Research in Personnel and Human Resources Management

research in personnel and human resources management journal

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

research in personnel and human resources management 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.

CategoryYearQuartile
Organizational Behavior and Human Resource Management2001Q3
Organizational Behavior and Human Resource Management2002Q1
Organizational Behavior and Human Resource Management2003Q1
Organizational Behavior and Human Resource Management2004Q1
Organizational Behavior and Human Resource Management2005Q1
Organizational Behavior and Human Resource Management2006Q1
Organizational Behavior and Human Resource Management2007Q1
Organizational Behavior and Human Resource Management2008Q2
Organizational Behavior and Human Resource Management2009Q1
Organizational Behavior and Human Resource Management2010Q2
Organizational Behavior and Human Resource Management2011Q1
Organizational Behavior and Human Resource Management2012Q1
Organizational Behavior and Human Resource Management2013Q3
Organizational Behavior and Human Resource Management2014Q1
Organizational Behavior and Human Resource Management2015Q2
Organizational Behavior and Human Resource Management2016Q2
Organizational Behavior and Human Resource Management2017Q1
Organizational Behavior and Human Resource Management2018Q2
Organizational Behavior and Human Resource Management2019Q1
Organizational Behavior and Human Resource Management2020Q2
Organizational Behavior and Human Resource Management2021Q1

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.

YearSJR
20010.186
20021.707
20032.340
20041.166
20051.714
20061.962
20071.283
20080.645
20091.272
20100.521
20111.578
20120.936
20130.327
20141.320
20150.777
20160.778
20171.183
20180.853
20190.867
20200.779
20211.283

Evolution of the number of published documents. All types of documents are considered, including citable and non citable documents.

YearDocuments
20008
20019
20029
200310
20048
20058
20069
20078
20088
20097
20106
20116
20126
20130
20146
20156
20166
20177
20187
20196
20208
20218

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 documentYearValue
Cites / Doc. (4 years)20000.000
Cites / Doc. (4 years)20010.250
Cites / Doc. (4 years)20020.941
Cites / Doc. (4 years)20031.462
Cites / Doc. (4 years)20041.444
Cites / Doc. (4 years)20052.750
Cites / Doc. (4 years)20061.743
Cites / Doc. (4 years)20071.571
Cites / Doc. (4 years)20081.697
Cites / Doc. (4 years)20091.333
Cites / Doc. (4 years)20101.500
Cites / Doc. (4 years)20111.759
Cites / Doc. (4 years)20122.074
Cites / Doc. (4 years)20131.440
Cites / Doc. (4 years)20141.556
Cites / Doc. (4 years)20152.667
Cites / Doc. (4 years)20161.222
Cites / Doc. (4 years)20171.889
Cites / Doc. (4 years)20182.000
Cites / Doc. (4 years)20192.231
Cites / Doc. (4 years)20202.923
Cites / Doc. (4 years)20212.429
Cites / Doc. (3 years)20000.000
Cites / Doc. (3 years)20010.250
Cites / Doc. (3 years)20020.941
Cites / Doc. (3 years)20031.462
Cites / Doc. (3 years)20041.250
Cites / Doc. (3 years)20052.148
Cites / Doc. (3 years)20061.577
Cites / Doc. (3 years)20071.200
Cites / Doc. (3 years)20081.120
Cites / Doc. (3 years)20091.400
Cites / Doc. (3 years)20101.130
Cites / Doc. (3 years)20111.714
Cites / Doc. (3 years)20121.579
Cites / Doc. (3 years)20131.111
Cites / Doc. (3 years)20142.000
Cites / Doc. (3 years)20150.667
Cites / Doc. (3 years)20161.250
Cites / Doc. (3 years)20171.889
Cites / Doc. (3 years)20182.053
Cites / Doc. (3 years)20191.850
Cites / Doc. (3 years)20202.300
Cites / Doc. (3 years)20212.143
Cites / Doc. (2 years)20000.000
Cites / Doc. (2 years)20010.250
Cites / Doc. (2 years)20020.941
Cites / Doc. (2 years)20031.222
Cites / Doc. (2 years)20041.105
Cites / Doc. (2 years)20052.111
Cites / Doc. (2 years)20061.313
Cites / Doc. (2 years)20070.706
Cites / Doc. (2 years)20081.294
Cites / Doc. (2 years)20091.188
Cites / Doc. (2 years)20101.067
Cites / Doc. (2 years)20111.308
Cites / Doc. (2 years)20120.917
Cites / Doc. (2 years)20131.167
Cites / Doc. (2 years)20140.500
Cites / Doc. (2 years)20150.667
Cites / Doc. (2 years)20161.250
Cites / Doc. (2 years)20171.667
Cites / Doc. (2 years)20181.923
Cites / Doc. (2 years)20191.429
Cites / Doc. (2 years)20202.231
Cites / Doc. (2 years)20211.500

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.

CitesYearValue
Self Cites20000
Self Cites20010
Self Cites20020
Self Cites20031
Self Cites20042
Self Cites20052
Self Cites20061
Self Cites20071
Self Cites20082
Self Cites20090
Self Cites20100
Self Cites20110
Self Cites20120
Self Cites20130
Self Cites20140
Self Cites20150
Self Cites20160
Self Cites20171
Self Cites20182
Self Cites20190
Self Cites20201
Self Cites20211
Total Cites20000
Total Cites20012
Total Cites200216
Total Cites200338
Total Cites200435
Total Cites200558
Total Cites200641
Total Cites200730
Total Cites200828
Total Cites200935
Total Cites201026
Total Cites201136
Total Cites201230
Total Cites201320
Total Cites201424
Total Cites20158
Total Cites201615
Total Cites201734
Total Cites201839
Total Cites201937
Total Cites202046
Total Cites202145

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.

CitesYearValue
External Cites per document20000
External Cites per document20010.250
External Cites per document20020.941
External Cites per document20031.423
External Cites per document20041.179
External Cites per document20052.074
External Cites per document20061.538
External Cites per document20071.160
External Cites per document20081.040
External Cites per document20091.400
External Cites per document20101.130
External Cites per document20111.714
External Cites per document20121.579
External Cites per document20131.111
External Cites per document20142.000
External Cites per document20150.667
External Cites per document20161.250
External Cites per document20171.833
External Cites per document20181.947
External Cites per document20191.850
External Cites per document20202.250
External Cites per document20212.095
Cites per document20000.000
Cites per document20010.250
Cites per document20020.941
Cites per document20031.462
Cites per document20041.250
Cites per document20052.148
Cites per document20061.577
Cites per document20071.200
Cites per document20081.120
Cites per document20091.400
Cites per document20101.130
Cites per document20111.714
Cites per document20121.579
Cites per document20131.111
Cites per document20142.000
Cites per document20150.667
Cites per document20161.250
Cites per document20171.889
Cites per document20182.053
Cites per document20191.850
Cites per document20202.300
Cites per document20212.143

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.

YearInternational Collaboration
20000.00
20010.00
20020.00
20030.00
20040.00
20050.00
20060.00
20070.00
20080.00
20090.00
20100.00
20110.00
20120.00
20130
201416.67
20150.00
20160.00
20170.00
201814.29
201966.67
20200.00
20210.00

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.

DocumentsYearValue
Non-citable documents20000
Non-citable documents20011
Non-citable documents20022
Non-citable documents20033
Non-citable documents20043
Non-citable documents20053
Non-citable documents20063
Non-citable documents20073
Non-citable documents20083
Non-citable documents20093
Non-citable documents20102
Non-citable documents20111
Non-citable documents20120
Non-citable documents20130
Non-citable documents20140
Non-citable documents20150
Non-citable documents20160
Non-citable documents20170
Non-citable documents20180
Non-citable documents20190
Non-citable documents20206
Non-citable documents202114
Citable documents20000
Citable documents20017
Citable documents200215
Citable documents200323
Citable documents200425
Citable documents200524
Citable documents200623
Citable documents200722
Citable documents200822
Citable documents200922
Citable documents201021
Citable documents201120
Citable documents201219
Citable documents201318
Citable documents201412
Citable documents201512
Citable documents201612
Citable documents201718
Citable documents201819
Citable documents201920
Citable documents202014
Citable documents20217

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.

DocumentsYearValue
Uncited documents20000
Uncited documents20017
Uncited documents20029
Uncited documents200315
Uncited documents200415
Uncited documents200512
Uncited documents200610
Uncited documents200712
Uncited documents200813
Uncited documents200911
Uncited documents201013
Uncited documents20116
Uncited documents20125
Uncited documents20139
Uncited documents20144
Uncited documents20156
Uncited documents20165
Uncited documents20174
Uncited documents20187
Uncited documents20198
Uncited documents20206
Uncited documents20218
Cited documents20000
Cited documents20011
Cited documents20028
Cited documents200311
Cited documents200413
Cited documents200515
Cited documents200616
Cited documents200713
Cited documents200812
Cited documents200914
Cited documents201010
Cited documents201115
Cited documents201214
Cited documents20139
Cited documents20148
Cited documents20156
Cited documents20167
Cited documents201714
Cited documents201812
Cited documents201912
Cited documents202014
Cited documents202113

Evolution of the percentage of female authors.

YearFemale Percent
200020.00
200123.81
200212.50
200342.86
200457.69
200542.11
200642.11
200731.25
200841.67
200933.33
201040.00
201150.00
201221.43
20130.00
201426.09
201537.50
201631.25
201746.15
201820.00
20190.00
20200.00
20210.00

Evolution of the number of documents cited by public policy documents according to Overton database.

DocumentsYearValue
Overton20000
Overton20010
Overton20020
Overton20030
Overton20040
Overton20050
Overton20060
Overton20070
Overton20080
Overton20090
Overton20100
Overton20110
Overton20120
Overton20130
Overton20140
Overton20150
Overton20160
Overton20170
Overton20180
Overton20190
Overton20200
Overton20210

Evoution of the number of documents related to Sustainable Development Goals defined by United Nations. Available from 2018 onwards.

DocumentsYearValue
SDG20184
SDG20192
SDG20202
SDG20212

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research in personnel and human resources management journal

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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 Behavioral and Experimental Economics

Journal of Vocational Behavior

  • ISSN: 0001-8791
  • 5 Year impact factor: 9.4
  • Impact factor: 5.2

Journal of Vocational Behavior

Organizational Dynamics

  • ISSN: 0090-2616
  • 5 Year impact factor: 2.1
  • Impact factor: 3.1

Organizational Dynamics

research in personnel and human resources management journal

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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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

  • Reviewing applications

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
  • Suitability/Fitness

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 .

A career with the U.S. government provides employees with a comprehensive benefits package. As a federal employee, you and your family will have access to a range of benefits that are designed to make your federal career very rewarding. Opens in a new window Learn more about federal benefits .

Review our benefits

Eligibility for benefits depends on the type of position you hold and whether your position is full-time, part-time or intermittent. Contact the hiring agency for more information on the specific benefits offered.

How You Will Be Evaluated

You will be evaluated for this job based on how well you meet the qualifications above.

  • Customer Service
  • Financial Accountability
  • Oral Communication
  • Planning and Evaluating
  • Technical Competence

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.

  • Your Resume showing the applicant's name, email address, work schedule, hours worked per week, dates of employment, and duties performed to include pay plan, series, and grade level for relevant federal experience. Your resume must show complete information for each job entry to support minimum qualifications.
  • Classified or government sensitive information
  • Social Security Number (SSN)
  • Photos of yourself
  • Personal information, such as age, gender, religious affiliation, etc.
  • Encrypted and digitally signed documents
  • This position has a basic education requirement, you MUST submit a copy of your transcripts for further consideration.
  • Cover Letter, optional
  • V eterans' Preference documentation, if applicable (e.g. DD-214 Member Copy 4 showing type of discharge/character of service; Current Active Duty members must submit a certification of expected discharge or release from active duty under honorable conditions dated within 120 days; SF-15 Form and related documentation; VA letter.) If you are a veteran and you are claiming 5-point veterans' preference, you must submit a copy of your DD-214 or other proof of eligibility.
  • If you are a veteran and you are claiming 10-point veterans' preference, you must also submit an SF-15, "Application for 10-Point Veterans' Preference" plus the proof required by that form. For more information on veterans' preference click here .
  • Career Transition Assistance Program/Interagency Career Transition Assistance Program documentation, if applicable (e.g. Certification of Expected Separation, Reduction-In-Force Separation Notice, or Notice of Proposed Removal; SF-50 that documents the RIF separation action; and most recent performance appraisal.)
  • Worker's Compensation Separation : Agency certification of inability to place employee AND Notification of Separation OR Separation SF-50.
  • Disability Annuity Termination : Notification from OPM of disability annuity termination AND Separation SF-50 of the last position held.
  • Military Reserve or National Guard Technician Special Disability Retirement Annuity under 5 U.S.C. 8337(h) or 8456 : Certification of special disability retirement annuity from a military department or National Guard Bureau AND Separation SF-50 of the last position held

If you are relying on your education to meet qualification requirements:

Education must be accredited by an accrediting institution recognized by the U.S. Department of Education in order for it to be credited towards qualifications. Therefore, provide only the attendance and/or degrees from schools accredited by accrediting institutions recognized by the U.S. Department of Education .

Failure to provide all of the required information as stated in this vacancy announcement may result in an ineligible rating or may affect the overall rating.

  • You will be taken to an online application. Complete the online application, verify the required documentation, and submit the application.
  • You will receive an email notification when your application has been received for the announcement.
  • To verify the status of your application, log into your USAJOBS account, https://my.usajobs.gov/Account/Login , select the Application Status link and then select the More Information link for this position. The Application Status page will display the status of your application, the documentation received and processed, and your responses submitted to the online application. Your uploaded documents may take several hours to clear the virus scan process.

Agency contact information

The questionnaire, resume review, and structured interview will assess your qualifications for the job, and will be used to identify the best qualified applicants to be referred to the hiring manager for further consideration and possible interviews. Your ratings in this Assessment Questionnaire are subject to evaluation and verification based on the documents and references you submit. Later steps in the selection process are specifically designed to verify your ratings. Deliberate attempts to falsify information may be grounds for not selecting you or for dismissing you from the position/agency during the probationary period. Once your online application is submitted you will receive a confirmation notification by email. Your application will be evaluated. You will receive another notification about the status of your application. You may be asked to complete a pre-cert structured interview, post-cert structured interview, written demonstration, and/or fit interview. We expect to make a job offer within 40 days after the closing date of the announcement. After making a tentative job offer, we will conduct a suitability/security background investigation. An agency may not request, orally, in writing, or electronically through the USA Jobs website or other electronic means, that an applicant for appointment for a position in the civil service disclose criminal history record information before the appointing agency extends a conditional offer of employment. If you believe you were asked about your criminal history improperly, contact the agency or visit https://www.opm.gov/about-us/careers-at-opm/opportunities/

The Federal hiring process is set up to be fair and transparent. Please read the following guidance.

  • Criminal history inquiries
  • Equal Employment Opportunity (EEO) Policy
  • Financial suitability
  • New employee probationary period
  • Privacy Act
  • Reasonable accommodation policy
  • Selective Service
  • Signature and false statements
  • Social security number request

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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

ORCID logo

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

PLOS

  • Published: September 9, 2024
  • https://doi.org/10.1371/journal.pone.0310125
  • Peer Review
  • Reader Comments

Table 1

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).

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  • 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

  • View Article
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  • 4. Chui, M., Yee, L., Hall, B., & Singla, A. (2023, August 1). The state of AI in 2023: Generative AI’s breakout year. QuantumBlack, AI by McKinsey. https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-in-2023-generative-ais-breakout-year#/ .
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  • 14. R Core Team (2024). R : A language and environment for statistical computing . R Foundation for Statistical Computing: Vienna, Austria.
  • 15. Wickham H. (2016). ggplot2: Elegant graphics for data analysis . Springer-Verlag: New York.

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Click here to enlarge figure

ParameterStandard
Proximate AnalysisHigher 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.
ParameterAverage Number of Shoots (pcs.) for 1 BushAverage Shoot Diameter (mm)
at 50 cm Height on 1 Bush
Average Shoot Weight (kg·bush )
Age of shootsonemanyonemanyonemany
‘Kataloński’19.00 ± 7.94 A *10.67 ± 1.15 A12.91 ± 0.51 A22.70 ± 3.03 A1.78 ± 0.38 A4.88 ± 1.38 A
‘Olbrzymi
z Halle’
12.00 ± 1.73 A8.67 ± 1.53 A12.90 ± 1.68 A20.77 ± 1.86 A1.33 ± 0.66 A4.44 ± 2.03 A
‘Olga’10.33 ± 4.51 A11.67 ± 1.15 A12.20 ± 1.93 A22.63 ± 8.06 A1.33 ± 0.00 A6.44 ± 2.52 A
‘Webba
Cenny’
12.67 ± 0.57 A10.00 ± 3.00 A13.93 ± 2.98 A20.90 ± 0.88A1.78 ± 0.77 A4.66 ± 0.66 A
p-value0.85120.56320.24890.36970.57890.6328
ParameterAge of ShootsHazelnut Varietyp-Value
‘Kataloński’‘Olbrzymi
z Halle’
‘Olga’‘Webba Cenny’
LHV
(MJ·kg )
One15.43 ± 0.09 BCa *16.85 ± 0.09 Aa15.26 ± 0.05 Ca15.61 ± 0.04 Ba<0.0001
Many15.97 ± 0.09 BCa17.44 ± 0.09 Aa15.79 ± 0.05 Ca16.16 ± 0.04 Ba<0.0001
p-value0.25890.13690.75320.1598
HHV (MJ·kg )One16.79 ± 0.09 Ac18.08 ± 0.09 Aa16.64 ± 0.05 Ac17.00 ± 0.04 Ab<0.0001
Many16.83 ± 0.5 Ac18.03 ± 0.04 Aa16.39 ± 0.03 Ac16.98 ± 0.06 Ab<0.0004
p-value0.48930.73690.83610.1774
M
(%)
One16.76 ± 0.10 Ca13.5 ± 0.06 Aa18.04 ± 0.27 Da17.48 ± 0.07 Ba<0.0001
Many17.35 ± 0.10 Ca13.97 ± 0.06 Aa18.67 ± 0.28 Da18.09 ± 0.07 Ba<0.0001
p-value0.31900.16870.92800.1969
V
(%)
One64.99 ± 0.35 Ca68.22 ± 0.34 Aa64.93 ± 0.36 Ca66.13 ± 0.39 Ba<0.0001
Many67.26 ± 0.36 Ca70.61 ± 0.35 Aa67.20 ± 0.37 Ca68.44 ± 0.40 Ba<0.0001
p-value0.27700.14650.80590.1710
A
(%)
One2.40 ± 0.08 Aa1.49 ± 0.05 BCa1.77 ± 0.24 Ba1.26 ± 0.06 Ca<0.0001
Many2.48 ± 0.08 Aa1.54 ± 0.051 BCa1.83 ± 0.25 Ba1.30 ± 0.06 Ca<0.0001
p-value0.30750.16260.89460.1898
FC
(%)
One15.85 ± 0.41 Ba16.78 ± 0.25 Aa15.26 ± 0.41 Ba15.12 ± 0.29 Ba0.0014
Many16.40 ± 0.42 Ba17.37 ± 0.26 Aa15.79 ± 0.42 Ba15.65 ± 0.30 Ba0.0019
p-value0.27180.14370.79090.1678
C
(%)
One42.28 ± 0.5 Ba45.29 ± 0.03 Aa41.05 ± 0.22 Ca42.76 ± 0.23 Ba<0.0001
Many43.76 ± 0.52 Ba46.88 ± 0.03 Aa42.48 ± 0.23 Ca44.26 ± 0.24 Ba<0.0001
p-value0.30170.15960.87790.1862
H
(%)
One7.78 ± 0.15 Aa7.1 ± 0.35 ABa7.63 ± 0.22 ABa7.09 ± 0.28 Ba<0.0001
Many8.05 ± 0.15 Aa7.35 ± 0.36 ABa7.89 ± 0.23ABa7.34 ± 0.29 Ba<0.0001
p-value0.28220.14920.82100.1742
N
(%)
One0.95 ± 0.02 ABa0.96 ± 0.01 Aa0.87 ± 0.03 Ba0.72 ± 0.05 Ca<0.0001
Many0.98 ± 0.02 ABa0.99 ± 0.01 Aa0.90 ± 0.03 Ba0.74 ± 0.05 Ca<0.0001
p-value0.31320.16560.91130.1933
S
(%)
One0.05 ± 0 Aa0.04 ± 0.01 Aa0.05 ± 0 Aa0.05 ± 0.02 Aa0.5463
Many0.051 ± 0 Aa0.04 ± 0.01 Aa0.05 ± 0 Aa0.05 ± 0.02 Aa0.6874
p-value0.29000.15330.84360.1790
O
(%)
One46.54 ± 0.55 Ba45.12 ± 0.34 Ca48.62 ± 0.65 Aa48.12 ± 0.13 Aa<0.0001
Many48.17 ± 0.57 Ba46.68 ± 0.35 Ca50.32 ± 0.67 Aa49.80 ± 0.13 Aa<0.0001
p-value0.32190.17020.93640.1987
H/COne1.84 ± 0.04 Aa1.57 ± 0.08 Ba1.86 ± 0.04 Aa1.66 ± 0.07 Ba0.0009
Many1.90 ± 0.04 Aa1.62 ± 0.08 Ba1.92 ± 0.0414 Aa1.71 ± 0.07 Ba0.0007
p-value0.29770.15740.86620.1838
N/COne0.02 ± 0.001 Aa0.02 ± 0.00 Aa0.02 ± 0.001 Aa0.02 ± 0.001 Ba<0.0001
Many0.02 ± 0.001 Aa0.021 ± 0.00 Aa0.02 ± 0.001 Aa0.02 ± 0.001 Ba<0.0001
p-value0.33050.17480.96150.2040
O/COne0.83 ± 0.02 Ba0.75 ± 0.01 Ca0.88 ± 0.02 Aa0.84 ± 0.01 Ba<0.0001
Many0.85 ± 0.02 Ba0.77 ± 0.01 Ca0.92 ± 0.02 Aa0.87 ± 0.01 Ba<0.0001
p-value0.26800.14170.77960.1654
ParameterAge of ShootsHazelnut Varietyp-Value
‘Kataloński’‘Olbrzymi
z Halle’
‘Olga’‘Webba Cenny’
CO
(kg·Mg )
One52.09 ± 0.62 Ba *55.79 ± 0.04 Aa50.57 ± 0.27 Ca52.67 ± 0.29 Ba<0.0001
Many53.91 ± 0.64 Ba57.74 ± 0.04 Aa52.34 ± 0.28 Ca54.51 ± 0.30 Ba<0.0001
p-value0.33590.45980.33150.4897
CO
(kg·Mg )
One1275.51 ± 15.15 Ba1366.23 ± 0.97 Aa1238.46 ± 6.55 Ca1289.89 ± 7.08 Ba<0.0001
Many1320.15 ± 15.68 Ba1414.05 ± 1.00 Aa1281.81 ± 6.78 Ca1335.04 ± 7.33 Ba<0.0001
p-value0.38590.52820.38080.5626
N
(kg·Mg )
One3.37 ± 0.06 ABa3.4 ± 0.05 Aa3.08 ± 0.1 Ba2.54 ± 0.19 Ca<0.0001
Many3.49 ± 0.06 ABa3.52 ± 0.05 Aa3.19 ± 0.10 Ba2.63 ± 0.19 Ca<0.0001
p-value0.41390.56650.40840.6034
SO
(kg·Mg )
One0.09 ± 0.01 Aa0.08 ± 0.01 Aa0.11 ± 0.01 Aa0.11 ± 0.04 Aa0.5463
Many0.09 ± 0.01 Aa0.09 ± 0.01 Aa0.11 ± 0.01 Aa0.11 ± 0.04 Aa0.5578
p-value0.37280.51040.36800.5436
Dust
(kg·Mg )
One3.03 ± 0.1 Aa1.88 ± 0.07 BCa2.24 ± 0.3 Ba1.6 ± 0.08 Ca<0.0001
Many3.14 ± 0.10 Aa1.95 ± 0.07 BCa2.32 ± 0.31 Ba1.66 ± 0.08 Ca<0.0001
p-value0.34770.47590.34310.5068
ParameterAge of ShootsHazelnut Varietyp-Value
‘Kataloński’‘Olbrzymi
z Halle’
‘Olga’‘Webba Cenny’
Vo (Nm ·kg )One0.89 ± 0.02 Aba *0.93 ± 0.02 Aa0.85 ± 0.02 Ba0.86 ± 0.01 Ba0.0033
Many0.93 ± 0.02 ABa0.96 ± 0.02 Aa0.88 ± 0.02 Ba0.89 ± 0.01 Ba0.0029
p-value0.52390.51890.49960.6123
V
(Nm ·kg )
One4.28 ± 0.07 ABa4.42 ± 0.10 Aa4.07 ± 0.09 Ba4.09 ± 0.06 Ba0.0033
Many4.43 ± 0.08 ABa4.57 ± 0.11 Aa4.21 ± 0.10 Ba4.23 ± 0.06 Ba0.0029
p-value0.56060.55520.53460.6552
V (Nm ·kg )One0.79 ± 0.01 Ba0.85 ± 0.00 Aa0.77 ± 0.00 Ca0.79 ± 0.00 Ba<0.0001
Many0.82 ± 0.01 Ba0.87 ± 0.00 Aa0.79 ± 0.00 Ca0.83 ± 0.00 Ba<0.0001
p-value0.58150.57600.55460.6797
V (Nm ·kg )One0.0003 ± 0.00 Aa0.0003 ± 0.00 Aa0.0004 ± 0.00 Aa0.0004 ± 0.00 Aa0.5463
Many0.0003 ± 0.00 Aa0.0003 ± 0.00 Aa0.0004 ± 0.00Aa0.0004 ± 0.00 Aa0.5498
p-value0.65030.61630.59340.7272
V (Nm ·kg )One1.08 ± 0.02 Aa0.96 ± 0.04 Ba1.08 ± 0.02 Aa1.01 ± 0.03 ABa0.0024
Many1.12 ± 0.01 Aa0.99 ± 0.04 Ba1.12 ± 0.03 Aa1.05 ± 0.03 ABa0.0029
p-value0.64550.63930.61560.7544
V (Nm ·kg )One4.15 ± 0.05 Aa4.26 ± 0.08 Aa3.91 ± 0.09 Ba3.81 ± 0.04 Ba<0.0001
Many4.29 ± 0.05 Aa4.41 ± 0.08 Aa4.05 ± 0.09 Ba3.94 ± 0.04 Ba<0.0001
p-value0.85230.81110.73980.6379
V (Nm ·kg )One4.94 ± 0.06 Aa5.11 ± 0.08 Aa4.68 ± 0.09 Ba4.61 ± 0.03 Ba<0.0001
Many5.10 ± 0.06 Aa5.29 ± 0.08 Aa4.84 ± 0.09 Ba4.77 ± 0.04 Ba<0.0001
p-value0.60770.60190.57950.7103
V (Nm ·kg )One6.71 ± 0.08 Aa6.78 ± 0.13 Aa6.41 ± 0.13 Ba6.28 ± 0.07 Ba0.0013
Many6.94 ± 0.08 Aa7.02 ± 0.14 Aa6.64 ± 0.14 Ba6.45 ± 0.07 Ba0.0019
p-value0.70500.66810.64330.7884
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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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Rostov-on-Don is deemed to have been founded in December 1749, when construction began on the Temernitskaya Customs House in the lower reaches of the Don, on the left bank of the River Temernik. Soon after, a berth, a storehouse, a quarantine house and garrison barracks were added to the customs house. A port was built later, and for a while it would be the only Russian port in the south, handling all trade with the countries around the Black, Aegean and Mediterranean Seas. September 23, 1761, Elizabeth I decreed for a new fortress to be built here, which would be christened St. Dimitry of Rostov Fortress, and would define the location of the future historic centre of Rostov-on-Don.

The St. Dimitry of Rostov Fortress had lost its military value by the beginning of the nineteenth century as the Russian frontier was now far removed from those parts. In August 1807, the fortress was designated as uyezd town – the administrative centre of a county – and a few government institutions were moved there from Taganrog. Rostov changed its name a few times. First it was shortened to Rostov Fortress, then just Rostov, and finally expanded to Rostov-on-Don so as to avoid confusion with another Rostov up north.

The Rostov Fortress was demolished in 1835. The ramparts were leveled, and the moats filled, giving the town a nearly two-fold surplus of land. Rostov-on-Don began to expand quickly, getting paved streets, the telegraph, a water supply system as well as other amenities. In fact, Rostov-on-Don received the telephone before Moscow did. Rostov’s railway station, built in 1875, was the biggest in Russia at the time, and Rostov became the key transport hub on the Vladikavkaz railway. Rostov started using electric lighting at the end of the nineteenth century, and trams started running in the city in the early 1900s.

Russia’s first automatic telephone switchboard for 6,000 numbers was installed in Rostov-on-Don in 1929. This was the first public switchboard: the very first such facility was built in Moscow in the early 1920s, but served only the leaders of the Soviet state, and was not available to the public.

Today the thriving city of Rostov-on-Don (Russian spelling Rostov-na-Donu) and its 1 million inhabitants is the capital and administrative center of Rostov oblast of Russia. The city is also the capital of South Federal District of the Russian Federation from 2000. It is standing on the banks of the Don River about 46 km from the Azov Sea. Rostov-on-Don is a major railway and highway junction of Southern Russia. There is also an international airport in the city. In addition to the COupe Mondiale World Accordion Championships, several matches of FIFA World Cup 2018 will be held in Rostov-on-Don.  

The city offers many attractions, including:

  • Don River Lookout, Beregovaya ulitsa. Often referred to as "The Enbankment", visitors and locals alike will enjoy a stroll along the riverside. More than a picturesque view, the Embankment is lined with several restaurants, statues, lighted fountains, and a few shops; It is the center of nightlife in Rostov. Several steamboats are docked along the bank, and tickets for hour-long excursions can be purchased at the ticket booths on location for about 200 rubles.
  • An Obelisk at Teatralnaya square, Teatralnaya ploshad. Affectionally dubbed "Stella", by locals, the obelisk appears as a winged tower, across the street from Maxim Gorky Drama Theatre. As one approaches the obelisk, inscriptions honoring the arts, science, agriculture, military, and education can be seen at the base. On the south side of the obelisk, the golden lady (Stella) hovers between the wings.
  • Pushkin Street. Visitors may enjoy a stroll down this highly ornate, landscaped boulevard, lined with thousands of trees, restaurants, food kiosks, flowers, benches, statues, and memorials. A favorite sight near the eastern end of the boulevard are the wrought-iron globes, depicting scenes from Pushkin's most popular works. Pushkin Street leads into both the City Park (Park Gorkovo) and October Revolution Park, where visitors will see more meticulously cultivated garden beds and other diversions such as amusement parks and souvenir kiosks.
  • Underground Tile Work, While perhaps not the most impressive sight, tile mosaics (depicting scenes of Soviet life, found on the walls of underground street crossings ("perekhody"), make for a momentary distraction. Though mosaics are found under several street crossings on Bolshaya Sadovaya Street, the most beautiful and well-maintained mosaics are under the intersection of Boshaya Sadovaya and Buddyonoskiy Prospect. Note: Appreciate the tile work as you walk; do not stop and stare, or you will block other pedestrians.
  • Public Parks: Many statues and monuments not listed here can be found in almost every public park and major street of Rostov.
  • Rostov State Opera and Ballet, (134 Bolshaya Sadovaya Street), Rostov has a nice Musical Theatre,which is not located far from the Maxim Gorky Drama Theatre. Most operas are sung in an original language. Ballets are exquisitely choreographed and are invariably accompanied by extravagant sets. Refreshments are almost always sold in the lobby during intermission (for an inflated price). Most Rostov locals will dress formally (but practically) while attending a ballet or opera. $10-$40 / 400-1500 rubles.
  • Rostov-on-Don ZooPark This zoo is well worth a visit, especially if one is accompanied by children. As one of the largest in Russia, the ZooPark is home to a staggering variety of animals, including giraffes, camels, polar bears, falcons, reptiles, fish, and simians. Entrance costs only 80 rubles for adults and 40 rubles for children. To get there, take the #6 Bus from the Central Rinok and exit at the stop "ZooPark".
  • Shopping at the Central Rinok, "Rinok" might be translated at "bazaar" or "farmer's market". This massive outdoor-and-indoor assortment of tiny shops and booths can be both exciting and intimidating for Westerners, who are unaccustomed to either haggling/ bargaining or being yelled at by shopkeepers. Shopping at a Rinok is one of the most memorable experiences that Russia has to offer for an adventurous North American, so don't be put off by the different feel of things. The majority of the Rinok is devoted to food and clothing, but you can buy anything here. Yes, anything (though it might take a while to find). Even if one does not speak the language, shopping at the Rinok is far preferable to shopping at the nearby, overpriced, department stores. Just let the money do the talking. The Rinok is located downtown, on Stanislavskovo Street, just four blocks south of the central intersection of Bolshaya Sadovaya Street and Buddyonovskiy Prospect.
  • Maxim Gorky Academic Drama Theatre. (1 Teatralnaya Ploshad), [5] Despite the name, this theatre is a venue not only for dramatic plays, but also comedies and concerts. The theatre is located on the eastern end of Bolshaya Sadovaya Street, directly across the street from the monument known as "Stella". Even throughout the summer months (when other theatres may be closed), Maxim Gorky Theatre still operates. Prices will vary depending on the show, but tickets are generally inexpensive when compared to other large theatres.
  • Stroll Through October Revolution Park, More than just a wooded area, this park is filled with things to do: amusement park rides, ping-pong tables, and a petting zoo, just to name a few.
  • Stroll Through the City Park (45 Bolshaya Sadovaya Street), Sometimes called "Park Gorkovo", this park is filled with beautiful flower beds, a full amusement park, restaurants, and souvenir kiosks.

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Southern Federal District, Russia

Southern federal district - overview.

The Southern Federal District is a federal district of the Russian Federation located in the south of its European part, in the lower reaches of the Volga River, north of the Greater Caucasus Range. The city of Rostov-on-Don is the administrative center of the district. The main sea resorts of Russia (Sochi, Anapa, Gelendzhik) are located on the territory of this federal district.

The population of the Southern Federal District is about 14,086,000 (2016), the area - 420,876 sq. km.

South district map, Russia

Southern federal district - features.

The federal district under the name of the North Caucasian Federal District was formed by the decree of the Russian President on May 13, 2000. On June 21, 2000, it was renamed the Southern Federal District. Initially, this federal district included the republics of the North Caucasus and the Stavropol Krai, which, in 2010, became parts of a separate North Caucasian Federal District.

In 2016, the Crimea and the city of Sevastopol were included in the Southern Federal District. Today, the Crimea is the object of territorial disagreements between Russia, de facto controlling this territory since March 2014, and Ukraine. Most of the UN member states continue to consider the entire Crimea as part of Ukraine.

In the west, the Southern Federal District borders Ukraine, in the east - Kazakhstan. In the east, it is also washed by the Caspian Sea, in the west - by the Azov Sea and the Black Sea.

South of Russia is characterized by a variety of landscapes (mixed, coniferous, deciduous forests, steppes, subalpine meadows). The average temperature in January is minus 4.4 degrees Celsius, in July - plus 24.6 degrees Celsius.

The Southern Federal District has significant reserves of thermal and mineral waters, tungsten, coal, oil on the shelf of the Caspian Sea.

The main branches of the local economy are extraction and processing of thermal and mineral waters, mining industry, tourism, agriculture, production of building materials.

Nature of the Southern Federal District

Krasnodar region

Krasnodar region

Author: Vladislav Shutyy

Rostov region

Rostov region

Author: Igor Tartanov

Astrakhan region

Astrakhan region

Author: Dvornikov Mikhail

Southern Federal District - Cities and Regions

South Federal District includes the following 6 federal subjects of Russian Federation:

The largest cities of South Federal District are:

  • Novorossiysk

South district of Russia photos

Sights of the southern federal district.

Cathedral in the Rostov region

Cathedral in the Rostov region

Author: Nikolay Sevastyanov

Church in the Volgograd region

Church in the Volgograd region

Author: Aleksandr Tuznichenko

Mosque in Maykop

Mosque in Maykop

Author: Radjeb Tsey

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Rating: 2.7 /5 (830 votes cast)

Invasion of the Parthenocissus inserta (Kern.) K. Fritsch. in Floodplain Forests of Rostov Oblast

  • Published: 01 April 2020
  • Volume 11 , pages 41–46, ( 2020 )

Cite this article

research in personnel and human resources management journal

  • B. L. Kozlovskiy 1 ,
  • O. I. Fedorinova 1 &
  • M. V. Kuropyatnikov 1  

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Naturalization of woody lianas poses a great danger to the ravine and floodplain forests of Rostov oblast. Therefore, the aim of the work was to study morphology, ecological and biological properties, and the invasive process of Partenocissus inserta . Studies were carried out in the Botanical Garden of the Southern Federal University and in the west of Rostov oblast in a floodplain oak forest at the confluence of the Mius and Krynka rivers. P. inserta penetrates into natural, seminatural, and disturbed communities and also is a part of the urban flora of Rostov-on-Don. In floodplain forests, it is a sub-edificator of the herbaceous layer, where it dominates and forms stable simple synusiae. This is the only representative of understory vegetation; it rises along the tree trunks to a height of 15–20 m, fixing itself in the unevenness of their bark by the twining tips of tendrils. It competes for the light with trees of the first story. This species changes the nature of the forest cenosis over a large area. The P. inserta naturalization strategy corresponds to the empty niche hypothesis.

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Agafonov, V.A. and Kaz’mina, E.S., Features of the flora of anthropogenic isolates on the example of the Bayern Munich floristic complex in the Voronezh city, Vestn. Voronezh Gos. Univ., Ser.: Khim. Biol. Farm. , 2013, no. 1, pp. 72–76.

Agafonov, V.A. and Tul’skii, A.V., Adventive fraction of forest zone flora in the Voronezh oblast, Vestn. Voronezh Gos. Univ., Ser.: Khim. Biol. Farm. , 2018, no. 2, pp. 68–74.

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This study was supported by the Ministry of Education and Science of the Russian Federation (project no. 6.6222.2017/8.9).

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Kozlovskiy, B.L., Fedorinova, O.I. & Kuropyatnikov, M.V. Invasion of the Parthenocissus inserta (Kern.) K. Fritsch. in Floodplain Forests of Rostov Oblast. Russ J Biol Invasions 11 , 41–46 (2020). https://doi.org/10.1134/S2075111720010075

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Received : 04 February 2019

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DOI : https://doi.org/10.1134/S2075111720010075

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Research in Personnel and Human Resources Management: Volume 31

Table of contents, research in personnel and human resources management, copyright page, list of contributors, global human resource management: bridging strategic and institutional perspectives.

The purpose of this paper is to bridge the boundaries separating strategic and comparative institutional perspectives on human resource systems and employment relations. Each research tradition has investigated the role and outcomes of corporations as they operate in an increasingly global economy. Researchers in these traditions, however, ask different research questions and draw on distinct social science disciplines, theoretical assumptions, and research methodologies. While they have pursued parallel but separate tracks, we argue that they have important lessons for each other. In this paper, we review the core characteristics and critiques of each research tradition, provide a series of examples of efforts to bridge their differences, and offer suggestions for future integration.

Designing Executive Risk-Taking: An Agenda for Improving Executive Outcomes Through Work Design

Executives exert a pervasive influence on the organizations they lead. As such, scholars have long considered how to calibrate the risks inherent in executive decision making, often relying on incentives and compensation to calibrate executive risk behavior. However, there are shortcomings that reduce the efficacy of this approach, largely because incentives and compensation do not alter the work environment itself, which play a significant role influencing executive risk behavior. Consequently, in this chapter, we propose a conceptualization that integrates executive risk-taking with work design, framing three central features of the strategic leader job and work environment that may be manipulated to channel and shape executive risk-taking. Specifically, accountability, discretion, and relationships are proposed as the key higher-order characteristics of the executive work context, and they are examined with respect to optimal calibration in order to maximize both executive performance and well-being, as well as organizational coordination and control. Implications of this conceptualization and directions for future research are discussed.

Orientation Programs that may Facilitate Newcomer Adjustment: A Literature Review and Future Research Agenda

Formal orientation programs play a potentially important role in newcomer adjustment, yet research aimed at understanding and improving the effects of these interventions has stagnated in recent years. The purpose of this chapter is to facilitate a redirection of researchers’ attention to such programs, and to suggest ways to integrate this body of research with recent developments in socialization and training literatures.

A Review of Recent Advances in Item Response Theory

The purpose of this chapter is to provide researchers a summary of some of the latest developments in item response theory (IRT), and to help these groups realize that psychometric tools can now be used for theory testing in addition to the traditional role of improving construct measurement. The author first reviews some of the fundamental tenets of classical test theory to contrast with IRT. He then describes recent advances in goodness-of-fit tests that have helped turn IRT into a model-testing tool. Finally, the author reviews several new test models that provide new flexibilities, summarizing summarize several examples of research that has used these new models in organizational research. At the end of this review, the author provides suggestions to help researchers better use these new IRT tools. Although there have been significant advances in IRT in the past decade, there has not been a systematic review of these developments. This review places those developments in context to provide readers a real appreciation of these breakthroughs.

The Impact of Leadership, Management, and HRM on Employee Reactions to Organizational Change

This chapter provides a review and extension of research pertaining to the respective roles of leaders, managers, and human resource management (HRM) practices on employee reactions to organizational change. Particular focus is given to the differential effects of these factors on employee commitment and resistance to change. A review of organizational change research between 2001 and 2011 revealed these outcomes to be two of the most commonly studied and consequential employee reactions to change. The review also demonstrates that research lacks precision in the treatment (conceptualization and measurement) of these constructs. For instance, sometimes the leadership and management of change are clearly differentiated, other times equated, and yet other times blurred. Arguments are presented that encourage researchers to refine theory and tests of leading and managing change, as well as their respective implications for change-related HRM. A specific case is made for the academic and practical utility of theory and practice that explicitly integrates leadership and management roles and responsibilities. Doing so will more closely align with and capture the inherent complexity of organizational change and employees’ experience. The intent is to urge both scholars and practitioners of change to adopt broader, yet more precise and practical, perspectives on how to influence employee reactions and change outcomes. To this end, special attention is paid to roles of leaders, managers, and HRM practices.

Off-the-job Embeddedness: A Reconceptualization and Agenda for Future Research

We propose that off-the-job embeddedness (OTJE) be reconceptualized as a separate and distinct, albeit related, construct from job embeddedness. We conceptualize OTJE as the totality of outside-work forces which keep an individual bound to his/her current geographical area and argue that this construct includes important factors which do not fall under the umbrella of “community embeddedness.” Moreover, we propose that these outside-work forces may embed individuals in their jobs either directly or indirectly (through the perceived or expressed preferences of spouses, children, and extended family). This paper identifies the key components of OJTE, addresses the measurement of OTJE, explains the relationships between job embeddedness and OTJE (and their respective components), highlights how OTJE can either amplify or counteract the effects of job embeddedness, and illustrates the direct and indirect effects of OTJE on both work-related and personal outcomes.

About the Authors

Rosemary Batt is the Alice Hanson Cook Professor of Women and Work at the ILR School, Cornell University. She is a Professor in Human Resource Studies and International and Comparative Labor and Editor of the ILR Review . She received her BA from Cornell University and her Ph.D. from MIT. Her research focuses on comparative international studies of management and employment relations, with particular attention to globalization and the restructuring of service industries, and its impact on low wageworkers. She coordinated the Global Call Center Research Project (http://www.globalcallcenter.org). Her current work focuses on the impact of financialization on management and employment relations. Her work has appeared in such journals as the AMJ , British Journal of Industrial Relations , the European Journal of Industrial Relations , ILR Review, Industrial Relations , IJHRM , and Personnel Psychology . She is coeditor of the Oxford Handbook on Work and Organization and coauthor of The New American Workplace , Cornell University Press (http://www.ilr.cornell.edu/directory/rb41/).

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