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Artificial intelligence and HRM: identifying future research Agenda using systematic literature review and bibliometric analysis

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Cited by 52 publication s

References 104 publication s, trends in multidiscipline management research: past, present and future of fiib business review.

This study aims to guide scholars on emerging research themes and publication trends by reviewing the published intellectual contributions in the FIIB Business Review (FBR)—a multidisciplined management journal since its inception. FBR has been indexed in Scopus and Academic journal guide (AJG) and continues to achieve new heights. This article has used bibliometric analysis of the leading publication themes of the journal between 2011 and 2022. This work uses the Scopus database to find the publication trend and most impactful papers, main themes and keywords of publication, futuristic themes and target ideas for FBR. In 2021, the average citation value for three years was 2.292 compared to 0.778 in 2020. Journal of Business Research, Journal of Marketing and Journal of Finance are the top three most cited journals in FBR. The journal has shown usefulness for scholars mainly related but not limited to strategic management, marketing, organizational behaviour and finance, among others, by contributing to areas like digital transformation, team problems, organizational conflicts, COVID-19 problems, sustainability and financial crises. Areas of future research and limitations are discussed.

Emotional Intelligence as Intangible Class Content for Effective Communication in Managing University Classes: A Bibliometrics Analysis

John Mayer and Peter Salavoy devised the term ‗Emotional Intelligence‘ in 1990, later popularized by psychologist Daniel Goleman psychologist Daniel Goleman and popularized through his scholarly work Emotional Intelligence in 1995. Since then this term has fascinated the world. The Harvard Business Review of 2003 determined that 80% of competencies distinguishing the top performers from the rest were owing to their high emotional intelligence. A lot of research has been done in the area since the last decade of the twentieth century conferring Emotional Intelligence (EI) a superior position to its equally important counterpart, the Intelligence Quotient (IQ). The present study aims to map the growing research, spread across domains covering Emotional Intelligence as a central theme, through a review of published articles in the Scopus database. In all, 860 articles were retrieved and refined to take 680 final articles. The data extracted was analysed based on different bibliographic parameters was visualized with the help of VOS viewer. Based on the contributions made by the countries, Spain topped the list with 137 contributions whereas the US topped the list of total link strength (1892). The overly visualization of the authors‘ bibliographic coupling shows that the most substantial authors were Extremera N. (with 20 publications), Merida-Lopez S. (with 13 publications), and Brackett M. A. (with 10 publications). In contrast, emotional intelligence, stress, self-efficacy, burnout, academic achievement have been the most recurrently considered and referred concepts. The most significant and prominent journal has been Frontiers in Psychology and Teaching and Teacher Education in this field. Frontiers in Psychology‖, Sustainability (Switzerland), and ―International Journal of Environmental Research and Public Health‖ topped the list of the latest publications in this literature.

The state of art, opportunities and challenges of blockchain in the insurance industry: a systematic literature review

Blockchain technologies are quickly changing the competitive business landscape. However, key stakeholders are still sceptical about how, when, and what blockchain might bring to their businesses. This study aims to analyse how blockchain might reshape the insurance industry from an economic and business perspective, as well as to identify which are the challenges and enablers that specifically affect blockchain adoption within this industry, through a Systematic Literature Review (SRL). We also classify existing challenges into five strategic areas: Social, Technological, Environmental, Economic, and Political. Additionally, we provide several recommendations to the manager on identifying the existing hurdles and smoothing transformation.

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• DOI: 10.1007/s11301-021-00249-2
• Corpus ID: 244743942

Artificial intelligence and HRM: identifying future research Agenda using systematic literature review and bibliometric analysis

• Neelam Kaushal , Rahul Pratap Singh Kaurav , +1 author N. Kaushik
• Published in Management Review Quarterly 29 November 2021
• Computer Science, Business

40 Citations

Analysis of research on artificial intelligence in human resources management: a bibliometric analysis, the interdisciplinary nature of ai and human resource management: a bibliometric analysis, human resource management and artificial intelligence: a bibliometric exploration, impact of ai-focussed technologies on social and technical competencies for hr managers – a systematic review and research agenda, applying artificial intelligence for talent retention: a systematic literature review, innovating hrm recruitment: a comprehensive review of ai deployment, artificial intelligence shaping talent intelligence and talent acquisition for smart employee management, artificial intelligence in corporate learning and development: current trends and future possibilities.

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How HR analytics evolved over time: a bibliometric analysis on Scopus database

Trends in multidiscipline management research: past, present and future of fiib business review, 142 references, a review paper on artificial intelligence at the service of human resources management.

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Understanding the interplay of artificial intelligence and strategic management: four decades of research in review

Research landscape of business intelligence and big data analytics: a bibliometrics study, artificial intelligence interchange human intervention in the recruitment process in indian software industry, trends and opportunities of artificial intelligence in human resource management: aspirations for public sector in bahrain, business intelligence and analytics: from big data to big impact, a conceptual artificial intelligence application framework in human resource management, adoption of artificial intelligence (ai) for talent acquisition in it/ites organizations, the transformation of human resource management and its impact on overall business performance: big data analytics and ai technologies in strategic hrm, applying artificial intelligence: implications for recruitment, related papers.

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Artificial intelligence and HRM: identifying future research Agenda using systematic literature review and bibliometric analysis

• Published: 29 November 2021
• Volume 73 , pages 455–493, ( 2023 )

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• Neelam Kaushal 1 ,
• Rahul Pratap Singh Kaurav   ORCID: orcid.org/0000-0001-9851-6854 2 ,
• Brijesh Sivathanu 3 &
• Neeraj Kaushik 1  

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The present research aims to identify significant contributors, recent dynamics, domains and advocates for future study directions in the arena of integration of Artificial Intelligence (AI) with Human Resource Management (HRM), in the context of various functions and practices in organizations. The paper adopted a methodology comprising of bibliometrics, network and content analysis (CA), on a sample of 344 documents extracted from the Scopus database, to identify extant research on this theme. Along with the bibliometric analysis, systematic literature review was done to propose an Artificial Intelligence and Human Resource Management Integration (AIHRMI) framework. Five clusters were recognized, and CA was conducted on the documents placed in the group of articles. It was found that vital research concentration in this arena is primarily about AI embeddedness in various HRM functions such as recruitment, selection, onboarding, training and learning, performance analysis, talent acquisition, as well as management and retention. The study proposes an AIHRMI framework developed from various studies considered in the current research. This model can provide guidance and future directions for several organizations in expansion of use of AI in HRM.

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( Source : Authors)

( Source : Authors). Note TP = Total publications, CoC = Co-citation count, CoA = Co-authorship, CoCu = Collaboration of countries, KF = Author key-word frequency, AJG = Academic journal guide, SNA = Social network analysis, NV = Network visualization, BtwCA = Between centrality analysis, PRA = Page rank analysis, TSA = Thematic structure analysis

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Abdeldayem MM, Aldulaimi SH (2020) Trends and opportunities of artificial intelligence in human resource management: aspirations for public sector in Bahrain. Int J Sci Technol Res 9(1):3867–3871

Google Scholar  

Acedo FJ, Barroso C, Casanueva C, Galán JL (2006) Co-authorship in management and organizational studies: an empirical and network analysis. J Manag Stud 43(5):957–983. https://doi.org/10.1111/j.1467-6486.2006.00625.x

Article   Google Scholar  

Anderson N (2003) Applicant and recruiter reactions to new technology in selection: a critical review and agenda for future research. Int J Sel Assess 11(2/3):121–136. https://doi.org/10.1111/1468-2389.00235

Angrave D, Charlwood A, Kirkpatrick I, Lawrence M, Stuart M (2016) HR and analytics: why HR is set to fail the big data challenge. Hum Res Manag J 26(1):1–11. https://doi.org/10.1111/1748-8583.12090

Aral S, Brynjolfsson E, Wu L (2012) Three-way complementarities: performance pay, human resource analytics, and information technology. Manage Sci 58(5):913–931

Bassi L (2011) Raging debates in HR analytics. People Strategy 34(2):14

Baxter M (2018) Information-age. https://www.information-age.com/business-analytics-intelligence-123477004/ . Accessed 26 June 2020

Becker B, Gerhart B (1996) The impact of human resource management on organizational performance: Progress and prospects. Acad Manag J 39(4):779–801

Berhil S, Benlahmar H, Labani N (2020) A review paper on artificial intelligence at the service of human resources management. Indones J Electr Eng Comput Sci 18(1):32–40. https://doi.org/10.11591/ijeecs.v18.i1.pp32-40

Beg MMH (2019) Re: employee-retention/talent-retention-using-artificial-intelligence. https://sightsinplus.com/insight/employee-retention/talent-retention-using-artificial-intelligence/

Bersin J (2018) Talent trends technology disruptions for 2018. https://www.isaconnection.org/assets/documents/2018BersinHRTechDisruptionsReport.pdf

Blanchard EG, Frasson C (2005) Making intelligent tutoring systems culturally aware: The use of Hofstede's cultural dimensions. In: IC-AI, pp 644–649

Block JH, Fisch C (2020) Eight tips and questions for your bibliographic study in business and management research. Manag Rev Q 70(3):307–312. https://doi.org/10.1007/s11301-020-00188-4

Blondel VD, Guillaume JL, Lambiotte R, Lefebvre E (2008) Fast unfolding of communities in large networks. J Stat Mech Theory Exp 10:P10008. https://doi.org/10.1088/1742-5468/2008/10/P10008

Bondarouk T, Brewster C (2016) Conceptualising the future of HRM and technology research. Int J Hum Resour Manag 27(21):2652–2671. https://doi.org/10.1080/09585192.2016.1232296

Brockmann P, Schuhbauer H, Hinze A (2019) Diversity as an Advantage: An Analysis of Career Competencies for IT Students. In: 16th international conference cognition and exploratory learning in digital age, CELDA, pp 209–216. https://doi.org/10.33965/celda2019_201911L026

Brynjolfsson E, Mitchell T (2017) What can machine learning do? Workforce implications. Science 358:1530–1534. https://doi.org/10.1126/science.aap8062

Brin S, Page L (1998) The anatomy of a large-scale hypertextual web search engine. Comput Netw ISDN Syst 30:107–117. https://doi.org/10.1016/S0169-7552(98)00110-X

Castriotta M, Loi M, Marku E, Naitana L (2019) What’s in a name? Exploring the conceptual structure of emerging organizations. Scientometrics 118(2):407–437. https://doi.org/10.1007/s11192-018-2977-2

Prentice C, Lopes SD, Wang X (2020) Emotional intelligence or artificial intelligence—an employee perspective. J Hosp Mark Manag 29(4):377–403. https://doi.org/10.1080/19368623.2019.1647124

Chamorro-Premuzic T, Winsborough D, Sherman RA, Hogan R (2016) New talent signals: Shiny new objects or a brave new world? Ind Organ Psychol 9(3):621–640. https://doi.org/10.1017/iop.2016.6

Chen H, Chiang RH, Storey VC (2012) Business intelligence and analytics: from big data to big impact. MIS Q. https://doi.org/10.2307/41703503

Chien CF, Chen LF (2008) Data mining to improve personnel selection and enhance human capital: a case study in high-technology industry. Expert Syst Appl 34(1):280–290. https://doi.org/10.1016/j.eswa.2006.09.003

Cisneros L, Ibanescu M, Keen C, Lobato-Calleros O, Niebla-Zatarain J (2018) Bibliometric study of family business succession between 1939 and 2017: mapping and analyzing authors’ networks. Scientometrics 117(2):919–951. https://doi.org/10.1007/s11192-018-2889-1

Colomo-Palacios R, González-Carrasco I, López-Cuadrado JL, Trigo A, Varajao JE (2014) I-Competere: using applied intelligence in search of competency gaps in software project managers. Inf Syst Front 16(4):607–625. https://doi.org/10.1007/s10796-012-9369-6

Dabirian A (2019) Employer branding in the IT industry: AN employer view. In: 2019 IEEE 43rd annual computer software and applications conference (COMPSAC), vol 1, pp 548–548. IEEE. https://doi.org/10.1109/COMPSAC.2019.00084

De Kok J, Uhlaner LM (2001) Organisation context and human resource management in the small firm. Small Bus Econ 17(4):273–291. https://doi.org/10.1023/A:1012238224409

DeSanctis G (1986) Human resource information systems: a current assessment. MIS Q 10:15–27. https://doi.org/10.2307/248875

Ding Y, Cronin B (2011) Popular and/or prestigious? Measures of scholarly esteem. Inf Process Manag 47(1):80–96. https://doi.org/10.1016/j.ipm.2010.01.002

Ding Y, Yan E, Frazho A, Caverlee J (2009) PageRank for ranking authors in co-citation networks. J Am Soc Inf Sci 60:2229–2243. https://doi.org/10.1002/asi.21171

Dubey R, Gunasekaran A, Childe SJ (2019) Big data analytics capability in supply chain agility. Manag Decis 57(8):2092–2112. https://doi.org/10.1108/MD-01-2018-01192092-2112

Duchessi P, O’Keefe R, O’Leary D (1993) A research perspective: artificial intelligence, management and organizations. Int J Intell Syst Accounting Financ Manag 2(3):151–159. https://doi.org/10.1002/j.1099-1174.1993.tb00039.x

Dwivedi YK, Hughes L, Ismagilova E, Aarts G, Coombs C, Crick T, Galanos V (2019) Artificial intelligence (AI): multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. Int J Inf Manage. https://doi.org/10.1016/j.ijinfomgt.2019.08.002

El-Rayes N, Fang M, Smith M, Taylor SM (2020) Predicting employee attrition using tree-based models. Int J Organ Anal 28(6):1273–1291. https://doi.org/10.1108/IJOA-10-2019-1903

Erixon F (2018) Recipe. Retrieved from the economic benefits of globalization for business and consumers. https://ecipe.org/publications/the-economic-benefits-of-globalization -

Fan CY, Fan PS, Chan TY, Chang SH (2012) Using hybrid data mining and machine learning clustering analysis to predict the turnover rate for technology professionals. Expert Syst Appl 39(10):8844–8851. https://doi.org/10.1016/j.eswa.2012.02.005

Fajar AN, Nurcahyo A, Sriratnasari SR (2018) SOA System architecture for interconnected modern higher education in Indonesia. Procedia Computer Science 135:354–360. https://doi.org/10.1016/j.procs.2018.08.184

Fisch C, Block J (2018) Six tips for your (systematic) literature review in business and management research. Manag Rev Q 68(2):103–106. https://doi.org/10.1007/s11301-018-0142-x

Frisk JE, Bannister F (2017) Improving the use of analytics and big data by changing the decision-making culture. Manag Decis 55(10):2074–2088. https://doi.org/10.1108/MD-07-2016-0460

Galanaki E, Lazazzara A, Parry E (2019) A cross-national analysis of E-HRM configurations: integrating the information technology and HRM perspectives. In: Lazazzara A, Nacamulli R, Rossignoli C, Za S (eds) Organizing for digital innovation. Lecture notes in information systems and organisation, vol 27. Springer, Cham, pp 261–276. https://doi.org/10.1007/978-3-319-90500-6_20

Chapter   Google Scholar  

Glänzel W, Schubert A (2004) Analyzing scientific networks through co-authorship. In: Glänzel W, Schmoch U (eds) Handbook of quantitative science and technology research. Springer, Dordrecht, pp 257–276. https://doi.org/10.1007/1-4020-2755-9_12

Gmür M (2003) Co-citation analysis and the search for invisible colleges: a methodological evaluation. Scientometrics 57(1):27–57. https://doi.org/10.1023/A:1023619503005

Góes ASDO, De Oliveira RCL (2020) A Process for human resource performance evaluation using computational intelligence: an approach using a combination of rule-based classifiers and supervised learning algorithms. IEEE Access 8:39403–39419. https://doi.org/10.1109/ACCESS.2020.2975485

Kluemper DH, Rosen PA (2009) Future employment selection methods: evaluating social networking web sites. J Manag Psychol 24(6):567–580. https://doi.org/10.1108/02683940910974134

Haines VY, Petit A (1997) Conditions for successful human resource information systems. Hum Resour Manag 36(2):261–275. https://doi.org/10.1002/(SICI)1099-050X(199722)36:2%3c261::AID-HRM7%3e3.0.CO;2-V

Hannon J, Jelf G, Brandes D (1996) Human resource information systems: operational issues and strategic considerations in a global environment. Int J Hum Resour Manag 7(1):245–269. https://doi.org/10.1080/09585199600000127

HireVue (2017) HireVue: case study. https://cdn2.hubspot.net/hubfs/464889/Hilton%20Aug%202017/2017_12_SuccessStory_Hilton_CustomerMarketing3.pdf?__hstc=&__hssc=&hsCtaTracking=b76cefe9-cece-4631-bef5-53084aa900e5%7Ca26c5bca-5fbe-46e3-b0e4-c1f79894f72c

Holland JH (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence, 1st edn. MIT Press, pp 1–207

Book   Google Scholar  

Hosseininezhad F, Nadali A, Balalpour M (2011) A Fuzzy Expert System for performance evaluation of HRM with 360 degree feedback approach (Case study: An Iranian IT company). In: 2011 6th international conference on computer sciences and convergence information technology (ICCIT), pp 487–491. IEEE.

Huai C, Chai L (2016) A bibliometric analysis on the performance and underlying dynamic patterns of water security research. Scientometrics 108(3):1531–1551. https://doi.org/10.1007/s11192-016-2019-x

Hughes JC, Rog E (2008) Talent management: a strategy for improving employee recruitment, retention and engagement within hospitality organizations. Int J Contemp Hosp Manag 20(7):743–757. https://doi.org/10.1108/09596110810899086

Huselid MA (1995) The impact of human resource management practices on turnover, productivity, and corporate financial performance. Acad Manag J 38(3):635–672

Iwamoto T (2019) Development of the HR tech market in Japan. In: 2019 Portland international conference on management of engineering and technology (PICMET). IEEE, pp 1–4. https://doi.org/10.23919/PICMET.2019.8893759

Jantan H, Hamdan AR, Othman ZA (2008) Potential intelligent techniques in human resource decision support system (HR DSS). In: 2008 international symposium on information technology. IEEE. vol 3, pp 1–9. https://doi.org/10.1109/ITSIM.2008.4632047

Jia Q, Guo Y, Li R, Li Y, Chen Y (2018) A conceptual artificial intelligence application framework in human resource management. In: Proceedings of the international conference on electronic business, pp 106–114. https://aisel.aisnet.org/iceb2018/91

Kamaruddin N, Rahman AWA, Lawi RAM (2019) Jobseeker-industry matching system using automated keyword selection and visualization approach. Indones J Electr Eng Comput Sci 13(3):1124–1129. https://doi.org/10.11591/ijeecs.v13.i3.pp1124-1129

Katz JS, Martin BR (1997) What is research collaboration? Res Policy 26:1–18. https://doi.org/10.1016/S0048-7333(96)00917-1

Keding C (2021) Understanding the interplay of artificial intelligence and strategic management: four decades of research in review. Manag Rev Q 71:91–134. https://doi.org/10.1007/s11301-020-00181-x

Khera SN, Divya (2018) Predictive modelling of employee turnover in Indian IT industry using machine learning techniques. Vision 23(1):12–21. https://doi.org/10.1177/0972262918821221

Kluemper DH, Rosen PA, Mossholder KW (2012) Social networking websites, personality ratings, and the organizational context: more than meets the eye? J Appl Soc Psychol 42(5):1143–1172. https://doi.org/10.1111/j.1559-1816.2011.00881.x

LaValle S, Lesser E, Shockley R, Hopkins MS, Kruschwitz N (2011) Big data, analytics and the path from insights to value. MIT Sloan Manag Rev 52(2):21–32

Lawler JJ, Elliot R (1996) Artificial intelligence in HRM: an experimental study of an expert system. J Manage 22(1):85–111. https://doi.org/10.1016/S0149-2063(96)90013-6

Legris P, Ingham J, Collerette P (2003) Why do people use information technology? A critical review of the technology acceptance model. Inf Manag 40(3):191–204. https://doi.org/10.1016/S0378-7206(01)00143-4

Leydesdorff L (2015) Bibliometrics/citation networks. In: Barnett GA (ed) Encyclopaedia of social networks. Sage Publications Inc, Thousand Oaks

Liang TP, Liu YH (2018) Research landscape of business intelligence and big data analytics: a bibliometrics study. Expert Syst Appl 111:2–10. https://doi.org/10.1016/j.eswa.2018.05.018

Lievens F, Sackett PR (2006) Video-based versus written situational judgment tests: a comparison in terms of predictive validity. J Appl Psychol 91(5):1181. https://doi.org/10.1037/0021-9010.91.5.1181

Liu C, Li Z, Zhou D, Shi Y (2019) Organizational innovation of Sci-Tech societies in the era of intelligence: a case study in China. In: 2019 Portland international conference on management of engineering and technology (PICMET). IEEE, pp 1–7. https://doi.org/10.23919/PICMET.2019.8893783

Liu J, Wang T, Li J, Huang J, Yao F, He R (2019) A data-driven analysis of employee promotion: the role of the position of organization. In: 2019 IEEE international conference on systems, man and cybernetics (SMC). IEEE, pp 4056–4062. https://doi.org/10.1109/SMC.2019.8914449

Liu J, Li J, Wang T, He R (2019) Will your classmates and colleagues affect your development in the workplace: predicting employees' growth based on interpersonal environment. In: 2019 IEEE fifth international conference on big data computing service and applications (Big Data Service). IEEE, pp 71–78. ISBN Information: INSPEC Accession Number: 19013694. https://doi.org/10.1109/BigDataService.2019.00016

Liu J, Long Y, Fang M, He R, Wang T, Chen G (2018) Analyzing employee turnover based on job skills. In: Proceedings of the international conference on data processing and applications, pp 16–21. https://doi.org/10.1145/3224207.3224209

Mahmoud AA, Shawabkeh TA, Salameh WA, Al Amro I (2019) Performance predicting in hiring process and performance appraisals using machine learning. In: 2019 10th international conference on information and communication systems (ICICS). IEEE, pp110–115. https://doi.org/10.1109/IACS.2019.8809154

Mallick Basu Chiradeep (2019) Re: how-ai-identifies-flight-risk-and-helps-retain-highvalue-employees. https://www.hrtechnologist.com/articles/digital-transformation/how-ai-identifies-flight-risk-and-helps-retain-highvalue-employees/

Marler JH, Boudreau JW (2017) An evidence-based review of HR Analytics. Int J Hum Resour Manag 28(1):3–26. https://doi.org/10.1080/09585192.2016.1244699

McAfee A, Brynjolfsson E, Davenport TH, Patil DJ, Barton D (2012) Big data: the management revolution. Harv Bus Rev 90(10):60–68

McCarthy J, Minsky ML, Rochester N, Shannon CE (1955) A proposal for the Dartmouth summer research project on artificial intelligence, august 31. AI Mag 27(4):12–12

McDonald K, Fisher S, Connelly CE (2017) e-HRM systems in support of “smart” workforce management: An exploratory case study of system success. Electronic HRM in the smart era, 87–108. https://doi.org/10.1108/978-1-78714-315-920161004 ; https://doi.org/10.1609/aimag.v27i4.1904

Mehrabad MS, Brojeny MF (2007) The development of an expert system for effective selection and appointment of job applicants in human resource management. Comput Ind Eng 53(2):306–312. https://doi.org/10.1016/j.cie.2007.06.023

Melin G, Persson O (1996) Studying research collaboration using co-authorships. Scientometrics 36:363–377

Mikhaylov SJ, Esteve M, Campion A (2018) Artificial intelligence for the public sector: opportunities and challenges of cross-sector collaboration. Philos Trans R Soc Math Phys Eng Sci 376(2128):20170357. https://doi.org/10.1098/rsta.2017.0357

Mitchell RS, Michalski JG, Carbonell TM (2013) An artificial intelligence approach. Springer, Berlin

Mitrofanova E, Mitrofanova A, Tarasenko V (2020) Immature digital expertise of the educational institution’s managerial staff as HR risk to education development. In: 13th international scientific and practical conference-artificial intelligence anthropogenic nature vs. social origin. Springer, Cham, pp 756–765. https://doi.org/10.1007/978-3-030-39319-9_84

Montuschi P, Gatteschi V, Lamberti F, Sanna A, Demartini C (2013) Job recruitment and job-seeking processes: how technology can help. IT Professional 16(5):41–49. https://doi.org/10.1109/MITP.2013.62

Moyo S, Doan TN, Yun JA, Tshuma N (2018) Application of machine learning models in predicting length of stay among healthcare workers in underserved communities in South Africa. Hum Resour Health 16(1):68. https://doi.org/10.1186/s12960-018-0329-1

Nawaz N, Gomes AM (2019) Artificial intelligence chatbots are new recruiters. Int J Adv Comput Sci Appl. https://doi.org/10.14569/IJACSA.2019.0100901

Nawaz N (2019) Artificial intelligence interchange human intervention in the recruitment process in Indian software industry. Int J Adv Trends Comput Sci Eng. https://doi.org/10.2139/ssrn.3521912

Newman MEJ (2001) The structure of scientific collaboration networks. Proc Natl Acad Sci 98:404–409. https://doi.org/10.1073/pnas.021544898

Newman MEJ (2004) Coauthorship networks and patterns of scientific collaboration. Proc Natl Acad Sci 101:5200–5205. https://doi.org/10.1073/pnas.0307545100

Nijjer S, Singh J, Raj S (2019) Developing HRIS for predictive attrition and retention management of Indian IT engineers-using ANN, ANOVA and Smart PLS.

Nilsson NJ (2005) Human-level artificial intelligence? Be serious! AI Mag 26(4):68–68. https://doi.org/10.1609/aimag.v26i4.1850

Noe R, Hollenbeck J, Gerhart B, Wright P (2006) Human resources management: gaining a competitive advantage, tenth global edition. McGraw-Hill Education, New York, MA

Norikumo S (2019) Changes in organizations due to management mechanization (case studies of life insurance companies in Japan). In: Intelligent decision technologies 2019. Springer, Singapore, pp 249–257. https://doi.org/10.1007/978-981-13-8303-8_22

O’Donovan D (2019) HRM in the organization: AN overview. In: Machado C, Davim J (eds) Management science management and industrial engineering. Springer, Cham, pp 75–110

Oracle (2019) Report of the 2019 state of artificial intelligence in talent acquisition. http://www.oracle.com/a/ocom/docs/artificial-intelligence-in-talent-acquisition.pdf

Otley D (1999) Performance management: a framework for management control systems research. Manag Account Res 10(4):363–382

Petruzzellis S, Licchelli O, Palmisano I, Bavaro V, Palmisano C (2006) Employee profiling in the total reward management. In: International symposium on methodologies for intelligent systems. Springer, Berlin, Heidelberg, pp 739–744

Petruzzellis S, Licchelli O, Palmisano I, Semeraro G, Bavaro V, Palmisano C (2006) Personalized incentive plans through employee profiling. In: ICEIS (2), pp 107–114.

Pillai R, Sivathanu B (2020) Adoption of artificial intelligence (AI) for talent acquisition in IT/ITeS organizations. Benchmarking Int J 27(9):2599–2629. https://doi.org/10.1108/BIJ-04-2020-0186

Pillai R, Sivathanu B (2021) Measure what matters: descriptive and predictive metrics of HRM-pathway toward organizational performance. Int J Prod Perf Manag. https://doi.org/10.1108/IJPPM-10-2020-0509

Prem E (2019) Artificial intelligence for innovation in Austria. Technol Innov Manag Rev 9(12):5–15. https://doi.org/10.22215/timreview/1287

Quinn A, Rycraft JR, Schoech D (2002) Building a model to predict caseworker and supervisor turnover using a neural network and logistic regression. J Technol Hum Serv 19(4):65–85. https://doi.org/10.1300/J017v19v04_05

Randstad (2020) How will artificial intelligence affect your talent acquisition strategy. https://www.randstad.com/workforce-insights/hr-tech/how-will-artificial-intelligence-affect-your-talent-acquisition-strategy/ . Accessed 15 Nov 2020.

Ramos Rodríguez AR, Ruíz Navarro J (2004) Changes in the intellectual structure of strategic management research: a bibliometric study of the Strategic Management Journal, 1980–2000. Strateg Manag J 25(10):981–1004. https://doi.org/10.1002/smj.397

Reddy AJM, Rani R, Chaudhary V (2019) Technology for sustainable HRM: an empirical research of health care sector. Int J Innovative Technol Exploring Eng 9(1):2919–2924

Rich E (1983) Users are individuals: individualising user models. Int J Man Mach Stud 18(3):199–214. https://doi.org/10.1016/S0020-7373(83)80007-8

Salin ED, Winston PH (1992) Machine learning and artificial intelligence: an introduction. Anal Chem 64(1):49A-60A

Sánchez LE, Santos-Olmo A, Álvarez E, Huerta M, Camacho S, Fernández-Medina E (2016) Development of an expert system for the evaluation of students’ curricula on the basis of competencies. Future Internet 8(2):22. https://doi.org/10.3390/fi8020022

Sheila LM, Steven G, Chad M, Mayank G (2018) The new age: artificial intelligence for human resource opportunities and functions. Ernst & Young LLP 1–8

Sivathanu B, Pillai R (2019) Technology and talent analytics for talent management—a game-changer for organizational performance. Int J Organ Anal 28(2):457–473. https://doi.org/10.1108/IJOA-01-2019-1634

Srivastava S (2019) Top 10 Countries leading the artificial intelligence race. Analyticsinsight.net. https://www.analyticsinsight.net/top-10-countries-leading-the-artificial-intelligence-race/ . Accessed 10 Sept 2021

Small H (2009) Critical thresholds for co-citation clusters and the emergence of the giant component. J Informetr 3(4):332–340. https://doi.org/10.1016/j.joi.2009.05.001

Small H (1973) Co-citation in the scientific literature: a new measure of the relationship between two documents. Wayback machine. J Am Soc Inf Sci 24:265–269. https://doi.org/10.1002/asi.4630240406

Small HG (1980) Co-citation context analysis and the structure of paradigms. J Doc 36:183–196. https://doi.org/10.1108/eb026695

StarMeUp OS (2018) Re: the-power-and-possibilities-of-ai-in-talent-management. https://www.peoplemattersglobal.com/article/learning-technology/the-power-and-possibilities-of-ai-in-talent-management-22418

Stone DL, Deadrick DL, Lukaszewski KM, Johnson R (2015) The influence of technology on the future of human resource management. Hum Resour Manag Rev 25(2):216–231

Strohmeier S, Piazza F (2015) Artificial intelligence techniques in human resource management—a conceptual exploration. In: Intelligent techniques in engineering management. Springer, Cham, pp 149–172. https://doi.org/10.1007/978-3-319-17906-3_7

Strozzi F, Colicchia C, Creazza A, Noè C (2017) Literature review on the ‘Smart Factory’concept using bibliometric tools. Int J Prod Res 55(22):6572–6591. https://doi.org/10.1080/00207543.2017.1326643

Stuart R, Norvig P (2016) Artificial intelligence: a modern approach, 3rd edn. Prentice-Hall Press, Upper Saddle River

Suen HY, Hung KE, Lin CL (2020) Intelligent video interview agent used to predict communication skill and perceived personality traits. Human-Centric Comput Inf Sci 10(1):1–12. https://doi.org/10.1186/s13673-020-0208-3

Svensson G (2010) SSCI and its impact factors: a “prisoner’s dilemma?” Eur J Mark 44(1/2):23–33. https://doi.org/10.1108/03090561011008583

Tecuci G (2012) Artificial intelligence. Wiley Interdiscip Rev Comput Stat 4(2):168–180

Tranfield D, Denyer D, Smart P (2003) Towards a methodology for developing evidence-informed management knowledge by means of systematic review. Br J Manag 14(3):207–222. https://doi.org/10.1111/1467-8551.00375

Tunger D, Eulerich M (2018) Bibliometric analysis of corporate governance research in German-speaking countries: applying bibliometrics to business research using a custom-made database. Scientometrics 117(3):2041–2059. https://doi.org/10.1007/s11192-018-2919-z

Tursunbayeva A (2019) Human resource technology disruptions and their implications for human resources management in healthcare organizations. BMC Health Serv Res 19(1):268. https://doi.org/10.1186/s12913-019-4068-3

Upadhyay AK, Khandelwal K (2018) Applying artificial intelligence: implications for recruitment. Strateg HR Rev 17(5):255–258. https://doi.org/10.1108/SHR-07-2018-0051

Van Esch P, Black JS, Ferolie J (2019) Marketing AI recruitment: the next phase in job application and selection. Comput Human Behav 90:215–222. https://doi.org/10.1016/j.chb.2018.09.009

Ved S, Kaundanya NS, Panda OP (2016) Applications and current achievements in the field of artificial intelligence. Imp J Interdiscip Res (IJIR) 2(11):932–936

Vinichenko MV, Makushkin SA, Rybakova MV, Chulanova OL, Kuznetsova IV, Lobacheva AS (2019) Using natural and artificial intelligence in the talent management system. Int J Recent Technol Eng 8(3):7417–7423

Vinichenko MV, Chulanova OL, Rybakova MV, Barkov SA, Malyshev MA (2020) The impact of artificial intelligence on behavior of people in the labor market. J Adv Res Dyn Control Syst 12(04):526–532. https://doi.org/10.5373/JARDCS/V12SP4/20201518

Wagner CS, Leydesdorff L (2005) Network structure, self-organization, and the growth of international collaboration in science. Res Policy 34:1608–1618. https://doi.org/10.1016/j.respol.2005.08.002

Wang X (2008) Research on decision support system of employee turnover risk management. In: 38th international conference on computers and industrial engineering, vol 1, pp625–631

Wang X, Wang C (2017) Research on intelligent evaluation system of human resources based on knowledge perspective. Boletin Tecnico/tech Bull 55:656–663

Wu ZX, Nkambou R, Bourdeau J (2012) Cultural intelligence decision support system for business activities. In: The second international conference on business intelligence and technology, BUSTECH.

Xu X, Chen X, Jia F, Brown S, Gong Y, Xu Y (2018) Supply chain finance: a systematic literature review and bibliometric analysis. Int J Prod Econ 204:160–173. https://doi.org/10.1016/j.ijpe.2018.08.003

Yu C, Wang X, Feng Z (2019) Coordinated multiagent reinforcement learning for teams of mobile sensing robots. In: Proceedings of the 18th international conference on autonomous agents and multiagent systems, pp 2297–2299

Zehir C, Karaboğa T, Başar D (2020) The transformation of human resource management and its impact on overall business performance: big data analytics and AI technologies in strategic HRM. In: Digital business strategies in blockchain ecosystems. Springer, Cham, pp 265–279. https://doi.org/10.1007/978-3-030-29739-8_12

Zhang H, Yuan W, Jiang H (2012) Performance evaluation on human resource management of China’s commercial banks based on improved BP neural networks. Int J Adv Comput Technol 4(11):361–365

Zupic I, Čater T (2015) Bibliometric methods in management and organization. Organ Res Methods 18(3):429–472. https://doi.org/10.1177/1094428114562629

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Research Methodology 5. Systematic Literature Review (SLR)

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Research Methodology 5. Systematic Literature Review (SLR). Romi Satria Wahon o [email protected] http ://romisatriawahono.net/rm WA/SMS : +6281586220090. Romi Satria Wahono. SD Sompok Semarang (1987) SMPN 8 Semarang (1990) SMA Taruna Nusantara Magelang (1993)

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Research Methodology5. Systematic Literature Review (SLR) Romi Satria [email protected]://romisatriawahono.net/rmWA/SMS: +6281586220090

Romi Satria Wahono • SD Sompok Semarang (1987) • SMPN 8 Semarang (1990) • SMA Taruna NusantaraMagelang (1993) • B.Eng, M.Eng and Ph.Din Software Engineering fromSaitama University Japan (1994-2004)Universiti Teknikal Malaysia Melaka (2014) • Research Interests: Software Engineering,Machine Learning • Founder danKoordinatorIlmuKomputer.Com • Peneliti LIPI (2004-2007) • Founder dan CEO PT Brainmatics Cipta Informatika

Course Outline

5. Systematic Literature Review (SLR) 5.1 Pengantar SLR 5.2 Tahapan Planning 5.3 Tahapan Conducting 5.4 Tahapan Reporting

5.1 Pengantar SLR

Literature Review • Literature Review is a critical and in depth evaluation of previous research (Shuttleworth, 2009)(https://explorable.com/what-is-a-literature-review) • A summary and synopsis of a particular area of research, allowing anybody reading the paper to establish the reasons for pursuing a particular research • A good Literature Review evaluates quality and findings of previous research

ManfaatMereviewLiteratur • Memperdalampengetahuantentangbidang yang diteliti (Textbooks) • Mengetahuihasilpenelitian yangberhubungan dan yang sudah pernahdilaksanakan (Related Research) (Paper) • Mengetahuiperkembanganilmupadabidang yang kitapilih (state-of-the-art) (Paper) • Memperjelasmasalahpenelitian (Paper)

Literature Review Methods • Typesand Methods of Literature Review: • Traditional Review • Systematic Literature Review or Systematic Review • Systematic Mapping Study (Scoping Study) • Tertiary Study • SLR is now well established review method in the field of software engineering (Kitchenham & Charters, Guidelines in performing Systematic Literature Reviews in Software Engineering, EBSE Technical Report version 2.3, 2007)

1. Traditional Review • Provides an overview of the research findings on particular topics • Advantages: produce insightful, valid syntheses of the research literature if conducted by the expert • Disadvantages: vulnerable to unintentional and intentional bias in the selection, interpretation and organization of content • Examples: • Liao et al., Intrusion Detection System: A Comprehensive Review, Journal of Network and Computer Applications, 36(2013) • Galar et al., A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), Vol. 42, No. 4, July 2012 • CagatayCatal, Software fault prediction: A literature review and current trends, Expert Systems with Applications 38 (2011)

2. Systematic Mapping Study • Suitable for a very broad topic • Identify clusters of evidence (making classification) • Direct the focus of future SLRs • To identify areas for future primary studies • Examples: • Neto et al., A systematic mapping study of software product lines testing, Information and Software Technology Vol. 53, Issue 5, May 2011 • Elberzhager et al., Reducing test effort: A systematic mapping study on existing approaches, Information and Software Technology 54 (2012)

3. Systematic Literature Review (SLR) • The purpose of a systematic literature reviews is to provide as complete a list as possible of all the published studies relating to a particular subject area • A process of identifying, assessing, and interpreting all available research evidence, to provide answers for a particular research question • A form of secondary study that uses a well-defined methodology • SLRs are well established in other disciplines, particularly medicine. They integrate an individual clinical expertise and facilitate access to the outcomes of the research (Kitchenham & Charters, Guidelines in performing Systematic Literature Reviews in Software Engineering, EBSE Technical Report version 2.3, 2007)

3. Systematic Literature Review (SLR) Examples of SLR: • Hall et al., A Systematic Literature Review on Fault Prediction Performance in Software Engineering, IEEE Transaction on Software Engineering, Vol. 38, No. 6, 2012 • Romi Satria Wahono, A Systematic Literature Review of Software Defect Prediction: Research Trends, Datasets, Methods and Frameworks, Journal of Software Engineering, Vol. 1, No. 1, April 2015 • Matthias Galster, Danny Weyns, Dan Tofan, BartoszMichalik, and Paris Avgeriou, Variability in Software Systems: A Systematic Literature Review, IEEE Transactions on Software Engineering, Vol 40, No 3, 2014

4. Tertiary study • Is a SLR of SLRs • To answer a more wider question • Uses the same method as in SLR • Potentially less resource intensive • Examples: • Kitchenham et al., Systematic literature reviews in software engineering – A tertiary study, Information and Software Technology 52 (2010) • Cruzes et al., Research synthesis in software engineering: A tertiary study, Information and Software Technology 53 (2011)

Tahapan SLR 1. Formulate the Review’sResearchQuestion 2. Develop the Review’sProtocol 5.1 PLANNING 1. Identify the RelevantLiterature 2. Perform Selection of PrimaryStudies 3. Perform DataExtraction 4. Assess Studies’ Quality 5. Conduct Synthesis of Evidence 5.2 CONDUCTING 1. Write Up the SLR Paper 2. Choose the Right Journal 5.3 REPORTING

5.1Tahapan Planning Formulate the Review’s Research Question Develop the Review’s Protocol

Formulate the Review’s Research Question • Features of good question: • The RQ is meaningful and important to practitioners and researchers. • The RQ will lead to changes in current software engineering practice or to increase confidence in the value of current practice • The RQ will identify discrepancies between commonly held beliefs and the reality • RQ can be derived primarily based on researcher’s interest • An SLR for PhD thesis should identify existing basis for the research work and where it fits in the current body of knowledge

The Research Question (RQ) • Is the most important part in any SLR • Is not necessarily the same as questions addressed in your research • Is used to guide the search process • Is used to guide the extraction process • Data analysis (synthesis of evidence) is expected to answer your SLR’s RQ

RQ and PICOC The formulation of RQs about effectiveness of a treatment should focus on 5 elements known as PICOC: • Population (P)- the target group for the investigation (e.g. people, software etc.) • Intervention (I) - specifies the investigation aspects or issues of interest to the researchers • Comparison (C)– aspect of the investigation with which the intervention is being compared to • Outcomes (O)– the effect of the intervention • Context (C)– the setting or environment of the investigation (Petticrew et al., Systematic Reviews in the Social Sciences: A Practical Guide, Blackwell Publishing, 2006)

Example of PICOC (Kitchenham et al., 2007) Kitchenham et al., A Systematic Review of Cross- vs. Within-Company Cost Estimation Studies, IEEE Transactions on Software Engineering, 33 (5), 2007

Example of PICOC (Wahono, 2015) Romi Satria Wahono, A Systematic Literature Review of Software Defect Prediction: Research Trends, Datasets, Methods and Frameworks, Journal of Software Engineering, Vol. 1, No. 1, pp. 1-16, April 2015

Example of RQs (Kitchenham, 2007) Kitchenham et al., A Systematic Review of Cross- vs. Within-Company Cost Estimation Studies, IEEE Transactions on Software Engineering, 33 (5), 2007 • RQ1: What evidence is there that cross-company estimation models are not significantly different from within-company estimation models for predicting effort for software/Web projects? • RQ2: What characteristics of the study data sets and the data analysis methods used in the study affect the outcome of within- and cross-company effort estimation accuracy studies? • RQ3: Which experimental procedure is most appropriate for studies comparing within- and cross-company estimation models?

Example of RQs (Davis et al., 2006) Davis et al., Effectiveness of Requirements Elicitation Techniques: Empirical Results Derived from a Systematic Review, 14th IEEE Requirements Engineering Conference, 2006 • RQ: What elicitation technique is most efficient in a particular setting?

Example of RQs (Radjenovic et al., 2013) Radjenovic et al., Software fault prediction metrics: A systematic literature review, Information and Software Technology, Vol. 8, No. 55, pp. 1397-1418, 2013 • RQ1: Which software metrics for fault prediction exist in literature? • RQ2: What data sets are used for evaluating metrics?

Example of RQ (Wahono, 2015) Romi Satria Wahono, A Systematic Literature Review of Software Defect Prediction: Research Trends, Datasets, Methods and Frameworks, Journal of Software Engineering, Vol. 1, No. 1, pp. 1-16, April 2015

2. Develop the Review’s Protocol • A plan that specifies the basic review procedures (method) • Components of a protocol: • Background • Research Questions • Search terms • Selection criteria • Quality checklist and procedures • Data extraction strategy • Data synthesis strategy

5.2Tahapan Conducting Identify the Relevant Literature Perform Selection of Primary Studies Perform Data Extraction Assess Studies’ Quality Conduct Synthesis of Evidence

1. Identifying Relevant Literature • Involves a comprehensive and exhaustive searching of studies to be included in the review • Define a search strategy • Search strategies are usually iterative and benefit from: • Preliminary searches (to identify existing review and volume of studies) • Trial searches (combination of terms from RQ) • Check the search results against list of known studies • Consult the experts in the field

Approach to Construct Search String • Derive major terms used in the review questions based on the PICOC • List the keywords mentioned in the article • Search for synonyms and alternative words • Use the boolean OR to incorporate alternative synonyms • Use the boolean AND to link major terms

Example of Search String (Kitchenham et al., 2007) • Kitchenham et al. (2007) used their structured questions to construct search strings for use with electronic databases: • Population: software OR application OR product OR Web OR WWW OR Internet OR World-Wide Web OR project OR development • Intervention: cross company OR cross organisation OR cross organization OR multiple-organizational OR multiple-organisational model OR modeling OR modelling effort OR cost OR resource estimation OR prediction OR assessment • Contrast: within-organisation OR within-organization OR within-organizational OR within-organisational OR single company OR single organisation • Outcome: Accuracy OR Mean Magnitude Relative Error • The search strings were constructed by linking the four OR lists using the Boolean AND

Example of Search String (Wahono, 2015) Search String: (software OR applicati* OR systems ) AND (fault* OR defect* OR quality OR error-prone) AND (predict* OR prone* OR probability OR assess* OR detect* OR estimat* OR classificat*) Romi Satria Wahono, A Systematic Literature Review of Software Defect Prediction: Research Trends, Datasets, Methods and Frameworks, Journal of Software Engineering, Vol. 1, No. 1, pp. 1-16, April 2015

Example of Search String (Salleh et al., 2011) • The complete search term initially used : (student* OR undergraduate*) AND (pair programming OR pair-programming) AND ((experiment* OR measurement OR evaluation OR assessment) AND (effective* OR efficient OR successful) • A very limited number of results retrieved when using the complete string, thus a much simpler string was derived. • Subject librarian suggested to revise the search string: “pair programming” OR “pair-programming”

Sources of Evidence • Digital libraries • Reference lists from relevant primary studies and review articles • Journals (including company journals such as the IBM Journal of Research and Development), grey literature (i.e. technical reports, work in progress) • Conference proceedings • Research registers • The Internet (google) • Direct contact specific researcher(s)

Studies SelectionStrategy(Wahono, 2015) • Publication Year: • 2000-2013 • Publication Type: • Journal • Conference Proceedings • Search String: softwareAND(fault* OR defect* OR quality OR error-prone) AND(predict* OR prone* OR probability OR assess* OR detect* ORestimat* ORclassificat*) • Selected Studies: • 71

Sources of Evidence (Kitchenham et al., 2007) • The search strings were used on 6 digital libraries: • INSPEC , El Compendex, Science Direct, Web of Science, IEEExplore, ACM Digital library • Search specific journals and conf. proceedings: • Empirical Software Engineering (J) • Information and Software Technology (J) • Software Process Improvement and Practice (J) • Management Science (J) • International Software Metrics Symposium (C) • International Conference on Software Engineering (C) • Manual search: • Evaluation and Assessment in Software Engineering (C) • Check references of each relevant article • Contact researchers

Managing Bibliography • Use relevant Bibliographic package to manage large number of references • E.g. Mendeley, EndNote, Zotero, JabRef Reference Manager etc.

Documenting the Search • The process of conducting SLR must be transparent and replicable • The review should be documented in sufficient detail • The search should be documented and changes noted • Unfiltered search results should be saved for possible reanalysis

2. Selection of Studies • Primary studies need to be assessed for their actual relevance • Set the criteria for including or excluding studies (decided earlier during protocol development, can be refined later) • Inclusion & exclusion criteria should be based on RQ • Selection process should be piloted • Study selection is a multistage process

Selection of Studies(Kitchenham et al., 2007) • Kitchenham et al. (2007) used the following inclusion criteria: • Any study that compared predictions of cross-company models with within-company models based on analysis of single company project data. • They used the following exclusion criteria: • Studies where projects were only collected from a small number of different sources (e.g. 2 or 3 companies) • Studies where models derived from a within-company data set were compared with predictions from a general cost estimation model.

Selection of Studies (Wahono, 2015)

Selection of Studies (Salleh et al., 2011) • Inclusion criteria: • to include any empirical studies of PP that involved highereducation students as the population of interest. • Exclusion criteria: • Papers presenting unsubstantiated claims made by the author(s), for which no evidence was available. • Papers about Agile/XP describing development practices other than PP, such as test-first programming, refactoring etc. • Papers that only described tools (software or hardware) that could support the PP practice. • Papers not written in English. • Papers involving students but outside higher education

3. Assessing Studies’ Quality • To provide more detailed Inclusion/Exclusion criteria • To check whether quality differences provide an explanation for differences in study results • As a means of weighting the importance of individual studies when results are being synthesized • To guide the interpretation of findings and determine the strength of inferences • To guide recommendations for further research

Assessing Studies’ Quality • Quality relates to the extent to which the study minimizes bias and maximizes internal and external validity(Khan et al. 2001) • Quality Concepts Definition (Kitchenham & Charter, 2007)

Assessing Studies’ Quality • Assessing quality of studies: • Methodology or design of the study • Analysis of studies’ findings • Quality checklist or instrument need to be designed to facilitate quality assessment • Most quality checklists include questions aimed at assessing the extent to which articles have addressed bias and validity

Study Quality Assessment (Salleh et al., 2011)

Study Quality Assessment(Kitchenham et al., 2007) Kitchenham et al. (2007) constructed a quality questionnaire based on 5 issues affecting the quality of the study: • Is the data analysis process appropriate? • Did studies carry out a sensitivity or residual analysis? • Were accuracy statistics based on the raw data scale? • How good was the study comparison method? • The size of the within-company data set(e.g < 10 projects considered poor quality)

4. Data Extraction • Involve reading the full text article • Data extracted from primary studies should be recorded using data extraction form • The form should be designed and piloted when the protocol is defined • Collect all the information that can be used to answer the RQ and the study’s quality criteria • Both quality checklist and review data can be included in the same form • In case of duplicates publications (reporting the same data), refer the most complete one • For validation, a set of papers should be reviewed by 2 or more researchers. Compare results and resolve any conflicts

5. Synthesis of Evidence • Involves collating and summarizing the results of the included primary studies • Key objectives of data synthesis(Cruzes & Dyba, 2011): • to analyze and evaluate multiple studies • to select appropriate methods for integrating or providing new interpretive explanations about them • Synthesis can be: • Descriptive (narrative/non-quantitative) • Quantitative (e.g. meta-analysis) (Cruzes et al., Research Synthesis in Software Engineering: A tertiary study, Information and Software Technology, 53(5), 2011)

Descriptive Synthesis (Narrative) “An approach to the synthesis of findings from multiple studies that relies primarily on the use of words and text to summarize and explain the findings of the synthesis. It adopts a textual approach to the process of synthesis to ‘tell the story’ of the findings from the included studies.” (Popay et al. 2006) • Use tables to tabulate information extracted from included studies (e.g. population, number of included studies, study quality etc.) • Tables should be structured to highlight similarity or differences of study outcomes • Were the findings consistent (homogeneous) or inconsistent?

Quantitative Synthesis (Meta-Analysis) • Meta-analysis can be used to aggregate results or to pool data from different studies • The outcome of a meta-analysis is an average effect size with an indication of how variable that effect size is between studies • Meta-analysis involves three main steps: 1. Decide which studies to be included in the meta-analysis 2. Estimate an effect size for each individual study 3. Combine the effect sizes from the individual studies to estimate and test the combined effect • Results of the meta-analysis can be presented in a forest plot

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