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Journal articles on the topic 'Human Resource Analytics'

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Published: 4 June 2021
Last updated: 2 February 2022

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1

Vineeta and Akanksha Bharti. "Human resource analytics." South Asian Journal of Marketing & Management Research 7, no. 5 (2017): 68. http://dx.doi.org/10.5958/2249-877x.2017.00031.5.

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King, Kylie Goodell. "Data Analytics in Human Resources." Human Resource Development Review 15, no. 4 (November 20, 2016): 487–95. http://dx.doi.org/10.1177/1534484316675818.

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The use of data analytics in the field of human resource development is becoming increasingly common. This rise in popularity is accompanied by skepticism about the ability of human resource professionals to effectively utilize data analytics to reap organizational benefits. This article provides a review of literature both supportive and critical of human resource analytics, argues for the involvement of academia in implementing analytical practices, and uses a case study to illustrate how quantitative tools may positively influence the management and development of human resources.
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Mirski, Peter, Reinhard Bernsteiner, and Dania Radi. "Analytics in Human Resource Management The OpenSKIMR Approach." Procedia Computer Science 122 (2017): 727–34. http://dx.doi.org/10.1016/j.procs.2017.11.430.

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Lunsford, Dale L. "An Output Model for Human Resource Development Analytics." Performance Improvement Quarterly 32, no. 1 (February 10, 2019): 13–35. http://dx.doi.org/10.1002/piq.21284.

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Shrivastava, Shweta, Kritika Nagdev, and Anupama Rajesh. "Redefining HR using people analytics: the case of Google." Human Resource Management International Digest 26, no. 2 (March 12, 2018): 3–6. http://dx.doi.org/10.1108/hrmid-06-2017-0112.

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Purpose This paper aims to highlight the importance of analytics and its advantages in the human resource domain using the example of Google, which has extensively used analytics to improve various aspects of people management. Design/methodology/approach The paper discusses human resource analytics and illustrates how it has been successfully implemented by Google to enable better decision-making. Findings Implementation of analytics in the area of human resources can make people-related decision-making objective, transparent and data-driven and, thus, make the function “quantitative” in nature. Originality/value Although analytics has been widely implemented in functions such as finance and marketing, it is yet to gain a strong foothold in the domain of human resources. This paper discusses how Google, a leading organization in the field of technology, has been able to take impactful people-related decisions with the help of analytics.
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Kels, Peter, and Uwe Vormbusch. "People Analytics im Personalmanagement: Auf dem Weg zur automatisierten Entscheidungskultur?" Industrielle Beziehungen. Zeitschrift für Arbeit, Organisation und Management 27, no. 1-2020 (April 16, 2020): 69–88. http://dx.doi.org/10.3224/indbez.v27i1.04.

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Personalabteilungen gelten traditionell als wenig datenaffin, geraten aber immer stärker unter Druck, die Wirksamkeit betrieblicher Personalmanagementaktivitäten und Mitarbeiterinvestitionen auszuweisen. Vor diesem Hintergrund wächst das Interesse an „Workforce-“ bzw. „People Analytics“, die innerhalb der Managementliteratur sowie von Beratungs- und Softwareeunternehmen als revolutionäres Tool und Wegbereiter eines datengetriebenen und evidenzbasierten Managements propagiert werden. Über eine algorithmenbasierte Analyse der Zusammenhänge zwischen dem Arbeitsverhalten und den Kompetenzen von Mitarbeitenden, der Personalmanagement- und Führungspraxis sowie unternehmensbezogenen Performancegrössen sollen die Qualität der Managemententscheide und die Ausschöpfung von Mitarbeiterpotenzialen massgeblich erhöht werden. Unser Beitrag analysiert auf Basis von zwei explorativen Unternehmensfallstudien die organisationale Einbettung von People Analytics aus einer kritischen Perspektive. Er arbeitet fünf Problemfelder für das Personalmanagement und die managerielle Entscheidungskultur sowie relevante arbeitspolitische Aspekte heraus.
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Durai D., Subhashini, Krishnaveni Rudhramoorthy, and Shulagna Sarkar. "HR metrics and workforce analytics: it is a journey, not a destination." Human Resource Management International Digest 27, no. 1 (January 14, 2019): 4–6. http://dx.doi.org/10.1108/hrmid-08-2018-0167.

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Purpose The main objective in adopting the use of metrics and analytics is to use the expertise of HR professionals in human resource management regarding their understanding of the best way to recruit, select, train, design, motivate, develop, evaluate, and retain employees at an organization to help achieve its goals more effectively. Design/methodology/approach The first and foremost step to generate metrics and analytics strategies in an organization is identification of existing problems faced by them. Owing to the changing environment and global requirement, the labor measurement also changes. The main focus is on the problems faced by the organization and human resources in the working environment. Findings Through the use of human resources measures and workforce analytics, decision-makers will gain the ability to more effectively manage and improve human resources programs and processes. This in turn improves the effectiveness of the workforce and organizational performance. Practical implications Metrics and analytics is a better problem-solving measure in organizations, because in any situations, decisions are made after analyzing the tactical choices. Social implications The development of effective human resource metrics and workforce analytics is likely to be seen in the future as a very important source of competitive advantage. Originality/value The use of human resource metrics and workforce analytics improves organizational effectiveness and strategic decision-making of managers that positively impact the organization’s performance as a whole.
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8

Marumo, Okaile R., and Tumisang Angela Mmopelwa. "Use of Data Mining Techniques in Human Resource Management." Zambia ICT Journal 1, no. 1 (December 11, 2017): 41–46. http://dx.doi.org/10.33260/zictjournal.v1i1.22.

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In the past few years, Analytics has rapidly risen in among organizations within the field of human resource management. To the present date, however, Human Resource Analytics has not been subject to a lot of scrutiny from educational researchers. The aim of this paper is so to look at Different Mining Techniques could be implemented in the HR Department to enhance or support their decision making process. This will improve existing practices of HR analytics and will deliver transformational change indeed
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Kapoor, Bhushan, and Yaggeta Kabra. "Current and Future Trends in Human Resources Analytics Adoption." Journal of Cases on Information Technology 16, no. 1 (January 2014): 50–59. http://dx.doi.org/10.4018/jcit.2014010105.

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While many organizations use business intelligence and analytics in business functions including Supply Chain, Finance, Accounting and Marketing, they have taken little advantage of this in the Human Resources (HR) management area. Seeing tremendous opportunities in the use of analytics, businesses are taking big measures, such as creating a culture of making critical decisions validated by data driven approaches and hiring analytics professionals in areas that promises high rates of return. Experts continue to emphasize the importance of analytics for HR to transform itself into a more effective resource for the organization. In this paper the authors study the current and the near future states of analytics in Human Resources area. With information collected from leading job search engines, SimplyHired.com and Indeed.com, the authors have modeled trends in hiring analytics professionals in different functional areas of business. The authors compared the HR analytics trend with trends in hiring analytics professionals in Supply Chain, Finance, Accounting and Marketing functions. The extent to which companies are hiring analytics professionals now should be a good indication of analytics adoptions in the future.
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Khan, Shaji A., and Jintong Tang. "The Paradox of Human Resource Analytics: Being Mindful of Employees." Journal of General Management 42, no. 2 (December 2016): 57–66. http://dx.doi.org/10.1177/030630701704200205.

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Suggested as a strategic necessity, practice of HR analytics along with its potential business benefits for organisations abounds in popular press. However, the issue of how employees perceive the use of predictive analytics pertaining to themselves and the impact such perceptions may have on proximal employee outcomes has received little attention. The current research reports on the results of an exploratory study that attempts to shed light on how employees' attributions of organisations' use of HR analytics relate to their commitment to the organisation. Based on this evidence, the current research provides managerial implications related to potential employee concerns with HR analytics and their ramifications for organisations, and calls for future research to investigate these issues more thoroughly and systematically.
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Reena, Dr R., Muhamed Muslim Ansari K, and Shilpa Santhosh Jayakrishnan. "EMERGING TRENDS IN HUMAN RESOURCE ANALYTICS IN UPCOMING DECADE." International Journal of Engineering Applied Sciences and Technology 04, no. 08 (December 31, 2019): 260–64. http://dx.doi.org/10.33564/ijeast.2019.v04i08.045.

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Kumar Rajbhar, Asim. "A Study on HR Analytics Transforming Human Resource Management." Journal of Investment and Management 6, no. 4 (2017): 92. http://dx.doi.org/10.11648/j.jim.20170604.12.

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Saxena, Mansi, Teena Bagga, and Sangeeta Gupta. "Fearless path for human resource personnel’s through analytics: a study of recent tools and techniques of human resource analytics and its implication." International Journal of Information Technology 13, no. 4 (May 4, 2021): 1649–57. http://dx.doi.org/10.1007/s41870-021-00677-z.

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Gupta, B. N., and Sadique Shaikh. "PEOPLE ANALYTICS: NOVEL APPROACH TO MODERN HUMAN RESOURCE MANAGEMENT PRACTICE." International Journal of Engineering Technologies and Management Research 5, no. 7 (March 21, 2020): 78–83. http://dx.doi.org/10.29121/ijetmr.v5.i7.2018.261.

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This research review papers shows how HR departments need to optimize the way their organizations use human resources, and to be as efficient as possible themselves. They need to make better and faster decisions, to match people better to requirements, and at the same time to reduce costs. As an HR director, you’re probably aware that you could achieve your aims more effectively through better use of data. For example, when a business need arises, you need to be able to see at a glance whether you have the right person available internally or need to look outside. In the latter case, you then typically face the additional challenge of scanning large volumes of applications or CVs/résumés. Fortunately, with today’s technology, it’s possible to automate much of the work of matching people to requirements. You can also bring together structured and unstructured data to learn more about the potential of your own staff, and take advantage of information available in social networks to find out about potential recruits. With recent advances, all this can be achieved without major investment in technology and related skills. Applying advanced analytics effectively to HR challenges is the aim of our new offer, People Analytics.
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Khan, Shaji A., and Jintong Tang. "The paradox of human resource analytics: Being mindful of employees." IEEE Engineering Management Review 45, no. 4 (2017): 90–96. http://dx.doi.org/10.1109/emr.2017.8233300.

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16

Aral, Sinan, Erik Brynjolfsson, and Lynn Wu. "Three-Way Complementarities: Performance Pay, Human Resource Analytics, and Information Technology." Management Science 58, no. 5 (May 2012): 913–31. http://dx.doi.org/10.1287/mnsc.1110.1460.

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Berk, Lauren, Dimitris Bertsimas, Alexander M. Weinstein, and Julia Yan. "Prescriptive analytics for human resource planning in the professional services industry." European Journal of Operational Research 272, no. 2 (January 2019): 636–41. http://dx.doi.org/10.1016/j.ejor.2018.06.035.

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18

Haywood, M. Elizabeth, and Anubha Mishra. "Building a culture of business analytics: a marketing analytics exercise." International Journal of Educational Management 33, no. 1 (January 7, 2019): 86–97. http://dx.doi.org/10.1108/ijem-03-2018-0107.

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Purpose The purpose of this paper is to describe how brief exercises in introductory and advanced marketing courses can help business students achieve a broader understanding of what Big data and data analytics mean in the workplace. These short analytics problems fit into the culture that we are building at our institution to create analytics cases for courses within our business curriculum. Design/methodology/approach A database of 1,500 customer reviews for a fictitious sporting company was created. Two exercises based on text mining and sentiment analysis were developed to be tested in introductory and advanced marketing course. Students were introduced to the basic concepts used in data analysis and the creation of R code for extracting sentiment words was demonstrated. Students then used pivot tables to identify patterns in the given data set. Students in the introductory course completed a short exercise while the students in the advanced class developed a detailed memo. Findings Results suggest that students in the introductory course are significantly more aware of the use of data in the industry as well as methods to deal with Big data after completing the exercise as compared to their knowledge at the beginning of the exercise. Students in the advanced course are able to identify patterns, detect shortcoming and propose strategic plans based on their analysis of the data. Originality/value Proposed exercises in the study are developed with an aim to help business schools develop a culture supportive of analytics. The purpose of these exercises is to make students aware of the importance of Big data and analytics early on in their curriculum and reinforce their exposure in an advanced course.
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Muriithi, Anne Wambui, and Paul Waithaka. "People Analytics and Performance of Deposit-Taking Micro Finance Institutions in Nyeri County, Kenya." International Journal of Current Aspects 3, no. V (October 28, 2019): 186–209. http://dx.doi.org/10.35942/ijcab.v3iv.70.

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People analytics is a data-driven approach to improving people-related decisions for advancing both individual and organizational success. While people have always been critical to the success of organizations, many business leaders still make key decisions about their workforce based on intuition, experience, advice, and guesswork. However, today leaders can improve their people decision-making based on the collection and systematic analysis of data. A closer look at the operations of many deposit taking micro-finance institutions reveals that they all face challenges related to human resources management. These firms invest in human development, only for the human capital to leave for greener pastures within a short period, impacting negatively and heavily on performance, survival and growth. It is therefore imperative that they undertake serious human resource evaluation, and people analytics can be a crucial tool for the success of this process. The aim of the study was thus to evaluate the effect of people analytics on the performance of Deposit Taking Micro Finance Institutions in Nyeri County, Kenya. The specific objectives guiding the study were: to determine the influence of technology adoption on the performance of deposit taking micro-finance institutions, effect of human resource data access on the performance of deposit taking micro-finance institutions, effect of data management capacity on the performance of deposit taking micro-finance institutions, and the effect of stewardship for people analytics on the performance of deposit taking micro-finance institutions in Nyeri County, Kenya. The study adopted the descriptive research design while targeting173 respondents comprising 8 human resource managers and 165 staff in the human resource department of 8 registered deposit taking micro-finance institutions in Nyeri County. Through stratified sampling method, all managers (8) and 30% (50) of the 165 staff comprised the sample size of 58 respondents. The selected respondents were considered key informants in the study area. Data was collected from primary sources using a semi-structured questionnaire. Data was analyzed with the aid of Statistical Package for Social Studies and excel computer software through descriptive (percentages, means, standard deviation), as well as inferential statistical methods (correlation and regression techniques). Tables and graphs were used for data presentation. Results showed that the micro finance institutions had established infrastructure for the application of technology. Descriptive and inferential analysis results indicated that technology adoption, human resource data access, data management and stewardship had a positive relationship with the performance of MFIs. Findings further indicated that out of the four independent variables, only three were significant: human resource data access, data management and stewardship. The study thus concluded that HR data access, data management and stewardship aspects of people analytics had significant effect on the performance of Microfinance Institutions. Technology adoption lowly affected people analytics and performance of micro finance institutions. To enhance data access and management, the study recommended that managers need to invest in new apps that are platforms for people analytics including cloud computing and artificial intelligence. They must also re-evaluate the techniques for human resource anaytics as well as capacity development in people analytics for managers and staff.
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Al-Ayed, Sura I. "The impact of strategic human resource management on organizational resilience: an empirical study on hospitals." Business: Theory and Practice 20 (March 26, 2019): 179–86. http://dx.doi.org/10.3846/btp.2019.17.

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The aim of this study was to explore the impact of strategic human resource management practices (strategic value of human resource practices, human resource analytics and high-performance work practices) on organizational resilience (cognitive, behavioral and contextual dimensions) in private hospitals. The required data for the purpose of this study were collected by a questionnaire developed on the basis of related works. The questionnaire was developed based on exploratory and confirmatory factor analyses. It consisted of 30 items divided into two domains, the first one to measure strategic resource management practices (15 items), and the second one to measure organizational resilience (15 items). The study population was consisted of employees working at private hospitals. The questionnaires were distributed to a random sample of 500 administrative staff working in private hospitals. A total of 449 valid responses were retrieved with a response rate of 89.9%, which is high percent because the researcher visited the hospitals and distributed the questionnaires personally. Using IBM SPSS 24.0 and AMOS 22, the results confirmed that strategic human resource management practices have a positive impact on organizational resilience. In terms of the separate effects of strategic HRM practices, the results showed that strategic value of human resource practices was the most influential variable on organizational resilience, followed by human resource analytics, then high performance work practices The study concluded that the hospital’s ability to be resilient requires an advance planning when formulating the human resources strategy that is supposed to be integrated into the hospital’s strategy.
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Lochab, Anshu, Sunil Kumar, and Himanshi. "Dilemma to decision: Human resource analytics for organisational performance-an empirical analysis." Asian Journal of Multidimensional Research (AJMR) 9, no. 2 (2020): 143. http://dx.doi.org/10.5958/2278-4853.2020.00028.2.

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AHMAD RANA, IMRAN, MUHAMMAD ALI YAB, and CH ABDUL REHMAN. "Quality Analytics Led Human Resource Management and Its Impact on Customer Satisfaction." International Review of Management and Business Research 9, no. 3 (September 30, 2020): 147–56. http://dx.doi.org/10.30543/9-3(2020)-11.

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Necula, Sabina-Cristiana, and Cătălin Strîmbei. "People Analytics of Semantic Web Human Resource Résumés for Sustainable Talent Acquisition." Sustainability 11, no. 13 (June 27, 2019): 3520. http://dx.doi.org/10.3390/su11133520.

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The purpose of this study was to define a data science architecture for talent acquisition. The approach was to propose analytics that derive data. The originality of this paper consists in proposing an architecture to work within the process of obtaining semantically enriched data by using data science and Semantic Web technologies. We applied the proposed architecture and developed a case study-based prototype that uses analytics techniques for résumé data integrated with Linked Data technologies. We conducted a case study to identify skills by applying classification via regression, k-nearest neighbors (k-NN), random forest, naïve Bayes, support vector machine, and decision tree algorithms to résumé data that we previously described with terms from publicly available ontologies. We labeled data from résumés using terms from existing human resource ontologies. The main contribution is the extraction of skills from résumés and the mining of data that was previously described with the Semantic Web.
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Levenson, Alec, and Alexis Fink. "Human capital analytics: too much data and analysis, not enough models and business insights." Journal of Organizational Effectiveness: People and Performance 4, no. 2 (June 5, 2017): 145–56. http://dx.doi.org/10.1108/joepp-03-2017-0029.

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Purpose The purpose of this paper is to address the barriers to the rapid development of effective HR analytics capabilities in organizations. Design/methodology/approach Literature and conceptual review of the current state of HR analytics. Findings “HR analytics” is used to refer to a too-wide array of measurement and analytical approaches, making strategic focus difficult. There is a misconception that doing more measurement of HR activities and human capital will necessarily lead to actionable insights. There is too much focus on incremental improvement of existing HR processes, detracting from diagnosing the problems with business performance. Too much time is spent on mining existing data, to the detriment of model building and testing, including collecting new more appropriate data. Too much energy is consumed with basic tasks of data management. Stakeholders avoid action by nitpicking the details of the data. Practical implications Practitioners who follow the guidance provided should find that their application of HR analytics leads to more relevant and actionable insights. Social implications More effective application of HR analytics should lead to better decision making in organizations and more effective resource allocation. Originality/value A new look at the field of HR analytics that synthesizes the research literature and current practice in organizations.
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Leslie, Esther. "This other atmosphere: against human resources, emoji and devices." Journal of Visual Culture 18, no. 1 (April 2019): 3–29. http://dx.doi.org/10.1177/1470412919825816.

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Frequently humans are invited to engage with modern visual forms: emoji, emoticons, pictograms. Some of these forms are finding their way into the workplace, understood as augmentations to workplace atmospheres. What has been called the ‘quantified workplace’ requires its workers to log their rates of stress, wellbeing and subjective sense of productivity on a scale of 1–5 or by emoji, in a context in which Human Resources (HR) professionals develop a vocabulary of Workforce Analytics, People Analytics, Human Capital Analytics or Talent Analytics, and all this in the context of managing the work environment or its atmosphere. Atmosphere is mood, a compote of emotions. Emotions are a part of a human package characterized as ‘the quantified self’, a self intertwined with – subject to but also compliant with – tracking and archiving. The logical step for managing atmospheres is to track emotions at a granular and large-scale level. Through the concept of the digital crowd, rated and self-rating, as well as emotion-tracking strategies, the human resource (as worker and consumer) engages in a new politics of the crowd, organized around what political philosopher Jodi Dean calls, affirmatively, ‘secondary visuality’, high-circulation communication fusing speech, writing and image as a new form. This is the visuality of communicative, or social media, capitalism. But to the extent that it is captured by HR, is it an exposure less to crowd-sourced democracy, and more a stage in turning the employee into an on-the-shelf item in a digital economy warehouse, assessed by Likert scales? While HR works on new atmospheres of work, what other atmospheres pervade the context of labour, and can these be deployed in the generation of other types of affect, ones that work towards the free association of labour and life?
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Rostamzadeh, Neda, Sheikh S. Abdullah, and Kamran Sedig. "Visual Analytics for Electronic Health Records: A Review." Informatics 8, no. 1 (February 23, 2021): 12. http://dx.doi.org/10.3390/informatics8010012.

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The increasing use of electronic health record (EHR)-based systems has led to the generation of clinical data at an unprecedented rate, which produces an untapped resource for healthcare experts to improve the quality of care. Despite the growing demand for adopting EHRs, the large amount of clinical data has made some analytical and cognitive processes more challenging. The emergence of a type of computational system called visual analytics has the potential to handle information overload challenges in EHRs by integrating analytics techniques with interactive visualizations. In recent years, several EHR-based visual analytics systems have been developed to fulfill healthcare experts’ computational and cognitive demands. In this paper, we conduct a systematic literature review to present the research papers that describe the design of EHR-based visual analytics systems and provide a brief overview of 22 systems that met the selection criteria. We identify and explain the key dimensions of the EHR-based visual analytics design space, including visual analytics tasks, analytics, visualizations, and interactions. We evaluate the systems using the selected dimensions and identify the gaps and areas with little prior work.
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LAL, PRERNA. "Transforming HR in the digital era." Human Resource Management International Digest 23, no. 3 (May 11, 2015): 1–4. http://dx.doi.org/10.1108/hrmid-03-2015-0051.

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Purpose – Shows how workforce analytics can help human resource (HR) management specialists to be more effective in the digital era. Design/methodology/approach – Draws on the author’s experience, plus a review of the literature. Findings – Reveals how analytics have transformed ways of managing a diversified workforce and helped to put HR at the center of organizational decision-making. Practical implications – Claims that the predictive power of analytics can help to make HR more effective. Social implications – Highlights how analytics can help to fit the right people to the right jobs, ensure they receive the right training and the right pay and stay loyal to the organization. Originality/value – Reveals that the power of analytics can be used to make virtually all HR functions more effective.
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Глухенькая and N. Glukhenkaya. "Study of Human Resource Management Sistems: Konvergentsialno-Integration Approach." Management of the Personnel and Intellectual Resources in Russia 3, no. 4 (August 15, 2014): 56–59. http://dx.doi.org/10.12737/5424.

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Study of human resources management systems depend largely on approaches and techniques applied. Integration and convergence approach is complex and thus implies that several theoretic approaches are applied in mix. Research methods depend on the scientific approach selected. Approaches and techniques considered in this paper, can be used in preparing graduation theses upon the specialty of «personnel manager» as well as by professionals in analytics and practical activities. The author proposes classification of methods and techniques applicable to preparing graduate projects. A set of scientific approaches is selected based on theoretic writings of A.Ya. Kibanov, V.M. Mishin and other theorists. Also presented is the classification of techniques based on various aspects of personnel management system of an enterprise. Integration and convergence approach is build on the fundamentals of other theoretic approaches. Personnel management systems analysis is of importance, as it is an important factor of organizational management enhancement.
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Chaurasia, Sushil S., Devendra Kodwani, Hitendra Lachhwani, and Manisha Avadhut Ketkar. "Big data academic and learning analytics." International Journal of Educational Management 32, no. 6 (August 13, 2018): 1099–117. http://dx.doi.org/10.1108/ijem-08-2017-0199.

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Purpose Although big data analytics (BDA) have great benefits for higher education institutions (HEIs), due to lack of sufficient evidence on how BDA investment can pay off, it is tough for HEIs practitioners to realize value from such adoption. The purpose of this paper is to propose a big data academic and learning analytics enabled business value model to explain BDA potential benefits and business value which can be obtained by developing such analytics capabilities in HEIs. Design/methodology/approach The study examined 47 case descriptions from 26 HEIs to investigate the causal association between the BDA current and potential benefits and business value creation path for big data academic and learning analytics success in HEIs. Findings The pressure of compliance with all legal and regulatory requirements and competition had pushed HEIs hard to adopt BDA tools. However, the study found out that application of risk and security and predictive analytics to higher education fields is still in its infancy. Using this theoretical model, the results provide new insights to higher education administrators on ways to create BDA capabilities for HEIs transformation and suggest an empirical foundation that can lead to more thorough analysis of BDA implementation. Originality/value A distinctive theoretical contribution of this study is its conceptualization of understanding business value from BDA in the typical setting of higher education. The study provides HEIs with an all-inclusive understanding of BDA and gives insights on how it helps to transform HEIs. The new perspectives associated with the big data academic and learning analytics enabled business value model will contribute to future research in this area.
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Varadaraj, Dr A., and Dr Belal Mahmoud Al Wadi. "A Study on Contribution of Digital Human Resource Management towards Organizational Performance." INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE AND BUSINESS ADMINISTRATION 7, no. 5 (July 2021): 43–51. http://dx.doi.org/10.18775/ijmsba.1849-5664-5419.2014.75.1004.

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Digital (HRM) Human Resource Management is digital upgrading in the field of Human Resource management. The working process of DHRM will take place through mobile, electronic media, social media through the internet, and also with the help of IT (information technology). All these resources will make HRM more significant in the present situation. Digital HRM is capable of doing Human work by the means of software, through several apps, and with the internet embedded in it. Digital Human Resource will assist organizations through the optimization of Social, Mobile, Analytics, and Cloud (SMAC) technology, towards management and responsibility in helping them to ensure that assumptions and expectations within the organization drive the right behavior. Digitalization in HRM will make it more efficient and relevant in the future. Without digital transformation, HRM will lag far behind the demands of the organization worldwide. This research paper tries to highlight the role of digital HRM in improving the performance of the organization. The data used for this research are secondary. The outcome of the research would be very important for a business organization to implement digital human resource management and also for improving and enhancing organizational performance.
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Pauleen, David J. "Dave Snowden on KM and big data/analytics: interview with David J. Pauleen." Journal of Knowledge Management 21, no. 1 (February 13, 2017): 12–17. http://dx.doi.org/10.1108/jkm-08-2016-0330.

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Purpose Dave Snowden has been an important voice in knowledge management over the years. As the founder and chief scientific officer of Cognitive Edge, a company focused on the development of the theory and practice of social complexity, he offers informative views on the relationship between big data/analytics and KM. Design/methodology/approach A face-to-face interview was held with Dave Snowden in May 2015 in Auckland, New Zealand. Findings According to Snowden, analytics in the form of algorithms are imperfect and can only to a small extent capture the reasoning and analytical capabilities of people. For this reason, while big data/analytics can be useful, they are limited and must be used in conjunction with human knowledge and reasoning. Practical implications Snowden offers his views on big data/analytics and how they can be used effectively in real world situations in combination with human reasoning and input, for example in fields from resource management to individual health care. Originality/value Snowden is an innovative thinker. He combines knowledge and experience from many fields and offers original views and understanding of big data/analytics, knowledge and management.
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Sousa, Maria José, and Ivo Dias. "Business Intelligence for Human Capital Management." International Journal of Business Intelligence Research 11, no. 1 (January 2020): 38–49. http://dx.doi.org/10.4018/ijbir.2020010103.

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This article presents the results of an exploratory study of the use of business intelligence (BI) tools to help to make decisions about human resources management in Portuguese organizations. The purpose of this article is to analyze the effective use of BI tools in integrating reports, analytics, dashboards, and metrics, which impacts on the decision making the process of human resource managers. The methodology approach was quantitative based on the results of a survey to 43 human resource managers and technicians. The data analysis technique was correlation coefficient and regression analysis performed by IBM SPSS software. It was also applied qualitative analysis based on a focus group to identify the impacts of business intelligence on the human resources strategies of Portuguese companies. The findings of this study are that: business intelligence is positively associated with HRM decision-making, and business intelligence will significantly predict HRM decision making. The research also examines the process of the information gathered with BI tools from the human resources information system on the decisions of the human resources managers and that impacts the performance of the organizations. The study also gives indications about the practices and gaps, both in terms of human resources management and in processes related to business intelligence (BI) tools. It points out the different factors that must work together to facilitate effective decision-making. The article is structured as follows: a literature review concerning the use of the business intelligence concept and tools and the link between BI and human resources management, methodology, and the main findings and conclusions.
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van den Heuvel, Sjoerd, and Tanya Bondarouk. "The rise (and fall?) of HR analytics." Journal of Organizational Effectiveness: People and Performance 4, no. 2 (June 5, 2017): 157–78. http://dx.doi.org/10.1108/joepp-03-2017-0022.

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Purpose Driven by the rapidly accelerating pace of technology-enabled developments within human resource management (HRM), human resource (HR) analytics is infiltrating the research and business agenda. As one of the first in its field, the purpose of this paper is to explore what the future of HR analytics might look like. Design/methodology/approach Using a sample of 20 practitioners of HR analytics, based in 11 large Dutch organizations, the authors investigated what the application, value, structure, and system support of HR analytics might look like in 2025. Findings The findings suggest that, by 2025, HR analytics will have become an established discipline, will have a proven impact on business outcomes, and will have a strong influence in operational and strategic decision making. Furthermore, the development of HR analytics will be characterized by integration, with data and IT infrastructure integrated across disciplines and even across organizational boundaries. Moreover, the HR analytics function may very well be subsumed in a central analytics function – transcending individual disciplines such as marketing, finance, and HRM. Practical implications The results of the research imply that HR analytics, as a separate function, department, or team, may very well cease to exist, even before it reaches maturity. Originality/value Empirical research on HR analytics is scarce, and studies on scenarios, values, and structures of expected developments in HR analytics are non-existent. This research intends to contribute to a better understanding of the development of HR analytics, to facilitate business and HR leaders in taking informed decisions on investing in the further development of the HR analytics discipline. Such investments may lead to an enhanced HR analytics capability within organizations, and cultivate the fact-based and data-driven culture that many organizations and leaders try to pursue.
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Vera-Baquero, Alejandro, Ricardo Colomo Palacios, Vladimir Stantchev, and Owen Molloy. "Leveraging big-data for business process analytics." Learning Organization 22, no. 4 (May 11, 2015): 215–28. http://dx.doi.org/10.1108/tlo-05-2014-0023.

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Purpose – This paper aims to present a solution that enables organizations to monitor and analyse the performance of their business processes by means of Big Data technology. Business process improvement can drastically influence in the profit of corporations and helps them to remain viable. However, the use of traditional Business Intelligence systems is not sufficient to meet today ' s business needs. They normally are business domain-specific and have not been sufficiently process-aware to support the needs of process improvement-type activities, especially on large and complex supply chains, where it entails integrating, monitoring and analysing a vast amount of dispersed event logs, with no structure, and produced on a variety of heterogeneous environments. This paper tackles this variability by devising different Big-Data-based approaches that aim to gain visibility into process performance. Design/methodology/approach – Authors present a cloud-based solution that leverages (BD) technology to provide essential insights into business process improvement. The proposed solution is aimed at measuring and improving overall business performance, especially in very large and complex cross-organisational business processes, where this type of visibility is hard to achieve across heterogeneous systems. Findings – Three different (BD) approaches have been undertaken based on Hadoop and HBase. We introduced first, a map-reduce approach that it is suitable for batch processing and presents a very high scalability. Secondly, we have described an alternative solution by integrating the proposed system with Impala. This approach has significant improvements in respect with map reduce as it is focused on performing real-time queries over HBase. Finally, the use of secondary indexes has been also proposed with the aim of enabling immediate access to event instances for correlation in detriment of high duplication storage and synchronization issues. This approach has produced remarkable results in two real functional environments presented in the paper. Originality/value – The value of the contribution relies on the comparison and integration of software packages towards an integrated solution that is aimed to be adopted by industry. Apart from that, in this paper, authors illustrate the deployment of the architecture in two different settings.
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Deepa, D. "Personality Aging Pattern (PAP): An Empirical Study on Personality Traits in Relation with Work Force Age Bands." Shanlax International Journal of Management 7, no. 1 (July 1, 2019): 57–60. http://dx.doi.org/10.34293/management.v7i1.563.

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As human resource interventions are more of subjective nature, Human Resource Analytics gives a metric system and a rational dimension. The author heads to establish a pattern for Personality change among work force as against Aging under three workforce age bands using BiG five Personality Inventory. The finding shows that all the five factors of personality take different patterns along the life span. Human Resource interventions may be designed and executed for the functions viz., hiring, appraisal, development and retention of workforce in any type of organization in the sublime of Personality Aging Pattern (PAP).
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Romanov, Maksim. "Priority technologies for the adoption of digitalised human resources management in hospitality industry." SHS Web of Conferences 110 (2021): 02011. http://dx.doi.org/10.1051/shsconf/202111002011.

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The intensification of digital transformations in human resources management involves the creation of innovative products and service solutions that can improve the management efficiency and develop the information infrastructure. The article identifies and substantiates the priority tools and technologies for the adoption of digitalised human resource management in the hospitality industry and describes the complex of HR metrics made up by the author for the key areas of personnel processes in the hospitality businesses and including the following main metrics: HR automation, HR analytics, HR marketing, smart recruitment, and e-training. The author presents the system of indicators for the evaluation of functional units of the model of digitalised human resources management in the hospitality industry and evaluates the priority tools of digitalised human resources management in terms of the hospitality industry in Moscow, St. Petersburg and the Krasnodar region.
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Yoon, Seung Won. "Explosion of people analytics, machine learning, and human resource technologies: Implications and applications for research." Human Resource Development Quarterly 32, no. 3 (August 15, 2021): 243–50. http://dx.doi.org/10.1002/hrdq.21456.

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Chalutz Ben-Gal, Hila. "An ROI-based review of HR analytics: practical implementation tools." Personnel Review 48, no. 6 (September 2, 2019): 1429–48. http://dx.doi.org/10.1108/pr-11-2017-0362.

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Purpose The purpose of this paper is to provide a return on investment (ROI) based review of human resources (HR) analytics. The objectives of this paper are twofold: first, to offer an integrative analysis of the literature on the topic of HR analytics in order to provide scholars and practitioners a comprehensive yet practical ROI-based view on the topic; second, to provide practical implementation tools in order to assist decision makers concerning questions of whether and in which format to implement HR analytics by highlighting specific directions as to where the expected ROI may be found. Design/methodology/approach This paper is a review paper in which a four-step review and analysis methodology is implemented. Findings Study results indicate that empirical and conceptual studies in HR analytics generate higher ROI compared to technical- and case-based studies. Additionally, study results indicate that workforce planning and recruitment and selection are two HR tasks, which yield the highest ROI. Practical implications The results of this study provide practical information for HR professionals aiming to adopt HR analytics. The ROI-based approach to HR analytics presented in this study provides a robust tool to compare and contrast different dilemma and associated value that can be derived from conducting the various types of HR analytics projects. Originality/value A framework is presented that aggregates the findings and clarifies how various HR analytics tools influence ROI and how these relationships can be explained.
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Greasley, Kay, and Pete Thomas. "HR analytics: The onto‐epistemology and politics of metricised HRM." Human Resource Management Journal 30, no. 4 (February 3, 2020): 494–507. http://dx.doi.org/10.1111/1748-8583.12283.

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Vankevich, Alena. "The new trends in human resource management in the context of the economy digitalization." University Economic Bulletin, no. 43 (November 20, 2019): 7–12. http://dx.doi.org/10.31470/2306-546x-2019-43-7-12.

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The subject of research is the changes of human resources management at the micro level in the conditions of the economy digitalization. The main directions of the transformation of the labor market in the economy digitalization conditions are the following: expansion of labor supply due to participation in economic activity of various socio-demographic groups of the population; increase in the volume of information about the labor market and its openness due to electronic resources; industry changes in the structure of the labor demand; the jobs polarization; the rapid renewal of professions and skills; the reduction of traditional sectors of the economy; the break of the national labor markets borders. It has been substantiated that these transformations change the requirements for the human resources management service in organizations and modify their functions. The main trends of the human resource management changes in the digitalization conditions are highlighted - the development of employment analysis under Big Data analytics; increased attenion to the formation of the HR- brand; changing the organizational role of the human resources department at the micro level; more active using the staff outsourcing; digitalization of human resource management technologies; expansion of interaction HR-departments and educational institutions, anticipating future skills and competencies; increasing the requirements for the HR manager, especially professional HR-specialists. As a result of the study, the directions for the formation of modern human resources departments in the organizations of the Republic of Belarus were determined, taking into account the course taken by the country to digitalization of the economy.
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Jintana, J., A. Sopadang, and S. Ramingwong. "Idea selection of new service for courier business: The opportunity of data analytics." International Journal of Engineering Business Management 13 (January 1, 2021): 184797902110421. http://dx.doi.org/10.1177/18479790211042191.

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E-commerce growth enforces the courier business to focus on developing a new business model decision. This paper aims to explore a suitable new business idea for courier business if the Data Analytics (DA) can be advantageous, using small courier company as a case study. The study investigates Logistics Service Provider (LSP) activities and the Gap Analysis and SWOT analysis were conducted to explore Data Analytics (DA) opportunity. Then, the alternative business models were pre-screened by the requirements of company, i.e. reasonable investment cost and the opportunity in using Data Analytics (DA). Ansoff Matrix is used to classify the alternative of new service idea into two segments; 1) offer development, i.e. Market development, and 2) New business development, i.e. Service development and Diversification. Fuzzy Analytic Hierarchy Process (FAHP) is used to select suitable business idea by weighted summation on five of Data Analytics (DA) accommodation criteria, i.e. company capability; the ability performs of demand; vision, strategy, and desire of executive, investment strategy and customer data opportunity. The business models that are mostly desirable are 1) Suppliers-Consignees recommendation, 2) Fulfillment service model and 3) Sourcing model. These three models were elaborated and discussed in the perspectives of company and customer. Additionally, this research proposes several challenges in Data Analytics (DA) related in logistics activity, key decision criteria and methods of idea selection implemented guidelines for logistics business practice.
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Poba-Nzaou, Placide, Malatsi Galani, and Anicet Tchibozo. "Transforming human resources management in the age of Industry 4.0: a matter of survival for HR professionals." Strategic HR Review 19, no. 6 (August 3, 2020): 273–78. http://dx.doi.org/10.1108/shr-06-2020-0055.

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Purpose This study aims to contribute to the old debate about the need for transformation of human resource (HR) professionals and HR services. It proposes the advent of people analytics as an unprecedented opportunity to support this transformation toward a more strategic positioning. Design/methodology/approach This paper carried out a review of the use or willingness to use analytics by HR professionals. Findings Although HR professionals have been able to transform themselves over the years from a posture largely dominated by the administrative role, to one that includes compliance, the transformation remains insufficient considering the challenges faced by organizations. The advent of the fourth industrial revolution has put people back at the center of organizations’ concerns, but HR seems to be neither equipped nor ready to seize this unprecedented opportunity to play a more strategic role. Originality/value Transforming human resource management to fit Industry 4.0 is not a necessity, but a matter of survival for HR professionals.
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Zhu, Dawei, Zhanyang Xu, Xiaolong Xu, Qingzhan Zhao, Lianyong Qi, and Gautam Srivastava. "Cognitive Analytics of Social Media Services for Edge Resource Pre-Allocation in Industrial Manufacturing." IEEE Transactions on Computational Social Systems 8, no. 2 (April 2021): 500–511. http://dx.doi.org/10.1109/tcss.2021.3052231.

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Dahlbom, Pauli, Noora Siikanen, Pasi Sajasalo, and Marko Jarvenpää. "Big data and HR analytics in the digital era." Baltic Journal of Management 15, no. 1 (December 24, 2019): 120–38. http://dx.doi.org/10.1108/bjm-11-2018-0393.

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Purpose The purpose of this paper is to focus on how the HR function takes advantage of human resource analytics (HRA), including big data (BD), and discuss factors hindering HRA and data utilization. Moreover, the authors discuss the implications of the HRA-induced role transformation of the human resource (HR) function. Design/methodology/approach This is an explorative case study based on qualitative interviews in nine leading Finnish companies. Findings The results indicate that both technical and human obstacles, operating with very basic HR processes and traditional information systems and poor data quality, hinder adoption of advanced HRA. This, combined with lacking skills in analytics and business understanding, inability to go beyond reporting, misconceptions related to BD and traditional compliance-oriented HR culture pose further challenges for the data analytics capacity and business partner role of the HR function. Senior executives expect no significant advancements of HRA, while HR professionals saw potential value in BD, although skepticism was not uncommon. The results point toward a need for increased cooperation with data analysts and HR professionals in provision and understanding the HR-related data for business-related decision making. Furthermore, cultural change and organizational redesign may be called for, in addition to overcoming technological obstacles related to BD, for it to have an impact on HR practices. HRA utilization and role transition of the HR function seem closely related and this transformation can be mutually reinforcing. Originality/value This study provides and theorizes explorative data on HRA within a group of some of the largest Finnish companies, pointing toward an immature state of the art in BD and HRA utilization and there being a relationship between HRA and the role transition of the HR function in organizations.
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Marthandan, Govindan, and Prem Lal Joshi. "Continuous internal auditing: can big data analytics help." International Journal of Accounting, Auditing and Performance Evaluation 16, no. 1 (2020): 25. http://dx.doi.org/10.1504/ijaape.2020.10028479.

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Joshi, Prem Lal, and Govindan Marthandan. "Continuous internal auditing: can big data analytics help." International Journal of Accounting, Auditing and Performance Evaluation 16, no. 1 (2020): 25. http://dx.doi.org/10.1504/ijaape.2020.106766.

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Poonam, Poonam Arora. "Redesigning Human Resource Management System through Cloud Computing: A Strategic Approach." Journal of Technology Management for Growing Economies 10, no. 2 (October 25, 2019): 73–79. http://dx.doi.org/10.15415/jtmge.2019.102006.

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Cloud computing is enabling human resource (HR) management functions to strategically integrate into business operations. Its acting as a game changer for HR amid intense competition to survive and grow. Digital disruption, transformation, agility has set the new benchmarks for organizations to automate and redesign their functions. Cloud Computing offers HR a completely new dimension with cloud centric benefits like reduction in communication gap, increased organizational excellence, effective talent strategies, data driven decision making, and supporting revenue in organizations. Cloud Computing has the right capability to redesign major HR functions with agility to match the changing requirements of business, automating transactional activities, smarter decision making and meeting the business expectations. Use of HR cloud in organization can result into structural benefits beyond technological advancements. Cloud HR gives organizations flexibility of location, prompt information processing, effective use of data analytics and strategic decision making. This paper discusses ways through which cloud computing could be integrated into HR functions. An analysis on applications of cloud computing in human resource areas with their outcomes is also presented in this paper. A brief note on future prospects of cloud computing in HRM is also discussed.
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Daradkeh, Mohammad Kamel. "An Empirical Examination of the Relationship Between Data Storytelling Competency and Business Performance." Journal of Organizational and End User Computing 33, no. 5 (September 2021): 42–73. http://dx.doi.org/10.4018/joeuc.20210901.oa3.

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With the proliferation of big data and business analytics practices, data storytelling has gained increasing importance as an effective means for communicating analytical insights to the target audience to support decision-making and improve business performance. However, there is a limited empirical understanding of the relationship between data storytelling competency, decision-making quality, and business performance. Drawing on the resource-based view (RBV), this study develops and validates the concept of data storytelling competency as a multidimensional construct consisting of data quality, story quality, storytelling tool quality, storyteller skills, and storyteller domain knowledge. It also develops a mediation model to examine the relationship between data storytelling competency and business performance, and whether this relationship is mediated by decision-making quality. Based on an empirical analysis of data collected from business analytics practitioners, the results of this study reveal that the data storytelling competency is positively linked to business performance, which is partially mediated by decision-making quality. These results provide a theoretical basis for further investigation of possible antecedents and consequences of data storytelling competency. They also offer guidance for practitioners on how to leverage data storytelling capabilities in business analytics practices to improve decision-making and business performance.
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DiClaudio, Michael. "People analytics and the rise of HR: how data, analytics and emerging technology can transform human resources (HR) into a profit center." Strategic HR Review 18, no. 2 (April 8, 2019): 42–46. http://dx.doi.org/10.1108/shr-11-2018-0096.

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Purpose Employee and workforce insights are the greatest competitive advantage for organizations dealing with the disruption and uncertainty driving dramatic changes in today’s workplace. Embedded in this is the growing expectation of the human resource (HR) function to understand how workforce analytics informs the business and fuels success. This paper aims to explore how the HR function can achieve this. Design/methodology/approach The evolution of the “Future of HR” and how it is moving from “descriptive and diagnostic” to “prescriptive and predictive.” Findings According to KPMG’s 2019 Future of HR survey: 37 per cent of respondents feel “very confident” about HR’s actual ability to transform and move them forward via key capabilities such as analytics and artificial intelligence (AI). Over the next year or two, 60 per cent say they plan to invest in predictive analytics. Among those who have invested in AI to date, 88 per cent call the investment worthwhile, with analytics listed as a main priority (33 per cent). Despite data’s remarkable ability to deliver news insights and enhance decision-making, 20 per cent of HR believe analytics will be a primary HR initiative for them over the next one to two years, and only 12 per cent cite analytics as a top management concern. Research limitations/implications Taking a page from meeting customer needs, innovative technologies such as AI and the cloud, data analytics can give an organization the potential to gather infinitely greater amounts of information about customers. Practical implications Today’s workforce analytics focuses mostly on what happened and why. For instance, you might have tools for identifying areas of high turnover and diagnosing the reasons. But thanks to advancements in technology and data analytics capabilities, HR is better-positioned to be the predictive engine required for the organization’s success. Social implications There has never been a better time for HR to create greater strategic value, as the potential for meaningful workforce insights and analytics comes within reach. Even advancements in cloud-based systems for human capital management are coming packaged with analytics and visualization capabilities, enabling HR leaders to integrate people data with other data sources, such as customer relationship management, for a full view of the business. Originality/value This paper will be of value to HR leaders and practitioners who wish to use predictive analytics and emerging technology to drive performance improvement and gain the insights about their workforces.
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Berhil, Siham, Habib Benlahmar, and Nasser Labani. "A review paper on artificial intelligence at the service of human resources management." Indonesian Journal of Electrical Engineering and Computer Science 18, no. 1 (April 1, 2020): 32. http://dx.doi.org/10.11591/ijeecs.v18.i1.pp32-40.

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<span>In the last few years, all companies have been interested in the analysis of data related to Human Resources and have focused on human capital, which is considered as the major factor influencing the company’s development and all its activities at all levels of human resource policies. Data analysis (HR analytics) will significantly improve business profitability over the next years.We started with an extensive survey of different human resources problems and risks reported by HR specialists, then a comprehensive review of recent research efforts on computer science techniques proposed to solve these problems and finally focusing on suggested artificial intelligence methods. This review article will be an archive and a reference for computer scientists working on HR by summarizing the IT solutions already made in human resources for the period between 2008 and 2018. It aims to present clearly the issues that HR researchers face and for which computer scientists seek solutions. It summarizes at the same time the recent and different methods, IT approaches and tools already used by highlighting those using artificial intelligence.</span>
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