Abstract
Weather change plays an important role in work-related accidents, it impairs people’s cognitive abilities, increasing the risk of injuries and accidents. Furthermore, weather conditions can cause an increase or decrease in daily sales in the retail sector by influencing individual behaviors. The increase in transactions, in turn, leads employees to fatigue and overload, which can also increase the risk of injuries and accidents. This work aims to conduct a case study in a company in the retail sector to verify whether the transactions records in stores and the weather conditions of each district in mainland Portugal impact the occurrence of work accidents, as well as to perform predictive analysis of the occurrence or non-occurrence of work accidents in each district using these data and comparing different machine learning techniques. The correlation analysis of the occurrence or non-occurrence of work accidents with weather conditions and some transactions pointed out the nonexistence of correlation between the data. Evaluating the precision and the confusion matrix of the predictive models, the study indicates a predisposition of the models to predict the non-occurrence of work accidents to the detriment of the ability to predict the occurrence of work accidents.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Adam-Poupart, A., et al.: Climate change and occupational health and safety in a temperate climate: potential impacts and research priorities in quebec, canada. Ind. Health 51(1), 68–78 (2013)
Al-Libawy, H., Al-Ataby, A., Al-Nuaimy, W., Al-Taee, M.A., Al-Taee, A.M.: A fatigue prediction cognitive model for naturalistic typing environment. In: 2017 10th International Conference on Developments in eSystems Engineering (DeSE), pp. 227–231. IEEE (2017)
Alkhaldi, M., Pathirage, C., Kulatunga, U., et al.: The role of human error in accidents within oil and gas industry in bahrain. In: 13th International Postgraduate Research Conference (IPGRC): Conference Proceedings, pp. 822–834. University of Salford (2017)
Anderson, V.P., Schulte, P.A., Sestito, J., Linn, H., Nguyen, L.S.: Occupational fatalities, injuries, illnesses, and related economic loss in the wholesale and retail trade sector. Am. J. Ind. Med. 53(7), 673–685 (2010)
Badorf, F., Hoberg, K.: The impact of daily weather on retail sales: an empirical study in brick-and-mortar stores. J. Retail. Consum. Serv. 52, 101921 (2020)
Berry, H.L., Bowen, K., Kjellstrom, T.: Climate change and mental health: a causal pathways framework. Int. J. Public Health 55, 123–132 (2010)
Chi, S., Han, S., Kim, D.Y.: Relationship between unsafe working conditions and workers’ behavior and impact of working conditions on injury severity in us construction industry. J. Constr. Eng. Manag. 139(7), 826–838 (2013)
Choi, Y., Choi, J.W.: The prediction of workplace turnover using machine learning technique. Inter. J. Bus. Analy. (IJBAN) 8(4), 1–10 (2021)
Gupta, S.D.: Point biserial correlation coefficient and its generalization. Psychometrika 25(4), 393–408 (1960)
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. SSS, Springer, New York (2009). https://doi.org/10.1007/978-0-387-84858-7
Im Kampe, E.O., Kovats, S., Hajat, S.: Impact of high ambient temperature on unintentional injuries in high-income countries: a narrative systematic literature review. BMJ Open 6(2), e010399 (2016)
Kendall, M.G.: Rank correlation methods. Griffin (1948)
Kim, C.W., McInerney, M.L., Alexander, R.P.: Job satisfaction as related to safe performance: a case for a manufacturing firm (2002)
Mendes, J., Lima, J., Costa, L., Rodrigues, N., Brandão, D., Leitão, P., Pereira, A.I.: Machine learning to identify olive-tree cultivars. In: Optimization, Learning Algorithms and Applications: Second International Conference, OL2A 2022, Póvoa de Varzim, Portugal, 24–25 October 2022 Proceedings, pp. 820–835. Springer (2023). https://doi.org/10.1007/978-3-031-23236-7_56
Miot, H.A.: Correlation analysis in clinical and experimental studies (2018)
Montgomery, D.C., Runger, G.C.: Applied statistics and probability for engineers, 5th edn. John Wiley & Sons, New York (2010)
Open-Meteo.com: Open-meteo: Open-meteo api. https://open-meteo.com/, Accessed March 2023
Parnaudeau, M., Bertrand, J.L.: The contribution of weather variability to economic sectors. Appl. Econ. 50(43), 4632–4649 (2018)
Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Putz Anderson, V., Schulte, P.A., Novakovich, J., Pfirman, D., Bhattacharya, A.: Wholesale and retail trade sector occupational fatal and nonfatal injuries and illnesses from 2006 to 2016: Implications for intervention. Am. J. Ind. Med. 63(2), 121–134 (2020)
Samy, L., Macey, P.M., Alshurafa, N., Sarrafzadeh, M.: An automated framework for predicting obstructive sleep apnea using a brief, daytime, non-intrusive test procedure. In: Proceedings of the 8th ACM International Conference on PErvasive Technologies Related to Assistive Environments, pp. 1–8 (2015)
Sandroto, C.W., Fransiska, J.: The importance of emotional intelligence for the sales associates profession as a mediation between job stress and job satisfaction. Inter. J. Manag. Econ. 57(4), 331–342 (2021)
dos Santos (FFMS), F.F.M.: Pordata. https://www.pordata.pt/portugal, Accessed 4 March 2023
Smith, A., Johnson, B.: Title of the article. Journal Name (2018)
Štulec, I., Petljak, K., Naletina, D.: Weather impact on retail sales: how can weather derivatives help with adverse weather deviations? J. Retail. Consum. Serv. 49, 1–10 (2019)
Tran, B.R., et al.: The impact of weather on retail sales. FRBSF Econ. Lett. 2022(23), 1–5 (2022)
Acknowledgement
The authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020) and SusTEC (LA/P/0007/2021). This work has been supported by NORTE-01-0247-FEDER-072598 iSafety: Intelligent system for occupational safety and well-being in the retail sector.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Borges, L.D. et al. (2024). Effect of Weather Conditions and Transactions Records on Work Accidents in the Retail Sector – A Case Study. In: Pereira, A.I., Mendes, A., Fernandes, F.P., Pacheco, M.F., Coelho, J.P., Lima, J. (eds) Optimization, Learning Algorithms and Applications. OL2A 2023. Communications in Computer and Information Science, vol 1981. Springer, Cham. https://doi.org/10.1007/978-3-031-53025-8_3
Download citation
DOI: https://doi.org/10.1007/978-3-031-53025-8_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-53024-1
Online ISBN: 978-3-031-53025-8
eBook Packages: Computer ScienceComputer Science (R0)