Purpose: This study aims to examine managers’ perceptions of digital banking’s (DB) effect on customer experience and banks’ financial performance. Design/methodology/approach: The research uses interviews from the senior UK bank managers to gather their views on DB’s impact on customer experience and financial performance. The interviews were thematically analysed to produce results and a model. Findings: The attributes affecting DB experience are as follows: service quality, functional quality, perceived value, service customisation, service speed, employee–customer engagement, brand trust, DB innovation, perceived usability and perceived risk. They affect customer experience, satisfaction and loyalty and financial performance. The research revealed relationships amongst these attributes (e.g. brand trust and loyalty). Research limitations/implications: The study is a UK bank specific and can be replicated in other developed countries’ banks, helping in further comparison. However, DB is conducted globally, which implies that the findings are robust enough to be potentially applied in other countries. The proposed model shows customer experience drivers and outcomes through managers’ views, which can be theoretically tested. Practical implications: The findings suggest important attributes (as above) for consideration to improve DB customer experience and financial performance. They show the relevance of employee–customer interaction, service personalisation, value proposition, quality service offering and DB experience, which have useful implications for improving DB design and interactive marketing. Originality/value: Gauging DB customer experience as perceived by bank managers has not been studied in this way, highlighting DB effectiveness, which is important for multi-channel marketing and banks’ financial performance, and advances theory.

With the rapid development of technology and the increasing use of social networks, many opportunities for the design and deployment of interconnected systems arise that could enable a paradigm shift in the ways we interact with cultural heritage. The project described in this paper aims to create a new type of conceptually led environment, a kind of Cyber–Physical–Social Eco-Society (CPSeS) system that would seamlessly blend the real with virtual worlds interactively using Virtual Reality, Robots, and Social Networking technologies, engendered by humans’ interactions and intentions. The project seeks to develop new methods of engaging the current generation of museum visitors, who are influenced by their exposure to modern technology such as social media, smart phones, Internet of Things, smart devices, and visual games, by providing a unique experience of exploring and interacting with real and virtual worlds simultaneously. The research envisions a system that connects visitors to events and/or objects separated either in time or in space, or both, providing social meeting points between them. To demonstrate the attributes of the proposed system, a Virtual Museum scenario has been chosen. The following pages will describe the RoboSHU: Virtual Museum prototype, its capabilities and features, and present a generic development framework that will also be applicable to other contexts and sociospatial domains.

A theoretical framework for generating navigation behaviour patterns in mixed human-robot groups in complex environments is proposed. This framework represents an essential part in the development of a multiple robot-human system for assisting fire-fighters in search and rescue operations in the GUARDIANS project. In order to produce the desired behaviours an artificial potential field method has been developed. We distinguish a three classes of agents: robots, humans and obstacles, and apply different potential functions to them. Depending on the situation, we switch from one function to another; this allows to generate desired behaviour patterns as well as to avoid certain local minima. Typical behaviour patterns are singled out and their stability is discussed. Stability analysis is based on geometric considerations, that permits to avoid bulky computations and provide graphic demonstrations of convergence. The proposed framework can be used in other robotic applications where a group of heterogenous agents is deployed.

Editorial: Emerging technologies for assistive robotics: current challenges and perspectives

A new multi-robot path planning (MRPP) algorithm for 2D static environments was developed and evaluated. It combines a roadmap method, utilising the visibility graph (VG), with the algebraic connectivity (second smallest eigenvalue (λ₂)) of the graph’s Laplacian and Dijkstra’s algorithm. The paths depend on the planning order, i.e., they are in sequence path-by-path, based on the measured values of algebraic connectivity of the graph’s Laplacian and the determined weight functions. Algebraic connectivity maintains robust communication between the robots during their navigation while avoiding collisions. The algorithm efficiently balances connectivity maintenance and path length minimisation, thus improving the performance of path finding. It produced solutions with optimal paths, i.e., the shortest and safest route. The devised MRPP algorithm significantly improved path length efficiency across different configurations. The results demonstrated highly efficient and robust solutions for multi-robot systems requiring both optimal path planning and reliable connectivity, making it well-suited in scenarios where communication between robots is necessary. Simulation results demonstrated the performance of the proposed algorithm in balancing the path optimality and network connectivity across multiple static environments with varying complexities. The algorithm is suitable for identifying optimal and complete collision-free paths. The results illustrate the algorithm’s effectiveness, computational efficiency, and adaptability.

A new multi-robot path planning algorithm (MRPPA) for 2D static environments is developed and evaluated. It combines a roadmap method, utilising the visibility graph (VG), with the algebraic connectivity (second smallest eigenvalue (λ2)) of the graph’s Laplacian and Dijkstra's algorithm. The paths depend on the planning order, i.e., they are in sequence path-by-path, based on the measured values of algebraic connectivity of the graph’s Laplacian and the determined weights functions. Algebraic connectivity maintains robust communication between the robots during their movements while avoiding collision. The algorithm efficiently balanced connectivity maintenance and path length minimisation thus improving the performance of path finding. It produced solutions with optimal paths, i.e., the shortest and safest route. The devised MRPPA significantly improved path length efficiency across different configurations. The results demonstrated a highly efficient and robust solution for multi-robot systems requiring both optimal path planning and reliable connectivity, making it well-suited in scenarios where communication between robots is necessary. Simulation results demonstrated the performance of the proposed algorithm in balancing the path optimality and network connectivity across multiple static environments with varying complexities. The algorithm is suitable for identifying optimal and complete collision-free paths. The results illustrated the algorithm's effectiveness, computational efficiency, and adaptability.