Abstract
Purpose Dyadic interactions between customers and service providers rarely occur in isolation. Still, there is a lack of systematic knowledge about the roles that different types of nontechnological third parties - that is, other customers, pets, other employees and other firms - can adopt in relation to customers and service providers during encounters. The present study aims to unravel these roles and highlight their implications for customers, service providers and/or third parties. Design/methodology/approach This research relies on a systematic review of literature in the Web of Science using a search string pertaining to the research study's objectives. In total, 2,726 articles were screened by title and abstract using clear inclusion and exclusion criteria, thereby extracting 189 articles for full-text eligibility. The final sample consisted of 139 articles for coding and analysis. Findings The analyses reveal that other customers, pets, other employees and other firms can adopt five roles: bystander, connector, endorser, balancer and partner. Each role has different implications for customers, service providers and/or third parties. Additionally, the five roles are associated with distinct constellations of the customer, the service provider and the third party. These roles and constellations are dynamic and not mutually exclusive. Originality/value This research contributes to the service encounter literature by providing a thorough understanding of the various third-party roles and their implications for customers, service providers and/or third parties during encounters. As such, this research sheds light on the conditions under which third parties become "significant others" in service encounters and identifies avenues for future research.

Abstract
Researchers are being prompted by funders and institutions to expose the variety of results of their projects and to submit a Data Management Plan as part of their funding requests. In this context, institutions are looking for solutions to provide support to research data management activities in general, including DMP creation. We propose a collaborative approach where a researcher and a data steward create a DMP, involving other parties as required. We describe this collaborative method and its implementation, by means of a set of case studies that show the importance of the data steward in the institution. Feedback from researchers shows that the DMP are simple enough to lead people to engage in data management, but present enough challenges to constitute an entry point to the next level, the machine-actionable DMP.

Abstract

Abstract
Context. Methods used to detect giant exoplanets can be broadly divided into two categories: indirect and direct. Indirect methods are more sensitive to planets with a small orbital period, whereas direct detection is more sensitive to planets orbiting at a large distance from their host star. This dichotomy makes it difficult to combine the two techniques on a single target at once.Aims. Simultaneous measurements made by direct and indirect techniques offer the possibility of determining the mass and luminosity of planets and a method of testing formation models. Here, we aim to show how long-baseline interferometric observations guided by radial-velocity can be used in such a way.Methods. We observed the recently-discovered giant planet beta Pictoris c with GRAVITY, mounted on the Very Large Telescope Interferometer.Results. This study constitutes the first direct confirmation of a planet discovered through radial velocity. We find that the planet has a temperature of T=125050 K and a dynamical mass of M=8.2 +/- 0.8 M-Jup. At 18.5 +/- 2.5 Myr, this puts beta Pic c close to a 'hot start' track, which is usually associated with formation via disk instability. Conversely, the planet orbits at a distance of 2.7 au, which is too close for disk instability to occur. The low apparent magnitude (M-K=14.3 +/- 0.1) favours a core accretion scenario.Conclusions. We suggest that this apparent contradiction is a sign of hot core accretion, for example, due to the mass of the planetary core or the existence of a high-temperature accretion shock during formation.

Abstract
Purpose - Supply Chain Management (SCM) is one of the most important parts of business, which includes supply chain quality management (SCQM) and supply chain risk management (SCRM). One of the consequences of an epidemic outbreak can be a lack of reliable data and difficulty in accessing this information, which can simultaneously disrupt supply and demand. Because epidemics of infectious diseases such as Covid-19 cause many deaths worldwide. Therefore, in order to effectively control these epidemics and also to prevent the failure of health systems and laboratory services, having a quality management program and supply chain risk management seems to be essential. The main purpose of this article is to carefully review the studies that have analyzed the results of SCQM, SCRM techniques of different countries and industries in response to the COVID-19 crisis. Design/methodology/approach - In this research, studies pursue and assess the problems and solutions based on a systematic literature review analysis. Findings - By considering the researches which have been done related to disruptions of COVID-19, - important disruptions and risk management plans are mentioned to provide a better comprehension of this issue. Research limitations/implications - Since this global pandemic is a completely new issue, analyzing and gathering reliable statics from companies was very a complicated task. In a different circumstance, exploring hidden disruptions costs and other related issues is continuing since thspread of this disease is not finished yet. Therefore, access to the related data for experts is limited that leads to publishing fewer case studies researches in this filed. Originality/value - In this paper, the implication of the pandemic situation (COVID-19) is investigated for SCQM.

Abstract
Fleet tracking technology collects real-time information about geolocation of vehicles as well as driving-related data. This information is typically used for location monitoring as well as for analysis of routes, vehicles and drivers. From an operational point of view, the geolocation simply identifies the state of a vehicle in terms of positioning and navigation. From a management point of view, the geolocation may be used to infer the state of a vehicle in terms of process (e.g., driving, fueling, maintenance, or lunch break). Meaningful information may be extracted from these inferred states using process mining. An innovative methodology for inferring process states from geolocation data is proposed in this paper. Also, it is presented the potential of applying process mining techniques on geolocation data for process discovery. © 2020 The Authors. Published by Elsevier B.V.