Research in Personnel and Human Resources Management
Subject Area and Category
- Organizational Behavior and Human Resource Management
Emerald Group Publishing Ltd.
Publication type
Book Series
2000-2012, 2014-2021
Information
How to publish in this journal
The set of journals have been ranked according to their SJR and divided into four equal groups, four quartiles. Q1 (green) comprises the quarter of the journals with the highest values, Q2 (yellow) the second highest values, Q3 (orange) the third highest values and Q4 (red) the lowest values.
Category | Year | Quartile |
---|---|---|
Organizational Behavior and Human Resource Management | 2001 | Q3 |
Organizational Behavior and Human Resource Management | 2002 | Q1 |
Organizational Behavior and Human Resource Management | 2003 | Q1 |
Organizational Behavior and Human Resource Management | 2004 | Q1 |
Organizational Behavior and Human Resource Management | 2005 | Q1 |
Organizational Behavior and Human Resource Management | 2006 | Q1 |
Organizational Behavior and Human Resource Management | 2007 | Q1 |
Organizational Behavior and Human Resource Management | 2008 | Q2 |
Organizational Behavior and Human Resource Management | 2009 | Q1 |
Organizational Behavior and Human Resource Management | 2010 | Q2 |
Organizational Behavior and Human Resource Management | 2011 | Q1 |
Organizational Behavior and Human Resource Management | 2012 | Q1 |
Organizational Behavior and Human Resource Management | 2013 | Q3 |
Organizational Behavior and Human Resource Management | 2014 | Q1 |
Organizational Behavior and Human Resource Management | 2015 | Q2 |
Organizational Behavior and Human Resource Management | 2016 | Q2 |
Organizational Behavior and Human Resource Management | 2017 | Q1 |
Organizational Behavior and Human Resource Management | 2018 | Q2 |
Organizational Behavior and Human Resource Management | 2019 | Q1 |
Organizational Behavior and Human Resource Management | 2020 | Q2 |
Organizational Behavior and Human Resource Management | 2021 | Q1 |
The SJR is a size-independent prestige indicator that ranks journals by their 'average prestige per article'. It is based on the idea that 'all citations are not created equal'. SJR is a measure of scientific influence of journals that accounts for both the number of citations received by a journal and the importance or prestige of the journals where such citations come from It measures the scientific influence of the average article in a journal, it expresses how central to the global scientific discussion an average article of the journal is.
Year | SJR |
---|---|
2001 | 0.186 |
2002 | 1.707 |
2003 | 2.340 |
2004 | 1.166 |
2005 | 1.714 |
2006 | 1.962 |
2007 | 1.283 |
2008 | 0.645 |
2009 | 1.272 |
2010 | 0.521 |
2011 | 1.578 |
2012 | 0.936 |
2013 | 0.327 |
2014 | 1.320 |
2015 | 0.777 |
2016 | 0.778 |
2017 | 1.183 |
2018 | 0.853 |
2019 | 0.867 |
2020 | 0.779 |
2021 | 1.283 |
Evolution of the number of published documents. All types of documents are considered, including citable and non citable documents.
Year | Documents |
---|---|
2000 | 8 |
2001 | 9 |
2002 | 9 |
2003 | 10 |
2004 | 8 |
2005 | 8 |
2006 | 9 |
2007 | 8 |
2008 | 8 |
2009 | 7 |
2010 | 6 |
2011 | 6 |
2012 | 6 |
2013 | 0 |
2014 | 6 |
2015 | 6 |
2016 | 6 |
2017 | 7 |
2018 | 7 |
2019 | 6 |
2020 | 8 |
2021 | 8 |
This indicator counts the number of citations received by documents from a journal and divides them by the total number of documents published in that journal. The chart shows the evolution of the average number of times documents published in a journal in the past two, three and four years have been cited in the current year. The two years line is equivalent to journal impact factor ™ (Thomson Reuters) metric.
Cites per document | Year | Value |
---|---|---|
Cites / Doc. (4 years) | 2000 | 0.000 |
Cites / Doc. (4 years) | 2001 | 0.250 |
Cites / Doc. (4 years) | 2002 | 0.941 |
Cites / Doc. (4 years) | 2003 | 1.462 |
Cites / Doc. (4 years) | 2004 | 1.444 |
Cites / Doc. (4 years) | 2005 | 2.750 |
Cites / Doc. (4 years) | 2006 | 1.743 |
Cites / Doc. (4 years) | 2007 | 1.571 |
Cites / Doc. (4 years) | 2008 | 1.697 |
Cites / Doc. (4 years) | 2009 | 1.333 |
Cites / Doc. (4 years) | 2010 | 1.500 |
Cites / Doc. (4 years) | 2011 | 1.759 |
Cites / Doc. (4 years) | 2012 | 2.074 |
Cites / Doc. (4 years) | 2013 | 1.440 |
Cites / Doc. (4 years) | 2014 | 1.556 |
Cites / Doc. (4 years) | 2015 | 2.667 |
Cites / Doc. (4 years) | 2016 | 1.222 |
Cites / Doc. (4 years) | 2017 | 1.889 |
Cites / Doc. (4 years) | 2018 | 2.000 |
Cites / Doc. (4 years) | 2019 | 2.231 |
Cites / Doc. (4 years) | 2020 | 2.923 |
Cites / Doc. (4 years) | 2021 | 2.429 |
Cites / Doc. (3 years) | 2000 | 0.000 |
Cites / Doc. (3 years) | 2001 | 0.250 |
Cites / Doc. (3 years) | 2002 | 0.941 |
Cites / Doc. (3 years) | 2003 | 1.462 |
Cites / Doc. (3 years) | 2004 | 1.250 |
Cites / Doc. (3 years) | 2005 | 2.148 |
Cites / Doc. (3 years) | 2006 | 1.577 |
Cites / Doc. (3 years) | 2007 | 1.200 |
Cites / Doc. (3 years) | 2008 | 1.120 |
Cites / Doc. (3 years) | 2009 | 1.400 |
Cites / Doc. (3 years) | 2010 | 1.130 |
Cites / Doc. (3 years) | 2011 | 1.714 |
Cites / Doc. (3 years) | 2012 | 1.579 |
Cites / Doc. (3 years) | 2013 | 1.111 |
Cites / Doc. (3 years) | 2014 | 2.000 |
Cites / Doc. (3 years) | 2015 | 0.667 |
Cites / Doc. (3 years) | 2016 | 1.250 |
Cites / Doc. (3 years) | 2017 | 1.889 |
Cites / Doc. (3 years) | 2018 | 2.053 |
Cites / Doc. (3 years) | 2019 | 1.850 |
Cites / Doc. (3 years) | 2020 | 2.300 |
Cites / Doc. (3 years) | 2021 | 2.143 |
Cites / Doc. (2 years) | 2000 | 0.000 |
Cites / Doc. (2 years) | 2001 | 0.250 |
Cites / Doc. (2 years) | 2002 | 0.941 |
Cites / Doc. (2 years) | 2003 | 1.222 |
Cites / Doc. (2 years) | 2004 | 1.105 |
Cites / Doc. (2 years) | 2005 | 2.111 |
Cites / Doc. (2 years) | 2006 | 1.313 |
Cites / Doc. (2 years) | 2007 | 0.706 |
Cites / Doc. (2 years) | 2008 | 1.294 |
Cites / Doc. (2 years) | 2009 | 1.188 |
Cites / Doc. (2 years) | 2010 | 1.067 |
Cites / Doc. (2 years) | 2011 | 1.308 |
Cites / Doc. (2 years) | 2012 | 0.917 |
Cites / Doc. (2 years) | 2013 | 1.167 |
Cites / Doc. (2 years) | 2014 | 0.500 |
Cites / Doc. (2 years) | 2015 | 0.667 |
Cites / Doc. (2 years) | 2016 | 1.250 |
Cites / Doc. (2 years) | 2017 | 1.667 |
Cites / Doc. (2 years) | 2018 | 1.923 |
Cites / Doc. (2 years) | 2019 | 1.429 |
Cites / Doc. (2 years) | 2020 | 2.231 |
Cites / Doc. (2 years) | 2021 | 1.500 |
Evolution of the total number of citations and journal's self-citations received by a journal's published documents during the three previous years. Journal Self-citation is defined as the number of citation from a journal citing article to articles published by the same journal.
Cites | Year | Value |
---|---|---|
Self Cites | 2000 | 0 |
Self Cites | 2001 | 0 |
Self Cites | 2002 | 0 |
Self Cites | 2003 | 1 |
Self Cites | 2004 | 2 |
Self Cites | 2005 | 2 |
Self Cites | 2006 | 1 |
Self Cites | 2007 | 1 |
Self Cites | 2008 | 2 |
Self Cites | 2009 | 0 |
Self Cites | 2010 | 0 |
Self Cites | 2011 | 0 |
Self Cites | 2012 | 0 |
Self Cites | 2013 | 0 |
Self Cites | 2014 | 0 |
Self Cites | 2015 | 0 |
Self Cites | 2016 | 0 |
Self Cites | 2017 | 1 |
Self Cites | 2018 | 2 |
Self Cites | 2019 | 0 |
Self Cites | 2020 | 1 |
Self Cites | 2021 | 1 |
Total Cites | 2000 | 0 |
Total Cites | 2001 | 2 |
Total Cites | 2002 | 16 |
Total Cites | 2003 | 38 |
Total Cites | 2004 | 35 |
Total Cites | 2005 | 58 |
Total Cites | 2006 | 41 |
Total Cites | 2007 | 30 |
Total Cites | 2008 | 28 |
Total Cites | 2009 | 35 |
Total Cites | 2010 | 26 |
Total Cites | 2011 | 36 |
Total Cites | 2012 | 30 |
Total Cites | 2013 | 20 |
Total Cites | 2014 | 24 |
Total Cites | 2015 | 8 |
Total Cites | 2016 | 15 |
Total Cites | 2017 | 34 |
Total Cites | 2018 | 39 |
Total Cites | 2019 | 37 |
Total Cites | 2020 | 46 |
Total Cites | 2021 | 45 |
Evolution of the number of total citation per document and external citation per document (i.e. journal self-citations removed) received by a journal's published documents during the three previous years. External citations are calculated by subtracting the number of self-citations from the total number of citations received by the journal’s documents.
Cites | Year | Value |
---|---|---|
External Cites per document | 2000 | 0 |
External Cites per document | 2001 | 0.250 |
External Cites per document | 2002 | 0.941 |
External Cites per document | 2003 | 1.423 |
External Cites per document | 2004 | 1.179 |
External Cites per document | 2005 | 2.074 |
External Cites per document | 2006 | 1.538 |
External Cites per document | 2007 | 1.160 |
External Cites per document | 2008 | 1.040 |
External Cites per document | 2009 | 1.400 |
External Cites per document | 2010 | 1.130 |
External Cites per document | 2011 | 1.714 |
External Cites per document | 2012 | 1.579 |
External Cites per document | 2013 | 1.111 |
External Cites per document | 2014 | 2.000 |
External Cites per document | 2015 | 0.667 |
External Cites per document | 2016 | 1.250 |
External Cites per document | 2017 | 1.833 |
External Cites per document | 2018 | 1.947 |
External Cites per document | 2019 | 1.850 |
External Cites per document | 2020 | 2.250 |
External Cites per document | 2021 | 2.095 |
Cites per document | 2000 | 0.000 |
Cites per document | 2001 | 0.250 |
Cites per document | 2002 | 0.941 |
Cites per document | 2003 | 1.462 |
Cites per document | 2004 | 1.250 |
Cites per document | 2005 | 2.148 |
Cites per document | 2006 | 1.577 |
Cites per document | 2007 | 1.200 |
Cites per document | 2008 | 1.120 |
Cites per document | 2009 | 1.400 |
Cites per document | 2010 | 1.130 |
Cites per document | 2011 | 1.714 |
Cites per document | 2012 | 1.579 |
Cites per document | 2013 | 1.111 |
Cites per document | 2014 | 2.000 |
Cites per document | 2015 | 0.667 |
Cites per document | 2016 | 1.250 |
Cites per document | 2017 | 1.889 |
Cites per document | 2018 | 2.053 |
Cites per document | 2019 | 1.850 |
Cites per document | 2020 | 2.300 |
Cites per document | 2021 | 2.143 |
International Collaboration accounts for the articles that have been produced by researchers from several countries. The chart shows the ratio of a journal's documents signed by researchers from more than one country; that is including more than one country address.
Year | International Collaboration |
---|---|
2000 | 0.00 |
2001 | 0.00 |
2002 | 0.00 |
2003 | 0.00 |
2004 | 0.00 |
2005 | 0.00 |
2006 | 0.00 |
2007 | 0.00 |
2008 | 0.00 |
2009 | 0.00 |
2010 | 0.00 |
2011 | 0.00 |
2012 | 0.00 |
2013 | 0 |
2014 | 16.67 |
2015 | 0.00 |
2016 | 0.00 |
2017 | 0.00 |
2018 | 14.29 |
2019 | 66.67 |
2020 | 0.00 |
2021 | 0.00 |
Not every article in a journal is considered primary research and therefore "citable", this chart shows the ratio of a journal's articles including substantial research (research articles, conference papers and reviews) in three year windows vs. those documents other than research articles, reviews and conference papers.
Documents | Year | Value |
---|---|---|
Non-citable documents | 2000 | 0 |
Non-citable documents | 2001 | 1 |
Non-citable documents | 2002 | 2 |
Non-citable documents | 2003 | 3 |
Non-citable documents | 2004 | 3 |
Non-citable documents | 2005 | 3 |
Non-citable documents | 2006 | 3 |
Non-citable documents | 2007 | 3 |
Non-citable documents | 2008 | 3 |
Non-citable documents | 2009 | 3 |
Non-citable documents | 2010 | 2 |
Non-citable documents | 2011 | 1 |
Non-citable documents | 2012 | 0 |
Non-citable documents | 2013 | 0 |
Non-citable documents | 2014 | 0 |
Non-citable documents | 2015 | 0 |
Non-citable documents | 2016 | 0 |
Non-citable documents | 2017 | 0 |
Non-citable documents | 2018 | 0 |
Non-citable documents | 2019 | 0 |
Non-citable documents | 2020 | 6 |
Non-citable documents | 2021 | 14 |
Citable documents | 2000 | 0 |
Citable documents | 2001 | 7 |
Citable documents | 2002 | 15 |
Citable documents | 2003 | 23 |
Citable documents | 2004 | 25 |
Citable documents | 2005 | 24 |
Citable documents | 2006 | 23 |
Citable documents | 2007 | 22 |
Citable documents | 2008 | 22 |
Citable documents | 2009 | 22 |
Citable documents | 2010 | 21 |
Citable documents | 2011 | 20 |
Citable documents | 2012 | 19 |
Citable documents | 2013 | 18 |
Citable documents | 2014 | 12 |
Citable documents | 2015 | 12 |
Citable documents | 2016 | 12 |
Citable documents | 2017 | 18 |
Citable documents | 2018 | 19 |
Citable documents | 2019 | 20 |
Citable documents | 2020 | 14 |
Citable documents | 2021 | 7 |
Ratio of a journal's items, grouped in three years windows, that have been cited at least once vs. those not cited during the following year.
Documents | Year | Value |
---|---|---|
Uncited documents | 2000 | 0 |
Uncited documents | 2001 | 7 |
Uncited documents | 2002 | 9 |
Uncited documents | 2003 | 15 |
Uncited documents | 2004 | 15 |
Uncited documents | 2005 | 12 |
Uncited documents | 2006 | 10 |
Uncited documents | 2007 | 12 |
Uncited documents | 2008 | 13 |
Uncited documents | 2009 | 11 |
Uncited documents | 2010 | 13 |
Uncited documents | 2011 | 6 |
Uncited documents | 2012 | 5 |
Uncited documents | 2013 | 9 |
Uncited documents | 2014 | 4 |
Uncited documents | 2015 | 6 |
Uncited documents | 2016 | 5 |
Uncited documents | 2017 | 4 |
Uncited documents | 2018 | 7 |
Uncited documents | 2019 | 8 |
Uncited documents | 2020 | 6 |
Uncited documents | 2021 | 8 |
Cited documents | 2000 | 0 |
Cited documents | 2001 | 1 |
Cited documents | 2002 | 8 |
Cited documents | 2003 | 11 |
Cited documents | 2004 | 13 |
Cited documents | 2005 | 15 |
Cited documents | 2006 | 16 |
Cited documents | 2007 | 13 |
Cited documents | 2008 | 12 |
Cited documents | 2009 | 14 |
Cited documents | 2010 | 10 |
Cited documents | 2011 | 15 |
Cited documents | 2012 | 14 |
Cited documents | 2013 | 9 |
Cited documents | 2014 | 8 |
Cited documents | 2015 | 6 |
Cited documents | 2016 | 7 |
Cited documents | 2017 | 14 |
Cited documents | 2018 | 12 |
Cited documents | 2019 | 12 |
Cited documents | 2020 | 14 |
Cited documents | 2021 | 13 |
Evolution of the percentage of female authors.
Year | Female Percent |
---|---|
2000 | 20.00 |
2001 | 23.81 |
2002 | 12.50 |
2003 | 42.86 |
2004 | 57.69 |
2005 | 42.11 |
2006 | 42.11 |
2007 | 31.25 |
2008 | 41.67 |
2009 | 33.33 |
2010 | 40.00 |
2011 | 50.00 |
2012 | 21.43 |
2013 | 0.00 |
2014 | 26.09 |
2015 | 37.50 |
2016 | 31.25 |
2017 | 46.15 |
2018 | 20.00 |
2019 | 0.00 |
2020 | 0.00 |
2021 | 0.00 |
Evolution of the number of documents cited by public policy documents according to Overton database.
Documents | Year | Value |
---|---|---|
Overton | 2000 | 0 |
Overton | 2001 | 0 |
Overton | 2002 | 0 |
Overton | 2003 | 0 |
Overton | 2004 | 0 |
Overton | 2005 | 0 |
Overton | 2006 | 0 |
Overton | 2007 | 0 |
Overton | 2008 | 0 |
Overton | 2009 | 0 |
Overton | 2010 | 0 |
Overton | 2011 | 0 |
Overton | 2012 | 0 |
Overton | 2013 | 0 |
Overton | 2014 | 0 |
Overton | 2015 | 0 |
Overton | 2016 | 0 |
Overton | 2017 | 0 |
Overton | 2018 | 0 |
Overton | 2019 | 0 |
Overton | 2020 | 0 |
Overton | 2021 | 0 |
Evoution of the number of documents related to Sustainable Development Goals defined by United Nations. Available from 2018 onwards.
Documents | Year | Value |
---|---|---|
SDG | 2018 | 4 |
SDG | 2019 | 2 |
SDG | 2020 | 2 |
SDG | 2021 | 2 |
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Journals in Human resources management
Journal of behavioral and experimental economics.
- ISSN: 2214-8043
- 5 Year impact factor: 1.9
- Impact factor: 1.6
Journal of Vocational Behavior
- ISSN: 0001-8791
- 5 Year impact factor: 9.4
- Impact factor: 5.2
Organizational Dynamics
- ISSN: 0090-2616
- 5 Year impact factor: 2.1
- Impact factor: 3.1
The Relationship Between Sustainable Human Resource Management and Green Human Resource Management- A Case of Medical Sector in Hyderabad, India
- P. Kranthi Koneru Lakshmaiah Education Foundation Deemed to be University, Off Campus, Hyderabad, ORCID: 0000-0001-6238-743X
- Dr Kiran Kumar Thoti Koneru Lakshmaiah Education Foundation Deemed to be University, Off Campus, Hyderabad, ORCID: 0000-0001-6238-743X
- Dr Shilpa Bhakar TAPMI School of Business, Manipal University Jaipur, Dhami Kalan, Ajmer Road, Jaipur, Rajasthan, ORCID ID: 0009-0004-4413-8068
- Dr R. Arun Department of MBA, St. Joseph’s College of Engineering, Chennai, India, ORCID ID: 0000-0002-5252-103
- Dr Biswo Ranjan Mishra Utkal University (DDCE), Odisha, ORCHID ID: 0009-0006-5394-9609
Background: Human Resource Management (SHRM) and Green Human Resource Management (GHRM) are the subjects of this investigation of their interplay in Hyderabad, India's industrial sector. There is a growing need to include eco-friendly practices into HRM due to the increased global focus on sustainability in medical industry. Aim: Sustainable The study's overarching goal is to deduce how green HRM (GHRM) programs and SHRM practices—which prioritize the well-being of employees and the longevity of organizations—are compatible with one another. Method: The study surveyed 409 medical employees in Hyderabad using a quantitative research approach based on questionnaires and the data was analysis using SMART PLS. Results : There is a strong positive relationship between SHRM and GHRM, according to the results, thus businesses that use thorough SHRM are also more likely to use GHRM strategies that work. Human resource managers can help promote a sustainable culture, gain a competitive edge, and advance environmental goals by implementing the sustainable and green practices suggested in the study's conclusion. Conclusion: The study adds to the expanding corpus of literature on sustainable business practices and have important implications for industrial policy and practice.
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Public Health in Europe
- School of Primary Care and Public Health (CAPHRI), Maastricht
- School of Public Health - Faculty of Health Sciences - University of Bielefeld, Germany
- School of Public Health and Health Management, University of Belgrade, Serbia
- APHEA, Agency for Public Health Education Accreditation, Brussels, Belgium
- ASP HER, Association of Schools of Public Health in the European Region, Brussels, Belgium
- EHMA, European Health Management Association, Dublin, Ireland
- EPHA, European Public Health Alliance, Brussels, Belgium
- EUPHA, European Public Health Association, Utrecht, Netherlands
- EuroHealthNet, European Partnership for Health Equity and Well-being, Brussels, Belgium
- WFPHA, World Federation of Public Health Associations, Geneva, Switzerland
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SJIF Impact Factor Evaluation [ 2020 = 6.051 ] SEEJPH is indexed in BASE, ERIH PLUS, EZB, Google Scholar, Index Copernicus, OAJI, OCLC, SCOPUS, PubMed, SHERPA/ROMEO, SIS
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Personnel Research Psychologist (Leader)
Are you an Industrial/Organizational psychologist with selection expertise? If hired, you will develop, implement, and evaluate assessment programs; consult with external and internal partners; provide assessment guidance, and manage assessment content to help our Assessment and Evaluation program make a positive impact across the Government. Human Resources Solutions has many job openings at various levels and locations. View them here: https://www.usajobs.gov/Search/Results?mco=OPM-HRS
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Open & closing dates
09/05/2024 to 09/07/2024
The salary range for this announcement reflects Rest of US GS-14/1 @ $122,198 to highest GS-14/10 San Jose-San Francisco-Oakland, CA @ $191,900
Pay scale & grade
2 vacancies in the following location:
- Anywhere in the U.S. (remote job)
Telework eligible
Not applicable, this is a remote position.
Travel Required
Occasional travel - Occasional Travel; Travel averages 10%-15% yearly but may exceed that percentage during periods of increased service delivery.
Relocation expenses reimbursed
Appointment type, work schedule.
Competitive
Promotion potential
Job family (series).
- 0180 Psychology
Supervisory status
Security clearance, position sensitivity and risk.
Non-sensitive (NS)/Low Risk
Trust determination process
- Credentialing
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Financial disclosure
Bargaining unit status, announcement number.
24-PEW-12510649-DE
Control number
This job is open to.
Federal employees who meet the definition of a "surplus" or "displaced" employee.
U.S. Citizens, Nationals or those who owe allegiance to the U.S.
- Lead and/or participate on project teams to develop, deploy, and evaluate the effectiveness of innovative assessment tools.
- Identify, prioritize, and manage the development and implementation of Governmentwide assessment solutions.
- Manage content and content strategy for large-scale, Governmentwide assessment programs, including designing and implementing maintenance approaches, analyzing data and/or interpreting analyses related to item performance and exposure, and managing item refresh activities.
- Serve as the primary liaison between OPM psychologists and technical personnel responsible for the development, deployment, and maintenance of assessment delivery and reporting tools.
- Advise OPM and agencies' leadership on the design, development, and implementation of assessment strategies for a wide variety of audiences and end users.
- Consult with legal counsel to review assessment procedures, respond to assessment inquiries, and confer on assessment-related topics.
- Establish program objectives, activities, timelines, budgets, and staffing plans.
- Lead and/or participate on project teams to develop and/or contract for innovative assessment tools and evaluate the effectiveness of the tools.
- Manage the development and implementation of custom assessment solutions for a variety of Federal agencies.
- Develop and maintain content (e.g., items, scenarios) for hosting on an assessment technology platform.
- Conduct Governmentwide and/or agency-specific job analyses.
- Develop and implement competency models and hiring/promotional assessments.
- Establish project objectives, activities, timelines, budgets, and staffing plans.
- Advise agencies and internal senior management on the design, development, and implementation of assessment strategies.
Requirements
Conditions of employment.
- Must be a U.S. Citizen or National
- Males born after 12-31-59 must be registered for Selective Service
- Suitable for Federal employment, determined by a background investigation
- May be required to successfully complete a probationary period
- if the duty station is finalized in the Washington, DC metropolitan area, employees in this position will be represented by the American Federation of Government Employees (AFGE) Local 32. (BU Code - 2286)
- if the duty station is finalized outside of the Washington, DC metropolitan area, this position will not be represented by a bargaining unit. (BU Code - 7777)
Qualifications
- Using knowledge of the principles, theories, and methods of industrial/organizational psychology or related field (e.g., applied social psychology, applied research/evaluation) to develop practical solutions to assessment problems, including experience with assessment development and validation methods; AND
- Experience applying knowledge of statistics and using a major statistical analysis package (e.g., SPSS, R, Python) and creating data visualizations to share results; AND
- Leading/managing projects that involved conducting job analyses, performing statistical analyses, and developing tests/assessment tools consistent with professional and legal requirements (e.g., Uniform Guidelines on Employee Selection Procedures); AND
- Planning and monitoring project or program budgets to include negotiating funding with management or other officials; monitoring budget progression including overages or underutilization; determining pricing for operational activities; determining estimated costs for products or services; and managing resources to ensure delivery within budgeted amounts.
You must meet all qualification and eligibility requirements by the closing date of this announcement.
This position has a basic education requirement listed under the Qualifications section of this announcement.
Additional information
Incentive payments may be considered. This job opportunity announcement may be used to fill additional similar vacancies across OPM. If you are unable to apply online or need to fax a document that you do not have in electronic form, view the following link for information regarding an Alternate Application. Click the following link for more information, https://help.usastaffing.gov/Apply/index.php?title=Alternate_Application_Information .
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You will be evaluated for this job based on how well you meet the qualifications above.
- Customer Service
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As a new or existing federal employee, you and your family may have access to a range of benefits. Your benefits depend on the type of position you have - whether you're a permanent, part-time, temporary or an intermittent employee. You may be eligible for the following benefits, however, check with your agency to make sure you're eligible under their policies.
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- This position has a basic education requirement, you MUST submit a copy of your transcripts for further consideration.
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Artificial intelligence in human resource development: An umbrella review protocol
Roles Conceptualization, Data curation, Methodology, Project administration, Supervision, Validation, Writing – original draft, Writing – review & editing
* E-mail: [email protected]
Affiliation Human Resource Development, The University of Texas at Tyler, Tyler, Texas, United States of America
Roles Conceptualization, Data curation, Methodology, Software, Supervision, Validation, Writing – review & editing
Roles Conceptualization, Data curation, Validation, Writing – review & editing
- Sangok Yoo,
- Kim Nimon,
- Sanket Ramchandra Patole
- Published: September 9, 2024
- https://doi.org/10.1371/journal.pone.0310125
- Peer Review
- Reader Comments
The recent surge in artificial intelligence (AI) has significantly transformed work dynamics, particularly in human resource development (HRD) and related domains. Scholars, recognizing the significant potential of AI in HRD functions and processes, have contributed to the growing body of literature reviews on AI in HRD and related domains. Despite the valuable insights provided by these individual reviews, the challenge of collectively interpreting them within the HRD domain remains unresolved. This protocol outlines the methodology for an umbrella review aiming to systematically synthesize existing reviews on AI in HRD. The review seeks to address key research questions regarding AI’s contributions to HRD functions and processes, as well as the opportunities and threats associated with its implementation by employing a technology-aided systematic approach. The coding framework will be used to synthesize the contents of the selected systematic reviews such as their search strategies, data synthesis approaches, and HRD-related findings. The results of this umbrella review are expected to provide insights for HRD scholars and practitioners, promoting continuous improvement in AI-driven HRD initiatives. This protocol is preregistered on the Open Science Framework ( https://doi.org/10.17605/OSF.IO/Z8NM6 ) on May 27, 2024.
Citation: Yoo S, Nimon K, Patole SR (2024) Artificial intelligence in human resource development: An umbrella review protocol. PLoS ONE 19(9): e0310125. https://doi.org/10.1371/journal.pone.0310125
Editor: Juan Correa, Critical Centrality Institute, MEXICO
Received: February 8, 2024; Accepted: August 23, 2024; Published: September 9, 2024
Copyright: © 2024 Yoo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All supplementary files are available in an open-access repository: https://osf.io/af6d7/ .
Funding: The author(s) received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Artificial intelligence (AI) refers to the ability of machines to perform near or human-like functions, such as learning, interaction, and problem-solving, encompassing the culmination of computers, computer-related technologies, machines, and information communication technology innovations and developments, giving computers the ability to perform [ 1 , 2 ]. The AI market is anticipated to reach a $407 billion by 2027, indicating substantial growth from its estimated revenue of $86.9 billion in 2022. This surge is projected to make a 21% net contribution to the United States GDP by 2030, highlighting the profound impact of AI on economic growth [ 3 ]. Reasonably, a considerable 64% of businesses believe artificial intelligence will enhance their overall productivity [ 3 ]. Furthermore, according to an annual McKinsey Global Survey conducted in mid-April 2023, generative AI (Gen AI) has captured significant attention across the business landscape. Individuals from various regions, industries, and seniority levels are incorporating Gen AI into their professional and personal activities in their workplaces [ 4 ].
The recent proliferation of AI has dramatically changed the way we work [ 2 , 5 ]. In the field of human resource development (HRD) and related areas, the integration of AI presents opportunities to optimize talent acquisition, streamline learning and development initiatives, and enhance the strategic values of HRD in the workplace [ 5 , 6 ]. The far-reaching impact of AI underscores the need for a nuanced understanding of its role in HRD functions.
In academia, a burgeoning interest in AI in the workplace is evident through the growing body of research, leading to a surge of literature reviews focused on AI in HRD and related areas (e.g., [ 2 , 5 , 7 ]). For example, [ 5 ] conducted a critical review of the literature on AI and its impact on workplace outcomes, specifically within HR functions. [ 6 ] delved into the literature on AI applications, with a particular emphasis on the learning and development function. Despite the valuable contributions of these endeavors, the question of how these individual reviews can be collectively interpreted within the field of HRD remains unanswered.
To attain a comprehensive understanding of the rapidly expanding knowledge base, there is a need to systematically synthesize existing reviews on AI in HRD and related areas. An umbrella review, representing the highest level of evidence, offers a comprehensive overview of existing systematic reviews in a specific field. It enables scholars to compare the findings of systematic reviews relevant to a specific review question [ 8 , 9 ].
Hence, the proposed review outlined in this protocol aims to unveil patterns, trends, and gaps in the current understanding of AI in HRD literature. Additionally, we expect that this umbrella review will provide HRD scholars and practitioners with insights into the evolving concepts and practices associated with AI in HRD, thereby promoting continuous improvement in AI-driven HRD initiatives. The key research questions to be addressed in our umbrella review are:
- RQ1 : How does AI contribute to HRD functions and processes ?
- RQ2 : What are the opportunities and threats of implementing AI in HRD ?
In pursuit of the objective, this protocol proposes a technology-aided umbrella review process to synthesize systematic literature reviews on AI in the field of HRD and related areas. This systematic approach is designed to alleviate subjectivity in the review process, including the selection of search terms, thereby enhancing the rigor and objectivity of this umbrella review.
Materials and methods
Design and setting of the study.
This technology-aided umbrella review protocol adheres to the guidelines of PRISMA-P (Preferred reporting items for systematic review and meta-analysis protocols), serving as a guide for planning and documenting review methods [ 10 , 11 ]. The completed PRISMA-P checklist to confirm essential and minimum components of a systematic review is available in the S1 File . To achieve a comprehensive understanding of AI implementation in HRD, this protocol is designed to systematically incorporate existing systematic literature reviews on AI in HRD and related areas, mitigating subjective decision-making during review conduct [ 10 ]. This protocol is pre-registered on the Open Science Framework (OSF): https://doi.org/10.17605/OSF.IO/Z8NM6 . In the main research using this protocol, we plan to incorporate guidelines from the updated PRISMA 2020 statement to ensure comprehensive reporting of our umbrella review [ 12 ].
Database and data management
A structured search will be conducted in the Scopus and Web of Science databases, selected for their relevance to the field of study and comprehensive coverage. The review process, encompassing screening, will be coordinated utilizing Rayyan to ensure a systematic and efficient workflow [ 13 ].
Search strategy
Keywords to create a comprehensive search string that will be used to search systematic reviews for this umbrella review were collected. Table 1 describes the final search sub-strings of each component. AI-, HRD-, and SLR-related strings include search terms combined using the Boolean operator OR. In the final search string, the Boolean operator AND will be used to combine the three sub-strings. As our umbrella review aims to synthesize existing systematic literature reviews, the SLR-related string includes one search term that narrows the scope of our project. The specific search term identification strategy and term matching details are illustrated in the supplementary files ( S2 and S3 Files).
- PPT PowerPoint slide
- PNG larger image
- TIFF original image
https://doi.org/10.1371/journal.pone.0310125.t001
Screening process
We will employ a two-stage screening strategy. First, the relevance of each article will be evaluated based on its title and abstract. Articles that meet the exclusion criteria will be excluded. The second stage will evaluate the relevance of articles based on full texts using the inclusion and exclusion criteria. The screening process will be coordinated using Rayyan.
Eligibility criteria
To uphold consistency and reproducibility in the screening process among coders, the inclusion and exclusion criteria are established. First, eligible studies are systemic literature reviews specifically focused on AI in the field of HRD and related areas. This inclusion criterion aims to contribute to the synthesis of high-quality evidence and insights derived from rigorous research methodologies. The initial search will be confined to peer-reviewed journal articles and conference proceedings written in English and published from 1995 onwards, aligning with the search practices in previous literature reviews on AI (e.g., [ 5 – 7 ]).
Regarding the exclusion criteria, first, studies that do not explicitly explore AI-related technology will be excluded, ensuring a targeted exploration of the subject matter. Second, studies unrelated to a workplace setting will be excluded, as this umbrella review is specifically tailored to the application of AI in the workplace. Third, non-systemic literature reviews, which lack a structured and systematic approach, will also be excluded to maintain the methodological rigor of the review. Fourth, as this umbrella review specifically targets systemic literature reviews, studies employing meta-analysis as the primary research methodology will not be considered for inclusion. Finally, as explained in the inclusion criteria, book chapters and non-referred articles will be excluded to maintain the scholarly standard and reliability of the information under consideration.
Data extraction
We will use Rayyan to extract data. Extraction fields will be set up with the relevant information from the studies, and Rayyan’s tagging and coding features will be used to categorize and organize the extracted data. Disagreements will be discussed and resolved using Rayyan’s conflict resolution feature. The extraction fields for recording the finally selected systematic review studies will include:
- Full study citation
- The number of citations
- Title, abstract, and keywords
- Publication outlet (e.g., journal) and year
- Database, journal types, research context, scope
- Search terms and string(s)
- Scope of AI-related technologies (e.g., AI, machine learning, large language model)
- Scope of HRD-related functions (e.g., training & development, organizational development)
- Analysis approaches (e.g., bibliometrics, contents analysis, topic modeling, clustering)
- HRD-related areas in which AI applies to
- The benefits and possibility of AI adoption in HRD functions
- The enablers and obstacles of AI adoption in HRD functions
- Contributing factors to the effectiveness of AI-based HRD practices
- Other key contents/findings of the study (e.g., Future research directions)
Data synthesis
Thematic coding will be a crucial part of this umbrella review, focusing on discerning patterns in the implementation of AI within HRD. By employing an HRD framework, the goal of the thematic coding is to systematically categorize and analyze relevant literature to identify recurrent themes and trends in AI adoption across various HRD contexts. Furthermore, thematic coding facilitates the identification of key opportunities and challenges associated with AI implementation in HRD. The synthesis can highlight common issues faced by organizations integrating AI into HRD practices and, conversely, showcase successful strategies and innovative approaches. Ultimately, the thematic coding approach provides a comprehensive understanding of the current state of AI in HRD and sets the stage for suggesting future research directions and practical recommendations to enhance AI-driven HRD initiatives.
In addition to thematic coding, the data synthesis plan incorporates descriptive statistics. Descriptive statistics involves quantifying the occurrence of specific themes or concepts related to AI implementation in HRD across the selected systematic literature reviews. Specifically, frequency analysis helps to identify the prevalence of certain trends, challenges, or opportunities and visualization techniques can be employed to present these findings in a clear and accessible manner. R will be utilized for statistical analysis and visualization. We plan to use the base package [ 14 ] for statistical analysis and ggplot2 [ 15 ] for visualization.
Conclusions
This protocol will guide an umbrella review process to synthesize existing systematic reviews on AI in HRD. This umbrella review aims to explore the intersection of AI and HRD using existing reviews in the field of HRD and related areas. The anticipated outcomes of this umbrella review are intended to unveil patterns, opportunities, and threats of AI implementation in HRD. They will provide insights into AI-driven HRD initiatives. All data and analyses will be placed in an open-access repository, and the URL will be provided in the final manuscript.
Despite the expected contributions of this project, several limitations should be discussed. First, the protocol’s reliance on systematic literature reviews may introduce a potential bias, as certain valuable perspectives from non-systematic reviews or other types of reviews may be overlooked. Second, the scope of the review is contingent upon the availability of relevant literature published in English from 1995 onwards; this temporal and linguistic restriction may exclude valuable insights from non-English publications or earlier works that could contribute to a more nuanced understanding of the historical development of AI in HRD. Lastly, it should be mentioned that as AI-related technology is evolving rapidly future updates to this umbrella review will be necessary to ensure that it includes the most updated trends and practices.
Supporting information
S1 file. prisma-p checklist ( https://osf.io/2935t )..
https://doi.org/10.1371/journal.pone.0310125.s001
S2 File. Search term identification strategy ( https://osf.io/vgck2 ).
https://doi.org/10.1371/journal.pone.0310125.s002
S3 File. VosViewer keywords and search terms matching ( https://osf.io/nxc7v ).
* Note : All supplementary files are available in an open-access repository: https://osf.io/af6d7/ .
https://doi.org/10.1371/journal.pone.0310125.s003
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The determination of woody biomass resources and their energy potential from hazelnut tree cultivation.
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Parameter | Standard | |
---|---|---|
Proximate Analysis | Higher Heating Value (HHV; MJ·kg ) | EN-ISO 1928:2022; Equipment LECO AC 600 [ ] |
Lower Heating Value (LHV; MJ·kg ) | ||
Ash (A; %) | EN-ISO 18122-01; Equipment LECO TGA 701 [ ] | |
Volatile matter (V; %) | EN-ISO 18123-01; Equipment LECO TGA 701 [ ] | |
Moisture (M; %) | EN-ISO 18134-3; Equipment LECO TGA 701 [ ] | |
Fixed carbon (FC; %) | FC = 100-V-A-M [ ] | |
Ultimate Analysis Emission factors calculated according to studies | Carbon (C;%) | EN-ISO 16948:2015-07, Equipment LECO CHNS 628 [ ] |
Hydrogen (H;%) | ||
Nitrogen (N; %) | ||
Sulphur (S; %) | EN-ISO 16994:2016-10; Equipment LECO CHNS 628 [ ] | |
Oxygen (O; %) | O = 100-A-H-C-S-N [ ] | |
Emission Factors Exhaust gas composition was calculated according to [ ] | Carbon monoxide Emission factor (E ) of chemically pure coal (CO; kg·Mg ) | , CO—carbon monoxide emission factor (kg·kg ), —molar mass ratio of carbon monoxide and carbon, E —emission factor of chemically pure coal (kg∙kg ), C/CO—part of the carbon emitted as CO (for biomass 0.06). |
Carbon dioxide emission factor (CO ; kg·Mg ) | CO —carbon dioxide emission factor (kg∙kg ), - molar mass ratio of carbon dioxide and pure coal, - molar mass ratio of carbon dioxide and carbon monoxide, - molar mass ratio of carbon and methane, E —methane emission factor, E —emission index of non-methane VOCs (for biomass 0.009). | |
Sulphur dioxide emission factor (SO ; kg·Mg ) | SO –sulphur dioxide emission factor (kg∙kg ), 2—molar mass ratio of SO and sulphur, S—sulphur content in fuel (%), r—coefficient determining the part of total sulphur retained in the ash. | |
Emission factor was calculated from (NO ; kg·Mg ) | , NO —NO emission factor (kg∙kg ),—molar mass ratio of nitrogen dioxide to nitrogen. The molar mass of nitrogen dioxide is considered due to the fact that nitrogen oxide in the air oxidises very soon to nitrogen dioxide, N/C—nitrogen-to-carbon ratio in biomass, NO /N—part of nitrogen emitted as NO (for biomass 0.122). | |
Exhaust gas composition [ , ] | Theoretical oxygen demand (V ; Nm ·kg ) | , C-biomass carbon content (%), H-biomass hydrogen content (%), S-biomass sulphur content (%), O-biomass oxygen content). |
The stoichiometric volume of dry air required to burn 1 kg of biomass (V ; Nm ·kg ) | Since the oxygen content in the air is 21%, which participates in the combustion process in the boiler, the stoichiometric volume of dry air required to burn 1 kg of biomass | |
Carbon dioxide content of the combustion products (V ; Nm ·kg ) | ||
Content of sulphur dioxide (V ; Nm ·kg ) | , | |
Water vapour content of the exhaust gas (V O; Nm ·kg ) | , is the component of water vapour volume from the hydrogen combustion process and the volume of moisture contained in the combustion air ; M-fuel moisture content (%), -air absolute humidity (kg H O·kg dry air). | |
The theoretical nitrogen content in the exhaust gas ( ; Nm ·kg ) | , Considering that the nitrogen in the exhaust comes from the fuel composition and the combustion air, and the nitrogen content in the air is 79%. | |
The total stoichiometric volume of dry exhaust gas ( Nm ·kg ) | ||
The total volume of exhaust gases ( ; Nm ·kg ) | Assuming that biomass combustion is carried out under stoichiometric conditions, i.e., using the minimum amount of air required for combustion (λ = 1), a minimum exhaust gas volume will be obtained. |
Parameter | Average Number of Shoots (pcs.) for 1 Bush | Average Shoot Diameter (mm) at 50 cm Height on 1 Bush | Average Shoot Weight (kg·bush ) | |||
---|---|---|---|---|---|---|
Age of shoots | one | many | one | many | one | many |
‘Kataloński’ | 19.00 ± 7.94 A * | 10.67 ± 1.15 A | 12.91 ± 0.51 A | 22.70 ± 3.03 A | 1.78 ± 0.38 A | 4.88 ± 1.38 A |
‘Olbrzymi z Halle’ | 12.00 ± 1.73 A | 8.67 ± 1.53 A | 12.90 ± 1.68 A | 20.77 ± 1.86 A | 1.33 ± 0.66 A | 4.44 ± 2.03 A |
‘Olga’ | 10.33 ± 4.51 A | 11.67 ± 1.15 A | 12.20 ± 1.93 A | 22.63 ± 8.06 A | 1.33 ± 0.00 A | 6.44 ± 2.52 A |
‘Webba Cenny’ | 12.67 ± 0.57 A | 10.00 ± 3.00 A | 13.93 ± 2.98 A | 20.90 ± 0.88A | 1.78 ± 0.77 A | 4.66 ± 0.66 A |
p-value | 0.8512 | 0.5632 | 0.2489 | 0.3697 | 0.5789 | 0.6328 |
Parameter | Age of Shoots | Hazelnut Variety | p-Value | |||
---|---|---|---|---|---|---|
‘Kataloński’ | ‘Olbrzymi z Halle’ | ‘Olga’ | ‘Webba Cenny’ | |||
LHV (MJ·kg ) | One | 15.43 ± 0.09 BCa * | 16.85 ± 0.09 Aa | 15.26 ± 0.05 Ca | 15.61 ± 0.04 Ba | <0.0001 |
Many | 15.97 ± 0.09 BCa | 17.44 ± 0.09 Aa | 15.79 ± 0.05 Ca | 16.16 ± 0.04 Ba | <0.0001 | |
p-value | 0.2589 | 0.1369 | 0.7532 | 0.1598 | ||
HHV (MJ·kg ) | One | 16.79 ± 0.09 Ac | 18.08 ± 0.09 Aa | 16.64 ± 0.05 Ac | 17.00 ± 0.04 Ab | <0.0001 |
Many | 16.83 ± 0.5 Ac | 18.03 ± 0.04 Aa | 16.39 ± 0.03 Ac | 16.98 ± 0.06 Ab | <0.0004 | |
p-value | 0.4893 | 0.7369 | 0.8361 | 0.1774 | ||
M (%) | One | 16.76 ± 0.10 Ca | 13.5 ± 0.06 Aa | 18.04 ± 0.27 Da | 17.48 ± 0.07 Ba | <0.0001 |
Many | 17.35 ± 0.10 Ca | 13.97 ± 0.06 Aa | 18.67 ± 0.28 Da | 18.09 ± 0.07 Ba | <0.0001 | |
p-value | 0.3190 | 0.1687 | 0.9280 | 0.1969 | ||
V (%) | One | 64.99 ± 0.35 Ca | 68.22 ± 0.34 Aa | 64.93 ± 0.36 Ca | 66.13 ± 0.39 Ba | <0.0001 |
Many | 67.26 ± 0.36 Ca | 70.61 ± 0.35 Aa | 67.20 ± 0.37 Ca | 68.44 ± 0.40 Ba | <0.0001 | |
p-value | 0.2770 | 0.1465 | 0.8059 | 0.1710 | ||
A (%) | One | 2.40 ± 0.08 Aa | 1.49 ± 0.05 BCa | 1.77 ± 0.24 Ba | 1.26 ± 0.06 Ca | <0.0001 |
Many | 2.48 ± 0.08 Aa | 1.54 ± 0.051 BCa | 1.83 ± 0.25 Ba | 1.30 ± 0.06 Ca | <0.0001 | |
p-value | 0.3075 | 0.1626 | 0.8946 | 0.1898 | ||
FC (%) | One | 15.85 ± 0.41 Ba | 16.78 ± 0.25 Aa | 15.26 ± 0.41 Ba | 15.12 ± 0.29 Ba | 0.0014 |
Many | 16.40 ± 0.42 Ba | 17.37 ± 0.26 Aa | 15.79 ± 0.42 Ba | 15.65 ± 0.30 Ba | 0.0019 | |
p-value | 0.2718 | 0.1437 | 0.7909 | 0.1678 | ||
C (%) | One | 42.28 ± 0.5 Ba | 45.29 ± 0.03 Aa | 41.05 ± 0.22 Ca | 42.76 ± 0.23 Ba | <0.0001 |
Many | 43.76 ± 0.52 Ba | 46.88 ± 0.03 Aa | 42.48 ± 0.23 Ca | 44.26 ± 0.24 Ba | <0.0001 | |
p-value | 0.3017 | 0.1596 | 0.8779 | 0.1862 | ||
H (%) | One | 7.78 ± 0.15 Aa | 7.1 ± 0.35 ABa | 7.63 ± 0.22 ABa | 7.09 ± 0.28 Ba | <0.0001 |
Many | 8.05 ± 0.15 Aa | 7.35 ± 0.36 ABa | 7.89 ± 0.23ABa | 7.34 ± 0.29 Ba | <0.0001 | |
p-value | 0.2822 | 0.1492 | 0.8210 | 0.1742 | ||
N (%) | One | 0.95 ± 0.02 ABa | 0.96 ± 0.01 Aa | 0.87 ± 0.03 Ba | 0.72 ± 0.05 Ca | <0.0001 |
Many | 0.98 ± 0.02 ABa | 0.99 ± 0.01 Aa | 0.90 ± 0.03 Ba | 0.74 ± 0.05 Ca | <0.0001 | |
p-value | 0.3132 | 0.1656 | 0.9113 | 0.1933 | ||
S (%) | One | 0.05 ± 0 Aa | 0.04 ± 0.01 Aa | 0.05 ± 0 Aa | 0.05 ± 0.02 Aa | 0.5463 |
Many | 0.051 ± 0 Aa | 0.04 ± 0.01 Aa | 0.05 ± 0 Aa | 0.05 ± 0.02 Aa | 0.6874 | |
p-value | 0.2900 | 0.1533 | 0.8436 | 0.1790 | ||
O (%) | One | 46.54 ± 0.55 Ba | 45.12 ± 0.34 Ca | 48.62 ± 0.65 Aa | 48.12 ± 0.13 Aa | <0.0001 |
Many | 48.17 ± 0.57 Ba | 46.68 ± 0.35 Ca | 50.32 ± 0.67 Aa | 49.80 ± 0.13 Aa | <0.0001 | |
p-value | 0.3219 | 0.1702 | 0.9364 | 0.1987 | ||
H/C | One | 1.84 ± 0.04 Aa | 1.57 ± 0.08 Ba | 1.86 ± 0.04 Aa | 1.66 ± 0.07 Ba | 0.0009 |
Many | 1.90 ± 0.04 Aa | 1.62 ± 0.08 Ba | 1.92 ± 0.0414 Aa | 1.71 ± 0.07 Ba | 0.0007 | |
p-value | 0.2977 | 0.1574 | 0.8662 | 0.1838 | ||
N/C | One | 0.02 ± 0.001 Aa | 0.02 ± 0.00 Aa | 0.02 ± 0.001 Aa | 0.02 ± 0.001 Ba | <0.0001 |
Many | 0.02 ± 0.001 Aa | 0.021 ± 0.00 Aa | 0.02 ± 0.001 Aa | 0.02 ± 0.001 Ba | <0.0001 | |
p-value | 0.3305 | 0.1748 | 0.9615 | 0.2040 | ||
O/C | One | 0.83 ± 0.02 Ba | 0.75 ± 0.01 Ca | 0.88 ± 0.02 Aa | 0.84 ± 0.01 Ba | <0.0001 |
Many | 0.85 ± 0.02 Ba | 0.77 ± 0.01 Ca | 0.92 ± 0.02 Aa | 0.87 ± 0.01 Ba | <0.0001 | |
p-value | 0.2680 | 0.1417 | 0.7796 | 0.1654 |
Parameter | Age of Shoots | Hazelnut Variety | p-Value | |||
---|---|---|---|---|---|---|
‘Kataloński’ | ‘Olbrzymi z Halle’ | ‘Olga’ | ‘Webba Cenny’ | |||
CO (kg·Mg ) | One | 52.09 ± 0.62 Ba * | 55.79 ± 0.04 Aa | 50.57 ± 0.27 Ca | 52.67 ± 0.29 Ba | <0.0001 |
Many | 53.91 ± 0.64 Ba | 57.74 ± 0.04 Aa | 52.34 ± 0.28 Ca | 54.51 ± 0.30 Ba | <0.0001 | |
p-value | 0.3359 | 0.4598 | 0.3315 | 0.4897 | ||
CO (kg·Mg ) | One | 1275.51 ± 15.15 Ba | 1366.23 ± 0.97 Aa | 1238.46 ± 6.55 Ca | 1289.89 ± 7.08 Ba | <0.0001 |
Many | 1320.15 ± 15.68 Ba | 1414.05 ± 1.00 Aa | 1281.81 ± 6.78 Ca | 1335.04 ± 7.33 Ba | <0.0001 | |
p-value | 0.3859 | 0.5282 | 0.3808 | 0.5626 | ||
N (kg·Mg ) | One | 3.37 ± 0.06 ABa | 3.4 ± 0.05 Aa | 3.08 ± 0.1 Ba | 2.54 ± 0.19 Ca | <0.0001 |
Many | 3.49 ± 0.06 ABa | 3.52 ± 0.05 Aa | 3.19 ± 0.10 Ba | 2.63 ± 0.19 Ca | <0.0001 | |
p-value | 0.4139 | 0.5665 | 0.4084 | 0.6034 | ||
SO (kg·Mg ) | One | 0.09 ± 0.01 Aa | 0.08 ± 0.01 Aa | 0.11 ± 0.01 Aa | 0.11 ± 0.04 Aa | 0.5463 |
Many | 0.09 ± 0.01 Aa | 0.09 ± 0.01 Aa | 0.11 ± 0.01 Aa | 0.11 ± 0.04 Aa | 0.5578 | |
p-value | 0.3728 | 0.5104 | 0.3680 | 0.5436 | ||
Dust (kg·Mg ) | One | 3.03 ± 0.1 Aa | 1.88 ± 0.07 BCa | 2.24 ± 0.3 Ba | 1.6 ± 0.08 Ca | <0.0001 |
Many | 3.14 ± 0.10 Aa | 1.95 ± 0.07 BCa | 2.32 ± 0.31 Ba | 1.66 ± 0.08 Ca | <0.0001 | |
p-value | 0.3477 | 0.4759 | 0.3431 | 0.5068 |
Parameter | Age of Shoots | Hazelnut Variety | p-Value | |||
---|---|---|---|---|---|---|
‘Kataloński’ | ‘Olbrzymi z Halle’ | ‘Olga’ | ‘Webba Cenny’ | |||
Vo (Nm ·kg ) | One | 0.89 ± 0.02 Aba * | 0.93 ± 0.02 Aa | 0.85 ± 0.02 Ba | 0.86 ± 0.01 Ba | 0.0033 |
Many | 0.93 ± 0.02 ABa | 0.96 ± 0.02 Aa | 0.88 ± 0.02 Ba | 0.89 ± 0.01 Ba | 0.0029 | |
p-value | 0.5239 | 0.5189 | 0.4996 | 0.6123 | ||
V (Nm ·kg ) | One | 4.28 ± 0.07 ABa | 4.42 ± 0.10 Aa | 4.07 ± 0.09 Ba | 4.09 ± 0.06 Ba | 0.0033 |
Many | 4.43 ± 0.08 ABa | 4.57 ± 0.11 Aa | 4.21 ± 0.10 Ba | 4.23 ± 0.06 Ba | 0.0029 | |
p-value | 0.5606 | 0.5552 | 0.5346 | 0.6552 | ||
V (Nm ·kg ) | One | 0.79 ± 0.01 Ba | 0.85 ± 0.00 Aa | 0.77 ± 0.00 Ca | 0.79 ± 0.00 Ba | <0.0001 |
Many | 0.82 ± 0.01 Ba | 0.87 ± 0.00 Aa | 0.79 ± 0.00 Ca | 0.83 ± 0.00 Ba | <0.0001 | |
p-value | 0.5815 | 0.5760 | 0.5546 | 0.6797 | ||
V (Nm ·kg ) | One | 0.0003 ± 0.00 Aa | 0.0003 ± 0.00 Aa | 0.0004 ± 0.00 Aa | 0.0004 ± 0.00 Aa | 0.5463 |
Many | 0.0003 ± 0.00 Aa | 0.0003 ± 0.00 Aa | 0.0004 ± 0.00Aa | 0.0004 ± 0.00 Aa | 0.5498 | |
p-value | 0.6503 | 0.6163 | 0.5934 | 0.7272 | ||
V (Nm ·kg ) | One | 1.08 ± 0.02 Aa | 0.96 ± 0.04 Ba | 1.08 ± 0.02 Aa | 1.01 ± 0.03 ABa | 0.0024 |
Many | 1.12 ± 0.01 Aa | 0.99 ± 0.04 Ba | 1.12 ± 0.03 Aa | 1.05 ± 0.03 ABa | 0.0029 | |
p-value | 0.6455 | 0.6393 | 0.6156 | 0.7544 | ||
V (Nm ·kg ) | One | 4.15 ± 0.05 Aa | 4.26 ± 0.08 Aa | 3.91 ± 0.09 Ba | 3.81 ± 0.04 Ba | <0.0001 |
Many | 4.29 ± 0.05 Aa | 4.41 ± 0.08 Aa | 4.05 ± 0.09 Ba | 3.94 ± 0.04 Ba | <0.0001 | |
p-value | 0.8523 | 0.8111 | 0.7398 | 0.6379 | ||
V (Nm ·kg ) | One | 4.94 ± 0.06 Aa | 5.11 ± 0.08 Aa | 4.68 ± 0.09 Ba | 4.61 ± 0.03 Ba | <0.0001 |
Many | 5.10 ± 0.06 Aa | 5.29 ± 0.08 Aa | 4.84 ± 0.09 Ba | 4.77 ± 0.04 Ba | <0.0001 | |
p-value | 0.6077 | 0.6019 | 0.5795 | 0.7103 | ||
V (Nm ·kg ) | One | 6.71 ± 0.08 Aa | 6.78 ± 0.13 Aa | 6.41 ± 0.13 Ba | 6.28 ± 0.07 Ba | 0.0013 |
Many | 6.94 ± 0.08 Aa | 7.02 ± 0.14 Aa | 6.64 ± 0.14 Ba | 6.45 ± 0.07 Ba | 0.0019 | |
p-value | 0.7050 | 0.6681 | 0.6433 | 0.7884 |
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Borkowska, A.; Maj, G.; Klimek, K.E.; Kapłan, M. The Determination of Woody Biomass Resources and Their Energy Potential from Hazelnut Tree Cultivation. Energies 2024 , 17 , 4536. https://doi.org/10.3390/en17184536
Borkowska A, Maj G, Klimek KE, Kapłan M. The Determination of Woody Biomass Resources and Their Energy Potential from Hazelnut Tree Cultivation. Energies . 2024; 17(18):4536. https://doi.org/10.3390/en17184536
Borkowska, Anna, Grzegorz Maj, Kamila E. Klimek, and Magdalena Kapłan. 2024. "The Determination of Woody Biomass Resources and Their Energy Potential from Hazelnut Tree Cultivation" Energies 17, no. 18: 4536. https://doi.org/10.3390/en17184536
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