Search results
1 – 10 of 788Stefano Di Lauro, Aizhan Tursunbayeva, Gilda Antonelli and Luigi Moschera
This research aims to explore whether or how organizations adopt people analytics (PA), its value and potential socio-technical factors that can enable or hinder PA projects by…
Abstract
Purpose
This research aims to explore whether or how organizations adopt people analytics (PA), its value and potential socio-technical factors that can enable or hinder PA projects by disrupting and reshaping human resource management. We do this by focusing on the Italian context.
Design/methodology/approach
We conduct a scoping review of data collected between 2018 and 2022 via Google Alerts (GA), a content change detection and notification service that is gaining popularity in scholarly research.
Findings
Our findings suggest that the diffusion of PA applications in Italy, especially those of a descriptive nature, is growing. Most of the existing PA applications are positioned in a positive technocratic light, envisioning the value of PA for both employees and organizations. The value for the latter appears to be direct, while the value for employees is realized through organizational initiatives. The findings also suggest that while enablers can vary between PA application types, the barriers, especially technological and environmental, are generic for both descriptive and predictive/prescriptive PA applications.
Originality/value
Theoretically, we propose a framework for analyzing PA applications, their values, enablers and barriers. Methodologically, we present and describe in detail a novel approach, drawing on GA that can be used to study PA in specific contexts. Practically, our study serves as a helpful point of reference for managers planning or implementing PA in Italy, for benchmarking PA in Italy over time and for comparative international studies.
Details
Keywords
Sara Antomarioni, Filippo Emanuele Ciarapica and Maurizio Bevilacqua
The research approach is based on the concept that a failure event is rarely random and is often generated by a chain of previous events connected by a sort of domino effect…
Abstract
Purpose
The research approach is based on the concept that a failure event is rarely random and is often generated by a chain of previous events connected by a sort of domino effect. Thus, the purpose of this study is the optimal selection of the components to predictively maintain on the basis of their failure probability, under budget and time constraints.
Design/methodology/approach
Assets maintenance is a major challenge for any process industry. Thanks to the development of Big Data Analytics techniques and tools, data produced by such systems can be analyzed in order to predict their behavior. Considering the asset as a social system composed of several interacting components, in this work, a framework is developed to identify the relationships between component failures and to avoid them through the predictive replacement of critical ones: such relationships are identified through the Association Rule Mining (ARM), while their interaction is studied through the Social Network Analysis (SNA).
Findings
A case example of a process industry is presented to explain and test the proposed model and to discuss its applicability. The proposed framework provides an approach to expand upon previous work in the areas of prediction of fault events and monitoring strategy of critical components.
Originality/value
The novel combined adoption of ARM and SNA is proposed to identify the hidden interaction among events and to define the nature of such interactions and communities of nodes in order to analyze local and global paths and define the most influential entities.
Details
Keywords
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.
Details