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This study aims to examine managers’ perceptions of digital banking’s (DB) effect on customer experience and banks’ financial performance.

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.

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).

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.

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.

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.

The Group of Unmanned Assistant Robots Deployed in Aggregative Navigation by Scent (GUARDIANS) multi‐robot team is to be deployed in a large warehouse in smoke. The team is to assist firefighters search the warehouse in the event or danger of a fire. The large dimensions of the environment together with development of smoke which drastically reduces visibility, represent major challenges for search and rescue operations. The GUARDIANS robots act alongside a firefighter and guide and accompany the firefighters on the site while indicating possible obstacles and the locations of danger and maintain communications links. The purpose of this paper is to focus on basic navigation behaviours of multi‐robot or human‐robot teams, which have to be achieved without central and on‐line control in both categories of GUARDIANS robots' tasks.

In order to fulfill the aforementioned tasks, the robots need to be able to perform certain behaviours. Among the basic behaviours are capabilities to stay together as a group, that is, generate a formation and navigate while keeping this formation. The control model used to generate these behaviours is based on the so‐called social potential field framework, which the authors adapt to the specific tasks required for the GUARDIANS scenario. All tasks can be achieved without central control, and some of the behaviours can be performed without explicit communication between the robots.

The GUARDIANS environment requires flexible formations of the robot team: the formation has to adapt itself to the circumstances. Thus, the application has forced the concept of a formation to be re‐defined. Using the graph‐theoretic terminology, it can be said that a formation may be stretched out as a path or be compact as a star or wheel. The developed behaviours have been implemented in simulation environments as well as on real ERA‐MOBI robots commonly referred to as Erratics. Advantages and shortcomings of the model, based on the simulations as well as on the implementation with a team of Erratics are discussed.

This paper discusses the concept of a robot formation in the context of a real world application of a robot team (Swarm).

This paper aims to discuss traffic patterns generated by swarms of robots while commuting to and from a base station.

The paper adopts a mathematical evaluation and robot swarm simulation. The swarm approach is bottom‐up: designing individual agents the authors are looking for emerging group behaviour patterns. Examples of group behaviour patterns are human‐driven motorized traffic which is rigidly structured in two lanes, while army ants develop a three‐lane pattern in their traffic. The authors copy army ant characteristics onto their robots and investigate whether the three lane traffic pattern may emerge. They follow a three‐step approach. The authors first investigate the mathematics and geometry of cases occurring when applying the artificial potential field method to three “perfect” robots. Any traffic pattern (two, three or more lanes) appears to be possible. Next, they use the mathematical cases to study the impact of limited visibility by defining models of sensor designs. In the final step the authors implement ant inspired sensor models and a trail following mechanism on the robots in the swarm and explore which traffic patterns do emerge in open space as well as in bounded roads.

The study finds that traffic lanes emerge in the swarm traffic; however the number of lanes is dependent on the initial situation and environmental conditions. Intrinsically the applied robot models do not determine a specific number of traffic lanes.

The paper presents a method for studying and simulating robot swarms.