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Service robotics, a branch of robotics that entails the development of robots able to assist humans in their environment, is of growing interest in the hospitality industry. Designing effective autonomous service robots, however, requires an understanding of Human–Robot Interaction (HRI), a relatively young discipline dedicated to understanding, designing, and evaluating robotic systems for use by or with humans. HRI has not yet received sufficient attention in hospitality robotic design, much like Human–Computer Interaction (HCI) in property management system design in the 1980s. This article proposes a set of introductory HRI guidelines with implementation standards for autonomous hospitality service robots.

A set of key user-centered HRI guidelines for hospitality service robots were extracted from 52 research articles. These are organized into service performance categories to provide more context for their application in hospitality settings.

Based on an extensive literature review, this article presents some HRI guidelines that may drive higher levels of acceptance of service robots in customer-facing situations. Deriving meaningful HRI guidelines requires an understanding of how customers evaluate service interactions with humans in hospitality settings and to what degree those will differ with service robots.

Robots are challenging assumptions on how hospitality businesses operate. They are being increasingly deployed by hotels and restaurants to boost productivity and maintain service levels. Effective HRI guidelines incorporate user requirements and expectations in the design specifications. Compilation of such information for designers of hospitality service robots will offer a clearer roadmap for them to follow.

Currently, online chat is in common use in e-commerce. By adding social interaction to the online context, companies hope to increase customers’ purchasing intentions. However, previous studies have not investigated how social presence is embedded in online business-to-business (B2B) chat conversations between buyers and sellers. Moreover, the functions of online chat in B2B sales have not been investigated.

The data was collected at a case company over the course of four years, from which the authors analyzed 157 online chat conversations between buyers (n = 157) and sellers (n = 9) with a theory-driven thematic analysis. In addition, data from the company’s customer relationship management system was collected to specify buyer types.

The results reveal that social presence was embedded in online B2B chat via buyers’ interactive, affective and relationship maintenance responses. Social presence differed depending on the type of buyer, with only existing customers having relationship maintenance responses. E-commerce B2B chat functions can be described as multiple and changing depending on the buyer–seller relationship stage.

Having data only from one case company limits the results to one type of industry.

The results can be used in sales training and when developing online chat services.

Results bring scientific utility to B2B sales and marketing research, as the authors build a bridge between social presence, the existing theoretical model on B2B buyer–seller relationship development and online chat as a communication medium. Other researchers may use this understanding when exploring B2B buyer–seller interaction in different digitalized communication media.