To see the other types of publications on this topic, follow the link: Human Resources Analytics.
Journal articles on the topic 'Human Resources Analytics'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Human Resources Analytics.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
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.
Full textAbstract:
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.
2
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.
Full textAbstract:
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.
3
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.
Full textAbstract:
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.
4
Nocker, Manuela, and Vania Sena. "Big Data and Human Resources Management: The Rise of Talent Analytics." Social Sciences 8, no. 10 (September 29, 2019): 273. http://dx.doi.org/10.3390/socsci8100273.
Full textAbstract:
The purpose of this paper is to discuss the opportunities talent analytics offers HR practitioners. As the availability of methodologies for the analysis of large volumes of data has substantially improved over the last ten years, talent analytics has started to be used by organizations to manage their workforce. This paper discusses the benefits and costs associated with the use of talent analytics within an organization as well as to highlight the differences between talent analytics and other sub-fields of business analytics. It will discuss a number of case studies on how talent analytics can improve organizational decision-making. From the case studies, we will identify key channels through which the adoption of talent analytics can improve the performance of the HR function and eventually of the whole organization. While discussing the opportunities that talent analytics offer organizations, this paper highlights the costs (in terms of data governance and ethics) that the widespread use of talent analytics can generate. Finally, it highlights the importance of trust in supporting the successful implementation of talent analytics projects.
5
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.
Full textAbstract:
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.
6
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.
Full textAbstract:
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?
7
Monte Santo Andrade, Sergio Henrique. "Data Analytics to Increase Performance in the Human Resources Area." Journal of Autonomous Intelligence 2, no. 4 (March 31, 2020): 15. http://dx.doi.org/10.32629/jai.v2i4.80.
Full textAbstract:
In a digital era, traditional areas like Human Resources have to adapt themselves to stay alive and competitive. The processes have been drasticallychanging from paper and talks into systems and workflows. Data is now morethan ever in the spotlight and have become an essential asset to ensure delivery, performance, quality and predictability. But first, data has to be organized, combined, verified, treated and transformed to become meaningful information, not forgetting automatized to be delivered in time and supporting decision making in a daily basis. Business Intelligence (BI) is the tool capable to do it and we are the minds to pull it off.
8
King, Kylie Goodell. "Data analytics in human resources: A case study and critical review." IEEE Engineering Management Review 45, no. 4 (2017): 97–102. http://dx.doi.org/10.1109/emr.2017.8233301.
Full text
9
Calvard, Thomas Stephen, and Debora Jeske. "Cross-Functional Reorganization: Human Resources, Information Technology, and Big Data Analytics (WITHDRAWN)." Academy of Management Proceedings 2017, no. 1 (August 2017): 12819. http://dx.doi.org/10.5465/ambpp.2017.12819abstract.
Full text
10
Pape, Tom. "Prioritising data items for business analytics: Framework and application to human resources." European Journal of Operational Research 252, no. 2 (July 2016): 687–98. http://dx.doi.org/10.1016/j.ejor.2016.01.052.
Full text
11
Chalutz Ben-Gal, Hila, Dan Avrahami, Dana Pessach, Gonen Singer, and Irad Ben Gal. "A Human Resources Analytics Examination of Turnover: Implications for Theory and Practice." Academy of Management Proceedings 2021, no. 1 (August 2021): 12152. http://dx.doi.org/10.5465/ambpp.2021.158.
Full text
12
Wang, Lijun, and Yuan Cheng. "Look Back and Leap Forward: A Review of Human Resources Analytics Literature." Academy of Management Proceedings 2021, no. 1 (August 2021): 13967. http://dx.doi.org/10.5465/ambpp.2021.13967abstract.
Full text
13
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.
Full textAbstract:
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.
14
Ghosh, Arunava, and Tuhin Sengupta. "J. Fitz-Enz and I. I. John Mattox, Predictive analytics for human resources." Human Resource Development International 20, no. 2 (November 29, 2016): 180–83. http://dx.doi.org/10.1080/13678868.2016.1258914.
Full text
15
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.
Full textAbstract:
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.
16
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.
Full textAbstract:
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.
17
Patre, Smruti. "Six Thinking Hats Approach to HR Analytics." South Asian Journal of Human Resources Management 3, no. 2 (December 2016): 191–99. http://dx.doi.org/10.1177/2322093716678316.
Full textAbstract:
Historically, the human resources (HR) function and functionaries have struggled to establish their credibility and value in the eyes of key stakeholders, including the top management and line managers. Arguably, one of the reasons for this struggle for acceptance is their inability to present data-driven, business-oriented proposals, requiring urgent attention to elevate the analytical capability of the HR function. This paper focuses on the role and significance of HR analytics to organizations through a Six Thinking Hats approach. It presents a holistic understanding of HR analytics encompassing the concept, benefits, limitations, and likely solutions.
18
Jabir, Brahim, Noureddine Falih, and Khalid Rahmani. "HR analytics a roadmap for decision making: case study." Indonesian Journal of Electrical Engineering and Computer Science 15, no. 2 (August 1, 2019): 979. http://dx.doi.org/10.11591/ijeecs.v15.i2.pp979-990.
Full textAbstract:
<p>In the socio-economic world, the human resources are in the most top phase of the enterprise evolution. This evolution began when the arithmetic, statistics are applicable over a vast of opportunities and used to identify problems and support decision. However, analytics has been emerged to provide predictions and understand the people performance based on available data.</p>In light of this vast amount of information, human resources services need to deploy a predictive management model and operating system of analytics that can be an efficient and an instead solution that can respond to the gaps of the traditional existing ones and facilitate the decision making. In this paper, we present a literature review of this HR analytics concept and a case study concerning the impact of interventions using an analytics solution.<p> </p>
19
Potluri, Rajasekhara Mouly, and Narasimha Rao Vajjhala. "Risks in Adoption and Implementation of Big Data Analytics." International Journal of Risk and Contingency Management 10, no. 3 (July 2021): 1–11. http://dx.doi.org/10.4018/ijrcm.2021070101.
Full textAbstract:
The research investigates the risks in adopting and implementing big data analytics in Indian micro, small, and medium enterprises (MSMEs). The researchers outlined a survey questionnaire for accumulating reactions from managers working in 50 Indian micro, small, and medium-sized enterprises on behalf of five vital commercial sectors. The application and use of big data analytics offer several significant problems for small companies as an investment in hardware and software resources are substantial. This study's findings provided experimental evidence on five critical challenges that Indian MSMEs face while adopting and implementing big data analytics: lack of human resources, data privacy and security, shortage of technological resources, deficiency of awareness, and financial implications. This study's findings emphasize the challenges that MSMEs face while leveraging big data analytics benefits. The research outcome will promote MSMEs' organizational leadership in planning and developing short-term and long-term information systems strategies.
20
Gandhe, Arnav. "Human Animal Conflict in State of Maharashtra using Data Analytics." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 14, 2021): 808–20. http://dx.doi.org/10.22214/ijraset.2021.35172.
Full textAbstract:
Maharashtra, a land rich in its biodiversity, well known for its wildlife. Maharashtra stands 3rd in terms of Human-Animal Conflict behind Uttarakhand and Karnataka. Human–Animal conflict refers to the interaction between wildlife and people leading to a resultant negative impact on people, their resources, wild animals and their habitat. The paper discusses a 2year study(1st Jan-2019 to 1st Jan 2021) carried out on human-animal interactions in Maharashtra -focusing on various factors involved under Human-animal conflict, and its current situations in the state. The Paper further focuses on use of advanced computer technologies, and techniques like Data Analytics & Statistical Analysis to study the actual current situation of Human-Animal Conflict in Maharashtra.
21
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.
Full textAbstract:
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.
22
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.
Full textAbstract:
<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>
23
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.
Full textAbstract:
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.
24
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.
Full textAbstract:
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.
25
Vasilieva, E. V. "ANALYTICS AND HUMAN-CENTERED DESIGN IN THE MANAGEMENT OF INTELLECTUAL RESOURCES OF THE CIVIL SERVICE." social & labor researches 38, no. 1 (2020): 98–113. http://dx.doi.org/10.34022/2658-3712-2020-38-1-98-113.
Full text
26
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.
Full textAbstract:
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.
27
Sohrabi, Babak, Iman Raeesi Vanani, and Ehsan Abedin. "Human Resources Management and Information Systems Trend Analysis Using Text Clustering." International Journal of Human Capital and Information Technology Professionals 9, no. 3 (July 2018): 1–24. http://dx.doi.org/10.4018/ijhcitp.2018070101.
Full textAbstract:
Human resources management has seen a significant change by the emergence of information systems from a traditional or popularly called personnel management to the modern one. The purpose of this article is to study the trends of information systems in the field of human resources management in combination with information systems through text mining approaches on a broad exploration of internationally published papers. Among text analytics methods for extracting trends, text clustering has been applied to the dataset of highly-ranked information systems journals. The data set was obtained from Scopus database for the period of 2013 to 2017. Afterwards, text clustering algorithms were applied and validated on the titles, abstracts and keywords. The results present practical and intuitive information which can help practitioners and scholars to grasp a useful overview and provides them with the opportunity to focus on trends in information systems in the field human resources management.
28
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.
Full textAbstract:
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.
29
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.
Full textAbstract:
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.
30
Chornous, Galyna O., and Viktoriya L. Gura. "Integration of Information Systems for Predictive Workforce Analytics: Models, Synergy, Security of Entrepreneurship." European Journal of Sustainable Development 9, no. 1 (February 1, 2020): 83. http://dx.doi.org/10.14207/ejsd.2020.v9n1p83.
Full textAbstract:
The era of information economy leads to redesigning not only business models of organizations but also to rethinking the human resources paradigm to harness the power of state-of-the-art technology for Human Capital Management (HCM) optimization. Predictive analytics and computational intelligence will bring transformative change to HCM. This paper deals with issues of HCM optimization based on the models of predictive workforce analytics (WFA) and Business Intelligence (BI). The main trends in the implementation of predictive WFA in the world and in Ukraine, as well as the need to protect business data for security of entrepreneurship and the tasks of predictive analysis in the context of proactive HCM were examined. Some models of effective integration of information systems for predictive WFA were proposed, their advantages and disadvantages were analyzed. These models combine ERP, HCM, BI, Predictive Analytics, and security systems. As an example, integration of HCM system, the analytics platform (IBM SPSS Modeler), BI system (IBM Planning Analytics), and security platform (IBM QRadar Security Intelligence Platform) for predicting the employee attrition was shown. This integration provides a cycle ‘prediction – planning – performance review – causal analysis’ to support protected data-driven decision making in proactive HCM The results of the research support ensuring the effective management of all spectrum of risks associated with the collection, storage and use of data.
Keywords: Workforce Analytics (WFA), Human Capital Management (HCM), Predictive Analytics, Proactive Management, BI, Information Systems (IS), Integration, Security of Entrepreneurship
31
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.
Full textAbstract:
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.
32
Vats, Satvik, and B. B. Sagar. "An independent time optimized hybrid infrastructure for big data analytics." Modern Physics Letters B 34, no. 28 (July 21, 2020): 2050311. http://dx.doi.org/10.1142/s021798492050311x.
Full textAbstract:
In Big data domain, platform dependency can alter the behavior of the business. It is because of the different kinds (Structured, Semi-structured and Unstructured) and characteristics of the data. By the traditional infrastructure, different kinds of data cannot be processed simultaneously due to their platform dependency for a particular task. Therefore, the responsibility of selecting suitable tools lies with the user. The variety of data generated by different sources requires the selection of suitable tools without human intervention. Further, these tools also face the limitation of recourses to deal with a large volume of data. This limitation of resources affects the performance of the tools in terms of execution time. Therefore, in this work, we proposed a model in which different data analytics tools share a common infrastructure to provide data independence and resource sharing environment, i.e. the proposed model shares common (Hybrid) Hadoop Distributed File System (HDFS) between three Name-Node (Master Node), three Data-Node and one Client-node, which works under the DeMilitarized zone (DMZ). To realize this model, we have implemented Mahout, R-Hadoop and Splunk sharing a common HDFS. Further using our model, we run [Formula: see text]-means clustering, Naïve Bayes and recommender algorithms on three different datasets, movie rating, newsgroup, and Spam SMS dataset, representing structured, semi-structured and unstructured, respectively. Our model selected the appropriate tool, e.g. Mahout to run on the newsgroup dataset as other tools cannot run on this data. This shows that our model provides data independence. Further results of our proposed model are compared with the legacy (individual) model in terms of execution time and scalability. The improved performance of the proposed model establishes the hypothesis that our model overcomes the limitation of the resources of the legacy model.
33
Fernandez, Julie. "The ball of wax we call HR analytics." Strategic HR Review 18, no. 1 (February 11, 2019): 21–25. http://dx.doi.org/10.1108/shr-09-2018-0077.
Full textAbstract:
Purpose The debate surrounding automating analytics processes continues as technology becomes more prominent and advanced in the workplace. Specifically, when it comes to HR analytics, it is important to recognize that human judgment as it is used in recruiting today is flawed. One tool that can provide further analysis and measurement beyond performance indicators and predictors is machine learning. Through automation, HR professionals may someday be able to compare characteristics, apply regression analysis to identify the influence of a characteristic and make adjustments based on new hires, retention and promotion results. Design/methodology/approach With more and more companies using artificial intelligence, it is difficult to see how it will revolutionize the HR process. As humans already have biases, will they transfer over to these artificial intelligence machines? Human judgment is already flawed in the recruiting process, so it is crucial to take a look into how it plays a role when AI is becoming built into the process as well. Findings Advancements in automation and HR technology are not slowing down anytime soon. As HR departments become increasingly reliant on advanced technologies and the numbers they produce, they also will experience the need for new skillsets required to deploy and use them. The HR process is rapidly changing, and as people, we must adapt now to see how AI is going to affect it. With a growing need for a center of expertise (COE) for HR data and technology, we will need to use this to focus resources on workforce analytics to drive business insights and recommendations. Originality/value This paper discusses the importance of understanding the implications of advanced analytics on recruiting and people management.
34
Basili, Roberto, Danilo Croce, and Giuseppe Castellucci. "Dynamic polarity lexicon acquisition for advanced Social Media analytics." International Journal of Engineering Business Management 9 (January 1, 2017): 184797901774491. http://dx.doi.org/10.1177/1847979017744916.
Full textAbstract:
Social media analytics tool aims at eliciting information and knowledge about individuals and communities, as this emerges from the dynamics of interpersonal communications in the social networks. Sentiment analysis (SA) is a core component of this process as it focuses onto the subjective levels of this knowledge, including the agreement/rejection, the perception, and the expectations by which individual users socially evolve in the network. Analyzing user sentiments thus corresponds to recognize subjective opinions and preferences in the texts they produce in social contexts, gather collective evidence across one or more communities, and trace some inferences about the underlying social phenomena. Automatic SA is a complex process, often enabled by hand-coded dictionaries, called polarity lexicons, that are intended to capture the a priori emotional aspects of words or multiword expressions. The development of such resources is an expensive, and, mainly, language and task-dependent process. Resulting polarity lexicons may be inadequate at fully covering Social Media phenomena, which are intended to capture global communities. In the area of SA over Social Media, this article presents an unsupervised and language independent method for inducing large-scale polarity lexicons from a specific but representative medium, that is, Twitter. The model is based on a novel use of Distributional Lexical Semantics methodologies as these are applied to Twitter. Given a set of heuristically annotated messages, the proposed methodology transfers the known sentiment information of subjective sentences to individual words. The resulting lexical resource is a large-scale polarity lexicon whose effectiveness is measured with respect to different SA tasks in English, Italian, and Arabic. Comparison of our method with different Distributional Lexical Semantics paradigms confirms the beneficial impact of our method in the design of very accurate SA systems in several natural languages.
35
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.
Full textAbstract:
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.
36
Demir, Kemal, and Eyüp Çalık. "İşgören Seçiminde İnsan Kaynakları Analitiği (İKA) Yaklaşımının Kullanılması (The Use of Human Resources Analytics Approach in Employee Selection)." Journal of Business Research - Turk 12, no. 4 (December 29, 2020): 3747–58. http://dx.doi.org/10.20491/isarder.2020.1070.
Full text
37
Hamilton, R. H., and William A. Sodeman. "The questions we ask: Opportunities and challenges for using big data analytics to strategically manage human capital resources." Business Horizons 63, no. 1 (January 2020): 85–95. http://dx.doi.org/10.1016/j.bushor.2019.10.001.
Full text
38
Wolf, Allison, Nesri Padayatchi, Kogieleum Naidoo, Iqbal Master, Barun Mathema, and Max R. O’Donnell. "Spatiotemporal Clustering of Multidrug-Resistant and Extensively Drug-Resistant Tuberculosis Is Associated With Human Immunodeficiency Virus Status and Drug-Susceptibility Patterns in KwaZulu-Natal, South Africa." Clinical Infectious Diseases 70, no. 10 (September 20, 2019): 2224–27. http://dx.doi.org/10.1093/cid/ciz913.
Full textAbstract:
Abstract Using an open-access spatiotemporal analytics program, we mapped spatiotemporal heterogeneity loci in tuberculosis (TB) cases (clusters) and dynamic changes, and characterized the drug-resistant TB clustering risk using routine microbiological data from KwaZulu-Natal, South Africa. The data may provide insight into transmission dynamics and support efficient deployment of public health resources.
39
Leon, Linda A., Kala Chand Seal, Zbigniew H. Przasnyski, and Ian Wiedenman. "Skills and Competencies Required for Jobs in Business Analytics." International Journal of Business Intelligence Research 8, no. 1 (January 2017): 1–25. http://dx.doi.org/10.4018/ijbir.2017010101.
Full textAbstract:
The explosive growth of business analytics has created a high demand for individuals who can help organizations gain competitive advantage by extracting business knowledge from data. What types of jobs satisfy this demand and what types of skills should individuals possess to satisfy this huge and growing demand? The authors perform a content analysis of 958 job advertisements posted during 2014-2015 for four types of positions: business analyst, data analyst, data scientist, and data analytics manager. They use a text mining approach to identify the skills needed for these job types and identify six distinct broad competencies. They also identify the competencies unique to a particular type of job and those common to all job types. Their job type categorization provides a framework that organizations can use to inventory their existing workforce competencies in order to identify critical future human resources. It can also guide individual professionals with their career planning as well as academic institutions in assessing and advancing their business analytics curricula.
40
Ramesh, S. "Can Analytical Mindset Save HR from Voodoo Practices?" NHRD Network Journal 12, no. 2 (April 2019): 123–34. http://dx.doi.org/10.1177/2631454119842386.
Full textAbstract:
Be it employee recognition or goal setting, human resources function is based on proven research. However, of late it has been overrun by fads and best practices. Voodoo HR is looking at companies from a totally different milieu and copying practices that worked for them. ‘Great place to work’ and ‘leadership development’ are but two of the trends that have swept organisations. Every organisation is unique in terms of culture, values and the business challenges they face. A ‘catch all’ approach based on industry practices may not be a panacea for all such challenges. To be meaningful, human resources should return to their own evidences and analytics to solve the unique problems of their companies. It is not an easy path and it may be the ‘in’ thing to go with the fad of the moment, but ultimately solving the unique challenges gives HR a leverage that fads don’t.
41
Bansal, Swati, and Monica Agarwal. "To Study the Gap between the Education and Industrial Expectations of Management Graduates." Shanlax International Journal of Management 7, no. 2 (October 3, 2019): 14–19. http://dx.doi.org/10.34293/management.v7i2.591.
Full textAbstract:
This study was conducted to examine the difference between the industry expectations for management graduates and provides practical recommendations for strategically aligning management curricula with the proficient curricula. By identifying specific skills essential for profession success, universities can provide an improved service for their graduates and the management industry. The respondents consist of students in various fields like Business analytics, finance, Human Resources, marketing, operations, information technology etc.
42
Li, Hui, Qianhui Huang, Yu Liu, and Lana X. Garmire. "Single cell transcriptome research in human placenta." Reproduction 160, no. 6 (December 2020): R155—R167. http://dx.doi.org/10.1530/rep-20-0231.
Full textAbstract:
Human placenta is a complex and heterogeneous organ interfacing between the mother and the fetus that supports fetal development. Alterations to placental structural components are associated with various pregnancy complications. To reveal the heterogeneity among various placenta cell types in normal and diseased placentas, as well as elucidate molecular interactions within a population of placental cells, a new genomics technology called single cell RNA-seq (or scRNA-seq) has been employed in the last couple of years. Here we review the principles of scRNA-seq technology, and summarize the recent human placenta studies at scRNA-seq level across gestational ages as well as in pregnancy complications, such as preterm birth and preeclampsia. We list the computational analysis platforms and resources available for the public use. Lastly, we discuss the future areas of interest for placenta single cell studies, as well as the data analytics needed to accomplish them.
43
Danylenko, O. A. "The Use of HR-Analytics in the Diagnosis of Staff Management System." Business Inform 7, no. 522 (2021): 252–59. http://dx.doi.org/10.32983/2222-4459-2021-7-252-259.
Full textAbstract:
The article delimitates and presents the author's own definitions of the concepts of «diagnosis», «monitoring», «analysis», «evaluation» of the staff management system, «staff audit (staff management),», «HR-analytics». Based on the review of domestic and foreign theoretical and practical studies of the current state of HR-analytics, the understanding of HR analytics is deepened; the possibilities of its implementation in the diagnosis of the staff management system are determined; methodical approaches to this process are described: the object, theme, subject, directions of HR-analytics at the organization level are formulated, stakeholders (interested parties), instruments and examples of the system of metrics/measures (criteria and indicators) operated in HR analytics are defined. It is determined that the diagnosis of the staff management system is a broader concept than HR analytics, which is its main component. HR analytics is based on monitoring and analysis of data with the formation of relevant estimates in conclusions/reports. Preparation of alternative forecasts, improvement of HR processes and staff management system are the final stages of diagnosis of the staff management system. The proposed approach of delimitation of the categorical apparatus of instruments in the sphere of assessment of the effectiveness of human resources use and staff management of an organization with clearly defined structural elements of the process of staff diagnosis will help theorists and practitioners to perform more clearly their work on the analysis and critical evaluation of HR processes with further improvement and alternative forecasting.
44
Zaman, Asim, Xiang Liu, and Zhipeng Zhang. "Video Analytics for Railroad Safety Research: An Artificial Intelligence Approach." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 10 (August 20, 2018): 269–77. http://dx.doi.org/10.1177/0361198118792751.
Full textAbstract:
The volume of video data in the railroad industry has increased significantly in recent years. Surveillance cameras are situated on nearly every part of the railroad system, such as inside the cab, along the track, at grade crossings, and in stations. These camera systems are manually monitored, either live or subsequently reviewed in an archive, which requires an immense amount of human resources. To make the video analysis much less labor-intensive, this paper develops a framework for utilizing artificial intelligence (AI) technologies for the extraction of useful information from these big video datasets. This framework has been implemented based on the video data from one grade crossing in New Jersey. The AI algorithm can automatically detect unsafe trespassing of railroad tracks (called near-miss events in this paper). To date, the AI algorithm has analyzed hours of video data and correctly detected all near-misses. This pilot study indicates the promise of using AI for automated analysis of railroad video big data, thereby supporting data-driven railroad safety research. For practical use, our AI algorithm has been packaged into a computer-aided decision support tool (named AI-Grade) that outputs near-miss video clips based on user-provided raw video data. This paper and its sequent studies aim to provide the railroad industry with next-generation big data analysis methods and tools for quickly and reliably processing large volumes of video data in order to better understand human factors in railroad safety research.
45
Potelis, Artūras. "Real Estate Investment Improvement with the Help of Information Technologies." Mokslas - Lietuvos ateitis 1, no. 3 (April 11, 2011): 61–65. http://dx.doi.org/10.3846/154.
Full textAbstract:
Investment process is a mechanism where people who have financial resources meet those who need money. To arrange such successful meeting in real estate market one has to deal with enormous amount of information. Information technologies can optimise market data interpretation through all the steps of investment process. By now computer programs like “RealVal” or “Realty Analytics 2009” are not commonly used in Lithuania but foreign practice shows how these innovations can prevent human mistakes, save time and money.
46
Fayoumi, Ayman G., and Amjad Fuad Hajjar. "Advanced Learning Analytics in Academic Education." International Journal on Semantic Web and Information Systems 16, no. 3 (July 2020): 70–87. http://dx.doi.org/10.4018/ijswis.2020070105.
Full textAbstract:
The integration of innovative data mining and decision-making techniques in the context of higher education is a bold initiative towards enhanced performance. Predictive and descriptive analytics add interesting insights for significant aspects the education. The purpose of this article is to summarize a novel approach for the adoption of artificial intelligence (AI) techniques towards forecasting of academic performance. The added value of applying AI techniques for advanced decision making in education is the realization that the scientific approach to standard problems in academia, like the enhancement of academic performance is feasible. For the purpose of this research the authors promote a research in Saudi Arabia. The vision of the Knowledge Society in the Kingdom of Saudi Arabia is a critical milestone towards digital transformation. The human capital and the integration of industry and academia has to be based on holistic approaches to skills and competencies management. One of the main objectives of an academic decision maker is to ensure that academic resources are adequately planned and that students are properly advised. To achieve such an objective, an extensive analysis of large volumes of data may be required. This research develops a decision support system (DSS) that is based on an artificial neural network (ANN) model that can be deployed for effective academic planning and advising. The system is based on evaluating academic metrics against academic performance for students. The model integrates inputs from relevant academic data sources into an autonomous ANN. A simulation of real data on an ANN is conducted to validate the system's accuracy. Moreover, an ANN is compared with different mathematical approaches. The system enables the quality assurance of planning, advising, and the monitoring of academic decisions. The overall contribution of this work is a novel approach to the deployment of Artificial Intelligent for advanced decision making in higher education. In future work this model is integrated with big data and analytics research for advanced visualizations
47
Mishra, Srikanta, Jared Schuetter, Akhil Datta-Gupta, and Grant Bromhal. "Robust Data-Driven Machine-Learning Models for Subsurface Applications: Are We There Yet?" Journal of Petroleum Technology 73, no. 03 (March 1, 2021): 25–30. http://dx.doi.org/10.2118/0321-0025-jpt.
Full textAbstract:
Algorithms are taking over the world, or so we are led to believe, given their growing pervasiveness in multiple fields of human endeavor such as consumer marketing, finance, design and manufacturing, health care, politics, sports, etc. The focus of this article is to examine where things stand in regard to the application of these techniques for managing subsurface energy resources in domains such as conventional and unconventional oil and gas, geologic carbon sequestration, and geothermal energy. It is useful to start with some definitions to establish a common vocabulary. Data analytics (DA)—Sophisticated data collection and analysis to understand and model hidden patterns and relationships in complex, multivariate data sets Machine learning (ML)—Building a model between predictors and response, where an algorithm (often a black box) is used to infer the underlying input/output relationship from the data Artificial intelligence (AI)—Applying a predictive model with new data to make decisions without human intervention (and with the possibility of feedback for model updating) Thus, DA can be thought of as a broad framework that helps determine what happened (descriptive analytics), why it happened (diagnostic analytics), what will happen (predictive analytics), or how can we make something happen (prescriptive analytics) (Sankaran et al. 2019). Although DA is built upon a foundation of classical statistics and optimization, it has increasingly come to rely upon ML, especially for predictive and prescriptive analytics (Donoho 2017). While the terms DA, ML, and AI are often used interchangeably, it is important to recognize that ML is basically a subset of DA and a core enabling element of the broader application for the decision-making construct that is AI. In recent years, there has been a proliferation in studies using ML for predictive analytics in the context of subsurface energy resources. Consider how the number of papers on ML in the OnePetro database has been increasing exponentially since 1990 (Fig. 1). These trends are also reflected in the number of technical sessions devoted to ML/AI topics in conferences organized by SPE, AAPG, and SEG among others; as wells as books targeted to practitioners in these professions (Holdaway 2014; Mishra and Datta-Gupta 2017; Mohaghegh 2017; Misra et al. 2019). Given these high levels of activity, our goal is to provide some observations and recommendations on the practice of data-driven model building using ML techniques. The observations are motivated by our belief that some geoscientists and petroleum engineers may be jumping the gun by applying these techniques in an ad hoc manner without any foundational understanding, whereas others may be holding off on using these methods because they do not have any formal ML training and could benefit from some concrete advice on the subject. The recommendations are conditioned by our experience in applying both conventional statistical modeling and data analytics approaches to practical problems.
48
Sevastjanova, Rita, Wolfgang Jentner, Fabian Sperrle, Rebecca Kehlbeck, Jürgen Bernard, and Mennatallah El-assady. "QuestionComb: A Gamification Approach for the Visual Explanation of Linguistic Phenomena through Interactive Labeling." ACM Transactions on Interactive Intelligent Systems 11, no. 3-4 (December 31, 2021): 1–38. http://dx.doi.org/10.1145/3429448.
Full textAbstract:
Linguistic insight in the form of high-level relationships and rules in text builds the basis of our understanding of language. However, the data-driven generation of such structures often lacks labeled resources that can be used as training data for supervised machine learning. The creation of such ground-truth data is a time-consuming process that often requires domain expertise to resolve text ambiguities and characterize linguistic phenomena. Furthermore, the creation and refinement of machine learning models is often challenging for linguists as the models are often complex, in-transparent, and difficult to understand. To tackle these challenges, we present a visual analytics technique for interactive data labeling that applies concepts from gamification and explainable Artificial Intelligence (XAI) to support complex classification tasks. The visual-interactive labeling interface promotes the creation of effective training data. Visual explanations of learned rules unveil the decisions of the machine learning model and support iterative and interactive optimization. The gamification-inspired design guides the user through the labeling process and provides feedback on the model performance. As an instance of the proposed technique, we present QuestionComb , a workspace tailored to the task of question classification (i.e., in information-seeking vs. non-information-seeking questions). Our evaluation studies confirm that gamification concepts are beneficial to engage users through continuous feedback, offering an effective visual analytics technique when combined with active learning and XAI.
49
Utomo, Atyoko, Dian Indiyati, and Gadang Ramantoko. "Talent Acquisition Implementation with People Analytic Approach." Budapest International Research and Critics Institute (BIRCI-Journal): Humanities and Social Sciences 4, no. 1 (January 15, 2021): 204–15. http://dx.doi.org/10.33258/birci.v4i1.1584.
Full textAbstract:
The current human resource (HR) fulfillment conditions in this company are still quite low. This can be seen from the percentage of HR fulfillment of approximately 60% of the total HR needs. The strategy of fulfilling human resources through the recruitment and selection process must be done quickly and optimally. The problem that arises is related to the optimization of the talent acquisition process carried out, so that the results obtained are in accordance with the target and have quality that meets the required. In this study, data analysis was used using the random forest method. The method is used to develop a model that can predict the pass level of participants in recruitment and selection quickly and precisely in accordance with the profile of each participant, and can provide insight on the projected achievement of individual performance on each participant if passed at the company, to assist management in making decisions about the participants accepted in the recruitment and selection process. The data population used is data on recruitment and selection participants in 2018. To carry out the process of predicting the graduation rate of prospective employees, data for prospective employees who register for the recruitment and selection process will be used with a total of 17,294 people. The analytical tool in this study uses a people analytic approach. The conclusion of this study is that making people analytics on the process of talent acquisition can be done using the Random Forest Classification method. This method aims to determine the class of each predicted data. Modeling has been made to predict performance achievements, but the performance of the model is still not showing the level of significance in accordance with the standard level of confidence, which is still below 0.05.
50
Isokpehi, Raphael D., Udensi K. Udensi, Matthew N. Anyanwu, Andreas N. Mbah, Matilda O. Johnson, Kafui Edusei, Michael A. Bauer, Roger A. Hall, and Omotayo R. Awofolu. "Knowledge Building Insights on Biomarkers of Arsenic Toxicity to Keratinocytes and Melanocytes." Biomarker Insights 7 (January 2012): BMI.S7799. http://dx.doi.org/10.4137/bmi.s7799.
Full textAbstract:
Exposure to inorganic arsenic induces skin cancer and abnormal pigmentation in susceptible humans. High-throughput gene transcription assays such as DNA microarrays allow for the identification of biological pathways affected by arsenic that lead to initiation and progression of skin cancer and abnormal pigmentation. The overall purpose of the reported research was to determine knowledge building insights on biomarker genes for arsenic toxicity to human epidermal cells by integrating a collection of gene lists annotated with biological information. The information sets included toxicogenomics gene-chemical interaction; enzymes encoded in the human genome; enriched biological information associated with genes; environmentally relevant gene sequence variation; and effects of non-synonymous single nucleotide polymorphisms (SNPs) on protein function. Molecular network construction for arsenic upregulated genes TNFSF18 (tumor necrosis factor [ligand] superfamily member 18) and IL1R2 (interleukin 1 Receptor, type 2) revealed subnetwork interconnections to E2F4, an oncogenic transcription factor, predominantly expressed at the onset of keratinocyte differentiation. Visual analytics integration of gene information sources helped identify RAC1, a GTP binding protein, and TFRC, an iron uptake protein as prioritized arsenic-perturbed protein targets for biological processes leading to skin hyperpigmentation. RAC1 regulates the formation of dendrites that transfer melanin from melanocytes to neighboring keratinocytes. Increased melanocyte den-dricity is correlated with hyperpigmentation. TFRC is a key determinant of the amount and location of iron in the epidermis. Aberrant TFRC expression could impair cutaneous iron metabolism leading to abnormal pigmentation seen in some humans exposed to arsenicals. The reported findings contribute to insights on how arsenic could impair the function of genes and biological pathways in epidermal cells. Finally, we developed visual analytics resources to facilitate further exploration of the information and knowledge building insights on arsenic toxicity to human epidermal keratinocytes and melanocytes.