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Organizations invest in information security (IS) technology to be more competitive; however, implementing IS measures creates environmental conditions, such as overload uncertainty, and complexity, which can cause employees technostress, eventually resulting in poor security performance. This study seeks to contribute to the intersection of research on regulatory focus (promotion and prevention) as a type of individual personality traits, technostress, and IS.

A survey questionnaire was developed, collecting 346 responses from various organizations, which were analyzed using the structural equation model approach with AMOS 22.0 to test the proposed hypotheses.

The results indicate support for both the direct and moderating effects of security technostress inhibitors. Moreover, a negative relationship exists between promotion-focused employees and facilitators of security technostress, which negatively affects strains (organizational commitment and compliance intention).

Organizations should develop various programs and establish a highly IS-aware environment to strengthen employees' behavior regarding IS. Furthermore, organizations should consider employees' focus types when engaging in efforts to minimize security technostress, as lowering technostress results in positive outcomes.

IS management at the organizational level is directly related to employees' compliance with security rather than being a technical issue. Using the transaction theory perspective, this study seeks to enhance current research on employees' behavior, particularly focusing on the effect of individuals' personality types on IS. Moreover, this study theorizes the role of security technostress inhibitors for understanding employees' IS behaviors.

This paper presents a review of the existing state-of-the-art literature on machine learning (ML) in logistics and supply chain management (LSCM) by analyzing the current literature, contemporary concepts, data and gaps and suggesting potential topics for future research.

A systematic/structured literature review in the subject discipline and a bibliometric analysis were organized. Information regarding industry involvement, geographic location, research design and methods, data analysis techniques, university, affiliation, publishers, authors, year of publications is documented. A wide collection of eight databases from 1994 to 2019 were explored using the keywords “Machine Learning” and “Logistics“, “Transportation” and “Supply Chain” in the title and/or abstract. A total of 110 articles were found, and information on a chain of variables was gathered.

Over the last few decades, the application of emerging technologies has attracted significant interest all around the world. Analysis of the collected data shows that only nine literature reviews have been published in this area. Further, key findings show that 53.8 per cent of publications were closely clustered on transportation and manufacturing industries and 54.7 per cent were centred on mathematical models and simulations. Neural network is applied in 22 papers as their exclusive algorithms. Finally, the main focuses of the current literature are on prediction and optimization, where detection is contributed by only seven articles.

This review is limited to examining only academic sources available from Scopus, Elsevier, Web of Science, Emerald, JSTOR, SAGE, Springer, Taylor and Francis and Wiley which contain the words “Machine Learning” and “Logistics“, “Transportation” and “Supply Chain” in the title and/or abstract.

This paper provides a systematic insight into research trends in ML in both logistics and the supply chain.