the training and development research paper

The Current State of Research on Training Effectiveness

• First Online: 30 July 2020

Cite this chapter

• Thomas N. Garavan 11 ,
• Fergal O’Brien 12 ,
• James Duggan 13 ,
• Claire Gubbins 14 ,
• Yanqing Lai 15 ,
• Ronan Carbery 16 ,
• Sinead Heneghan 17 ,
• Ronnie Lannon 18 ,
• Maura Sheehan 19 &
• Kirsteen Grant 20  

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5 Citations

This chapter addresses the current state of research on training effectiveness in organisations. It summarises the key findings on what we know about training effectiveness, the research emphasis given to different components of the model, and how research informs the ways in which organisations should approach learning and development to maximise effectiveness. The chapter highlights the role of training needs analysis, the types of attendance policies that should be used, the most effective design of training delivery to maximise effectiveness, the relative effectiveness of training methods, the organisation of training content, the importance of learning or training transfer, and the types of outcomes that are derived from learning and development.

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Abdullah, M. M. B., Uli, J., & Tarí, J. J. (2008). The influence of soft factors on quality improvement and performance: Perceptions from managers. Total Quality Management Journal, 20 (5), 436–452.

Google Scholar  

Aguinis, H., & Kraiger, K. (2009). Benefits of training and development for individuals and teams, organizations, and society. Annual Review of Psychology, 60 (1), 451–474.

Ahmad, S., & Schroeder, R. G. (2003). The impact of human resource management practices on operational performance: Recognizing country and industry differences. Journal of Operations Management, 21 (1), 19–43.

Akrofi, S. (2016). Evaluating the effects of executive learning and development on organisational performance: Implications for developing senior manager and executive capabilities. International Journal of Training and Development, 20 (3), 177–199.

Alliger, G. M., Tannenbaum, S., Bennett, W., Traver, H., & Shotland, A. (1997). A meta-analysis of the relations among training criteria. Personnel Psychology, 50, 341–358.

Altinay, L., Altinay, E., & Gannon, J. (2008). Exploring the relationship between the human resource management practices and growth in small service firms. The Service Industries Journal, 28 (7), 919–937.

Aragon, M. I. B., & Sanz-Valle, R. (2013). Does training managers pay off? The International Journal of Human Resource Management, 24 (8), 1671–1684.

Aragon, M. I. B., Jiménez-Jiménez, D., & Sanz-Valle, R. (2014). Training and performance: The mediating role of organisational learning. Business Research Quarterly, 17 (3), 161–173.

Aragon-Sanchez, A., Aragon, M. I. B., & Sanz-Valle, R. (2003). Effect of training on business results. International Journal of Human Resource Management, 14 (6), 956–980.

Arthur, W., Jr., Bennett, W., Jr., Edens, P. S., & Bell, S. T. (2003). Effectiveness of training in organizations: A meta-analysis of design and evaluation features. Journal of Applied Psychology, 88 (2), 234–245.

Arunprasad, P. (2017). Inevitable knowledge strategy: A paradigm shift in strategic HRM practices to augment firm’s performance. Employee Relations, 39 (3), 753–774.

Audea, T., Teo, S., & Crawford, J. (2005). HRM professionals and their perceptions of HRM and firm performance in the Philippines. International Journal of Human Resource Management, 16 (4), 532–552.

Ballot, G., Fakhfakh, F., & Taymaz, E. (2006). Who benefits from training and R&D, the firm or the workers? British Journal of Industrial Relations, 44 (3), 473–495.

Banerjee, P., Gupta, R., & Bates, R. (2016). Influence of organisational learning culture on knowledge worker’s motivation to transfer training: Testing moderating effects of learning transfer climate. Current Psychology, 36, 606–617.

Barling, J., Weber, T., & Kelloway, K. (1996). Effects of transformational leadership training on attitudinal and financial outcomes: A Field experiment. Journal of Applied Psychology, 81 (6), 827–832.

Barrett, A., & O’Connell, P. J. (2001). Does training generally work? The returns to in-company training. Industrial and Labour Relations Review, 54 (3), 647–662.

Bashir, N., & Long, C. S. (2015). The relationship between training and organisational commitment among academicians in Malaysia. Journal of Managerial Development, 34 (10), 1227–1245.

Bauer, K. N., Orvis, K. A., Ely, K., & Surface, E. A. (2016). Re-examination of motivation in learning contexts: Meta-analytically investigating the role type of motivation plays in the prediction of key training outcomes. Journal of Business Psychology, 31, 33–50.

Beinicke, A., & Bipp, T. (2018). Evaluating training outcomes in corporate eLearning and classroom training. Vocations and Learning, 11 (3), 501–528.

Bell, J. L., & Grushecky, S. T. (2006). Evaluating the effectiveness of a logger safety training program. Journal of Safety Research, 37 (1), 53–61.

Berk, A., & Kaše, R. (2010). Establishing the value of flexibility created by training: Applying real options methodology to a single HR practice. Organization Science, 21 (3), 765–780.

Beugelsdijk, S. (2008). Strategic human resource practices and product innovation. Organization Studies, 29, 821–847.

Beynon, H., Grimshaw, D., Rubery, J., & Ward, K. (2002). Managing employment change: The new realities of work . Oxford: Oxford University Press.

Bezrukova, K., Spell, C. S., Perry, J. L., & Jehn, K. A. (2016). A meta-analytical integration of over 40 years of research on diversity training evaluation . Available from http://scholarship.sha.cornell.edu/articles/974 .

Bhatti, W. A., Zaheer, A., & Rehman, K. (2011). The effect of knowledge management practices on organisational performance: A conceptual study. African Journal of Business Management, 5, 2847–2853.

Birdi, K. S., Patterson, M. G., & Wood, S. J. (2007). Learning to perform? A comparison of learning practices and organisational performance in profit and non-profit-making sectors in the UK. International Journal of Training and Development, 11 (4), 265–281.

Birley, S., & Westhead, P. (1990). Private business sales environments in the United Kingdom. Journal of Business Venturing, 5 (6), 349–373.

Birou, L., Green, K., & Inman, R. (2019). Sustainability knowledge and training: Outcomes and firm performance. Journal of Manufacturing Technology Management, 30 (2), 294–311.

Black, S., & Lynch, L. (1996). Human-capital investments and productivity. American Economic Review, 86 (2), 263–267.

Blume, B. D., Ford, J. K., Baldwin, T. T., & Huang, J. L. (2010). Transfer of training: A meta-analytic review. Journal of Management, 36 (4), 1065–1105.

Boon, M., & van der Eijken, B. (1997). Employee training and productivity in Dutch manufacturing firms. Statistics Netherlands , research paper 9716, Voorburg, The Netherlands.

Boselie, P., & Paauwe, J. (2005). Human resource function competencies in European companies. Personnel Review, 34 (5), 550–566.

Browning, V. (2006). The relationship between HRM practices and service behaviour in South African service organisations. International Journal of Human Resource Management, 17 (7), 1321–1338.

Buch, R., Dysvik, A., Kuvaas, B., & Nerstad, C. G. (2015). It takes three to tango: Exploring the interplay among training intensity, job autonomy, and supervisor support in predicting knowledge sharing. Human Resource Management, 54 (4), 623–635.

Buch, R., Kuvaas, B., Dysvik, A., & Schyns, B. (2014). If and when social and economic leader-member exchange relationships predict follower work effort: The moderating role of work motivation. Leadership and Organisational Development Journal, 35 (8), 725–739.

Burke, M., & Day, R. R. (1986). A cumulative study of the effectiveness of managerial training. Journal of Applied Psychology, 71 (2), 232–245.

Burton, M. D., & O’Reilly, C. (2004). Walking the talk: The impact of high commitment values and practices on technology start-ups . Cornell University, School of Industrial and Labor Relations. Available from http://digitalcommons.ilr.cornell.edu/workingpapers/99 .

Cappelli, P., & Neumark, D. (2001). Do “high-performance” work practices improve establishment-level outcomes? Industrial and Labor Relations Review, 54 (4), 737–775.

Cascio, W. F. (2000). Managing a virtual workplace. Academy of Management Perspectives, 14, 81–90.

Cerasoli, C. P., Alliger, G. M., Donsbach, J. S., Mathieu, J. E., Tannenbaum, S. I., & Orvis, K. A. (2018). Antecedents and outcomes of informal learning behaviours: A meta-analysis. Journal of Business and Psychology, 33 (2), 203–230.

Chatterjee, S., Dandapat, D., & Bagchi, B. (2018). Efficiency of growth drivers: An analysis of select Indian industries . New York: Springer.

Chi, N. W., Yu, C. Y., & YunLin, C. Y. (2008). Does training facilitate SME’s performance? International Journal of Human Resource Management, 19 (10), 1962–1975.

Chinomona, R. (2013). Business owner’s expertise, employee skills training and business performance: A small business perspective. Journal of Applied Business Research, 29 (6), 1883–1896.

Cho, T., & Yoon, S. (2009). Human resource management, individualism-collectivism, and individual performance among public employees: A test of the main and moderating effects. The Korean Journal of Policy Studies, 23 (2), 57–87.

Cho, Y., & Egan, T. M. (2009). Action learning research: A systematic review and conceptual framework. Human Resource Development Review, 8 (4), 431–462.

Chowhan, J. (2016). Unpacking the black box: Understanding the relationship between strategy, HRM practices, innovation and organizational performance. Human Resource Management Journal, 26 (2), 112–133.

CIPD. (2017). To gig or not to gig? Stories from the modern economy . Survey Report. Available from https://www.cipd.co.uk/Images/to-gig-or-not-to-gig_2017-stories-from-the-modern-economy_tcm18-18955.pdf .

Cjoi, Y., & Dickson, D. R. (2009). A case study into the benefits of management training programs: Impacts on hotel employee turnover and satisfaction level. Journal of Human Resources in Hospitality & Tourism, 9 (1), 103–116.

Cobblah, M. C., & Van der Walt, T. B. (2016). Staff training and development programmes and work performance in the university libraries in Ghana. Information Development, 33 (4), 375–392.

Coetzee, R., Visagie, J., & Ukpere, W. I. (2014). Responding to change in a large organisation from a leadership angle. Mediterranean Journal of Social Sciences, 5 (20), 827.

Colquitt, J. A., LePine, J. A., & Noe, R. A. (2000). Toward an integrative theory of training motivation: A meta-analytic path analysis of 20 years of research. Journal of Applied Psychology, 85 (5), 678–707.

Cooke, N. J., Gorman, J. C., Duran, J. L., & Taylor, A. R. (2007). Team cognition in experienced command-and-control teams. Journal of Experimental Psychology: Applied, 13, 146–157.

D’Arcimoles, C. (1997). Human resource policies and company performance: A quantitative approach using longitudinal data. Organizational Studies, 18, 857–874.

Darwish, T. K., Singh, S., & Mohamed, A. F. (2013). The role of strategic HR practices in organisational effectiveness: An empirical investigation in the country of Jordan. International Journal of Human Resource Management, 24 (17), 3343–3362.

Delaney, T. J., & Huselids, M. A. (1996). The impact of human resource practices on perceptions of organisational performance. Academy of Management Journal, 39 (4), 949–969.

Deng, J., Menguc, B., & Benson, J. (2003). The impact of human resource management on export performance of Chinese manufacturing enterprises. Thunderbird International Business Review, 45 (4), 409–420.

Dermol, V., & Cater, T. (2013). The influence of training and training transfer factors on organisational learning and performance. Personnel Review, 42 (3), 324–348.

Dhar, R. L. (2015). Service quality and the training of employees: The mediating role of organizational commitment. Tourism Management, 46, 419–430.

Diaz-Fernandez, M., Bornay-Barrachina, M., & Lopez-Cabrales, A. (2015). Innovation and firm performance: The role of human resource management practices. Evidence-Based HRM, 3 (1), 64–80.

Donovan, J. J., & Radosevich, D. J. (1999). A meta-analytic review of the distribution of practice effect: Now you see it, now you don’t. Journal of Applied Psychology, 84 (5), 795–805.

Dostie, B. (2018). The impact of training on innovation. ILR Review, 71 (1), 64–87.

Dysvik, A., & Kuvaas, B. (2008). The relationship between perceived training opportunities, work motivation and employee outcomes. International Journal of Training and Development, 12 (3), 138–157.

Ellinger, A. D., Ellinger, A. E., & Keller, S. B. (2003). Supervisory coaching behavior, employee satisfaction, and warehouse employee performance: A dyadic perspective in the distribution industry. Human Resource Development Quarterly, 14 (4), 435–458.

Ellis, A. P. J., Bell, B. S., Ployhart, R. E., Hollenbeck, J. R., & Ilgen, D. R. (2005). An evaluation of generic teamwork skills training and action teams: Effects on cognitive and skills-based outcomes. Personnel Psychology, 58, 641–672.

Ely, R. (2004). A field study of group diversity, participation in diversity education programs, and performance. Journal of Organizational Behaviour, 25, 755–780.

Esteban-Lloret, N. N., Aragon-Sanchez, A., & Carrasco, A. (2016). Determinants of employee training: Impact on organisational legitimacy and organisational performance. International Journal of Human Resource Management, 29 (6), 1–22.

Feng, T., Wang, D., & Prajogo, D. (2014). Incorporating human resource management initiatives into customer services: Empirical evidence from Chinese manufacturing firms. Industrial Marketing Management, 43, 126–135.

Fletcher, L., Alfes, K., & Robinson, D. (2016). The relationship between perceived training and development and employee retention: The mediating role of work attitudes. International Journal of Human Resource Management, 29 (18), 2701–2728.

Frenkel, S. J., & Bednall, T. (2016). How training and promotion opportunities, career expectations, and two dimensions of organizational justice explain discretionary work effort. Human Performance, 29 (1), 16–32.

Gainey, T. W., & Klass, B. S. (2003). The outsourcing of training and development: Factors impacting client satisfaction. Journal of Management, 29 (2), 207–229.

Garavan, T., McCarthy, A., Lai, Y., Murphy, K., Sheehan, M., & Carbery, R. (2020). Training and organisational performance: A meta-analysis of temporal, institutional, and organisational context moderators. Human Resource Management Journal , in-press.

García-Bernal, J., Gargallo-Castel, A., Marzo-Navarro, M., & Rivera-Torres, P. (2005). Job satisfaction: Empirical evidence of gender differences. Women in Management Review, 20 (4), 279–288.

Gavino, M. C., Wayne, S. J., & Erdogan, B. (2012). Discretionary and transactional human resource practices and employee outcomes: The role of perceived organizational support. Human Resource Management, 51 (5), 665–686.

Gelade, G. A., & Ivery, M. (2003). The impact of human resource management and work climate on organizational performance. Personnel Psychology, 56, 383–404.

Georgellis, Y., Lange, T., & Tabbuma, V. (2012). The impact of life events on job satisfaction. Journal of Vocational Behaviour, 80 (2), 464–473.

Giangreco, M. F., Suter, J. C., & Doyle, M. B. (2010). Paraprofessionals in inclusive schools: A review of recent research. Journal of Educational and Psychological Consultation, 20 (1), 41–57.

Glaub, M. E., Frese, M., Fischer, S., & Hoppe, M. (2014). Increasing personal initiative in small business managers or owners leads to entrepreneurial success: A theory-based controlled randomized field intervention for evidence-based management. Academy of Management Learning & Education, 13 (3), 354–379.

Glaveli, N., & Karassavidou, E. (2011). Exploring a possible route through which training affects organizational performance: The case of a Greek bank. The International Journal of Human Resource Management, 22 (14), 2892–2923.

Gooderham, P., Ringdal, K., & Parry, E. (2006). The impact of human resource management practices on the performance of European firms . Copenhagen Business School Conference on HRM and Knowledge-Related Performance.

Gould-Williams, J. (2007). HR practices, organizational climate and employee outcomes: Evaluating social exchange relationships in local government. The International Journal of Human Resource Management, 18 (9), 1627–1647.

Grossman, R., & Salas, E. (2011). The transfer of training: What really matters? International Journal of Training and Development, 15 (2), 103–120.

Guan, X., & Frenkel, S. (2018). How HR practice, work engagement and job crafting influence employee performance. Chinese Management Studies, 12, 591–607.

Gupta-Potnuru, R. M., & Sahoo, C. K. (2016). HRD interventions, employee competencies and organizational effectiveness: An empirical study. European Journal of Training and Development, 40 (5), 345–365.

Gurbuz, S., & Mert, I. S. (2011). Impact of the strategic human resource management on organizational performance: Evidence from Turkey. The International Journal of Human Resource Management, 22 (8), 1803–1822.

Gutiérrez-Gutiérrez, L. J., Bustinza, O. F., & Barrales-Moline, V. (2012). Six sigma, absorptive capacity and organisational learning orientation. International Journal of Production Research, 50 (3), 661–675.

Guzzo, R. A., Jette, R. D., & Katzell, R. A. (1985). The effects of psychologically based intervention programs on worker productivity: A meta-analysis. Personnel Psychology, 38 (2), 275–291.

Harel, G. H., & Tzafrir, S. S. (1999). The effect of human resource management practices on the perceptions of organizational and market performance of the firm. Human Resource Management, 38 (3), 185–199.

Hassan, M. G., Hassan, S., Khan, M. F. A., & Iqbal, A. (2013). Impact of HR practices on employee satisfaction and employee loyalty: An empirical study of government owned public sector banks of Pakistan. Middle East Journal of Scientific Research, 16 (1), 1–8.

Heyler, R., & Lee, D. (2014). The role of work experience in the future employability of higher education graduates. Higher Education Quarterly, 68 (3), 348–372.

Holton, E. F., Bates, R. A., & Ruona, W. E. A. (2000). Development of a generalised learning transfer inventory. Human Resource Development Quarterly, 11 (4), 333–360.

Holzer, H. J., Block, R., Cheatman, M., & Knott, J. H. (1993). Are training subsidies for firms effective? The Michigan experience. Industrial and Labour Relations Review, 46 (4), 625–636.

Horgan, J., & Muhlau, P. (2006). Human resource systems and employee performance in Ireland and the Netherlands: A test of the complementarity hypothesis. International Journal of Human Resource Management, 17 (3), 414–439.

Howard, M. C., & Gutworth, M. B. (2020). A meta-analysis of virtual reality training programs for social skill development. Computers and Education, 144 , 1–19.

Howardson, G. N., & Behrend, T. S. (2016). Coming full circle with reactions: Understanding the structure and correlates of trainee reactions through the affect circumplex. Academy of Management Learning & Education, 15 (3), 471–492.

Huang, J. L., Blume, B. D., Ford, J. K., & Baldwin, T. T. (2015). A tale of two transfers: Disentangling maximum and typical transfer and their respective predictors. Journal of Business Psychology, 30, 709–732.

Huang, J. L., Ford, J. K., & Ryan, A. M. (2017). Ignored no more: Within-person variability enables better understanding of training transfer. Personal Psychology, 70, 557–596.

Hughes, A., Zajac, S., Woods, A. L., & Salas, E. (2019). The role of work environment in training sustainment: A meta-analysis. Human Factors, 62 (1), 1–18.

Hurtz, G. M., & Williams, K. J. (2009). Attitudinal and motivational antecedents of participation in voluntary employee development activities. Journal of Applied Psychology, 94 (3), 635–653.

Jiang, J., Wang, S., & Zhao, S. (2012). Does HRM facilitate employee creativity and organizational innovation? A study of Chinese firms. International Journal of Human Resource Management, 23 (19), 4025–4047.

Jimenez-Jimenez, D., & Sanz-Valle, R. (2005). Innovation and human resource management fit: An empirical study. International Journal of Manpower, 26 (4), 364–381.

Jung, Y., & Takeuchi, N. (2019). Testing mediation effects of social and economic exchange in linking organizational training investment to employee outcomes. Personnel Review, 48 (2), 306–323.

Kaminski, M. (2001). Unintended consequences: Organisational practices and their impact on workplace safety and productivity. Journal of Occupational Health Psychology, 6 (2), 127–138.

Kampkotter, P., & Marggraf, K. (2015). Do employees reciprocate to intra-firm trainings? An analysis of absenteeism and turnover rates. International Journal of Human Resource Management, 26 (22), 2888–2907.

Karami, A., Jones, B. M., & Kakabadse, N. K. (2008). Does strategic human resource management matter in high-tech sector? Some learning points for SME managers. Corporate Governance International Journal of Business in Society, 8 (1), 7–17.

Katou, A. A., Budhwar, P. S., & Patel, C. (2014). Content vs. process in the HRM‐ performance relationship: An empirical examination. Human Resource Management 53 , 527–544.

Keith, N., & Frese, M. (2008). Effectiveness of error management training: A meta-analysis. Journal of Applied Psychology, 93, 59–69.

Kim, Y., & Ployhart, R. E. (2014). The effects of staffing and training on firm productivity and profit growth before, during and after the Great Recession. Journal of Applied Psychology, 99 (3), 361–389.

Kirkpatrick, D. L. (1987). Evaluation of training. In R. L. Craig (Ed.), Training and development handbook: A guide to human resource development (pp. 301–319). New York: McGraw-Hill.

Kitching, J., & Blackburn, R. A. (2002). The nature of training and motivation to train in small firms . Small Business Research Centre, Kingston University. Available from http://dera.ioe.ac.uk/4691/1/RR330.pdf .

Kluger, A. N., & DeNisi, A. (1996). The effects of feedback interventions on performance: A historical review, a meta-analysis, and a preliminary feedback intervention theory. Psychological Bulletin, 119 (2), 254–284.

Koch, M. J., & McGrath, R. G. (1996). Improving labour productivity: Human resource management policies do matter. Strategic Management Journal, 17 (5), 335–354.

Kodwani, A. (2017). Decoding training effectiveness: The role of organisational factors. Journal of Workplace Learning, 29 (3), 200–216.

Kodwani, A. D., & Prashar, S. (2019). Exploring the influence of pre-training factors on training effectiveness-moderating role of trainees’ reaction: A study in the public sector in India. Human Resource Development International, 22 (3), 283–304.

Kooij, D., Guest, D., Clinton, M., Knight, T., Jansen, P., & Dikkers, J. (2013). How the impact of HR practices on employee well-being and performance changes with age? Human Resource Management Journal, 23 (1), 18–35.

Kown, K., & Rupp, D. E. (2013). High-performer turnover and firm performance: The moderating role of human capital investment and firm reputation. Journal of Organisational Behaviour, 34 (1), 129–150.

Kraiger, K., & Ford, J. K. (2007). The expanding role of workplace training: Themes and trends influencing training research and practice. In L. L. Koppes (Ed.), Historical perspectives in industrial and organizational psychology (pp. 281–309). Mahwah, NJ: Lawrence Erlbaum Associates Publishers.

Kraiger, K., Ford, J. K., & Salas, E. (1993). Application of cognitive, skill-based and affective theories of learning outcomes to new methods of training evaluation. Journal of Applied Psychology, 78, 311–328.

Kulik, C. C., & Kulik, J. A. (1991). Effectiveness of computer-based instruction: An updated analysis. Computers in Human Behaviour, 7, 75–94.

Kumar, S., & Liu, D. (2005). Impact of globalisation on entrepreneurial enterprises in the world markets. International Journal of Management and Enterprise Development, 2 (1), 46–64.

Lacerenza, C. N., Reyes, D. L., Marlow, S. L., Joseph, D. L., & Salas, E. (2017). Leadership training design, delivery, and implementation: A meta-analysis. Journal of Applied Psychology, 102 (12), 1686–1718.

Lam, T., Lo, A., & Chan, J. (2002). New employees’ turnover intentions and organizational commitment in the Hong Kong hotel industry. Journal of Hospitality & Tourism Research, 26 (3), 217–234.

Lau, C. M., & Ngo, H. Y. (2004). The HR system, organisational culture, and product innovation. International Business Review, 13 (6), 685–703.

Laursen, K., & Foss, N. (2003). New human resource management practices, complementarities and the impact on innovation performance. Cambridge Journal of Economics, 27 (2), 243–263.

LeBlanc, G., & Nguyen, N. (1996). Cues used by customers evaluating corporate image in service firms: An empirical study in financial institutions. International Journal of Service Industry Management, 7 (2), 44–56.

Liao, H., & Chuang, A. (2004). A multilevel investigation of factors influencing employee service performance and customer outcomes. The Academy of Management Journal, 47 (1), 41–58.

Litz, R. A., & Stewart, A. C. (2000). Trade-name franchise membership as a human resource management strategy: Does buying group training deliver ‘true value’ for small retailers? Entrepreneurship Theory and Practice, 25 (1), 125–135.

Liu, M. T., Wong, I. A., Shi, G., Chu, R., & Brock, J. L. (2014). The impact of corporate social responsibility (CSR) performance and perceived brand quality on customer-based brand preference. Journal of Services Marketing, 28 (3), 181–194.

Liu, N. C., & Liu, M. S. (2011). Human resource practices and individual knowledge-sharing behaviour—An empirical study for Taiwanese R&D professionals. The International Journal of Human Resource Management, 22 (4), 981–997.

Lowell, V. L., & Morris, J. (2019). Leading changes to professional training in the multigenerational office: Generational attitudes and preferences towards learning and technology. Performance Improvement Quarterly, 32 (2), 111–135.

Lux, M., Strohmaier, M., Dösinger, G., & Tochtermann, K. (2007). The web 2.0 way of learning with technologies. International Journal of Learning Technology, 3 (1), 87–107.

McNamara, T. K., Parry, E., Lee, J., & Pitt-Catsouphes, M. (2011). The effect of training on organisational performance: Differences by age composition and cultural context. The International Journal of Human Resource Management, 23 (6), 1226–1244.

Meschi, P., & Metais, E. (1998). A socio-economic study of companies through their training policies: New empirical considerations in the French context. MIR: Management International Review, 38 (1), 25–48.

Miller, D., & Lee, J. (2001). The people make the process: Commitment to employees, decision making, and performance. Journal of Management, 27 (2), 163–189.

Murray, B., & Raffaele, G. C. (1997). Single-site, results-level evaluation of quality awareness training. Human Resource Development Quarterly, 8 (3), 229–245.

Nasurdin, A. M., Ahmad, N. H., & Tan, C. L. (2014). Cultivating service-oriented citizenship behavior among hotel employees: The instrumental roles of training and compensation. Service Business . https://doi.org/10.1007/s11628-014-0230-5 .

Article   Google Scholar  

Neuman, G. A., Edwards, J. E., & Raju, N. S. (1989). Organizational development interventions: A meta-analysis of their effects on satisfaction and other attitudes. Personnel Psychology, 42 (3), 461–489.

Odle-Dusseau, H. N., Hammer, L. B., Crain, T., & Bodner, T. E. (2015). The influence of family-supportive supervisor training on employee job performance and attitudes: An organizational work-family intervention. Journal of Occupational Health Psychology, 21 (3), 296–308.

Okay-Somerville, B., & Scholarios, D. (2019). A multilevel examination of skills-oriented human resource management and perceived skill utilization during recession: Implications for the well-being of all workers. Human Resource Management, 58 (2), 139–154.

Otoo, F. (2019). Human resource development (HRD) practices and banking industry effectiveness: The mediating role of employee competencies. European Journal of Training and Development, 43 (3/4), 250–271.

Park, Y., & Jacobs, R. L. (2011). The influence of investment in workplace learning on learning outcomes and organisational performance. Human Resource Development Quarterly, 22 (4), 437–458.

Peccei, R., & Rosenthal, R. (2001). Delivering customer-oriented behaviour through empowerment: An empirical test of HRM assumptions. Journal of Management Studies, 38 (6), 831–857.

Piaralal, N. K., Mat, N., Piaralal, S. K., & Bhatti, M. A. (2014). Human resource management factors and service recovery performance in Malaysian life insurance industry. European Journal of Training and Development, 38 (6), 524–552.

Pratono, A. H., & Mahmood, R. (2016). Entrepreneurial orientation and firm performance: How can small and medium-sized enterprises survive environmental turbulence? Pacific Science Review: Humanities and Social Sciences, 1 (2), 85–91.

Quratulain, S., Khan, A. K., Sabharwal, M., & Javed, B. (2019). Effect of self-efficacy and instrumentality beliefs on training implementation behaviours: Testing the moderating effect of organisational climate. Review of Public Personnel Administration , in-press.

Raineri, A. (2017). Linking human resources practices with performance: The simultaneous mediation of collective affective commitment and human capital. The International Journal of Human Resource Management, 28 (22), 3149–3178.

Riley, S. M., Michael, S. C., & Mahoney, J. T. (2017). Human capital matters: Market valuation of firm investments in training and the role of complementary assets. Strategic Management Journal, 38 (9), 1895–1914.

Sahoo, M., & Mishra, S. (2019). Effects of trainee characteristics, training attitudes and training need analysis on motivation to transfer training. Management Research Review, 42 (2), 215–238.

Salas, E., Diaz-Granados, D., Klein, C., Burke, S., & Stagl, K. C. (2008). Does team training improve team performance? Human Factors, 50 (6), 903–933.

Salas, E., Weaver, S. J., & Shuffler, M. L. (2012). Learning, training, and development in organizations. In S. W. J. Kozlowski (Ed.), Oxford library of psychology. The Oxford handbook of organizational psychology, Vol. 1 (pp. 330–372). Oxford: Oxford University Press.

Santos, J. P., Caetano, A., & Tavares, S. M. (2015). Is training leaders in functional leadership a useful tool for improving the performance of leadership functions and team effectiveness? The Leadership Quarterly, 26 (3), 470–484.

Shaw, J. D., Delery, J. E., Jenkins, G. D., & Gupta, N. (1998). An organizational-level analysis of voluntary and involuntary turnover. Academy of Management Journal, 41, 511–525.

Shen, J., & Tang, C. (2018). How does training improve customer service quality? The roles of transfer of training and job satisfaction. European Management Journal, 36 (6), 708–716.

Sitzmann, T. (2011). A meta-analytic examination of the instructional effectiveness of computer-based simulation games. Personnel Psychology, 64, 489–528.

Sitzmann, T., & Weinhardt, J. M. (2015). Training engagement theory: A multilevel perspective on the effectiveness of work-related training. Journal of Management, 44 (2), 732–756.

Sitzmann, T., Brown, K. G., Casper, W. J., & Ely, K. (2008). A review and meta-analysis of the nomological network of trainee reactions. Journal of Applied Psychology, 93 (2), 280–295.

Sitzmann, T., Kraiger, K., Stewart, D., & Wisher, R. (2006). The comparative effectiveness of web-based and classroom instruction: A meta-analysis. Personnel Psychology, 59 (3), 623–664.

Stone, D. L., & Deadrick, D. L. (2015). Challenges and opportunities affecting the future of human resource management. Human Resource Management Review, 25 (2), 139–145.

Sung, S. Y., & Choi, J. N. (2012). Effects of team knowledge management on the creativity and financial performance of organisational teams. Organisational Behaviour and Human Decision Processes, 118 (1), 4–13.

Sung, S. Y., & Choi, J. N. (2014). Do organizations spend wisely on employees? Effects of training and development investments on learning and innovation in organizations. Journal of Organisational Behaviour, 35, 393–412.

Tan, J., Hall, R. J., & Boyce, C. (2003). The role of employee reactions in predicting training effectiveness. Human Resource Development Quarterly, 14 (4), 397–411.

Taylor, P. J., Russ-Eft, D. F., & Chan, D. W. L. (2005). A meta-analytic review of behaviour modelling training. Journal of Applied Psychology, 90 (4), 692–709.

Taylor, P. J., Russ-Eft, D. F., & Taylor, H. (2009). Transfer of management training from alternative perspectives. Journal of Applied Psychology, 94, 104–121.

Tessema, M. T., & Soeters, J. (2006). Challenges and prospects of HRM in developing countries: Testing the HRM-performance link in the Eritean civil service. International Journal of Human Resource Management, 17 , 105–186.

Tharenou, P., Saks, A. M., & Moore, C. (2007). A review of critique of research on training and organisational-level outcomes. Human Resource Management Review, 17, 251–273.

Tracey, J. B., Tannenbaum, S. I., & Kavanaugh, M. J. (1995). Applying trained skills on the job: The importance of the work environment. Journal of Applied Psychology, 80, 239–252.

Tremblay, M., Cloutier, J., Simard, G., Chenevert, D., & Vandenberghe, C. (2010). The role of HRM practices, procedural justice, organizational support and trust in organizational commitment and in-role and extra-role performance. The International Journal of Human Resource Management, 21 (3), 405–433.

Tzabbar, D., Tzafrir, S., & Baruch, Y. (2017). A bridge over troubled water: Replication, integration and extension of the relationship between HRM practices and organisational performance using moderating meta-analysis. Human Resource Management Review, 27 (1), 134–148.

Tzafrir, S. (2005). The relationship between trust, HRM practices and firm performance. International Journal of Human Resource Management, 16 (9), 1600–1622.

Úbeda-García, M. (2005). Training and business performance: The Spanish case. The International Journal of Human Resource Management, 16 (9), 1691–1710.

Ubeda-Garcia, M., Marco, B., Sabater, V., & Garcia-Lillo, F. (2013). Training policy and organisational performance in the Spanish hotel industry. International Journal of Human Resource Management, 24 (15), 2851–2875.

Unger, J. M., Rauch, A., Frese, M., & Rosenbusch, N. (2011). Human capital and entrepreneurial success: A meta-analytical review. Journal of Business Venturing, 26 (3), 341–358.

Wickramasinghe, V., & Liyanage, S. (2013). Effects of high performance work practices on job performance in project-based organizations. Project Management Journal, 44 (3), 64–77.

Wright, P. M., Dunford, B. B., & Snell, S. A. (2001). Human resources and the resource-based view of the firm. Journal of Management, 27 (6), 701–721.

Yang, B., Watkins, K. E., & Marsick, V. J. (2004). The construct of the learning organization: Dimensions, measurement, and validation. Human Resource Development Quarterly, 15 (1), 31–55.

Yang, T., Xun, J., & He, X. (2015). British SMEs’ e-commerce technological investments and firm performance: An RBV perspective. Technology Analysis & Strategic Management, 27 (5), 586–603.

Zhang, D. (2004). Virtual mentor and the lab system: Toward building an interactive, personalized, and in an intelligent eLearning environment. Journal of Computer Information Systems, 44, 35–43.

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Garavan, T.N. et al. (2020). The Current State of Research on Training Effectiveness. In: Learning and Development Effectiveness in Organisations. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-48900-7_5

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The Role of Training and Development on Organizational Effectiveness

Ismael, N. B., Othman, B. J., Gardi, B., Hamza, P. A., Sorguli, S., Aziz, H. M., Ahmed, S. A., Sabir, B. Y., Ali, B. J., Anwar, G. (2021). The Role of Training and Development on Organizational effectiveness. International Journal of Engineering, Business and Management, 5(3), 15–24

10 Pages Posted: 28 May 2021

Nechirwan Burhan Ismael

Cihan University

Baban Jabbar Othman

Knowledge university, bayar gardi, pshdar abdalla hamza, kurdistan technical institute, sarhang sorguli.

Knowledge University - College of Administration and Financial Sciences

Hassan Mahmood Aziz

Shahla ali ahmed, near east university, bawan yassin sabir, bayad jamal ali.

Komar University for Science and Technology

Govand Anwar

Knowledge University - Department of Business Administration

Date Written: May 22, 2021

The aim of this study is to investigate the relationship between training and development with organizational effectiveness. The research data is collected by developing questionnaire, the research is of qualitative method which tends to address the quality of things in a depth rather than numerical data about the questionnaire , its composed of two parts, the first one interested in collecting personal data, which the second section is shining light on the areas of (training, development and organization effectiveness) the sample were both male and female The researcher had collected the data at private universities by using a random sample , 120 questioners were delivered to different levels of employee at private universities and 102 of them responded to it , the analysis was undertaken by using SPSS. In this study, the researchers tried to shine a light on the training and development and how they can affect effectiveness of an organization for which the researchers decided to choose private universities to distribute my questionnaire and receiving them after they have filled it. the first research question that the researchers have found there is relationship between training and development and also the second research question that the researchers have found that there a direct impact of development programs on the organizational effectiveness and its progress and development is essential for an effective organization.

Keywords: Training, Development, Organizational Effectiveness, Private Universities

Suggested Citation: Suggested Citation

Cihan University ( email )

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Knowledge University - College of Administration and Financial Sciences ( email )

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Bayad Jamal Ali (Contact Author)

Komar university for science and technology ( email ).

Sulaimani Qularesi Kurdistan, Sulaimani Iraq

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Offensive content is pervasive in social media and a reason for concern to companies and government organizations. Several studies have been recently published investigating methods to detect the various forms of such content (e.g., hate speech, cyberbullying, and cyberaggression). The clear majority of these studies deal with English partially because most annotated datasets available contain English data. In this article, we take advantage of available English datasets by applying cross-lingual contextual word embeddings and transfer learning to make predictions in low-resource languages. We project predictions on comparable data in Arabic, Bengali, Danish, Greek, Hindi, Spanish, and Turkish. We report results of 0.8415 F1 macro for Bengali in TRAC-2 shared task [23], 0.8532 F1 macro for Danish and 0.8701 F1 macro for Greek in OffensEval 2020 [58], 0.8568 F1 macro for Hindi in HASOC 2019 shared task [27], and 0.7513 F1 macro for Spanish in in SemEval-2019 Task 5 (HatEval) [7], showing that our approach compares favorably to the best systems submitted to recent shared tasks on these three languages. Additionally, we report competitive performance on Arabic and Turkish using the training and development sets of OffensEval 2020 shared task. The results for all languages confirm the robustness of cross-lingual contextual embeddings and transfer learning for this task.

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Manpower training and development is essential to the success and productivity of every organization.  Although technology and the internet have enabled global collaboration and competition, employees are still the organization’s competitive advantage.  Manpower training and development enables employees to develop skills and competence necessary to enhance bottom-line results for their organizations.

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Training and development in sport officials: A systematic review

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EMPLOYEE TRAINING AND DEVELOPMENT AS A MODEL FOR ORGANIZATIONAL SUCCESS

This article reviewed employee training and development as a model for organizational performance and effectiveness. The various literatures reviewed on the topic in question showed that, training and development had positively correlated and claimed statistical significant relationship with employee performance and effectiveness and can advance organizational growth and success. In order to improve employee training and development as well as improved performance and service delivery, it is recommended that management should increase the number of employees taking part in training and development.

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The aim of this study was to explore the impact of training and development on employee performance. This study conducted under the framework of banking sector of Pakistan. Study finding reveal development leads to better employee performance, training and development both increase the employee performance. Organizations need to spend on training and development of its employees for sustainable long term competitive edge.

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The core theme of this study is to investigate relationship between human resource management and firm performance. This empirical study was three elements of human resources management that is training, skills and experience. The proposed study uses a sample size of 50 respondents. The target population is professionals working in different manufacturing sectors. The result shows that training, skills and experience are positively related with firm performance. Human capital management practices have a positive impact on firm performance. The organizations must enhance human capital by providing continuous training programs to update existing employee skills and abilities.

Global Journal of Management, Social Sciences and Humanites

Prof.Dr.Abdul Ghafoor Awan , Rizwan Rasheed

The objective of this research paper was to analyze various aspects of training and development as well as their impacts on the performance of organizations. For this purpose, primary data was collected from 300 employees of IT companies through a structured questionnaire randomly. The selected independent variables were Training Delivery Style, Training Design, Off-thejob Training and On-the-Job Training while dependent variable was Organizational performance. Training and Development of employees was a mediating variable. The statistical techniques such as Descriptive Statistics, Reliability test, Correlation analysis and Multiple Regression Analysis were used to analyze data. Our findings show that On-the-Job training, Training design, Delivery style and Off-the-Job training have positive and significant impact on the performance of organizations.

IJMSBR Open Access Journal , mohammed salah

"A case Study of Jordanian Private Sector transportation companies located in the Southern region of Jordan. A particular reference is made to the Govern ate of Maan ". ABSTRACT The Success or failure of modern business organizations depends on the quality of their human resources. Well trained and highly developed employees are considered as corner stone for such success. Hence the purpose of the study was to investigate the relationship between training , development, training and development and employees performance and productivity in selected Jordanian Private Sector transportation companies located in the Southern region of Jordan. The study was based on set of hypotheses that HOs: hypothesized no relationships between variables, while H1-H6 hypothesized the existence of relationships between stated variables. A quantitative approach is used Relevant data was collected through structured questionnaire. Subjects for the study consisted of 254 employees which constituted 60% of the total target population of 420 people. 254 structured questionnaire were distributed to employees on job location, 212 questionnaires were returned and only 188 were suitable for statistical analysis. SPSS version 16 has been used to for data analysis. Both descriptive and inferential statistics were used for data analysis. The statistical tools were aligned with the objective of the research. For this purpose, frequency tables, percentages, means and standard deviations were computed and substantively interpreted. Inferential statistics like Pearson product moment correlation coefficient (r) and linear regression were used to determine if there is a significant positive relationship existed between the independent variables (training and development) and dependent variables (performance and productivity). The findings indicated that training and development were positively correlated and claimed statistically significant relationship with employee performance and productivity. Analysis and interpretations were made at 0.05 level of significance. The study concluded that training and development have important impact on employee performance and productivity. Therefore, it was recommended that effective training programs and carefully set development plans should be provided to all employees to enable them to enhance their skills and upgrade their knowledge. Finally, foreseeable future research can be conducted to cover other variables like (capabilities, involvement so on) which might affect performance and productivity.

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The purpose of this research was to look into the effect of training and development on employee performance at the Ministry of Agriculture for Livestock and Fisheries in Chilanga District. The research questions for the study were as follows: 1) Determine the role of training and development in relation to job performance among ministry employees; 2) Investigate whether training and development affect public service delivery; and 3) Identify methods for implementing training and development to improve job performance. This study's research design was descriptive rather than experimental. The sample population in Chilanga District consisted of 30 professional personnel from the Ministry of Livestock and Fisheries. Secondary data was gathered through journals, unpublished papers, and published materials, while primary data was gathered using a semi-structured questionnaire particularly constructed for the project. SPSS version 23 was used to analyze the data. According to the findings, employee training and education have a considerable impact on the work performance of individuals inside a business. Several recommendations were made in light of these findings. To begin, it was proposed that a well-coordinated training program be formed with defined eligibility criteria, a schedule, a duration, and an application process for interested staff. This initiative should be available to all eligible workers, rather than just a small number of people. Second, the ministry should set aside funds at the start of each year to ensure that the training program runs smoothly. This proactive strategy enables the departments involved to plan appropriately with the resources available. Finally, clear criteria must be developed to guarantee that individuals attending training receive training that is relevant to their job tasks and duties

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2. gan architecture, hierarchy, and variants, 2.1. where do the generated images originate noise vs. image, 2.2. improvement of quality for generated images, 3. selecting the appropriate gan for the research objectives, 3.1. three considerations, 3.1.1. image-to-image translation gans and interindividual anatomic variance, 3.1.2. high-quality image resolution and contrast, 3.1.3. unsupervised detection models, 3.2. examples of gan application to brain mr imaging, 3.2.1. cyclegan—brain infarction images for augmentation, 3.2.2. psp encoder-combined stylegan—brain vessel images for unsupervised anomaly detection, 4. input data training, 4.1. image data preprocessing protocols, 4.2. training saturation, 4.3. performance improvement and ablation study, 5. performance evaluation, 5.1. quantitative evaluation, 5.2. qualitative evaluation, 5.3. diagnostic performance evaluation, 6. conclusions, 7. future directions, supplementary materials, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

• Gao, L.; Zhang, L.; Liu, C.; Wu, S. Handling imbalanced medical image data: A deep-learning-based one-class classification approach. Artif. Intell. Med. 2020 , 108 , 101935. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Gao, M.; Huang, J.; Huang, X.; Zhang, S.; Metaxas, D.N. Simplified labeling process for medical image segmentation. Med. Image Comput. Comput. Assist. Interv. 2012 , 15 Pt 2 , 387–394. [ Google Scholar ]
• Xun, S.; Li, D.; Zhu, H.; Chen, M.; Wang, J.; Li, J.; Chen, M.; Wu, B.; Zhang, H.; Chai, X.; et al. Generative adversarial networks in medical image segmentation: A review. Comput. Biol. Med. 2022 , 140 , 105063. [ Google Scholar ] [ CrossRef ]
• Osuala, R.; Kushibar, K.; Garrucho, L.; Linardos, A.; Szafranowska, Z.; Klein, S.; Glocker, B.; Díaz, O.; Lekadir, K. A Review of Generative Adversarial Networks in Cancer Imaging: New Applications, New Solutions. arXiv 2021 , arXiv:2107.09543. [ Google Scholar ]
• Huang, G.; Jafari, A.H. Enhanced balancing GAN: Minority-class image generation. Neural Comput. Appl. 2023 , 35 , 5145–5154. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Kannan, R.J.; Krithika alias Anbu Devi, M.; Suganthi, K.; Kommers, P.S.A.; Quadir Md, A. Review of Medical Image Synthesis using GAN Techniques. ITM Web Conf. 2021 , 37 , 01005. [ Google Scholar ]
• Karras, T.; Laine, S.; Aila, T. A Style-Based Generator Architecture for Generative Adversarial Networks. In Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 15–20 June 2019; pp. 4396–4405. [ Google Scholar ]
• Bermano, A.H.; Gal, R.; Alaluf, Y.; Mokady, R.; Nitzan, Y.; Tov, O.; Patashnik, O.; Cohen-Or, D. State-of-the-Art in the Architecture, Methods and Applications of StyleGAN. Comput. Graph. Forum 2022 , 41 , 591–611. [ Google Scholar ] [ CrossRef ]
• Karras, T.; Aila, T.; Laine, S.; Lehtinen, J. Progressive Growing of GANs for Improved Quality, Stability, and Variation. arXiv 2017 , arXiv:1710.10196. [ Google Scholar ]
• Radford, A.; Metz, L.; Chintala, S. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. arXiv 2015 , arXiv:1511.06434. [ Google Scholar ]
• Zhu, J.Y.; Park, T.; Isola, P.; Efros, A.A. Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks. In Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy, 22–29 October 2017; pp. 2242–2251. [ Google Scholar ]
• Isola, P.; Zhu, J.Y.; Zhou, T.; Efros, A.A. Image-to-Image Translation with Conditional Adversarial Networks. In Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 21–26 July 2017; pp. 5967–5976. [ Google Scholar ]
• Mirza, M.; Osindero, S. Conditional generative adversarial nets. arXiv 2014 , arXiv:1411.1784. [ Google Scholar ]
• Xia, X.; Pan, X.; Li, N.; He, X.; Ma, L.; Zhang, X.; Ding, N. GAN-based anomaly detection: A review. Neurocomputing 2022 , 493 , 497–535. [ Google Scholar ] [ CrossRef ]
• Park, S.; Lee, K.; Ko, B.; Kim, N. Unsupervised anomaly detection with generative adversarial networks in mammography. Sci. Rep. 2023 , 13 , 2925. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Lee, S.; Jeong, B.; Kim, M.; Jang, R.; Paik, W.; Kang, J.; Chung, W.J.; Hong, G.S.; Kim, N. Emergency triage of brain computed tomography via anomaly detection with a deep generative model. Nat. Commun. 2022 , 13 , 4251. [ Google Scholar ] [ CrossRef ]
• Goodfellow, I.; Pouget-Abadie, J.; Mirza, M.; Xu, B.; Warde-Farley, D.; Ozair, S.; Courville, A.; Bengio, Y. Generative Adversarial Networks. In Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, Montreal, QC, Canada, 8–13 December 2014; Volume 3. [ Google Scholar ]
• Alotaibi, A. Deep Generative Adversarial Networks for Image-to-Image Translation: A Review. Symmetry 2020 , 12 , 1705. [ Google Scholar ] [ CrossRef ]
• Quintana-Quintana, O.J.; De León-Cuevas, A.; González-Gutiérrez, A.; Gorrostieta-Hurtado, E.; Tovar-Arriaga, S. Dual U-Net-Based Conditional Generative Adversarial Network for Blood Vessel Segmentation with Reduced Cerebral MR Training Volumes. Micromachines 2022 , 13 , 823. [ Google Scholar ] [ CrossRef ]
• Anirudh, R.; Thiagarajan, J.J.; Bremer, P.-T.; Kim, H. Lung nodule detection using 3D convolutional neural networks trained on weakly labeled data. In Proceedings of the Medical Imaging 2016: Computer-Aided Diagnosis, San Diego, CA, USA, 27 February–3 March 2016. [ Google Scholar ]
• Hwang, S.; Kim, H.-E. Self-Transfer Learning for Weakly Supervised Lesion Localization. In Proceedings of the Medical Image Computing and Computer-Assisted Intervention—MICCAI 2016: 19th International Conference, Athens, Greece, 17–21 October 2016. [ Google Scholar ]
• Feng, X.; Yang, J.; Laine, A.; Angelini, E. Discriminative Localization in CNNs for Weakly-Supervised Segmentation of Pulmonary Nodules. In Proceedings of the Medical Image Computing and Computer Assisted Intervention—MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, 11–13 September 2017; Volume 10435. [ Google Scholar ]
• Richardson, E.; Alaluf, Y.; Patashnik, O.; Nitzan, Y.; Azar, Y.; Shapiro, S.; Cohen-Or, D. Encoding in style: A stylegan encoder for image-to-image translation. In Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA, 20–25 June 2021; pp. 2287–2296. [ Google Scholar ]
• Tov, O.; Alaluf, Y.; Nitzan, Y.; Patashnik, O.; Cohen-Or, D. Designing an encoder for StyleGAN image manipulation. ACM Trans. Graph. 2021 , 40 , 133. [ Google Scholar ] [ CrossRef ]
• Zhou, T.; Li, Q.; Lu, H.; Cheng, Q.; Zhang, X. GAN review: Models and medical image fusion applications. Inf. Fusion 2023 , 91 , 134–148. [ Google Scholar ] [ CrossRef ]
• Abdusalomov, A.B.; Nasimov, R.; Nasimova, N.; Muminov, B.; Whangbo, T.K. Evaluating Synthetic Medical Images Using Artificial Intelligence with the GAN Algorithm. Sensors 2023 , 23 , 3440. [ Google Scholar ] [ CrossRef ]
• Wicaksono, K.P.; Fujimoto, K.; Fushimi, Y.; Sakata, A.; Okuchi, S.; Hinoda, T.; Nakajima, S.; Yamao, Y.; Yoshida, K.; Miyake, K.K.; et al. Super-resolution application of generative adversarial network on brain time-of-flight MR angiography: Image quality and diagnostic utility evaluation. Eur. Radiol. 2022 , 33 , 936–946. [ Google Scholar ] [ CrossRef ]
• Belue, M.J.; Harmon, S.A.; Masoudi, S.; Barrett, T.; Law, Y.M.; Purysko, A.S.; Panebianco, V.; Yilmaz, E.C.; Lin, Y.; Jadda, P.K.; et al. Quality of T2-weighted MRI re-acquisition versus deep learning GAN image reconstruction: A multi-reader study. Eur. J. Radiol. 2024 , 170 , 111259. [ Google Scholar ] [ CrossRef ]
• Chen, Y.; Lin, H.; Zhang, W.; Chen, W.; Zhou, Z.; Heidari, A.A.; Chen, H.; Xu G:, I.C.y.c.l.e.-G.A.N. Improved cycle generative adversarial networks for liver medical image generation. Biomed. Signal Process. Control 2024 , 92 , 106100. [ Google Scholar ] [ CrossRef ]
• Song, Y.; Chong, N.Y. S-CycleGAN: Semantic Segmentation Enhanced CT-Ultrasound Image-to-Image Translation for Robotic Ultrasonography. arXiv 2024 , arXiv:2406.01191. [ Google Scholar ]
• Esmaeili, M.; Toosi, A.; Roshanpoor, A.; Changizi, V.; Ghazisaeedi, M.; Rahmim, A.; Sabokrou, M. Generative Adversarial Networks for Anomaly Detection in Biomedical Imaging: A Study on Seven Medical Image Datasets. IEEE Access 2023 , 11 , 17906–17921. [ Google Scholar ] [ CrossRef ]
• Lyu, J.; Fu, Y.; Yang, M.; Xiong, Y.; Duan, Q.; Duan, C.; Wang, X.; Xing, X.; Zhang, D.; Lin, J.; et al. Generative Adversarial Network-based Noncontrast CT Angiography for Aorta and Carotid Arteries. Radiology 2023 , 309 , e230681. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Wang, B.; Pan, Y.; Xu, S.; Zhang, Y.; Ming, Y.; Chen, L.; Liu, X.; Wang, C.; Liu, Y.; Xia, Y. Quantitative Cerebral Blood Volume Image Synthesis from Standard MRI Using Image-to-Image Translation for Brain Tumors. Radiology 2023 , 308 , e222471. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Armanious, K.; Jiang, C.; Fischer, M.; Küstner, T.; Hepp, T.; Nikolaou, K.; Gatidis, S.; Yang, B. MedGAN: Medical image translation using GANs. Comput. Med. Imaging Graph. 2020 , 79 , 101684. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Kong, L.; Lian, C.; Huang, D.; Li, Z.; Hu, Y.; Zhou, Q. Breaking the Dilemma of Medical Image-to-image Translation. In Proceedings of the 2021 Conference on Neural Information Processing Systems, Online, 6–14 December 2021. [ Google Scholar ]
• Makhlouf, A.; Maayah, M.; Abughanam, N.; Catal, C. The use of generative adversarial networks in medical image augmentation. Neural Comput. Appl. 2023 , 35 , 24055–24068. [ Google Scholar ] [ CrossRef ]
• Saeed, A.Q.; Sheikh Abdullah, S.N.H.; Che-Hamzah, J.; Abdul Ghani, A.T.; Abu-ain, W.A.k. Synthesizing Retinal Images using End-To-End VAEs-GAN Pipeline-Based Sharpening and Varying Layer. Multimed. Tools Appl. 2024 , 83 , 1283–1307. [ Google Scholar ] [ CrossRef ]
• Khan, S.; Huh, J.; Ye, J.C. Contrast and Resolution Improvement of POCUS Using Self-consistent CycleGAN. In Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health ; Springer International Publishing: Cham, Switzerland, 2021; pp. 158–167. [ Google Scholar ]
• Suryawati, E.; Pardede, H.F.; Zilvan, V.; Ramdan, A.; Krisnandi, D.; Heryana, A.; Yuwana, R.S.; Kusumo, R.B.S.; Arisal, A.; Supianto, A.A. Unsupervised feature learning-based encoder and adversarial networks. J. Big Data 2021 , 8 , 118. [ Google Scholar ] [ CrossRef ]
• Schlegl, T.; Seeböck, P.; Waldstein, S.; Langs, G.; Schmidt-Erfurth, U. f-AnoGAN: Fast Unsupervised Anomaly Detection with Generative Adversarial Networks. Med. Image Anal. 2019 , 54 , 30–44. [ Google Scholar ] [ CrossRef ]
• Sabuhi, M.; Zhou, M.; Bezemer, C.-P.; Musilek, P. Applications of Generative Adversarial Networks in Anomaly Detection: A Systematic Literature Review. IEEE Access 2021 , 9 , 161003–161029. [ Google Scholar ] [ CrossRef ]
• Schlegl, T.; Seeböck, P.; Waldstein, S.M.; Schmidt-Erfurth, U.; Langs, G. Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery. In Information Processing in Medical Imaging ; Springer International Publishing: Cham, Switzerland, 2017; pp. 146–157. [ Google Scholar ]
• Akcay, S.; Atapour-Abarghouei, A.; Breckon, T.P. GANomaly: Semi-supervised Anomaly Detection via Adversarial Training. In Computer Vision—ACCV 2018 ; Springer International Publishing: Cham, Switzerland, 2019; pp. 622–637. [ Google Scholar ]
• Chen, L.; Li, Y.; Deng, X.; Liu, Z.; Lv, M.; Zhang, H. Dual Auto-Encoder GAN-Based Anomaly Detection for Industrial Control System. Appl. Sci. 2022 , 12 , 4986. [ Google Scholar ] [ CrossRef ]
• Nakao, T.; Hanaoka, S.; Nomura, Y.; Murata, M.; Takenaga, T.; Miki, S.; Watadani, T.; Yoshikawa, T.; Hayashi, N.; Abe, O. Unsupervised Deep Anomaly Detection in Chest Radiographs. J. Digit. Imaging 2021 , 34 , 418–427. [ Google Scholar ] [ CrossRef ]
• Goceri, E. Medical image data augmentation: Techniques, comparisons and interpretations. Artif. Intell. Rev. 2023 , 56 , 12561–12605. [ Google Scholar ] [ CrossRef ]
• Shorten, C.; Khoshgoftaar, T.M. A survey on Image Data Augmentation for Deep Learning. J. Big Data 2019 , 6 , 60. [ Google Scholar ] [ CrossRef ]
• Meyes, R.; Lu, M.; Puiseau CWd Meisen, T. Ablation Studies in Artificial Neural Networks. arXiv 2019 , arXiv:1901.08644. [ Google Scholar ]
• Sheikholeslami, S.; Meister, M.; Wang, T.; Payberah, A.H.; Vlassov, V.; Dowling, J. AutoAblation: Automated Parallel Ablation Studies for Deep Learning. In Proceedings of the 1st Workshop on Machine Learning and Systems 2021, Edinburgh, UK, 26 April 2021. [ Google Scholar ]
• Singh, R.; Sethi, A.; Saini, K.; Saurav, S.; Tiwari, A.; Singh, S. Attention-guided generator with dual discriminator GAN for real-time video anomaly detection. Eng. Appl. Artif. Intell. 2024 , 131 , 107830. [ Google Scholar ] [ CrossRef ]
• Mindlin, D.; Schilling, M.; Cimiano, P. ABC-GAN: Spatially Constrained Counterfactual Generation for Image Classification Explanations. In Explainable Artificial Intelligence ; Springer Nature: Cham, Switzerland, 2023; pp. 260–282. [ Google Scholar ]
• Woodland, M.; Wood, J.; Anderson, B.M.; Kundu, S.; Lin, E.; Koay, E.; Odisio, B.; Chung, C.; Kang, H.C.; Venkatesan, A.M.; et al. Evaluating the Performance of StyleGAN2-ADA on Medical Images. In Simulation and Synthesis in Medical Imaging ; Springer International Publishing: Cham, Switzerland, 2022; pp. 142–153. [ Google Scholar ]
• Long, X.; Tian, C. Spatial and channel attention-based conditional Wasserstein GAN for direct and rapid image reconstruction in ultrasound computed tomography. Biomed. Eng. Lett. 2024 , 14 , 57–68. [ Google Scholar ] [ CrossRef ]
• Liang, D.; Zhang, S.; Zhao, Z.; Wang, G.; Sun, J.; Zhao, J.; Li, W.; Xu, L.X. Two-stage generative adversarial networks for metal artifact reduction and visualization in ablation therapy of liver tumors. Int. J. Comput. Assist. Radiol. Surg. 2023 , 18 , 1991–2000. [ Google Scholar ] [ CrossRef ]
• Alshawi, R.; Hoque, M.T.; Ferdaus, M.M.; Abdelguerfi, M.; Niles, K.; Prathak, K.; Tom, J.; Klein, J.; Mousa, M.; Lopez, J.J. Dual Attention U-Net with Feature Infusion: Pushing the Boundaries of Multiclass Defect Segmentation. arXiv 2023 , arXiv:231214053. [ Google Scholar ]
• Jeong, J.J.; Tariq, A.; Adejumo, T.; Trivedi, H.; Gichoya, J.W.; Banerjee, I. Systematic Review of Generative Adversarial Networks (GANs) for Medical Image Classification and Segmentation. J. Digit. Imaging 2022 , 35 , 137–152. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Laino, M.E.; Cancian, P.; Politi, L.S.; Della Porta, M.G.; Saba, L.; Savevski, V. Generative Adversarial Networks in Brain Imaging: A Narrative Review. J. Imaging 2022 , 8 , 83. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Mudeng, V.; Kim, M.; Choe, S.-w. Prospects of Structural Similarity Index for Medical Image Analysis. Appl. Sci. 2022 , 12 , 3754. [ Google Scholar ] [ CrossRef ]
• Nečasová, T.; Burgos, N.; Svoboda, D. Chapter 25—Validation and evaluation metrics for medical and biomedical image synthesis. In Biomedical Image Synthesis and Simulation ; Burgos, N., Svoboda, D., Eds.; Academic Press: Cambridge, MA, USA, 2022; pp. 573–600. [ Google Scholar ]
• Zhang, H.; Li, H.; Dillman, J.R.; Parikh, N.A.; He, L. Multi-Contrast MRI Image Synthesis Using Switchable Cycle-Consistent Generative Adversarial Networks. Diagnostics 2022 , 12 , 816. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Yoshida, M.; Teramoto, A.; Kudo, K.; Matsumoto, S.; Saito, K.; Fujita, H. Automated Extraction of Cerebral Infarction Region in Head MR Image Using Pseudo Cerebral Infarction Image by CycleGAN. Appl. Sci. 2022 , 12 , 489. [ Google Scholar ] [ CrossRef ]
• Ji, J.; Zhao, Y.; Zhang, Y.; Wang, C.; Ma, X.; Huang, F.; Yao, J. Infrared and visible image fusion of generative adversarial network based on multi-channel encoding and decoding. Infrared Phys. Technol. 2023 , 134 , 104853. [ Google Scholar ] [ CrossRef ]
• Borji, A. Pros and cons of GAN evaluation measures: New developments. Comput. Vis. Image Underst. 2022 , 215 , 103329. [ Google Scholar ] [ CrossRef ]
• Kalantar, R.; Messiou, C.; Winfield, J.M.; Renn, A.; Latifoltojar, A.; Downey, K.; Sohaib, A.; Lalondrelle, S.; Koh, D.M.; Blackledge, M.D. CT-Based Pelvic T(1)-Weighted MR Image Synthesis Using UNet, UNet++ and Cycle-Consistent Generative Adversarial Network (Cycle-GAN). Front. Oncol. 2021 , 11 , 665807. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Van Nguyen, H.; Trong, T.H. Brain MRI Images Generating Method Based on Cyclegan. J. Sci. Technol. Issue Inf. Commun. Technol. 2022 , 20 , 13–18. [ Google Scholar ] [ CrossRef ]
• Avbersek, L.K.; Repovs, G. Deep learning in neuroimaging data analysis: Applications, challenges, and solutions. Front. Neuroimaging 2022 , 1 , 981642. [ Google Scholar ] [ CrossRef ]
• Baur, C.; Wiestler, B.; Muehlau, M.; Zimmer, C.; Navab, N.; Albarqouni, S. Modeling Healthy Anatomy with Artificial Intelligence for Unsupervised Anomaly Detection in Brain MRI. Radiol. Artif. Intell. 2021 , 3 , e190169. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Ahmad, B.; Sun, J.; You, Q.; Palade, V.; Mao, Z. Brain Tumor Classification Using a Combination of Variational Autoencoders and Generative Adversarial Networks. Biomedicines 2022 , 10 , 223. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Tariq, U.; Qureshi, R.; Zafar, A.; Aftab, D.; Wu, J.; Alam, T.; Shah, Z.; Ali, H. Brain Tumor Synthetic Data Generation with Adaptive StyleGANs. In Proceedings of the 2023 Irish Conference on Artificial Intelligence and Cognitive Science, Letterkenny, Ireland, 7–8 December 2023; Springer Nature: Cham, Switzerland, 2023; pp. 147–159. [ Google Scholar ]
• Information eXtraction from Images Dataset. Available online: http://brain-development.org/ixi-dataset/ (accessed on 31 July 2024).

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Yoon, J.T.; Lee, K.M.; Oh, J.-H.; Kim, H.-G.; Jeong, J.W. Insights and Considerations in Development and Performance Evaluation of Generative Adversarial Networks (GANs): What Radiologists Need to Know. Diagnostics 2024 , 14 , 1756. https://doi.org/10.3390/diagnostics14161756

Yoon JT, Lee KM, Oh J-H, Kim H-G, Jeong JW. Insights and Considerations in Development and Performance Evaluation of Generative Adversarial Networks (GANs): What Radiologists Need to Know. Diagnostics . 2024; 14(16):1756. https://doi.org/10.3390/diagnostics14161756

Yoon, Jeong Taek, Kyung Mi Lee, Jang-Hoon Oh, Hyug-Gi Kim, and Ji Won Jeong. 2024. "Insights and Considerations in Development and Performance Evaluation of Generative Adversarial Networks (GANs): What Radiologists Need to Know" Diagnostics 14, no. 16: 1756. https://doi.org/10.3390/diagnostics14161756

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How to cite ChatGPT

Use discount code STYLEBLOG15 for 15% off APA Style print products with free shipping in the United States.

We, the APA Style team, are not robots. We can all pass a CAPTCHA test , and we know our roles in a Turing test . And, like so many nonrobot human beings this year, we’ve spent a fair amount of time reading, learning, and thinking about issues related to large language models, artificial intelligence (AI), AI-generated text, and specifically ChatGPT . We’ve also been gathering opinions and feedback about the use and citation of ChatGPT. Thank you to everyone who has contributed and shared ideas, opinions, research, and feedback.

In this post, I discuss situations where students and researchers use ChatGPT to create text and to facilitate their research, not to write the full text of their paper or manuscript. We know instructors have differing opinions about how or even whether students should use ChatGPT, and we’ll be continuing to collect feedback about instructor and student questions. As always, defer to instructor guidelines when writing student papers. For more about guidelines and policies about student and author use of ChatGPT, see the last section of this post.

Quoting or reproducing the text created by ChatGPT in your paper

If you’ve used ChatGPT or other AI tools in your research, describe how you used the tool in your Method section or in a comparable section of your paper. For literature reviews or other types of essays or response or reaction papers, you might describe how you used the tool in your introduction. In your text, provide the prompt you used and then any portion of the relevant text that was generated in response.

Unfortunately, the results of a ChatGPT “chat” are not retrievable by other readers, and although nonretrievable data or quotations in APA Style papers are usually cited as personal communications , with ChatGPT-generated text there is no person communicating. Quoting ChatGPT’s text from a chat session is therefore more like sharing an algorithm’s output; thus, credit the author of the algorithm with a reference list entry and the corresponding in-text citation.

When prompted with “Is the left brain right brain divide real or a metaphor?” the ChatGPT-generated text indicated that although the two brain hemispheres are somewhat specialized, “the notation that people can be characterized as ‘left-brained’ or ‘right-brained’ is considered to be an oversimplification and a popular myth” (OpenAI, 2023).

OpenAI. (2023). ChatGPT (Mar 14 version) [Large language model]. https://chat.openai.com/chat

You may also put the full text of long responses from ChatGPT in an appendix of your paper or in online supplemental materials, so readers have access to the exact text that was generated. It is particularly important to document the exact text created because ChatGPT will generate a unique response in each chat session, even if given the same prompt. If you create appendices or supplemental materials, remember that each should be called out at least once in the body of your APA Style paper.

When given a follow-up prompt of “What is a more accurate representation?” the ChatGPT-generated text indicated that “different brain regions work together to support various cognitive processes” and “the functional specialization of different regions can change in response to experience and environmental factors” (OpenAI, 2023; see Appendix A for the full transcript).

Creating a reference to ChatGPT or other AI models and software

The in-text citations and references above are adapted from the reference template for software in Section 10.10 of the Publication Manual (American Psychological Association, 2020, Chapter 10). Although here we focus on ChatGPT, because these guidelines are based on the software template, they can be adapted to note the use of other large language models (e.g., Bard), algorithms, and similar software.

The reference and in-text citations for ChatGPT are formatted as follows:

• Parenthetical citation: (OpenAI, 2023)
• Narrative citation: OpenAI (2023)

Let’s break that reference down and look at the four elements (author, date, title, and source):

Author: The author of the model is OpenAI.

Date: The date is the year of the version you used. Following the template in Section 10.10, you need to include only the year, not the exact date. The version number provides the specific date information a reader might need.

Title: The name of the model is “ChatGPT,” so that serves as the title and is italicized in your reference, as shown in the template. Although OpenAI labels unique iterations (i.e., ChatGPT-3, ChatGPT-4), they are using “ChatGPT” as the general name of the model, with updates identified with version numbers.

The version number is included after the title in parentheses. The format for the version number in ChatGPT references includes the date because that is how OpenAI is labeling the versions. Different large language models or software might use different version numbering; use the version number in the format the author or publisher provides, which may be a numbering system (e.g., Version 2.0) or other methods.

Bracketed text is used in references for additional descriptions when they are needed to help a reader understand what’s being cited. References for a number of common sources, such as journal articles and books, do not include bracketed descriptions, but things outside of the typical peer-reviewed system often do. In the case of a reference for ChatGPT, provide the descriptor “Large language model” in square brackets. OpenAI describes ChatGPT-4 as a “large multimodal model,” so that description may be provided instead if you are using ChatGPT-4. Later versions and software or models from other companies may need different descriptions, based on how the publishers describe the model. The goal of the bracketed text is to briefly describe the kind of model to your reader.

Source: When the publisher name and the author name are the same, do not repeat the publisher name in the source element of the reference, and move directly to the URL. This is the case for ChatGPT. The URL for ChatGPT is https://chat.openai.com/chat . For other models or products for which you may create a reference, use the URL that links as directly as possible to the source (i.e., the page where you can access the model, not the publisher’s homepage).

Other questions about citing ChatGPT

You may have noticed the confidence with which ChatGPT described the ideas of brain lateralization and how the brain operates, without citing any sources. I asked for a list of sources to support those claims and ChatGPT provided five references—four of which I was able to find online. The fifth does not seem to be a real article; the digital object identifier given for that reference belongs to a different article, and I was not able to find any article with the authors, date, title, and source details that ChatGPT provided. Authors using ChatGPT or similar AI tools for research should consider making this scrutiny of the primary sources a standard process. If the sources are real, accurate, and relevant, it may be better to read those original sources to learn from that research and paraphrase or quote from those articles, as applicable, than to use the model’s interpretation of them.

We’ve also received a number of other questions about ChatGPT. Should students be allowed to use it? What guidelines should instructors create for students using AI? Does using AI-generated text constitute plagiarism? Should authors who use ChatGPT credit ChatGPT or OpenAI in their byline? What are the copyright implications ?

On these questions, researchers, editors, instructors, and others are actively debating and creating parameters and guidelines. Many of you have sent us feedback, and we encourage you to continue to do so in the comments below. We will also study the policies and procedures being established by instructors, publishers, and academic institutions, with a goal of creating guidelines that reflect the many real-world applications of AI-generated text.

For questions about manuscript byline credit, plagiarism, and related ChatGPT and AI topics, the APA Style team is seeking the recommendations of APA Journals editors. APA Style guidelines based on those recommendations will be posted on this blog and on the APA Style site later this year.

Update: APA Journals has published policies on the use of generative AI in scholarly materials .

We, the APA Style team humans, appreciate your patience as we navigate these unique challenges and new ways of thinking about how authors, researchers, and students learn, write, and work with new technologies.

American Psychological Association. (2020). Publication manual of the American Psychological Association (7th ed.). https://doi.org/10.1037/0000165-000

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