Navigating organizational change for successful implementation of HR analytics
(English) Context: Numerous studies affirm the positive impact of Human Resources Analytics (HRA) on gaining a competitive edge and enhancing Human Resources (HR) strategic role therefore given the importance of knowing how to implement an HRA function in organizations successfully, this Ph.D. thesi...
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| Tipo de recurso: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | CBUC, CESCA |
| Repositorio: | TDR. Tesis Doctorales en Red |
| OAI Identifier: | oai:www.tdx.cat:10803/694262 |
| Acceso en línea: | http://hdl.handle.net/10803/694262 https://dx.doi.org/10.5821/dissertation-2117-427951 |
| Access Level: | acceso embargado |
| Palabra clave: | Àrees temàtiques de la UPC::Economia i organització d'empreses 65 - Gestió i organització. Administració i direcció d'empreses. Publicitat. Relacions públiques. Mitjans de comunicació de masses |
| Sumario: | (English) Context: Numerous studies affirm the positive impact of Human Resources Analytics (HRA) on gaining a competitive edge and enhancing Human Resources (HR) strategic role therefore given the importance of knowing how to implement an HRA function in organizations successfully, this Ph.D. thesis brings relevant insights of the most critical variables, and the barriers to be faced during this process so that future researchers and practitioners have the enough knowledge and tools to address it with success. Purpose: The Ph.D. thesis is divided into two interrelated purposes: (1) identifying the most crucial factors through which organizations base their HRA implementation and (2) the learning barriers to face during the organizational change that suppose implementing those factors to become a data-driven decision-making organization. Method: For the culmination of both purposes, we have conducted an inductive/explorative study using semi-structured interviews as the qualitative data collection method. In this case, the sample consists of ten managers, seven HRA function leaders, and three from HR departments who had carried out analytics projects. Those ten leaders work for ten different organizations in distinct sectors and across varying levels of analytical maturity. We have based on a couple of frameworks, one of organizational learning and another of HRA to design and create our own to guide the research. Results: Through empirical qualitative research, we obtained the five key HRA factors highlighted in the literature: Data, Technology Support, Culture, People and Project Design. Additionally, we considered the analytical maturity level of our organization's sample and related to this, the results show that Data quality and accessibility are crucial in descriptive and predictive stages. On the contrary, prescriptive organizations seem to be more focused on Technology, to support advanced analytical models. As far as organizational change is concerned, our results show that the intensity of the learning barriers in HRA decreases as the organizations increase their analytical maturity level. Besides, communication problems seem to be in the descriptive organizations, especially with the Data and Technology departments and the management. On the other hand, in predictive organizations, HR and/or HRA departments seem to lack autonomy in accessing the Data, building their teams, and choosing the technology they need to mature the function. Finally, other factors, such as ethics, budget, and trust in the HR function, were shown in the results. Conclusions / Implications: This allows researchers and practitioners to design and implement an HRA function supported by a framework that guarantees success in the process. Originality: This Ph.D. thesis extends existing theory about HRA success factors (SSFF), as well as organizational learning, developing a new model including both disciplines for implementing HRA function in organizations successfully. |
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