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1 – 10 of over 3000Tina Peeters, Jaap Paauwe and Karina Van De Voorde
The purpose of this paper is to explore the key ingredients that people analytics teams require to contribute to organizational performance. As the information that is currently…
Abstract
Purpose
The purpose of this paper is to explore the key ingredients that people analytics teams require to contribute to organizational performance. As the information that is currently available is fragmented, it is difficult for organizations to understand what it takes to execute people analytics successfully.
Design/methodology/approach
To identify the key ingredients, a narrative literature review was conducted using both traditional people analytics and broader business intelligence literature. The findings were summarized in the People Analytics Effectiveness Wheel.
Findings
The People Analytics Effectiveness Wheel identifies four categories of ingredients that a people analytics team requires to be effective. These are enabling resources, products, stakeholder management and governance structure. Under each category, multiple sub-themes are discussed, such as data and infrastructure; senior management support; and knowledge, skills, abilities and other characteristics (KSAOs) (enablers).
Practical implications
Many organizations are still trying to set up their people analytics teams, and many others are struggling to improve decision-making by using people analytics. For these companies, this paper provides a comprehensive overview of the current literature and describes what it takes to contribute to organizational performance using people analytics.
Originality/value
This paper is designed to provide organizations and researchers with a comprehensive understanding of what it takes to execute people analytics successfully. By using the People Analytics Effectiveness Wheel as a guideline, scholars are now better equipped to research the processes that are required for the ingredients to be truly effective.
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Mara Soncin and Marta Cannistrà
This study aims to investigate the organisational structure to exploit data analytics in the educational sector. The paper proposes three different organisational configurations…
Abstract
Purpose
This study aims to investigate the organisational structure to exploit data analytics in the educational sector. The paper proposes three different organisational configurations, which describe the connections among educational actors in a national system. The ultimate goal is to provide insights about alternative organisational settings for the adoption of data analytics in education.
Design/methodology/approach
The paper is based on a participant observation approach applied in the Italian educational system. The study is based on four research projects that involved teachers, school principals and governmental organisations over the period 2017–2020.
Findings
As a result, the centralised, the decentralised and the network-based configurations are presented and discussed according to three organisational dimensions of analysis (organisational layers, roles and data management). The network-based configuration suggests the presence of a network educational data scientist that may represent a concrete solution to foster more efficient and effective use of educational data analytics.
Originality/value
The value of this study relies on its systemic approach to educational data analytics from an organisational perspective, which unfolds the roles of schools and central administration. The analysis of the alternative organisational configuration allows moving a step forward towards a structured, effective and efficient system for the use of data in the educational sector.
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Anne Fleur van Veenstra, Francisca Grommé and Somayeh Djafari
Public sector data analytics concerns the process of retrieving data, data analysis, publication of the results as well as re-using the data by government organizations to improve…
Abstract
Purpose
Public sector data analytics concerns the process of retrieving data, data analysis, publication of the results as well as re-using the data by government organizations to improve their operations and enhance public policy. This paper aims to explore the use of public sector data analytics in the Netherlands and the opportunities and challenges of this use.
Design/methodology/approach
This paper finds 74 applications of public sector data analytics, identified by a Web search and consultation with policymakers. The applications are categorized by application type, organization(s) involved and application domain, and illustrative examples are used to elaborate opportunities and challenges.
Findings
Public sector data analytics is most frequently used for inspection and enforcement of social services and for criminal investigation. Even though its usage is often experimental, it raises concerns for scope creep, repeated targeting of the same (group of) individuals, personal data use by third parties and the transparency of governmental processes.
Research limitations/implications
Drawing on desk research, it was not always possible to identify which type of data or which technology was used in the applications that were found. Furthermore, the case studies are illustrative rather than providing an in-depth overview of opportunities and challenges of the use of data analytics in government.
Originality/value
Most studies either perform a literature overview or present a single case study; this paper presents a more comprehensive overview of how a public sector uses data analytics.
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Prashanth Madhala, Hongxiu Li and Nina Helander
The information systems (IS) literature has indicated the importance of data analytics capabilities (DAC) in improving business performance in organizations. The literature has…
Abstract
Purpose
The information systems (IS) literature has indicated the importance of data analytics capabilities (DAC) in improving business performance in organizations. The literature has also highlighted the roles of organizations’ data-related resources in developing their DAC and enhancing their business performance. However, little research has taken resource quality into account when studying DAC for business performance enhancement. Therefore, the purpose of this paper is to understand the impact of resource quality on DAC development for business performance enhancement.
Design/methodology/approach
We studied DAC development using the resource-based view and the IS success model based on empirical data collected via 19 semi-structured interviews.
Findings
Our findings show that data-related resource (including data, data systems, and data services) quality is vital to the development of DAC and the enhancement of organizations’ business performance. The study uncovers the factors that make up each quality dimension, which is required for developing DAC for business performance enhancement.
Originality/value
Using the resource quality view, this study contributes to the literature by exploring the role of data-related resource quality in DAC development and business performance enhancement.
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B.S. Patil and M.R. Suji Raga Priya
The purpose of this study is to target utilizing Human resources (HRs) data analytics that may enhance strategic business, but little study has examined how it affects components…
Abstract
Purpose
The purpose of this study is to target utilizing Human resources (HRs) data analytics that may enhance strategic business, but little study has examined how it affects components. Data analytics, HRM and strategic business require empirical investigations and how to over come HR data analytics implementation issues.
Design/methodology/approach
A semi-systematic methodology for its evaluation allows for a more complete examination of the literature that emerges theoretical framework and a structured survey questionnaire for quantitative data collection from IT sector personnel. SPSS analyses data.
Findings
Future research is essential for organisations to exploit HR data analytics’ performance-enhancing potential. Data analytics should complement human judgment, not replace it. This paper details these transitions, the important contributions to theory and practice and future research.
Research limitations/implications
Data analytics has grown rapidly and might make HRM practices faster, more efficient and data-driven. HR data analytics may improve strategic business. HR data analytics on employee retention, engagement and organisational success is insufficient. HR data analytics may boost performance, but there is limited proof. The authors do not know how HRM data analytics influences firms and employees.
Originality/value
Data analytics offers HRM new opportunities, along with technical and ethical challenges. This study makes a significant contribution to HR data analytics, evidence-based practice and strategic business literature. In addition to estimating turnover risk, identifying engagement factors and planning interventions to increase retention and engagement, HR data analytics can also estimate the risk of employee attrition.
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Salvatore V. Falletta and Wendy L. Combs
The purpose of the paper is to explore the meaning of Human Resources (HR) analytics and introduce the HR analytics cycle as a proactive and systematic process for ethically…
Abstract
Purpose
The purpose of the paper is to explore the meaning of Human Resources (HR) analytics and introduce the HR analytics cycle as a proactive and systematic process for ethically gathering, analyzing, communicating and using evidence-based HR research and analytical insights to help organizations achieve their strategic objectives.
Design/methodology/approach
Conceptual review of the current state and meaning of HR analytics. Using the HR analytics cycle as a framework, the authors describe a seven-step process for building evidence-based and ethical HR analytics capabilities.
Findings
HR analytics is a nascent discipline and there are a multitude of monikers and competing definitions. With few exceptions, these definitions lack emphasis on evidence-based practice (i.e. the use of scientific research findings in adopting HR practices), ethical practice (i.e. ethically gathering and using HR data and insights) and the role of broader HR research and experimentation. More importantly, there are no practical models or frameworks available to help guide HR leaders and practitioners in doing HR analytics work.
Practical implications
The HR analytics cycle encompasses a broader range of HR analytics practices and data sources including HR research and experimentation in the context of social, behavioral and organizational science.
Originality/value
This paper introduces the HR analytics cycle as a practical seven-step approach for making HR analytics work in organizations.
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Rosita Capurro, Raffaele Fiorentino, Stefano Garzella and Alessandro Giudici
The purpose of this paper is to analyze, from a dynamic capabilities perspective, the role of big data analytics in supporting firms' innovation processes.
Abstract
Purpose
The purpose of this paper is to analyze, from a dynamic capabilities perspective, the role of big data analytics in supporting firms' innovation processes.
Design/methodology/approach
Relevant literature is reviewed and critically assessed. An interpretive methodology is used to analyze empirical data from interviews of big data analytics experts at firms within digitally related sectors.
Findings
This study shows how firms leverage big data to gain “richer” and “deeper” data at the inter-sections between the digital and physical worlds. The authors provide evidence for the importance of counterintuitive strategies aimed at developing innovative products, services or solutions with characteristics that may initially diverge, even significantly, from established customer/user needs.
Practical implications
The authors’ findings offer insights to help practitioners manage innovation processes in the physical world while taking investments in big data analytics into account.
Originality/value
The authors provide insights into the evolution of scholarly research on innovation directed toward opportunities to create a competitive advantage by offering new products, services or solutions diverging, even significantly, from established customer demand.
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According to the significant growth of literature and continued adoption of people analytics in practice, it has been promised that people analytics will inform evidence-based…
Abstract
Purpose
According to the significant growth of literature and continued adoption of people analytics in practice, it has been promised that people analytics will inform evidence-based decision-making and improve business outcomes. However, existing people analytics literature remains underdeveloped in understanding whether and how such promises have been realized. Accordingly, this study aims to investigate the current reality of people analytics and uncover the debates and challenges that are emerging as a result of its adoption.
Design/methodology/approach
This study conducts a systematic literature review of peer-reviewed articles focused on people analytics published in the Association of Business School (ABS) ranked journals between 2011 and 2021.
Findings
The review illustrates and critically evaluates several emerging debates and issues faced by people analytics, including inconsistency among the concept and definition of people analytics, people analytics ownership, ethical and privacy concerns of using people analytics, missing evidence of people analytics impact and readiness to perform people analytics.
Practical implications
This review presents a comprehensive research agenda demonstrating the need for collaboration between scholars and practitioners to successfully align the promise and the current reality of people analytics.
Originality/value
This systematic review is distinct from existing reviews in three ways. First, this review synthesizes and critically evaluates the significant growth of peer-reviewed articles focused on people analytics published in ABS ranked journals between 2011 and 2021. Second, the study adopts a thematic analysis and coding process to identify the emerging themes in the existing people analytics literature, ensuring the comprehensiveness of the review. Third, this study focused and expanded upon the debates and issues evolving within the emerging field of people analytics and offers an updated agenda for the future of people analytics research.
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Mikael Öhman, Ala Arvidsson, Patrik Jonsson and Riikka Kaipia
The purpose of this study is to elaborate on how analytics capability develops within the PSM function. This study is an in-depth exploration of how analytics capability develops…
Abstract
Purpose
The purpose of this study is to elaborate on how analytics capability develops within the PSM function. This study is an in-depth exploration of how analytics capability develops within the purchasing and supply management (PSM) function.
Design/methodology/approach
A multiple case study was conducted of the PSM function of six case firms, in which primary data were collected through semi-structured interviews with PSM analytics stakeholders. The data were analyzed based on an analytics capability framework derived from the literature. Cases were chosen based on them having advanced PSM practices and ongoing analytics projects in the PSM area.
Findings
The findings shed light on how the firms develop their analytics capability in the PSM functional area. While we identify several commonalities in this respect, the authors also observe differences in how firms organize for analytics, bringing analytics and PSM decision-makers together. Building on the knowledge-based view of the firm, The authors offer a theoretical explanation of our observations, highlighting the user-driven side of analytics development, which has largely been unrecognized by prior literature. The authors also offer an explanation of the observed dual role that analytics takes in cross-functional initiatives.
Research limitations/implications
The exploratory nature of our study limits the generalizability of our results. Further, our limited number of cases and interviewees indicate that there is still much to explore in the phenomenon of developing analytics capability.
Practical implications
Our findings can help firms gain a better understanding of how they could develop their analytics capability and what issues they need to consider when seeking leveraging data through analytics for PSM decisions.
Originality/value
This paper is, to the best knowledge of the authors, the first empirical study of analytics capability in PSM.
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This research proposes a framework to conceptualise the potential realm of data regarding shipping connectivity for application of data analytics which can be used to generate…
Abstract
Purpose
This research proposes a framework to conceptualise the potential realm of data regarding shipping connectivity for application of data analytics which can be used to generate deeper insights with respect to the state of such linkages and potential areas for practical application.
Design/methodology/approach
The study method involved comprehensive presentation of different perspectives of assessing shipping connectivity and levels of data contained within container shipping services and proposed potential application to analyse profitability, performance, competitiveness, risk and environmental impact.
Findings
Advances in capabilities to handle large volumes of data offer scope for an integrated approach which utilises all available data from various stakeholders in analyses of liner shipping connectivity. Research shows how different types of data contained in container shipping services are related and can be organised for application of data analytics.
Research limitations/implications
Research implications are offered to shipping lines, port managers and operators and policymakers.
Practical implications
This research presented a conceptual framework that captures the range of data involved in container shipping services and how data analytics can be practically applied in an integrated manner.
Originality/value
This paper is the first in literature to discuss in detail the different levels of data that reside within shipping services that constitute liner shipping connectivity for application of data analytics.
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