesis is based on the established models for the adoption of business analytics and finding two projects performed in Australia and Europe to develop and evaluate approaches to adoption of learning analytics in higher education.

The paper first defines learning analytics and touches on lessons learned from some well-known case studies. The paper then reviews the current state of institutional adoption of learning analytics by examining evidence produced in several studies conducted worldwide. The paper next outlines an approach to learning analytics adoption that could aid system-wide institutional transformation. The approach also highlights critical challenges that require close attention in order for learning analytics to make a long-term impact on research and practice of learning and teaching.

The paper proposed approach that can be used by senior leaders, practitioners and researchers interested in adoption of learning analytics in higher education. The proposed approach highlights the importance of the socio-technical nature of learning analytics and complexities pertinent to innovation adoption in higher education institutions.

This study contributes to the supply chain management (SCM) literature differently. It offers insightful information about the use and adoption of technologies for small and medium-sized enterprises (SMEs) in developing countries. Some challenges regarding the predictive supply chain business analytics (SCBA) tools and their prediction remain unexplored and require addressing and examination in developing economies. Therefore, this study examines the substantial roles of relative advantage (RA) and compatibility (Comp.) in using technology in predictive SCBA adoption among SMEs in developing countries.

This paper performed a quantitative survey-based study to analyze the substantial role of RA and Comp. with the aim of using predictive SCBA adoption. To this end, the author conducted an online survey through which 262 SMEs from developing countries (i.e. Jordan, Kingdom of Saudi Arabia (KSA), the United Arab Emirates (UAE), Egypt, Turkey and Qatar) only responded by email.

The partial least squares structural equation modeling (PLS-SEM) clearly shows a significant relationship between RA and predictive SCBA adoption. Still, Comp. does not significantly affect the use of predictive SCBA adoption.

Such findings of this study can provide insightful implications for stakeholders and policymakers regarding the importance of using predictive SCBA adoption in SMEs in developing countries.

This paper aims at identifying the key factors influencing the adoption intention of data analytics for predictive maintenance (PdM) from the lens of the Technology–Organization–Environment (TOE) framework in the Singapore Process Industries context. The research model aids practitioners and researchers in developing a holistic maintenance strategy for large-scale asset-heavy process industries.

The TOE framework has been used in this study to consider a wide set of TOE factors and develop a research model with the support of literature. A survey is undertaken and the structural equation modelling (SEM) technique is adopted to test the hypotheses of the proposed model.

This research highlights the significant roles of digital infrastructure readiness, security and privacy, top management support, organizational competence, partnership with external consultants and government support in influencing adoption intention of data analytics for PdM. Perceived challenges related to organizational restructuring and process automation are not found significant in influencing the adoption intention.

This paper reports valuable insights on adoption intention of data analytics for PdM with relevant implications for the various stakeholders such as the leaders and senior managers of process manufacturing industry companies, government agencies, technology consultants and service providers.

This research uniquely validates the model for the adoption of data analytics for PdM in the process industries using the TOE framework. It reveals the significant technology, organizational and environmental factors influencing the adoption intention and highlights the relevant insights and implications for stakeholders.

Drawing on information processing view (IPV) theory, the objective of this study is to explore big data analytics (BDA) in pharmaceutical supply chain (PSC) for better business intelligence. Supply chain operations reference (SCOR) model is used to identify and discuss the likely benefits of BDA adoption in five processes: plan, source, make, deliver and return.

Semi-structured interviews with managers in a triad comprising pharmaceutical manufacturers, wholesalers/distributors and public hospital pharmacies were undertaken. NVivo software was used for thematic data analysis.

The findings revealed that BDA capability would be more practical and helpful in planning, delivery and return processes within PSC. Sourcing and making processes are perceived to be less beneficial.

The study informs managers about the strategic role of BDA capabilities in SCOR processes for improved business intelligence.

Adoption of BDA in SCOR processes within PSC is a step towards resolving the challenges of drug shortages, counterfeiting and inventory optimisation through timely decision. Despite its innumerable benefits of BDA, Australian PSC is far behind in BDA investment. The study advances the IPV theory by illustrating and strengthening the fact that data sharing and analytics can generate real-time business intelligence helping in better health care support through BDA-enabled PSC.

While business intelligence and analytic (BIA) systems have been developed by large corporations around the world, micro-, small- and medium-sized enterprises (MSMEs) have recently paid attention and deployed BIA adoption, particularly during the Covid-19 pandemic disruption. This study sheds light on how MSMEs adopt the BIA systems and then proposes a framework for the BIA adoption process in the context of MSMEs.

The multiple case research design and interpretivism approach are employed for expanding the theoretical boundary of the strategic management fields in BIA adoption by MSMEs. In total, 35 semi-structured interviews were conducted with senior managers and owners involved in BIA adoption from 17 participating MSMEs.

The research study identifies three BIA adoption stages with specific technical and managerial features in the path of BIA adoption in each stage, corresponding to the level of BIA maturity of MSMEs. The authors also highlight other factors that directly influence the successful adoption and transformation from each stage to another.

The research study identifies three BIA adoption stages with specific technical and managerial features in the path of BIA adoption at each stage that corresponds to the level of BIA maturity of MSMEs. Besides, this study also extends the current literature on BIA adoption in an organisation during the Covid-19 pandemic by identifying several contextual barriers that directly influence the BIA adoption.

Research findings can help business leaders and owners of MSMEs to determine the BIA maturity of their organisation. Furthermore, the authors’ framework can also be used by consultancies and standard setters to develop detailed BIA adoption strategies and tactics that support MSMEs' digitalisation towards BIA adoption.

The research study’s results highlight that contextual factors, leadership competencies, motivations and barriers for BIA adoption can also be used to help MSMEs' leaders and owners to trigger, advance or eliminate challenges for the adoption of BIA initiatives in MSMEs.

The purpose of this paper is to explore the role of storytelling in data interpretation, decision-making and individual-level adoption of business analytics (BA).

Existing theory is extended by introducing the concept of BA data-driven storytelling and by synthesizing insights from BA, storytelling, behavioral research, linguistics, psychology and neuroscience. Using theory-building methodology, a model with propositions is introduced to demonstrate the relationship between storytelling, data interpretation quality, decision-making quality, intention to use BA and actual BA use.

BA data-driven storytelling is a narrative sensemaking heuristic positively influencing human behavior towards BA use. Organizations deliberately disseminating BA data-driven stories can improve the quality of individual data interpretation and decision-making, resulting in increased individual utilization of BA on a daily basis.

To acquire a deeper understanding of BA data-driven storytelling in behavioral operational research (BOR), future studies should test the theoretical model of this study and focus on exploring the complexity and diversity in individual attitudes toward BA.

This study provides practical guidance for business practitioners who struggle with interpreting vast amounts of complex data, making data-driven decisions and incorporating BA into daily operations.

This cross-disciplinary study develops existing BOR, storytelling and BA literature by showing how a novel BA data-driven storytelling approach can facilitate BA adoption in organizations.

Based on previous literature on big data analytics (BDA) and supply chain (SC) management, the purpose of this paper is to address the factors determining firms’ intention to adopt BDA in their daily operations. Specifically, this study classifies potential factors into four categories: technological, organizational, environmental factors, and SC characteristics.

Drawing on the innovation diffusion theory, a model consisted of direct technological and organizational factors as well as moderators was proposed. Subsequently, survey data was collected from 210 organizations. Then we used SPSS and SmartPLS to analyze the collected data.

The empirical results revealed that perceived benefits and top management support can significantly influence the adoption intention. And environmental factors, such as competitors’ adoption, government policy, and SC connectivity, can significantly moderate the direct relationships between driving factors and the adoption intention.

Given the fact that big data (BD) usage in logistics and SC management is still in the start-up stage, the interpretations toward BDA might vary from different perspectives, thus causing some ambiguity in understanding the meaning and potential BD has. In addition, we collected data through questionnaires completed by IT managers, whose viewpoint may not fully represent that of an organization.

This paper tests the organizational adoption intention of BDA and extends the literature streams of BD and SC management simultaneously.

This research helps top managers assess the benefits of BDA as well as how to adjust their business strategy along the changes of environment and SC maturity.

This paper contributes to the literature of organizational adoption intention of BDA and extends the literature streams of BD and SC management simultaneously.