Big data and human resource management research: An integrative review and new directions for future research

Zhang, Yucheng; Xu, Shan; Zhang, Long; Yang, Mengxi

doi:10.1016/j.jbusres.2021.04.019

Cited by 52 publications

(25 citation statements)

References 96 publications

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“…An understanding that highlights the responsibilities of sociology in assuming the decisive role that it has set itself since its foundation; a role aimed at understanding, in the Weberian sense, the complexity of relationships and their evolution over time and space. Other frontiers of the social sciences have long started a fruitful discussion on these issues giving life to new fertile research fields such as those connected to the development of computational social science (Gloor, 2007 ; Abbington, 2019 ; Alvarez, 2020 ), the SAM approach (Cheung, Jak, 2016 ), research in the field of human resources (Zhang et al, 2021 ) and Humanities (Chen, Yu, 2018 ); while sociology, especially in Italy, despite the mobilisation of more recent times, struggles to claim its active role in this field, under the weight of the age-old, and never completely resolved, quantity-quality clash.…”

Section: Introductionmentioning

confidence: 99%

Sociology between big data and research frontiers, a challenge for educational policies and skills

Capogna

2022

Qual Quant

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The paper focuses on the challenges posed within sociology and social research by the transformations created by the “data society”. To this end, the paper outlines some of the most significant elements for new frontier research which sociology is forced to confront also in relation to the challenges for educational policies and skills. While leading literature decries the need to promote alphabetising data, otherwise defined as data literacy , the idea proposed here is that it is necessary to work towards understanding data . An understanding that highlights the role sociology has firmly set itself since its foundation, that of studying and explaining the complexity of the relationships that characterize social life in every context and period.

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Section: Introductionmentioning

confidence: 99%

Sociology between big data and research frontiers, a challenge for educational policies and skills

Capogna

2022

Qual Quant

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“…This study builds on Davenport and Harris (2007) arguments that the availability of quality data is the first step in building BDPA. Zhang et al. (2021) argue that BDPA capability is a “ must-have ” capability for both human resource management researchers and managers.…”

Section: Discussionmentioning

confidence: 99%

“…This study builds on arguments that the availability of quality data is the first step in building BDPA. Zhang et al (2021) argue that BDPA capability is a "must-have" capability for both human resource management researchers and managers. The role of big data and predictive analytics development, grievance handling, and employee involvement) under the mediating effect of BDPA has a positive and significant influence on the ER.…”

Section: Contributions To Theorymentioning

confidence: 99%

“…Many human resource management and information management scholars are making significant efforts to shape the practice, but find it extremely difficult (Garcia-Arroyo and Osca, 2021; Rombaut and Guerry, 2020;Hamilton and Sodeman, 2020). Despite significant attempts, the research and practice often remain as two solitudes (Shah et al, 2017;Calvard and Jeske, 2018;Zhang et al, 2021). The existing literature has remained silent on the factors that influence employee retention (ER).…”

Section: Introductionmentioning

confidence: 99%

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The role of big data and predictive analytics in the employee retention: a resource-based view

Singh¹,

Sharma

Foropon

et al. 2022

IJM

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PurposeThe authors have attempted to understand how big data and predictive analytics (BDPA) can help retain employees in the organization.Design/methodology/approachThis study is grounded in the positivism philosophy. The authors have used a resource-based view (RBV) to develop their research hypotheses. The authors tested their research hypotheses using primary data gathered using a single-informant questionnaire. The authors obtained 254 usable responses. The authors performed the assumptions test, performed confirmatory factor analysis (CFA) to test the validity of the proposed theoretical model, and further tested their research hypotheses using hierarchical regression analysis.FindingsThe statistical result suggests that the various human resource management strategies play a significant role in improving retention under the mediating effect of the BDPA.Research limitations/implicationsThe authors have grounded their study in the positivism philosophy. Moreover, the authors tested their hypotheses using single-informant cross-sectional data. Hence, the authors cannot ignore the effects of the common method bias on their research findings. Moreover, the research findings are based on a particular setting. Thus, the authors caution the readers that their findings must be examined in the light of their study limitations.Practical implicationsThe study provided empirical findings based on survey data. Hence, the authors provide numerous guidelines to the practitioners that how the organization can invest in creating BDPA that helps analyze complex data to extract meaningful and relevant information. This information related to employee turnaround may guide top management to reduce the dissatisfaction level among the employees working in high-stress environments resulting from a high degree of uncertainty.Social implicationsThe study helps understand the complex factors that affect the morale of the employee. In the high-paced environment, the employees are often exposed to various negative forces that affect their morale which further affect their productivity. Due to lack of awareness and adequate information, most of the employees and their issues are not dealt with effectively and efficiently by their line managers. Thus, the BDPA can help tackle the most complex problem of society in a significant way.Originality/valueThis study offers some useful contributions to the literature which attempts to unfold the complex nexus between human resource management, information management and strategy. The study contributes to the BDPA literature and how it helps in the retention of employees is one of the areas which still remains elusive to the academic community. Moreover, the managers are still skeptical about the application of BDPA in understanding human-related issues due to a lack of understanding of how and to what extent the employee-related information can be stored and processed. This study’s findings further open the new avenues of research that need to be tackled.

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“…They then proceeded with using a clustering algorithm based on Big Data research paradigm. Based on the discoveries, they proposed a general theoretical framework of Inductive vs. Deductive, where Inductive represents prediction paradigm of data mining and theory building, and Deductive means the explanation paradigm of theory testing [22].…”

Section: Big Datamentioning

confidence: 99%

Big Data—Knowledge Discovery in Production Industry Data Storages—Implementation of Best Practices

2021

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CRISP-DM (cross-industry standard process for data mining) methodology was developed as an intuitive tool for data scientists, to help them with applying Big Data methods in the complex technological environment of Industry 4.0. The review of numerous recent papers and studies uncovered that most of papers focus either on the application of existing methods in case studies, summarizing existing knowledge, or developing new methods for a certain kind of problem. Although all of these types of research are productive and required, we identified a lack of complex best practices for a specific field. Therefore, our goal is to propose best practices for the data analysis in production industry. The foundation of our proposal is based on three main points: the CRISP-DM methodology as the theoretical framework, the literature overview as an expression of current needs and interests in the field of data analysis, and case studies of projects we were directly involved in as a source of real-world experience. The results are presented as lists of the most common problems for selected phases (‘Data Preparation’ and ‘Modelling’), proposal of possible solutions, and diagrams for these phases. These recommendations can help other data scientists avoid certain problems or choose the best way to approach them.

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Big data and human resource management research: An integrative review and new directions for future research

Cited by 52 publications

References 96 publications

Sociology between big data and research frontiers, a challenge for educational policies and skills

Sociology between big data and research frontiers, a challenge for educational policies and skills

The role of big data and predictive analytics in the employee retention: a resource-based view

Big Data—Knowledge Discovery in Production Industry Data Storages—Implementation of Best Practices

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