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Throughout human history, the occurrence of disasters has been inevitable, leading to significant human, financial and emotional consequences. Therefore, it is crucial to establish a well-designed plan to efficiently manage such situations when disaster strikes. The purpose of this study is to develop a comprehensive program that encompasses multiple aspects of postdisaster relief.

A multiobjective model has been developed for postdisaster relief, with the aim of minimizing social dissatisfaction, economic costs and environmental damage. The model has been solved using exact methods for different scenarios. The objective is to achieve the most optimal outcomes in the context of postdisaster relief operations.

A real case study of an earthquake in Haiti has been conducted. The acquired results and subsequent management analysis have effectively assessed the logic of the model. As a result, the model’s performance has been validated and deemed reliable based on the findings and insights obtained.

Ultimately, the model provides the optimal quantities of each product to be shipped and determines the appropriate mode of transportation. Additionally, the application of the epsilon constraint method results in a set of Pareto optimal solutions. Through a comprehensive examination of the presented solutions, valuable insights and analyses can be obtained, contributing to a better understanding of the model’s effectiveness.

Accurate prediction of the structural condition of urban critical infrastructure is crucial for predictive maintenance. However, the existing prediction methods lack precision due to limitations in utilizing heterogeneous sensing data and domain knowledge as well as insufficient generalizability resulting from limited data samples. This paper integrates implicit and qualitative expert knowledge into quantifiable values in tunnel condition assessment and proposes a tunnel structure prediction algorithm that augments a state-of-the-art attention-based long short-term memory (LSTM) model with expert rating knowledge to achieve robust prediction results to reasonably allocate maintenance resources.

Through formalizing domain experts' knowledge into quantitative tunnel condition index (TCI) with analytic hierarchy process (AHP), a fusion approach using sequence smoothing and sliding time window techniques is applied to the TCI and time-series sensing data. By incorporating both sensing data and expert ratings, an attention-based LSTM model is developed to improve prediction accuracy and reduce the uncertainty of structural influencing factors.

The empirical experiment in Dalian Road Tunnel in Shanghai, China showcases the effectiveness of the proposed method, which can comprehensively evaluate the tunnel structure condition and significantly improve prediction performance.

This study proposes a novel structure condition prediction algorithm that augments a state-of-the-art attention-based LSTM model with expert rating knowledge for robust prediction of structure condition of complex projects.

Considering the significant increase in researchers’ interest in human resource management (HRM) in the public sector domain, this study aims to focus on producing a scale of HRM practices customized for the context of public organizations.

Experts and semantic analysis were performed for the scale development (qualitative stage), and exploratory and confirmatory factor analysis through structural equation modeling was conducted for the scale validation (quantitative stage).

The public HRM practices scale (public HRMPS) is composed of 19 items, distributed along four factors/dimensions, named training, development and education; relationship; work conditions; and competency and performance appraisal. The scale showed evidence of internal and construct validity (convergent, divergent, criterion-related and discriminant), as well as reliability and content validity.

The public HRMPS can be applied in relational studies to test structural models of prediction, mediation and moderation to evaluate relationships with organizational behavior variables, such as leader-members exchange, engagement at work, life quality at work and well-being at work, among others.

The public HRMPS may also serve as a useful diagnostic tool for the decision-making process made by public managers so they can promote a strategic, evidence-based HRM. Furthermore, the transforming role of strategic HRM can be operationalized by adopting practices gathered in the public HRMPS, advancing toward new HRM strategies to promote healthier and more productive work environments.

Healthier and more productive environments translate into real impacts for society, the first beneficiary of public services with more quality, efficiency and accountability.

The public HRMPS is the first attempt to produce an operationally valid and reliable measure to evaluate strategic HRM practices, responding to calls in the literature concerning the need for an integrated, comprehensive and customized HRM practices scale for the public service context.

Weick and Sutcliffe identified five principles that enable high-reliability organizations (HROs) to address environmental complexity and manage unexpected events. The current study aims to adopt this sensemaking perspective to analyze accidents within a typical HRO sector, namely maritime transport.

Through a retrospective case study analysis, this study focused on seven oil tanker accidents, using them as illustrative examples.

Findings show how the five principles contributed to the accidents' occurrence, explaining how failures in sensemaking affected the crew's capability to both prevent errors and cope with their consequences, thus leading to disasters.

Overall, the study offers an applicative contribution showing how this model may provide a reliable framework for analyzing the psychosocial factors affecting an accident. This approach deepens the understanding of how latent factors are enacted and how the prevention and error management phases interrelate within a comprehensive flow of the entire accident sequence. Furthermore, the study emphasizes consistent patterns that emerge across multiple accidents within the same sector, in order to learn valuable lessons to improve safety measures in the future.

This study constitutes an exemplary application in support of how Weick and Sutcliffe’s model is valuable for investigating HROs. It offers a second-order interpretative framework to understand accidents and underscores the interplay among these factors during the dynamic development of an accident.

The logistics industry has undergone a tremendous transformation. This transformation is necessary to cope with the fundamental changes in customer expectations and the need for digitalization imposed by the pandemic, changes in the socioeconomic world, and innovative technology solutions. This paper aims to present digital transformation as an integrated framework for transforming the operating model and applying advanced solutions to the ecosystem of a quintile logistics (5PL) company. 5PL operators are typically an ecosystem. Loosely coupled or self-organized entities that collaborate in a symbiotic relationship represent this ecosystem. They aim to jointly develop capabilities, create innovative services or solutions, share knowledge, facilitate transactions, and leverage network synergies in a logistics environment to provide optimized or novel customer- or partner-centric solutions (Lamberjohann and Otto, 2020).

Currently, there is no single definition of an integrated logistics operations model in 5PL practice, so the qualitative method used in this paper allows for investigation from an exploratory perspective. The paper follows a qualitative research methodology, collecting and analyzing data/facts through interviews and visits to subject matter experts, industry practitioners, and academic researchers, combined with an extensive review of academic publications, industry reports, and written and media content from established organizations in the marketplace. This paper follows a qualitative research methodology, as it is an inquiry rather than a statistical study. The qualitative method allows the study of the concepts of phenomena and definitions, their characteristics, and the defining features that serve as the basis (Berg, 2007). It emphasizes generalized interpretation and deeper understanding of concepts, which would be more difficult in quantitative, statistically based research. Fact-finding was conducted in two ways: in-depth interviews with experts from academia, information and communication technology organizations, and key players in the logistics industry; and academic publications, industry reports, and written and media content from established national and international organizations in the market.

The operations model introduced considers six aspects: persons, processes, platforms, partners, protection and preservation. A virtual team approach can support the personal side of the 5PL ecosystem’s digital transformation. Managing a 5PL ecosystem should be based on collaborative planning, forecasting, and replenishment methods (Parsa et al., 2020). A digital platform can support trust among the stakeholders in the ecosystem. A blockchain solution can powerfully support the 5PL ecosystem from partner relationships’ points of view. The implementation of a cybersecurity reference model is important for protection (Bandari, 2023). Reverse logistics and an integrated approach support the preservation of the ecosystem.

While the author has experience applying the different components of the operations model presented, it would be interesting to find a 5PL that would use all the components presented in an integrated way. The operations model presented applies to any similar ecosystem with minor adaptations.

This paper addresses operations models and digital transformation challenges for optimizing 5PL operators. It provides several opportunities and considerations for 5PL operators interested in improving their management and operations to cope with the growing challenges of today’s world.

The competitiveness and long-term performance of 5PL operators depend on selecting and carefully implementing their operations models. This paper emphasizes the importance of using advanced operations models.

The operations model derives from the author’s personal experiences in research and the innovative application of these models to logistics operators (DHL, UPS, Poste Italiane and others). This paper brings together academic and industry perspectives and operations models in an integrated business digital transformation. This paper defines an original optimal operations model for a 5PL operator and can add sustainable value to organizations and society. In doing so, it outlines different solution requirements, the critical success factors and the challenges for solutions and brings logistical performance objectives when implementing a digital business transformation.

The purpose of this study is to investigate the application of non-destructive testing methods in measuring bearing oil film thickness to ensure that bearings are in a normal lubrication state. The oil film thickness is a crucial parameter reflecting the lubrication status of bearings, directly influencing the operational state of bearing transmission systems. However, it is challenging to accurately measure the oil film thickness under traditional disassembly conditions due to factors such as bearing structure and working conditions. Therefore, there is an urgent need for a nondestructive testing method to measure the oil film thickness and its status.

This paper introduces methods for optically, electrically and acoustically measuring the oil film thickness and status of bearings. It discusses the adaptability and measurement accuracy of different bearing oil film measurement methods and the impact of varying measurement conditions on accuracy. In addition, it compares the application scenarios of other techniques and the influence of the environment on detection results.

Ultrasonic measurement stands out due to its widespread adaptability, making it suitable for oil film thickness detection in various states and monitoring continuous changes in oil film thickness. Different methods can be selected depending on the measurement environment to compensate for measurement accuracy and enhance detection effectiveness.

This paper reviews the basic principles and latest applications of optical, electrical and acoustic measurement of oil film thickness and status. It analyzes applicable measurement methods for oil film under different conditions. It discusses the future trends of detection methods, providing possible solutions for bearing oil film thickness detection in complex engineering environments.

Social commerce has brought about a significant transformation in consumer experience due to diverse factors. As a result, users often find themselves prone to impulsive buying behaviour when exposed to such an environment. Prior research was limited to demonstrating the expanding influence of celebrities on social media and the linkage between social engagement and impulse buying context. Furthermore, the impulse buying tendency of consumers on social media in the context of celebrity posts has yet to be validated. This paper aims to assess the influence of consumer awareness, consumer trust and observational learning on the latent state-trait (LST) theory regarding celebrity posts on impulse buying tendencies.

The empirical research builds on a sample survey involving 750 students from the “Big Four” economics universities in Hanoi. The proposed model was analysed using a partial least squares structural equation modelling technique.

The authors find that consumer trust and observational learning from celebrity’ posts positively affect impulse buying tendency. Yet celebrity influence awareness directly impacts trust in celebrity’ posts rather than directly impacting impulse buying tendency. Perceiving the importance of interactive and authentic posts by a celebrity in influencing consumers’ purchase behaviour on social media, this research offers valuable insights for stakeholders in the digital celebrity sphere of communication and marketing.

Perceiving the importance of interactive and authentic posts by a celebrity in influencing consumers’ purchase behaviour on social media, this research offers valuable insights for stakeholders in the digital celebrity sphere of communication and marketing.

From a theoretical perspective, this expands the applicability of the LST theory in social commerce to promote impulse buying tendencies. Second, this contributes to the literature on the emerging phenomenon of social media celebrities, as existing literature does not clarify their influence on impulse buying behaviour. Third, this research applies the concept of observational learning in online shopping through key features of social media platforms, namely, likes, shares and comments, to investigate their influence on the impulse buying tendency of consumers. Concerning managerial implications, the authors propose practical recommendations for practitioners, particularly those involved or interested in the commercial services industry and social media marketing (namely, celebrities and partner companies).

Social commerce (s-commerce) has gained widespread popularity as a social platform where customers engage in resource-sharing activities such as information exchange, advice-seeking and expressing their opinions on mutual interests. However, existing studies have not fully comprehended the drivers of electronic customer-to-customer interaction (eCCI) and how such behavior contributes to the customer “stick” on s-commerce sites. This study develops the Motivation–Opportunity–Ability (MOA) theory and investigates the impact of MOA factors on eCCI, which in turn affects customer stickiness.

A survey was used to acquire data from 455 valid respondents, and the research employed a combination of fuzzy-set qualitative comparative analysis (fsQCA) and structural equation modeling.

The results revealed associations between perceived self-efficacy, intrinsic motivation, tie strength with other customers, eCCI and customer stickiness.

Considering the limited availability of complete eCCI frameworks in existing scholarly works, the authors present valuable perspectives on the role of consumer characteristics as both antecedents and consequences of eCCI. Additionally, this study proposes a research agenda for the field of eCCI on s-commerce sites.

This study is centered on the identification of the most appropriate Technology Adoption (TA) model for investigating the adoption of Industry 4.0 technologies within pharmaceutical and related enterprises. The aim is to facilitate a smooth transition to advanced technologies while concurrently achieving environmental sustainability.

Selection of a suitable TA theory is carried out using a hybrid multi-criteria decision-making (MCDM) approach incorporating PIvot Pairwise RElative Criteria Importance Assessment (PIPRECIA) and Fuzzy Measurement of alternatives and ranking according to Compromise solution (F-MARCOS) methods. A group of three experts is formulated for the ranking of criteria and alternatives based on those criteria.

The results indicate that out of all six TA models considered unified theory of acceptance and use of technology (UTAUT) model gets the highest utility function value, followed by the technical adoption model (TAM). Further, sensitivity analysis is conducted to confirm the validity of the MCDM model employed.

Challenging times like COVID-19 pointed out the importance of technology in the pharmaceutical and healthcare sectors. TA studies in this area can help in the identification of critical factors that can assist pharmaceutical firms in their efforts to embrace emerging technologies, enhance their outputs and increase their efficiency.

The novelty of this research lies in the fact that the utilization of a TA theory prior to its implementation has not been witnessed in existing scholarly literature. The utilization of a TA theory, specifically within the pharmaceutical industry, can assist enterprises in directing their attention toward pertinent factors when contemplating the implementation of emerging technologies and achieving sustainable development.

This article develops an innovative multidisciplinary conceptual framework in the field of refugee entrepreneurship by combining the theory of mixed embeddedness with the concepts of intersectionality and agency. Focusing on the phenomenon of refugee entrepreneurship, this conceptual framework addresses the following questions: how is entrepreneurship informed by the various intersectional positions of refugees? And how do refugees exert their agency based on these intersecting identities?

By revising the mixed embeddedness approach and combining it with an intersectional approach, this study aims to develop a multidimensional conceptual framework.

This research illustrates how the intersectional positions of refugees impact their entrepreneurial motivations, resources and strategies. The authors' findings show that refugee entrepreneurship not only contributes to the economic independence of refugees in new societies but also creates opportunities for refugees to exert their agency.

This conceptual framework can be applied in empirical research and accordingly contributes to refugee entrepreneurship studies and intersectionality theory.