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1 – 10 of over 2000Salvatore 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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Aya Rizk, Anna Ståhlbröst and Ahmed Elragal
Within digital innovation, there are two significant consequences of the pervasiveness of digital technology: (1) the increasing connectivity is enabling a wider reach and scope…
Abstract
Purpose
Within digital innovation, there are two significant consequences of the pervasiveness of digital technology: (1) the increasing connectivity is enabling a wider reach and scope of innovation structures, such as innovation networks and (2) the unprecedented availability of digital data is creating new opportunities for innovation. Accordingly, there is a growing domain for studying data-driven innovation (DDI), especially in contemporary contexts of innovation networks. The purpose of this study is to explore how DDI processes take form in a specific type of innovation networks, namely federated networks.
Design/methodology/approach
A multiple case study design is applied in this paper. We draw our analysis from data collected over six months from four cases of DDI. The within-analysis is aimed at constructing the DDI process instance in each case, while the crosscase analysis focuses on pattern matching and cross-case synthesis of common and unique characteristics in the constructed processes.
Findings
Evidence from the crosscase analysis suggests that the widely accepted four-phase digital innovation process (including discovery, development, diffusion and post-diffusion) does not account for the explorative nature of data analytics and DDI. We propose an extended process comprising an explicit exploration phase before development, where refinement of the innovation concept and exploring social relationships are essential. Our analysis also suggests two modes of DDI: (1) asynchronous, i.e. data acquired before development and (2) synchronous, i.e. data acquired after (or during) development. We discuss the implications of these modes on the DDI process and the participants in the innovation network.
Originality/value
The paper proposes an extended version of the digital innovation process that is more specifically suited for DDI. We also provide an early explanation to the variation in DDI process complexities by highlighting the different modes of DDI processes. To the best of our knowledge, this is the first empirical investigation of DDI following the process from early stages of discovery till postdiffusion.
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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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Aleksi Harju, Jukka Hallikas, Mika Immonen and Katrina Lintukangas
The purpose of this study is to investigate the role of procurement digitalization in reducing uncertainty in the supply chain (SC) and how it relates to mitigating SC risks and…
Abstract
Purpose
The purpose of this study is to investigate the role of procurement digitalization in reducing uncertainty in the supply chain (SC) and how it relates to mitigating SC risks and improving SC resilience (SCRES).
Design/methodology/approach
Based on survey data collected from the procurement functions of 147 Finnish firms, this study conceptualizes data analytics, information sharing and procurement process digitalization as drivers of procurement digitalization and investigates their impact on SC risk management and SCRES by using partial least squares path modeling.
Findings
Procurement digitalization through data analytics and digital process maturity requires effective information sharing among SC partners and SC risk management to be able to improve SCRES. Procurement digitalization increases information-processing capacities and reduces uncertainty in the SC.
Originality/value
This study contributes to the understanding on the relationships between procurement digitalization and SCRES.
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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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Rose Clancy, Dominic O'Sullivan and Ken Bruton
Data-driven quality management systems, brought about by the implementation of digitisation and digital technologies, is an integral part of improving supply chain management…
Abstract
Purpose
Data-driven quality management systems, brought about by the implementation of digitisation and digital technologies, is an integral part of improving supply chain management performance. The purpose of this study is to determine a methodology to aid the implementation of digital technologies and digitisation of the supply chain to enable data-driven quality management and the reduction of waste from manufacturing processes.
Design/methodology/approach
Methodologies from both the quality management and data science disciplines were implemented together to test their effectiveness in digitalising a manufacturing process to improve supply chain management performance. The hybrid digitisation approach to process improvement (HyDAPI) methodology was developed using findings from the industrial use case.
Findings
Upon assessment of the existing methodologies, Six Sigma and CRISP-DM were found to be the most suitable process improvement and data mining methodologies, respectively. The case study revealed gaps in the implementation of both the Six Sigma and CRISP-DM methodologies in relation to digitisation of the manufacturing process.
Practical implications
Valuable practical learnings borne out of the implementation of these methodologies were used to develop the HyDAPI methodology. This methodology offers a pragmatic step by step approach for industrial practitioners to digitally transform their traditional manufacturing processes to enable data-driven quality management and improved supply chain management performance.
Originality/value
This study proposes the HyDAPI methodology that utilises key elements of the Six Sigma DMAIC and the CRISP-DM methodologies along with additions proposed by the author, to aid with the digitisation of manufacturing processes leading to data-driven quality management of operations within the supply chain.
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Larissa Alves Sincorá, Marcos Paulo Valadares de Oliveira, Hélio Zanquetto-Filho and Marcelo Bronzo Ladeira
The survival and growth of organizations presently depend on managing processes and capabilities to effectively use large volumes of data from different sources to assist…
Abstract
Purpose
The survival and growth of organizations presently depend on managing processes and capabilities to effectively use large volumes of data from different sources to assist organizations’ strategic and operational goals. This paper aims to test the relationship between organizational analytical capabilities (OAC), the performance results in organizational resilience (OR) and the business process management maturity (BPMM).
Design/methodology/approach
Based on a survey of companies operating in the state of Espírito Santo, Brazil, a conceptual model was proposed and tested using the partial least squares algorithm.
Findings
The results confirm the proposed theoretical hypotheses that OAC and BPMM positively impact OR. In addition, the results show that OAC exert a moderating effect on the relationship between BPMM and OR.
Practical implications
It is understood that stimulating the practice of data and information analysis in the organizational routine translates into a relevant managerial behavior, as this attitude leverages the knowledge development and understanding about how to manage unexpected risk events, enabling companies to assess their ability to react to disruptions, even in terms of operational failures.
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Li Chen, Dirk Ifenthaler, Jane Yin-Kim Yau and Wenting Sun
The study aims to identify the status quo of artificial intelligence in entrepreneurship education with a view to identifying potential research gaps, especially in the adoption…
Abstract
Purpose
The study aims to identify the status quo of artificial intelligence in entrepreneurship education with a view to identifying potential research gaps, especially in the adoption of certain intelligent technologies and pedagogical designs applied in this domain.
Design/methodology/approach
A scoping review was conducted using six inclusive and exclusive criteria agreed upon by the author team. The collected studies, which focused on the adoption of AI in entrepreneurship education, were analysed by the team with regards to various aspects including the definition of intelligent technology, research question, educational purpose, research method, sample size, research quality and publication. The results of this analysis were presented in tables and figures.
Findings
Educators introduced big data and algorithms of machine learning in entrepreneurship education. Big data analytics use multimodal data to improve the effectiveness of entrepreneurship education and spot entrepreneurial opportunities. Entrepreneurial analytics analysis entrepreneurial projects with low costs and high effectiveness. Machine learning releases educators’ burdens and improves the accuracy of the assessment. However, AI in entrepreneurship education needs more sophisticated pedagogical designs in diagnosis, prediction, intervention, prevention and recommendation, combined with specific entrepreneurial learning content and entrepreneurial procedure, obeying entrepreneurial pedagogy.
Originality/value
This study holds significant implications as it can shift the focus of entrepreneurs and educators towards the educational potential of artificial intelligence, prompting them to consider the ways in which it can be used effectively. By providing valuable insights, the study can stimulate further research and exploration, potentially opening up new avenues for the application of artificial intelligence in entrepreneurship education.
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Chiehyeon Lim, Min-Jun Kim, Ki-Hun Kim, Kwang-Jae Kim and Paul Maglio
The proliferation of customer-related data provides companies with numerous service opportunities to create customer value. The purpose of this study is to develop a framework to…
Abstract
Purpose
The proliferation of customer-related data provides companies with numerous service opportunities to create customer value. The purpose of this study is to develop a framework to use this data to provide services.
Design/methodology/approach
This study conducted four action research projects on the use of customer-related data for service design with industry and government. Based on these projects, a practical framework was designed, applied, and validated, and was further refined by analyzing relevant service cases and incorporating the service and operations management literature.
Findings
The proposed customer process management (CPM) framework suggests steps a service provider can take when providing information to its customers to improve their processes and create more value-in-use by using data related to their processes. The applicability of this framework is illustrated using real examples from the action research projects and relevant literature.
Originality/value
“Using data to advance service” is a critical and timely research topic in the service literature. This study develops an original, specific framework for a company’s use of customer-related data to advance its services and create customer value. Moreover, the four projects with industry and government are early CPM case studies with real data.
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