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
The modern digital era is characterized by a plethora of emerging technologies, methodologies and techniques that are employed in the manufacturing industries with intent to improve productivity, to optimize processes and to reduce operational costs. Yet, algorithms and methodological approaches for improvement of energy consumption and environmental impact are not integrated with the current operational and planning tools used by manufacturing companies. One possible reason for this is the difficulty in bridging the gap between the most advanced energy related ICT tools, developed within the scope of the industry 4.0 era, and the legacy systems that support most manufacturing operational and planning processes. Consequently, this paper proposes a conceptual architecture model for a digital energy management platform, which is comprised of an IIoT-based platform, strongly supported by energy digital twin for interoperability and integrated with AI-based energy data-driven services. This conceptual architecture model enables companies to analyse their energy consumption behaviour, which allows for the understanding of the synergies among the variables that affect the energy demand, and to integrate this energy intelligence with their legacy systems in order to achieve a more sustainable energy demand. © 2020 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the FAIM 2021.

Abstract

Abstract

Abstract

Abstract