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This study investigates the relationships between data governance (DG), business analytics capabilities (BAC), and decision-making performance (DMP), with a focus on the mediating effects of big data literacy (BDL) and data analytics competency (DAC).

The study was conducted with 178 experienced managers in public service organizations, using a quantitative approach. Structural equation modeling (SEM) and mediation tests were employed to analyze the data.

The findings reveal that DG and BDL are critical antecedents for developing analytical capabilities. Big data literacy mediates the relationship between DG and BAC, while BAC mediates the relationship between DG and DMP. Furthermore, DAC mediates the relationship between BA capabilities and DMP, explaining most of the effect of BAC on DMP.

These results highlight the importance of DG in fostering BDL and analytical skills for improved decision-making in organizations.

By prioritizing DG practices that promote BDL and analytical capabilities, organizations can leverage business analytics to enhance decision-making.

This paper aims to identify, discuss and provide suggestions for how the phenomenon of business analytics and its elements may influence management accounting and the accountant.

This paper not only identifies a number of studies from academic journals but also reports from professional consultancies and professional accounting bodies concerning future opportunities and implications for management accounting in combination with business analytics.

First, it was found that both academic articles and professional accounting bodies suggest changes for management accounting. Second, it shows that topics such holistic views, fact-based decisions, predictions, visualization and specific hard core skills are the most important for the accountant. Finally, the paper demonstrates that there are different ambition levels for the management accountant, depending on if s(he) wants to be on a descriptive, on a predictive or on a prescriptive level.

Even though the paper is general in nature, the paper discusses a phenomenon that for some reason has been ignored by practitioners and researchers. The true value of the paper therefore lies in making practitioners and researchers more aware of the possibilities of business analytics for management accounting, and through that, making the management accountant a real value driver for the company.

The study objective was to understand what components of organizational culture and capability combined with analytic skillsets are needed to allow organizations to exploit real-time analytic technologies to create supply chain performance improvements.

The authors relied on information processing theory to support a hypothesized model, which is empirically tested using an ordinary least squares equation model, and survey data from a sample of 208 supply chain executives across multiple industries.

The authors found strong support for the concept that real-time analytics will require specialized analytical skills for the managers who use them in their daily work, as well as an analytics-focused organizational culture that promotes data visibility and fact-based decision-making.

Based on the study model, the authors found that a cultural bias to embrace analytics and a strong background in statistical fluency can produce decision-makers who can make sense of a sea of data, and derive significant supply chain performance improvements.

The research was initiated through five workshops and presentations with supply chain executives leading real-time analytics initiatives within their organizations, which were then mapped onto survey items and tested. The authors complement our findings with direct observations from managers that lend unique insights into the field.

In recent times, the field of corporate intelligence has gained substantial prominence, employing advanced data analysis techniques to yield pivotal insights for instantaneous strategic and tactical decision-making. Expanding beyond rudimentary post observation and analysis, social media analytics unfolds a comprehensive exploration of diverse data streams encompassing social media platforms and blogs, thereby facilitating an all-encompassing understanding of the dynamic social customer landscape. During an extensive evaluation of social media presence, various indicators such as popularity, impressions, user engagement, content flow, and brand references undergo meticulous scrutiny. Invaluable intelligence lies within user-generated data stemming from social media platforms, encompassing valuable customer perspectives, feedback, and recommendations that have the potential to revolutionize numerous operational facets, including supply chain management. Despite its intrinsic worth, the actual business value of social media data is frequently overshadowed due to the pervasive abundance of content saturating the digital realm. In response to this concern, the present study introduces a cutting-edge system known as the Enterprise Just-in-time Decision Support System (EJDSS).

Leveraging deep learning techniques and advanced analytics of social media data, the EJDSS aims to propel business operations forward. Specifically tailored to the domain of marketing, the framework delineates a practical methodology for extracting invaluable insights from the vast expanse of social data. This scholarly work offers a comprehensive overview of fundamental principles, pertinent challenges, functional aspects, and significant advancements in the realm of extensive social data analysis. Moreover, it presents compelling real-world scenarios that vividly illustrate the tangible advantages companies stand to gain by incorporating social data analytics into their decision-making processes and capitalizing on emerging investment prospects.

To substantiate the efficacy of the EJDSS, a detailed case study centered around reverse logistics resource recycling is presented, accompanied by experimental findings that underscore the system’s exceptional performance. The study showcases remarkable precision, robustness, F1 score, and variance statistics, attaining impressive figures of 83.62%, 78.44%, 83.67%, and 3.79%, respectively.

This scholarly work offers a comprehensive overview of fundamental principles, pertinent challenges, functional aspects, and significant advancements in the realm of extensive social data analysis. Moreover, it presents compelling real-world scenarios that vividly illustrate the tangible advantages companies stand to gain by incorporating social data analytics into their decision-making processes and capitalizing on emerging investment prospects.

The purpose of this paper is to focus on how the HR function takes advantage of human resource analytics (HRA), including big data (BD), and discuss factors hindering HRA and data utilization. Moreover, the authors discuss the implications of the HRA-induced role transformation of the human resource (HR) function.

This is an explorative case study based on qualitative interviews in nine leading Finnish companies.

The results indicate that both technical and human obstacles, operating with very basic HR processes and traditional information systems and poor data quality, hinder adoption of advanced HRA. This, combined with lacking skills in analytics and business understanding, inability to go beyond reporting, misconceptions related to BD and traditional compliance-oriented HR culture pose further challenges for the data analytics capacity and business partner role of the HR function. Senior executives expect no significant advancements of HRA, while HR professionals saw potential value in BD, although skepticism was not uncommon. The results point toward a need for increased cooperation with data analysts and HR professionals in provision and understanding the HR-related data for business-related decision making. Furthermore, cultural change and organizational redesign may be called for, in addition to overcoming technological obstacles related to BD, for it to have an impact on HR practices. HRA utilization and role transition of the HR function seem closely related and this transformation can be mutually reinforcing.

This study provides and theorizes explorative data on HRA within a group of some of the largest Finnish companies, pointing toward an immature state of the art in BD and HRA utilization and there being a relationship between HRA and the role transition of the HR function in organizations.

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.

Business analytics (BA) has been a breakthrough technological development in recent years. Although scholars have suggested several solutions in using these technologies to facilitate decision-making, there are as of yet limited studies on how analysts, in practice, improve decision makers' understanding of business environments. This study uses sensemaking theory and proposes a model of how data analysts generate analytical outcomes to improve decision makers' understanding of the business environment.

This study employs an interpretive field study with thematic analysis. The authors conducted 32 interviews with data analysts and consultants in Australia and New Zealand. The authors then applied thematic analysis to the collected data.

The thematic analysis discovered four main sensegiving activities, including data integration, trustworthiness analysis, appropriateness analysis and alternative selection. The proposed model demonstrates how these activities support the properties of sensemaking and result in improved decision-making.

This study provides strong empirical evidence for the theory development and practice of sensemaking. It brings together two distinct fields – sensemaking and business analytics – and demonstrates how the approaches advocated by these two fields could improve analytics applications. The findings also propose theoretical implications for information system development (ISD).

This study demonstrates how data analysts could use analytical tools and social mechanisms to improve decision makers' understanding of the business environment.

This study is the first known empirical study to conceptualize the theory of sensemaking in the context of BA and propose a model for analytical sensegiving in organizations.

Driven by the rapidly accelerating pace of technology-enabled developments within human resource management (HRM), human resource (HR) analytics is infiltrating the research and business agenda. As one of the first in its field, the purpose of this paper is to explore what the future of HR analytics might look like.

Using a sample of 20 practitioners of HR analytics, based in 11 large Dutch organizations, the authors investigated what the application, value, structure, and system support of HR analytics might look like in 2025.

The findings suggest that, by 2025, HR analytics will have become an established discipline, will have a proven impact on business outcomes, and will have a strong influence in operational and strategic decision making. Furthermore, the development of HR analytics will be characterized by integration, with data and IT infrastructure integrated across disciplines and even across organizational boundaries. Moreover, the HR analytics function may very well be subsumed in a central analytics function – transcending individual disciplines such as marketing, finance, and HRM.

The results of the research imply that HR analytics, as a separate function, department, or team, may very well cease to exist, even before it reaches maturity.

Empirical research on HR analytics is scarce, and studies on scenarios, values, and structures of expected developments in HR analytics are non-existent. This research intends to contribute to a better understanding of the development of HR analytics, to facilitate business and HR leaders in taking informed decisions on investing in the further development of the HR analytics discipline. Such investments may lead to an enhanced HR analytics capability within organizations, and cultivate the fact-based and data-driven culture that many organizations and leaders try to pursue.

This paper aims to contribute to the literature on human resources (HR) digitalization, specifically on HR analytics, disentangling the concept of analytics applied to HR and explaining the factors that hinder companies from moving to analytics. Therefore, the central research questions addressed in this study are: what does HR analytics encompass? What impedes the adoption of analytics in HR within organizations?

The authors performed a comprehensive literature review on analytics as applied in HR. The authors relied on two of the major multidisciplinary publication databases (i.e. Scopus and WoS). A total of 64 manuscripts from 2010 to 2019 were content analyzed.

The results reveal that there is an ongoing confusion on HR analytics conceptualization. Yet, it seems that there is an emerging consensus on what HR analytics encompasses. The authors have identified 14 different barriers for HR analytics adoption grouped into four categories, namely, data and models, software and technology, people and management. Grounding on them the authors propose a set of 14 key factors to help to successfully adopt HR Analytics in companies.

This paper brings clarity over the conceptualization of HR analytics by offering a comprehensive definition. Additionally, it facilitates business and HR leaders in making informed decisions on adopting and implementing HR analytics. Moreover, it assists HR researchers in positioning their paper more explicitly in current debates and encouraging them to develop some future avenues of research departing from some questions posed.

The purpose of this paper is to analyze the inadequacies of current business education in the tackling of the educational challenges inherent to the advent of a data-driven business world. It presents an analysis of the implications of digitization and more specifically big data analytics (BDA) and data science (DS) on organizations with a special emphasis on decision-making processes and the function of managers. It argues that business schools and other educational institutions have well responded to the need to train future data scientists but have rather disregarded the question of effectively preparing future managers for the new data-driven business era.

The approach involves analysis and review of the literature.

The development of analytics skills shall not pertain to data scientists only, it must rather become an organizational cultural component shared among all employees and more specifically among decision makers: managers. In the data-driven business era, managers turn into manager-scientists who shall possess skills at the crossroad of data management, analytical/modeling techniques and tools, and business. However, the multidisciplinary nature of big data analytics and data science (BDADS) seems to collide with the dominant “functional silo design” that characterizes business schools. The scope and breadth of the radical digitally enabled change, the author are facing, may necessitate a global questioning about the nature and structure of business education.

For the sake of transparency and clarity, academia and the industry must join forces to standardize the meaning of the terms surrounding big data. BDA/DS training programs, courses, and curricula shall be organized in such a way that students shall interact with an array of specialists providing them a broad enough picture of the big data landscape. The multidisciplinary nature of analytics and DS necessitates to revisit pedagogical models by developing experiential learning and implementing a spiral-shaped pedagogical approach. The attention of scholars is needed as there exists an array of unexplored research territories. This investigation will help bridge the gap between education and the industry.

The findings will help practitioners understand the educational challenges triggered by the advent of the data-driven business era. The implications will also help develop effective trainings and pedagogical strategies that are better suited to prepare future professionals for the new data-driven business world.

By demonstrating how the advent of a data-driven business era is impacting the function and role of managers, the paper initiates a debate revolving around the question about how business schools and higher education shall evolve to better tackle the educational challenges associated with BDADS training. Elements of response and recommendations are then provided.