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Modeling the teacher job satisfaction by artificial neural networks

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Abstract

This article uses the artificial neural networks (ANNs) method to investigate the association between various dimensions of demographic and coaching leadership with the job satisfaction of teachers in Korean schools. ANN models demonstrate a superior capability to model the relationship with higher predictive accuracy than multiple regression analysis. A user-friendly standalone software is developed for prediction and estimating the relative importance of independent variables on job satisfaction. The graphical representation of results provides strong evidence of complexity, signifying that nonlinear representations understand the relationship between demographic and coaching dimensions with job satisfaction. Eventually, the proposed framework is a practical and accurate method to tackle influential factors and assessment problems in the organization.

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References

  • Alonderiene R, Majauskaite M (2016) Leadership style and job satisfaction in higher education institutions. Int J Educ Manag 30(1):140–164

    Article  Google Scholar 

  • Anthony DP, van Nieuwerburgh CJ (2018) A thematic analysis of the experience of educational leaders introducing coaching into schools. Int J Mentor Coach Educ 7(4):343–356

    Article  Google Scholar 

  • Atmojo M (2015) The influence of transformational leadership on job satisfaction, organizational commitment, and employee performance. Int Res J Bus Stud 5 (2)

  • Bademo Y, Tefera BF (2016) assessing the desired and actual levels of teachers’ participation in decision-making in secondary schools of Ethiopia. Educ Res Rev 11(13):1236–1242

    Article  Google Scholar 

  • Behery M, Al-Nasser A (2016) Examining the impact of leadership style and coaching on employees’ commitment and trust: mediation effect of bullying and job alienation. Int J Organ Anal 24(2):291–314

    Article  Google Scholar 

  • Bishop CM (1995) Neural networks for pattern recognition

  • Bogler R (2001) The influence of leadership style on teacher job satisfaction. Educ Adm Q 37(5):662–683

    Article  Google Scholar 

  • Collie RJ, Shapka JD, Perry NE (2012) School climate and social–emotional learning: predicting teacher stress, job satisfaction, and teaching efficacy. J Educ Psychol 104(4):1189

    Article  Google Scholar 

  • Daniëls E, Hondeghem A, Dochy F (2019) A review on leadership and leadership development in educational settings. Educ Res Rev 27:110–125. https://doi.org/10.1016/j.edurev.2019.02.003

    Article  Google Scholar 

  • Davis J, Wilson SM (2000) Principals’ efforts to empower teachers: effects on teacher motivation and job satisfaction and stress. Clear House 73(6):349–353

    Article  Google Scholar 

  • de Moura AA, Bernardes A, Balsanelli AP, Dessotte CAM, Gabriel CS, Zanetti ACB (2020) Leadership and job satisfaction in the mobile emergency care service context. Rev Lat Am Enferm 28:1–10. https://doi.org/10.1590/1518-8345.3455.3260

    Article  Google Scholar 

  • DeTienne KB, DeTienne DH, Joshi SA (2003) Neural networks as statistical tools for business researchers. Organ Res Methods 6(2):236–265. https://doi.org/10.1177/1094428103251907

    Article  Google Scholar 

  • Eun-Hee L (2000) Moderating effects of communication of the relationship between job stressors and burnout and job satisfacton in male and female teachers. Korean J Str Res 8(2):47–62

    Google Scholar 

  • Goff P, Edward Guthrie J, Goldring E, Bickman L (2014) Changing principals’ leadership through feedback and coaching. J Educ Admin 52(5):682–704

    Article  Google Scholar 

  • Grant AM (2007) Relational job design and the motivation to make a prosocial difference. Acad Manag Rev 32(2):393–417

    Article  Google Scholar 

  • Griffith J (2004) Relation of principal transformational leadership to school staff job satisfaction, staff turnover, and school performance. J Educ Admin 42(3):333–356

    Article  Google Scholar 

  • Hameed AA, Karlik B, Salman MS (2016) Backpropagation algorithm with variable adaptive momentum. Knowl-Based Syst 114:79–87. https://doi.org/10.1016/j.knosys.2016.10.001

    Article  Google Scholar 

  • Hitt DH, Tucker PD (2016) Systematic review of key leader practices found to influence student achievement: a unified framework. Rev Educ Res 86(2):531–569

    Article  Google Scholar 

  • Hulin CL, Judge TA (2003) Job attitudes. In: Handbook of psychology, pp 255–276

  • Hunt JM, Weintraub JR (2004) Learning Developmental Coaching. J Manag Educ 28(1):39–61

    Article  Google Scholar 

  • Hunt JM, Weintraub JR (2006) The coaching organization: a strategy for developing leaders. Sage, London

    Google Scholar 

  • James M, Hunt JRW (2002) The coaching manager: developing top talent in business. SAGE Publications, London

    Google Scholar 

  • Jiang Z, Zhang Z, Friedrich K (2007) Prediction on wear properties of polymer composites with artificial neural networks. Compos Sci Technol 67(2):168–176. https://doi.org/10.1016/j.compscitech.2006.07.026

    Article  Google Scholar 

  • Kim H-D, Cruz AB (2016) The influence of coaches’ leadership styles on athletes’ satisfaction and team cohesion: a meta-analytic approach. Int J Sports Sci Coach 11(6):900–909

    Article  Google Scholar 

  • Knowles RT, Hawkman AM, Nielsen SR (2020) The social studies teacher-coach: a quantitative analysis comparing coaches and non-coaches across how/what they teach. J Soc Stud Res 44(1):117–125. https://doi.org/10.1016/j.jssr.2019.04.001

    Article  Google Scholar 

  • Ladegard G, Gjerde S (2014) Leadership coaching, leader role-efficacy, and trust in subordinates. A mixed methods study assessing leadership coaching as a leadership development tool. Leadersh Q 25(4):631–646

    Article  Google Scholar 

  • LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436. https://doi.org/10.1038/nature14539

    Article  Google Scholar 

  • Lee A, Geem Z, Suh K-D (2016) Determination of optimal initial weights of an artificial neural network by using the harmony search algorithm: application to breakwater armor stones. Appl Sci 6(6):164

    Article  Google Scholar 

  • Lefstein A, Vedder-Weiss D, Tabak I, Segal A (2018) Learner agency in scaffolding: the case of coaching teacher leadership. Int J Educ Res 90:209–222

    Article  Google Scholar 

  • Leithwood K, Sun J (2012) The nature and effects of transformational school leadership: a meta-analytic review of unpublished research. Educ Adm Q 48(3):387–423

    Article  Google Scholar 

  • Lippmann RP (1987) An introduction to computing with neural nets. IEEE ASSP Mag 4(2):4–22. https://doi.org/10.1109/MASSP.1987.1165576

    Article  Google Scholar 

  • Lu M, AbouRizk SM, Hermann UH (2001) Sensitivity analysis of neural networks in spool fabrication productivity studies. J Comput Civ Eng 15(4):299–308. https://doi.org/10.1061/(ASCE)0887-3801(2001)15:4(299)

    Article  Google Scholar 

  • Ma X, MacMillan RB (1999) Influences of workplace conditions on teachers’ job satisfaction. J Educ Res 93(1):39–47

    Article  Google Scholar 

  • Matzler K, Renzl B (2006) The relationship between interpersonal trust, employee satisfaction, and employee loyalty. Total Qual Manag Bus Excell 17(10):1261–1271

    Article  Google Scholar 

  • Minbashian A, Bright JE, Bird KD (2010) A comparison of artificial neural networks and multiple regression in the context of research on personality and work performance. Organ Res Methods 13(3):540–561

    Article  Google Scholar 

  • Nguni S, Sleegers P, Denessen E (2006) Transformational and transactional leadership effects on teachers’ job satisfaction, organizational commitment, and organizational citizenship behavior in primary schools: the Tanzanian case. Sch Eff Sch Improv 17(2):145–177

    Article  Google Scholar 

  • Palocsay SW, White MM (2004) Neural network modeling in cross-cultural research: a comparison with multiple regression. Organ Res Methods 7(4):389–399. https://doi.org/10.1177/1094428104268030

    Article  Google Scholar 

  • Park J, Wee K (2020) A study on the relationship among coaching leadership job autonomy and job commitment. Asia-Pac J Educ Manag Res 5(1):1–8. https://doi.org/10.21742/AJEMR.2020.5.1.01

    Article  Google Scholar 

  • Pearson LC, Moomaw W (2005) The relationship between teacher autonomy and stress, work satisfaction, empowerment, and professionalism. Educ Res Q 29(1):38–54

    Google Scholar 

  • Plint AC, Johnson DW, Patel H, Wiebe N, Correll R, Brant R, Mitton C, Gouin S, Bhatt M, Joubert G (2009) Epinephrine and dexamethasone in children with bronchiolitis. N Engl J Med 360(20):2079–2089

    Article  Google Scholar 

  • Reddy NS, Lee CS, Kim JH, Semiatin SL (2006) Determination of the beta-approach curve and beta-transus temperature for titanium alloys using sensitivity analysis of a trained neural network. Mater Sci Eng A 434(1–2):218–226. https://doi.org/10.1016/j.msea.2006.06.104

    Article  Google Scholar 

  • Reddy NS, Lee YH, Kim JH, Lee CS (2008) High temperature deformation behavior of Ti–6Al–4V alloy with an equiaxed microstructure: a neural networks analysis. Met Mater Int 14(2):213–221. https://doi.org/10.3365/met.mat.2008.04.213

    Article  Google Scholar 

  • Reddy NS, Krishnaiah J, Young HB, Lee JS (2015a) Design of medium carbon steels by computational intelligence techniques. Comput Mater Sci 101:120–126. https://doi.org/10.1016/j.commatsci.2015.01.031

    Article  Google Scholar 

  • Reddy NS, Panigrahi BB, Ho CM, Kim JH, Lee CS (2015b) Artificial neural network modeling on the relative importance of alloying elements and heat treatment temperature to the stability of α and β phase in titanium alloys. Comput Mater Sci 107:175–183. https://doi.org/10.1016/j.commatsci.2015.05.026

    Article  Google Scholar 

  • Ripley BD (2014) Pattern recognition and neural networks. Pattern Recognit Neural Netw. https://doi.org/10.1017/CBO9780511812651

    Article  MATH  Google Scholar 

  • Sadan MK, Ahn HJ, Chauhan GS, Reddy NS (2016) Quantitative estimation of poly(methyl methacrylate) nano-fiber membrane diameter by artificial neural networks. Eur Polym J 74:91–100. https://doi.org/10.1016/j.eurpolymj.2015.11.014

    Article  Google Scholar 

  • Shaw J, Newton J (2014) Teacher retention and satisfaction with a servant leader as principal. Education 135(1):101–106

    Google Scholar 

  • Skaalvik EM, Skaalvik S (2010) Teacher self-efficacy and teacher burnout: a study of relations. Teach Teach Educ 26(4):1059–1069

    Article  Google Scholar 

  • Somers MJ (1999) Application of two neural network paradigms to the study of voluntary employee turnover. J Appl Psychol 84(2):177–185. https://doi.org/10.1037/0021-9010.84.2.177

    Article  Google Scholar 

  • Somers MJ (2001) Thinking differently: assessing nonlinearities in the relationship between work attitudes and job performance using a Bayesian neural network. J Occup Organ Psychol 74(1):47–61. https://doi.org/10.1348/096317901167226

    Article  Google Scholar 

  • Somers MJ, Casal JC (2009a) Using artificial neural networks to model nonlinearity: the case of the job satisfaction-job performance relationship. Organ Res Methods 12(3):403–417. https://doi.org/10.1177/1094428107309326

    Article  Google Scholar 

  • Somers MJ, Casal JC (2009b) Using artificial neural networks to model nonlinearity: the case of the job satisfaction—job performance relationship. Organ Res Methods 12(3):403–417

    Article  Google Scholar 

  • Spector PE (1997) Job satisfaction: application, assessment, causes, and consequences, vol 3. Sage Publications, London

    Google Scholar 

  • Stowell SJ (1987) Leadership and the coaching process in organizations. ProQuest Information & Learning

  • Sukirno D, Siengthai S (2011) Does participative decision making affect lecturer performance in higher education? Int J Educ Manag 25(5):494–508

    Article  Google Scholar 

  • Sun A, Xia J (2018) Teacher-perceived distributed leadership, teacher self-efficacy and job satisfaction: a multilevel SEM approach using the 2013 TALIS data. Int J Educ Res 92:86–97

    Article  Google Scholar 

  • Teemant A, Wink J, Tyra S (2011) Effects of coaching on teacher use of sociocultural instructional practices. Teach Teach Educ 27(4):683–693

    Article  Google Scholar 

  • Troesch LM, Bauer CE (2017) Second career teachers: job satisfaction, job stress, and the role of self-efficacy. Teach Teach Educ 67:389–398

    Article  Google Scholar 

  • Vogt F, Rogalla M (2009) Developing adaptive teaching competency through coaching. Teach Teach Educ 25(8):1051–1060

    Article  Google Scholar 

  • Yu XH, Chen GA (1997) Efficient backpropagation learning using optimal learning rate and momentum. Neural Netw 10(3):517–527. https://doi.org/10.1016/S0893-6080(96)00102-5

    Article  Google Scholar 

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Acknowledgements

N. S. Reddy acknowledges YSJ and PPS for the inspiration.

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Author notes

  1. BangWon Seok and Kuk-hoan Wee contributed equally to this work.

Authors and Affiliations

  1. Business Department, Gyeongnam National University, 150-1, Chiram-dong, Jinju-si, Gyeongsangnam-do, South Korea

    Bang Won Seok

  2. Sungduk University, Shinnyeng-Myeon a College Road 105, Yeongcheon-City, Gyeongbuk, South Korea

    Kuk-hoan Wee

  3. Department of Social Welfare, Dongguk University, 123, Dongdae-ro, Gyeongju-City, Gyeongsangbuk-do, South Korea

    Ju-young Park

  4. Department of Humanities and Sciences, Srinivasa Ramanujan Institute of Technology, Anantapur, 515701, India

    D. Anil Kumar

  5. Virtual Materials Lab, School of Materials Science and Engineering, Engineering Research Institute, Gyeongsang National University, 501 Jinju-daero, Jinju, 660-701, South Korea

    N. S. Reddy

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  1. Bang Won Seok
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Correspondence to Ju-young Park or N. S. Reddy.

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"All procedures performed in studies involving human participants were following the ethical standards of the institutional and national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.". This study does not contain clinical studies on human participants or animals performed by any authors.

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Seok, B.W., Wee, Kh., Park, Jy. et al. Modeling the teacher job satisfaction by artificial neural networks. Soft Comput 25, 11803–11815 (2021). https://doi.org/10.1007/s00500-021-05958-0

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