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•This articles explores the challenges of adoption of Human resource analytics.•We propose a comprehensive framework for adoption of HR Analytics for practitioners and ...academicians.•We are introducing framework synthesis as a method for systematic review.•The paper promotes the practice of big data and analytics in Human resource management.
Data analytics has gained importance in human resource management (HRM) for its ability to provide insights based on data-driven decision-making processes. However, integrating an analytics-based approach in HRM is a complex process, and hence, many organizations are unable to adopt HR Analytics (HRA). Using a framework synthesis approach, we first identify the challenges that hinder the practice of HRA and then develop a framework to explain the different factors that impact the adoption of HRA within organizations. This study identifies the key aspects related to the technological, organizational, environmental, data governance, and individual factors that influence the adoption of HRA. In addition, this paper determines 23 sub-dimensions of these five factors as the crucial aspects for successfully implementing and practicing HRA within organizations. We also discuss the implications of the framework for HR leaders, HR Managers, CEOs, IT Managers and consulting practitioners for effective adoption of HRA in organization.
The recent advancement in internet 2.0 creates a scope to connect people worldwide using society 2.0 and web 2.0 technologies. This new era allows the consumer to directly connect with other ...individuals, business corporations, and the government. People are open to sharing opinions, views, and ideas on any topic in different formats out loud. This creates the opportunity to make the "Big Social Data" handy by implementing machine learning approaches and social data analytics. This study offers an overview of recent works in social media, data science, and machine learning to gain a wide perspective on social media big data analytics. We explain why social media data are significant elements of the improved data-driven decision-making process. We propose and build the "Sunflower Model of Big Data" to define big data and bring it up to date with technology by combining 5 V’s and 10 Bigs. We discover the top ten social data analytics to work in the domain of social media platforms. A comprehensive list of relevant statistical/machine learning methods to implement each of these big data analytics is discussed in this work. "Text Analytics" is the most used analytics in social data analysis to date. We create a taxonomy on social media analytics to meet the need and provide a clear understanding. Tools, techniques, and supporting data type are also discussed in this research work. As a result, researchers will have an easier time deciding which social data analytics would best suit their needs.
•PA has emerged alongside a cluster of related terms describing digital innovations for modelling and optimising organisational human capital.•Trends in terminology reflect evolving priorities, ...methodological and technological innovations, communities of practice and rebranding.•The PA literature is multi-disciplinary, extending beyond HR or management, with the data and computer sciences playing an increasing role.•Gaps in training exist and ethical issues are rarely considered, despite risks to employees and organisations.•PA research is at an early stage and more studies are needed to build the evidence-base.
This mixed-method ‘scoping review’ mapped the emergence of the term People Analytics (PA), the value propositions offered by vendors of PA tools and services and the PA skillsets being sought by professionals. Analysis of academic research and online search traffic since 2002 revealed changes in the relative trajectory of PA and conceptually related terms over the past fifteen years, indicating both the re-branding of similar innovations and a differentiation of priorities and communities of practice. The market in commercial PA tools and services is diverse, offering numerous functional and strategic benefits, although published evidence of these outcomes remains sparse. Companies marketing PA systems and services emphasise benefits to employers more than to personnel. Across the sources examined, including specialised online courses, PA was largely aligned with HRM, however its development reflects the shifting focus of HR departments from supporting functional to strategic organisational requirements. Consideration of ethical issues was largely absent.
Business intelligence and analytics (BI&A) has emerged as an important area of study for both practitioners and researchers, reflecting the magnitude and impact of data-related problems to be solved ...in contemporary business organizations. This introduction to the MIS Quarterly Special Issue on Business Intelligence Research first provides a framework that identifles the evolution, applications, and emerging research areas of BI&A. BI& A 1.0, BI&A 2.0, and BI&A 3.0 are defined and described in terms of their key characteristics and capabilities. Current research in BI&A is analyzed and challenges and opportunities associated with BI&A research and education are identified. We also report a bibliometric study of critical BI&A publications, researchers, and research topics based on more than a decade of related academic and industry publications. Finally, the six articles that comprise this special issue are introduced and characterized in terms of the proposed BI&A research framework.
To date, health care industry has not fully grasped the potential benefits to be gained from big data analytics. While the constantly growing body of academic research on big data analytics is mostly ...technology oriented, a better understanding of the strategic implications of big data is urgently needed. To address this lack, this study examines the historical development, architectural design and component functionalities of big data analytics. From content analysis of 26 big data implementation cases in healthcare, we were able to identify five big data analytics capabilities: analytical capability for patterns of care, unstructured data analytical capability, decision support capability, predictive capability, and traceability. We also mapped the benefits driven by big data analytics in terms of information technology (IT) infrastructure, operational, organizational, managerial and strategic areas. In addition, we recommend five strategies for healthcare organizations that are considering to adopt big data analytics technologies. Our findings will help healthcare organizations understand the big data analytics capabilities and potential benefits and support them seeking to formulate more effective data-driven analytics strategies.
•A big data analytics architecture for healthcare organizations is built.•We identify five big data analytics capabilities from 26 big data cases.•We present several strategies for being successful with big data analytics in healthcare settings.•We provide a comprehensive understanding of the potential benefits of big data analytics.
The process of using analytic data to inform instructional decision-making is acknowledged to be complex; however, details of how it occurs in authentic teaching contexts have not been fully ...unpacked. This study investigated five university instructors’ use of a learning analytics dashboard to inform their teaching. The existing literature was synthesized to create a template for inquiry that guided interviews, and inductive qualitative analysis was used to identify salient emergent themes in how instructors 1) asked questions, 2) interpreted data, 3) took action, and 4) checked impact. Findings showed that instructors did not always come to analytics use with specific questions, but rather with general areas of curiosity. Questions additionally emerged and were refined through interaction with the analytics. Data interpretation involved two distinct activities, often along with affective reactions to data: reading data toidentify noteworthy patterns and explaining their importance in the course using contextual knowledge. Pedagogical responses to the analytics included whole-class scaffolding, targeted scaffolding, and revising course design, as well two new non-action responses: adopting a wait-and-see posture and engaging in deep reflection on pedagogy. Findings were synthesized into a model of instructor analytics use that offers useful categories of activities for future study and support
The amount of data produced and communicated over the Internet is significantly increasing, thereby creating challenges for the organizations that would like to reap the benefits from analyzing this ...massive influx of big data. This is because big data can provide unique insights into, inter alia, market trends, customer buying patterns, and maintenance cycles, as well as into ways of lowering costs and enabling more targeted business decisions. Realizing the importance of big data business analytics (BDBA), we review and classify the literature on the application of BDBA on logistics and supply chain management (LSCM) – that we define as supply chain analytics (SCA), based on the nature of analytics (descriptive, predictive, prescriptive) and the focus of the LSCM (strategy and operations). To assess the extent to which SCA is applied within LSCM, we propose a maturity framework of SCA, based on four capability levels, that is, functional, process-based, collaborative, agile SCA, and sustainable SCA. We highlight the role of SCA in LSCM and denote the use of methodologies and techniques to collect, disseminate, analyze, and use big data driven information. Furthermore, we stress the need for managers to understand BDBA and SCA as strategic assets that should be integrated across business activities to enable integrated enterprise business analytics. Finally, we outline the limitations of our study and future research directions.
As the use of analytics becomes increasingly important in today's business landscape, The Marketing Analytics Practitioner's Guide (MAPG) provides a thorough understanding of marketing management ...concepts and their practical applications, making it a valuable resource for professionals and students alike.The four-volume compendium of MAPG provides an in-depth look at marketing management concepts and their practical applications, equipping readers with the knowledge and skills needed to effectively inform daily marketing decisions and strategy development and implementation. It seamlessly blends the art and science of marketing, reflecting the discipline's evolution in the era of data analytics. Whether you're a seasoned marketer or new to the field, the MAPG is an essential guide for mastering the use of analytics in modern marketing practices.Volume I is focused on Brand and Consumer. Part I of this volume is dedicated to understanding the concepts and methods of brand sensing and brand equity. It delves into the analytic techniques used to track and profile brand image, and explains the key components of brand equity, how to measure it, and what factors drive it. It provides readers with a comprehensive framework for measuring and understanding brand equity and the tools to pursue its growth.Part II of this volume focuses on understanding consumers through qualitative and quantitative research methods, segmentation, customer satisfaction, customer value management, consumer panels, consumer analytics and big data. The volume covers the analytic tools used to extract insights from consumer transactions, which are becoming increasingly important in today's data-driven world. It also covers the use of consumer analytics and big data specifically within consumer markets.
As the use of analytics becomes increasingly important in today's business landscape, The Marketing Analytics Practitioner's Guide (MAPG) provides a thorough understanding of marketing management ...concepts and their practical applications, making it a valuable resource for professionals and students alike.The four-volume compendium of MAPG provides an in-depth look at marketing management concepts and their practical applications, equipping readers with the knowledge and skills needed to effectively inform daily marketing decisions and strategy development and implementation. It seamlessly blends the art and science of marketing, reflecting the discipline's evolution in the era of data analytics. Whether you're a seasoned marketer or new to the field, the MAPG is an essential guide for mastering the use of analytics in modern marketing practices.Volume II, Parts III to V, is dedicated to Product, Advertising, Packaging, Biometrics, Price and Promotion. Part III focuses on the product development process, covering the analytic methods and procedures used to screen ideas, concepts, and products during development, launch, and post-launch.Part IV delves into advertising, packaging, and biometrics. The fundamentals, concepts, and core themes of advertising are covered in a chapter that explains how advertising works and what makes it effective and impactful. The chapter on Advertising Analytics focuses on audience engagement, both behavioural and attitudinal, and the analytic techniques and research processes used to test and track advertising.The chapter on packaging is devoted to the analytics and research techniques employed throughout the stages of packaging development and the chapter on biometrics covers biometric techniques and the relevant technologies, devices, metrics, and applications of these techniques that are useful to practitioners.Finally, Part V deals with price and promotion, covering a variety of pricing research methods and techniques for promotions evaluation. This will help the reader to gain an understanding of the importance and application of pricing and promotions in marketing strategy.
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