Modelling & Visualizations - Technology bibliographies - in Harvard style

In-text: (Accenture, 2021)

Your Bibliography: Accenture, 2021. Accenture Launches 360° Value Reporting Experience. [online] Accenture. Available at: <https://newsroom.accenture.com/news/accenture-launches-360-degree-value-reporting-experience.htm#:~:text=Accenture%20defines%20%E2%80%9C360%C2%B0%20Value,%2C%20Experience%2C%20Sustainability%20and%20Financial.> [Accessed 8 May 2022].

In-text: (Alduayj and Rajpoot, 2018)

Your Bibliography: Alduayj, S. and Rajpoot, K., 2018. Predicting Employee Attrition using Machine Learning. 2018 International Conference on Innovations in Information Technology (IIT), [online] Available at: <https://ieeexplore.ieee.org/abstract/document/8605976> [Accessed 8 May 2022].

In-text: (Anderson, 2020)

Your Bibliography: Anderson, B., 2020. Employee Turnover vs. Attrition: Context is the Key - BambooHR Blog. [online] Bamboohr.com. Available at: <https://www.bamboohr.com/blog/employee-turnover-vs-attrition-context-is-the-key/> [Accessed 8 May 2022].

In-text: (Binder, Gefeller, Schmid and Mayr, 2014)

Your Bibliography: Binder, H., Gefeller, O., Schmid, M. and Mayr, A., 2014. The Evolution of Boosting Algorithms. Methods of Information in Medicine, [online] 53(06), pp.419-427. Available at: <https://www.thieme-connect.com/products/ejournals/abstract/10.3414/ME13-01-0122> [Accessed 8 May 2022].

In-text: (Brooks, 2021)

Your Bibliography: Brooks, R., 2021. Uncovering Industries With an Elevated Risk of Employee Attrition. [online] Workday. Available at: <https://blog.workday.com/en-us/2021/uncovering-industries-with-an-elevated-risk-of-employee-attrition.html> [Accessed 8 May 2022].

In-text: (Buzeti, Klun and Stare, 2016)

Your Bibliography: Buzeti, J., Klun, M. and Stare, J., 2016. Evaluation of measures to reduce employee turnover in Slovenian organisations. E+M Ekonomie a Management, [online] 19(1), pp.121-131. Available at: <https://otik.uk.zcu.cz/bitstream/11025/21478/1/Buzeti.pdf> [Accessed 8 May 2022].

In-text: (Charilaou and Battat, 2022)

Your Bibliography: Charilaou, P. and Battat, R., 2022. Machine learning models and over-fitting considerations. World Journal of Gastroenterology, [online] 28(5), pp.605-607. Available at: <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8905023/?msclkid=04680531cf0811ec9645e23d999f6e5c> [Accessed 8 May 2022].

In-text: (Chartered Institute of Personnel and Development, 2021)

Your Bibliography: Chartered Institute of Personnel and Development, 2021. Employee Turnover & Retention | Factsheets | CIPD. [online] CIPD. Available at: <https://www.cipd.co.uk/knowledge/strategy/resourcing/turnover-retention-factsheet#gref> [Accessed 8 May 2022].

In-text: (Choueiry, 2022)

Your Bibliography: Choueiry, G., 2022. Which Variables Should You Include in a Regression Model? – Quantifying Health. [online] Quantifyinghealth.com. Available at: <https://quantifyinghealth.com/variables-to-include-in-regression/> [Accessed 8 May 2022].

In-text: (Deloitte, 2022)

Your Bibliography: Deloitte, 2022. Reducing Voluntary Turnover Through Predictive Analytics | Deloitte US. [online] Deloitte. Available at: <https://www2.deloitte.com/us/en/pages/deloitte-analytics/articles/finding-the-balance.html> [Accessed 8 May 2022].

In-text: (Ernst & Young, 2021)

Your Bibliography: Ernst & Young, 2021. More than half of employees globally would quit their jobs if not provided post-pandemic flexibility, EY survey finds. [online] Ernst & Young. Available at: <https://www.ey.com/en_gl/news/2021/05/more-than-half-of-employees-globally-would-quit-their-jobs-if-not-provided-post-pandemic-flexibility-ey-survey-finds> [Accessed 8 May 2022].

In-text: (Fallucchi, Coladangelo, Giuliano and William De Luca, 2020)

Your Bibliography: Fallucchi, F., Coladangelo, M., Giuliano, R. and William De Luca, E., 2020. Predicting Employee Attrition Using Machine Learning Techniques. Computers, [online] 9(4), p.86. Available at: <https://www.mdpi.com/2073-431X/9/4/86> [Accessed 8 May 2022].

In-text: (Favaro and Silkin, 2019)

Your Bibliography: Favaro, B. and Silkin, L., 2019. People analytics and GDPR: the challenge for HR — Future of Work Hub. [online] futureofworkhub. Available at: <https://www.futureofworkhub.info/comment/2019/10/28/people-analytics-and-gdpr-the-challenge-for-hr> [Accessed 8 May 2022].

In-text: (Fong et al., 2020)

Your Bibliography: Fong, S., Li, G., Dey, N., Gonzalez-Crespo, R. and Herrera-Viedma, E., 2020. Finding an Accurate Early Forecasting Model from Small Dataset: A Case of 2019-nCoV Novel Coronavirus Outbreak. International Journal of Interactive Multimedia and Artificial Intelligence, [online] 6(1), p.132. Available at: <https://arxiv.org/abs/2003.10776> [Accessed 8 May 2022].

In-text: (Gartner, 2022)

Your Bibliography: Gartner, 2022. Definition of Attrition - Gartner Human Resources Glossary. [online] Gartner. Available at: <https://www.gartner.com/en/human-resources/glossary/attrition> [Accessed 8 May 2022].

In-text: (Gartner, 2022)

Your Bibliography: Gartner, 2022. Gartner Says Total Annual Employee Turnover Will Likely Jump by Nearly 20% From the Prepandemic Annual Average. [online] Gartner. Available at: <https://www.gartner.com/en/newsroom/04-28-2022-gartner-says-us-total-annual-employee-turnover-will-likely-jump-by-nearly-twenty-percent-from-the-prepandemic-annual-average> [Accessed 8 May 2022].

In-text: (Handelman et al., 2019)

Your Bibliography: Handelman, G., Kok, H., Chandra, R., Razavi, A., Huang, S., Brooks, M., Lee, M. and Asadi, H., 2019. Peering Into the Black Box of Artificial Intelligence: Evaluation Metrics of Machine Learning Methods. American Journal of Roentgenology, [online] 212(1), pp.38-43. Available at: <https://www.ajronline.org/doi/full/10.2214/AJR.18.20224> [Accessed 8 May 2022].

In-text: (IBM, 2022)

Your Bibliography: IBM, 2022. What is Overfitting?. [online] IBM.com. Available at: <https://www.ibm.com/cloud/learn/overfitting> [Accessed 8 May 2022].

In-text: (Ipsos, 2022)

Your Bibliography: Ipsos, 2022. Half of British workers have considered quitting their job, looked or applied for another job. [online] Ipsos. Available at: <https://www.ipsos.com/en-uk/half-of-british-workers-have-considered-quitting-their-job#:~:text=New%20research%20by%20Ipsos%20in,employer%20about%20resigning%20(6%25).> [Accessed 8 May 2022].

In-text: (Iqbal, 2010)

Your Bibliography: Iqbal, A., 2010. Employee Turnover: Causes, Consequences and Retention Strategies in the Saudi Organizations. The Business Review, Cambridge, [online] 16(2), pp.275-281. Available at: <https://www.researchgate.net/publication/215912138_Employee_Turnover_Causes_Consequences_and_Retention_Strategies_in_Saudi_Organizations> [Accessed 8 May 2022].

In-text: (Krishnamurthy, 2019)

Your Bibliography: Krishnamurthy, P., 2019. Understanding Data Bias. [online] Medium. Available at: <https://towardsdatascience.com/survey-d4f168791e57> [Accessed 8 May 2022].

In-text: (Lin and Huang, 2020)

Your Bibliography: Lin, C. and Huang, C., 2020. Employee turnover intentions and job performance from a planned change: the effects of an organizational learning culture and job satisfaction. International Journal of Manpower, [online] 42(3), pp.409-423. Available at: <https://www.emerald.com/insight/content/doi/10.1108/IJM-08-2018-0281/full/html> [Accessed 8 May 2022].

In-text: (McKinsey Global Institute, 2021)

Your Bibliography: McKinsey Global Institute, 2021. The future of work after COVID-19. [online] McKinsey & Company. Available at: <https://www.mckinsey.com/featured-insights/future-of-work/the-future-of-work-after-covid-19> [Accessed 8 May 2022].

In-text: (ONS, 2021)

Your Bibliography: ONS, 2021. Employee earnings in the UK - Office for National Statistics. [online] Ons.gov.uk. Available at: <https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/earningsandworkinghours/bulletins/annualsurveyofhoursandearnings/2021> [Accessed 8 May 2022].

In-text: (O'Reilly-Shah et al., 2020)

Your Bibliography: O'Reilly-Shah, V., Gentry, K., Walters, A., Zivot, J., Anderson, C. and Tighe, P., 2020. Bias and ethical considerations in machine learning and the automation of perioperative risk assessment. British Journal of Anaesthesia, [online] 125(6), pp.843-846. Available at: <https://www.bjanaesthesia.org/action/showPdf?pii=S0007-0912%2820%2930631-0> [Accessed 8 May 2022].

In-text: (Pannucci and Wilkins, 2010)

Your Bibliography: Pannucci, C. and Wilkins, E., 2010. Identifying and Avoiding Bias in Research. Plastic and Reconstructive Surgery, [online] 126(2), pp.619-625. Available at: <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2917255/> [Accessed 8 May 2022].

In-text: (PwC, 2010)

Your Bibliography: PwC, 2010. Failure to retain competent employees costing UK businesses £42bn a year - Press room. [online] Pwc.blogs.com. Available at: <https://pwc.blogs.com/press_room/2010/10/failure-to-retain-competent-employees-costing-uk-businesses-42bn-a-year.html> [Accessed 8 May 2022].

In-text: (Ro, 2022)

Your Bibliography: Ro, C., 2022. Why there's no 'best' month to quit your job. [online] BBC.com. Available at: <https://www.bbc.com/worklife/article/20220103-why-theres-no-best-month-to-quit-your-job> [Accessed 8 May 2022].

In-text: (Shankar, Rajanikanth, Sivaramaraju and Murthy, 2018)

Your Bibliography: Shankar, R., Rajanikanth, J., Sivaramaraju, V. and Murthy, K., 2018. PREDICTION OF EMPLOYEE ATTRITION USING DATAMINING. 2018 ieee international conference on system, computation, automation and networking (icscan), [online] pp.1-8. Available at: <https://ieeexplore.ieee.org/abstract/document/8541242> [Accessed 8 May 2022].

In-text: (SHRM, 2022)

Your Bibliography: SHRM, 2022. Essential Elements of Employee Retention | Lynchburg Regional SHRM. [online] Lrshrm.shrm.org. Available at: <https://lrshrm.shrm.org/blog/2017/10/essential-elements-employee-retention> [Accessed 8 May 2022].

In-text: (Singh, 2022)

Your Bibliography: Singh, A., 2022. Gaussian Mixture Models | Clustering Algorithm Python. [online] Analytics Vidhya. Available at: <https://www.analyticsvidhya.com/blog/2019/10/gaussian-mixture-models-clustering/> [Accessed 8 May 2022].

In-text: (Srivastava and Nair, 2017)

Your Bibliography: Srivastava, D. and Nair, P., 2017. Employee Attrition Analysis Using Predictive Techniques. Information and Communication Technology for Intelligent Systems (ICTIS 2017) - Volume 1, [online] 1, pp.293-300. Available at: <https://link.springer.com/chapter/10.1007/978-3-319-63673-3_35> [Accessed 8 May 2022].

In-text: (Talent, 2022)

Your Bibliography: Talent, 2022. Accenture Salary in United Kingdom - Average Salary. [online] Talent.com. Available at: <https://uk.talent.com/salary?job=accenture> [Accessed 8 May 2022].

In-text: (Xu, Zhang and Miao, 2020)

Your Bibliography: Xu, J., Zhang, Y. and Miao, D., 2020. Three-way confusion matrix for classification: A measure driven view. Information Sciences, [online] 507, pp.772-794. Available at: <https://www.sciencedirect.com/science/article/abs/pii/S0020025519306024> [Accessed 8 May 2022].

In-text: (Yildiz, 2018)

Your Bibliography: Yildiz, S., 2018. An empirical analysis of the leader–member exchange and employee turnover intentions mediated by mobbing: evidence from sport organisations. Economic Research-Ekonomska Istraživanja, [online] 31(1), pp.480-497. Available at: <https://hrcak.srce.hr/file/295512> [Accessed 8 May 2022].

In-text: (Ying, 2019)

Your Bibliography: Ying, X., 2019. An Overview of Overfitting and its Solutions. Journal of Physics: Conference Series, [online] 1168(2), p.022022. Available at: <https://iopscience.iop.org/article/10.1088/1742-6596/1168/2/022022/meta> [Accessed 8 May 2022].

In-text: (Zhao et al., 2018)

Your Bibliography: Zhao, Y., Hryniewicki, M., Cheng, F., Fu, B. and Zhu, X., 2018. Employee Turnover Prediction with Machine Learning: A Reliable Approach. Advances in Intelligent Systems and Computing, [online] pp.737-758. Available at: <https://link.springer.com/chapter/10.1007/978-3-030-01057-7_56> [Accessed 8 May 2022].