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Logistics collaboration has emerged a prevalent strategy to mitigate challenge individuals and organizations encounter. A successful collaboration, however, depends on certain trustworthy behaviors partner exhibit. To that end, understanding aspects constituting behavioral uncertainty and mechanisms by which such aspects affect partner trust is a necessary. This necessity counts on emergent behavioral trust uncertainties, constituted by partner's actions and interactions occurring during collaboration. While this is a necessary requirement, most of the studies in the literature lack to take into account the influence of behavioral uncertainty on collaboration and partner trust. To that effect, this paper uncovers outlined limitation by establishing behavioral factors influencing partner trust in operational stage of logistics collaboration. To accomplish this objective, a systematic literature review (SLR) is deployed to consolidate research domains of logistics, supply chain, collaboration, and trust. SLR proceeds by defining a review protocol, followed by a search process conducted in 5 databases using 20 search terms on articles published between 2001 and 2015 inclusively. Among findings this SLR has revealed are four behavioral factors and thirteen criteria proposed to affect partner trust. Additionally, these factors constitute success and measurable criteria needed for empirical investigation which may employ experimental and/or case-study methods. Moreover, synthesized factors extend further an understanding of behavioral trust in ad hoc collaborative networks, a large part of which being supported by networks of humans and computers. Keywords Trust Á Partner trust Á Resource sharing Á Behavioral trust Á Logistics collaboration This article is part of a focus collection on ''Dynamics in Logistics: Digital Technologies and Related Management Methods''.

BackgroundResource allocation in patient care relies heavily on individual judgements of healthcare professionals. Such professionals perform coordinating functions by managing the timing and execution of a multitude of care processes for multiple patients. Based on advances in simulation, new technologies that could be used for establishing realistic representations have been developed. These simulations can be used to facilitate understanding of various situations, coordination training and education in logistics, decision-making processes, and design aspects of the healthcare system. However, no study in the literature has synthesized the types of simulations models available for non-technical skills training and coordination of care.MethodsA systematic literature review, following the PRISMA guidelines, was performed to identify simulation models that could be used for training individuals in operative logistical coordination that occurs on a daily basis. This article reviewed papers of simulation in healthcare logistics presented in the Web of Science Core Collections, ACM digital library, and JSTOR databases. We conducted a screening process to gather relevant papers as the knowledge foundation of our literature study. The screening process involved a query-based identification of papers and an assessment of relevance and quality.ResultsTwo hundred ninety-four papers met the inclusion criteria. The review showed that different types of simulation models can be used for constructing scenarios for addressing different types of problems, primarily for training and education sessions. The papers identified were classified according to their utilized paradigm and focus areas. (1) Discrete-event simulation in single-category and single-unit scenarios formed the most dominant approach to developing healthcare simulations and dominated all other categories by a large margin. (2) As we approached a systems perspective (cross-departmental and cross-institutional), discrete-event simulation became less popular and is complemented by system dynamics or hybrid modeling. (3) Agent-based simulations and participatory simulations have increased in absolute terms, but the share of these modeling techniques among all simulations in this field remains low.ConclusionsAn extensive study analyzing the literature on simulation in healthcare logistics indicates a growth in the number of examples demonstrating how simulation can be used in healthcare settings. Results show that the majority of studies create situations in which non-technical skills of managers, coordinators, and decision makers can be trained. However, more system-level and complex system-based approaches are limited and use methods other than discrete-event simulation.

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Abstract-This paper addresses the implications of combining learning analytics and serious games for improving game quality, monitoring and assessment of player behavior, gaming performance, game progression, learning goals achievement, and user's appreciation. We introduce two modes of serious games analytics: in-game (real time) analytics, and post-game (off-line) analytics. We also explain the GLEANER framework for in-game analytics and describe a practical example for offline analytics. We conclude with a brief outlook on future work, highlighting opportunities and challenges towards a solid uptake of SGs in authentic educational and training settings.

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Deployment of serious games (SGs) and their insertion in higher education (HE) curricula is still low. The literature lacks papers describing deployment of SGs in HE critically showing educational benefits and providing guidelinesand good practices for their use. With the present work, we intend to make a first step in this direction, by reporting our experience in using state of the art managerial SGs in MSc engineering/business courses in four different European universities. In order to describe and analyse the educational characteristics and effectiveness of each game, we propose to use two models that we have straightforwardly extracted from two major pedagogical paradigms: the Bloom's revised cognitive learning goals taxonomy and the Kolb's experiential learning cycle. Based on our experience, we also propose a set of lessons and good practices to incentivize and better support deployment of SGs in HE courses

A data-driven approach in production logistics is adopted as a response to challenges such as low visibility and system rigidity. One important step for such a transition is to identify the enabling technologies from a value-creating perspective. The existing corpus of literature has discussed the benefits and applications of smart technologies in overall manufacturing or logistics. However, there is limited discussion specifically on a production logistics level, from a systematic perspective. This paper addresses two issues in this respect by conducting a systematic literature review and analyzing 142 articles. First, it covers the gap in literature concerning mapping the application of these smart technologies to specific production logistic activities. Ten groups of technologies were identified and production logistics activities divided into three major categories. A quantitative share assessment of the technologies in production logistics activities was carried out. Second, the ultimate goal of implementing these technologies is to create business value. This is addressed in this research by presenting the “production logistics data lifecycle” and the importance of having a balanced holistic perspective in technology development. The result of this paper is beneficial to build a ground to transit towards a data-driven state by knowing the applications and use cases described in the literature for the identified technologies.