Literature Review Draft
The social sciences and digital technologies have evolved significantly over the past twenty years. Recent social and technological methodologies provide opportunities to improve safety management systems (SMS). These improvements are especially necessary for the aviation industry, since even the most insignificant technical, human factor, or organizational failures can lead to significant financial or even tragic consequences. The study on aviation SMS regulatory and technological improvements will allow systematizing and unifying of both legal and technical approaches, which will improve existing SMSs and develop new ones.
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Framework for Safety Management Systems in Airworthiness Organisations by Batuwangala et al.
Framework for Safety Management Systems in Airworthiness Organisations by Batuwangala et al. explores the latest trends in international aviation regulatory policy. Researchers have found that today, most of the current regulations are aimed at the aviation industry’s organizational aspect, namely the area of aircraft design and manufacturing (Batuwangala et al., 2018). The authors also describe the history of SMS in aviation and SMS regulatory practice. However, this research has little original component as it is more of a review. It is worth mentioning that the researchers consider only North American and European national and international organizations.
Aviation safety regulation in the multi-stakeholder environment of an airport by Murphy and Efthymiou
Murphy and Efthymiou investigate the impact of multi-stakeholder groups originated regulation on SMS in airport operations. In their case study, the authors conclude that this approach positively affects various safety variables (Murphy & Efthymiou, 2017). Researchers also note that further regulations should promote communication between different groups of stakeholders and employees (Murphy & Efthymiou, 2017). This case study’s disadvantage is that the fieldwork was held in only one location, namely Dublin Airport.
A Strategy to Improve Aviation Safety of AirAsia Indonesia by Oktaviani et al.
The authors investigate what methods AirAsia Indonesia has applied to improve its SMS performance. The authors use qualitative methodology as well as research techniques such as “observation, interviews, and literature review” and “SWOT analysis” (Oktaviani et al., 2019, p. 304). Oktaviani et al. (2019) argue that regulations that reward aggressive strategies, as well as SMS that are systematically predetermined by regulations, improve overall SMS performance. However, the conceptual drawbacks of the study are the lack of quantitative methods.
A data-mining approach to the identification of risk factors in safety management systems by Shi et al.
Researchers have developed an original data mining approach to improve SMS performance. According to Shi et al. (2017), this program implies the interaction of three main software categories, namely topical mining techniques, data-streaming algorithms, and three different classification algorithms (Shi et al., 2017). This data mining approach’s conceptual feature is that it automatically identifies the incident types without human intervention (Shi et al., 2017). The method proposed by the authors considers only the risk factors topic and does not include other SMS variables.
Challenges of implementation and practical deployment of aviation safety knowledge management software by Vittek et al.
The authors of the work discuss the current technological problems related to SMS software. According to Vittek et al. (2016), “the article deals mainly with practical issues concerning the deployment of reporting software” (p. 316). The authors argue that SMS improvements are needed in analyzing dynamic knowledge and knowledge gathering processes (Vittek et al., 2016). It is important to note that the researchers do not consider theoretical and conceptual SMS problems.
Structuring risk assessment process with tallying in aviation safety management by Uyar
The researcher criticizes the current conventional risk-oriented assessment process. Uyar (2019) also discusses the concept of the uncertainty assessment process. The author “… suggests using “tallying checklists” which are based on unit weight additive linear model, for making the probability and severity value assignments more consistent” (Uyar, 2019, p. 65). The disadvantages may be the researcher’s bias concerning the risk assessment process and the article’s narrow topic.
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Towards ontology-based safety information management in the aviation industry by Kostov et al.
The authors discuss the topic of improving SMS through the prism of safety information management. Researchers criticize the current hierarchical model of safety information management as well as the incompatibility of multiple frameworks. Kostov et al. (2016) suggest the original “Aviation Safety Ontology” as a possible software enhancement developed using “Linked Data principles” (p. 242). One of the positive aspects of the study is the description of testing the proposed model.
Design and implementation of international civil aviation security information database management system by Li et al.
The study is a detailed description of a new unified international aviation database. Li et al. (2019) believe that a unified database will improve both the aviation industry’s operation in general and SMS performance in particular. It can be said that the lack of self-criticism and reflection are the disadvantages of this study. This work is essential for the current research as an example of a unified technological system for improving SMS.
What are the common patterns of applied aviation SMS regulations and technologies that provide high-quality SMS performance?
A literature review reveals that only two groups of researchers are exploring the common improvement patterns of technologies. Qualitative independent variables of this research are improvement patterns of regulations such as international and collective approaches and the involvement of ISAO and EASA. Another qualitative independent variable is the improvement pattern of technologies, which is software applicability. The risk factors are the qualitative dependent variable, and the accident rate is the qualitative dependent one.
The research’s first hypothesis is that the most effective SMS regulations are those with international and collective patterns. The second hypothesis is that the most effective SMS technologies are those that belong to the software sphere.
Batuwangala, E., Silva, J., & Wild, G. (2018). The regulatory framework for safety management systems in airworthiness organisations. Aerospace, 5(4), 117. Web.
Kostov, B., Ahmad, J., & Křemen, P. (2016). Towards ontology-based safety information management in the aviation industry. In OTM confederated international conferences “On the move to meaningful internet systems” (pp. 242-251). Springer.
Li, H., Yang, X., & Feng, S. (2019). Design and implementation of international civil aviation security information database management system. In IOP Conference series: Earth and environmental science, vol. 252, no. 5 (pp. 1-10). IOP Publishing.
Murphy, G., & Efthymiou, M. (2017). Aviation safety regulation in the multi-stakeholder environment of an airport. Journal of Air Transport Studies, 8(2), 1-26.
Oktaviani, D., Mustikasari, R., Kania, D. D., Firdaus, M. I., & Setyowati, T. M. (2019). A Strategy to improve aviation safety of AirAsia Indonesia. Advances in Transportation and Logistics Research, 2, 304-310.
Shi, D., Guan, J., Zurada, J., & Manikas, A. (2017). A data-mining approach to identification of risk factors in safety management systems. Journal of Management Information Systems, 34(4), 1054-1081. Web.
Vittek, P., Lališ, A., Stojić, S., & Plos, V. (2016). Challenges of implementation and practical deployment of aviation safety knowledge management software. In International conference on knowledge engineering and the semantic web (pp. 316-327). Springer.
Uyar, T. (2019). Structuring risk assessment process with tallying in aviation safety management. The International Journal of Aerospace Psychology, 29(3-4), 65-73. Web.