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Retailers increasingly endeavour to implement artificial intelligence (AI) innovations, such as humanoid social robots (HSRs), to enhance customer experience. This paper investigates the interactive effect of HSR intelligence and consumers' speciesism on their perceptions of retail robots as sales assistants.

Three online experiments testing the effects of HSRs' intellectual intelligence on individuals' perceived competence and, consequently, their decision to shop at a retail store that uses HSRs as sales assistants are reported. Furthermore, the authors examine whether speciesism attenuates these effects such that a mediation effect is likely to be observed for individuals low in speciesism but not for those with high levels of speciesism. Data for all studies were collected on Prolific and analysed with SPSS to perform a logistic regression and PROCESS 4.0 (Hayes, 2022) for the mediation and moderated-mediation analysis.

The findings show that the level of speciesism moderates the relationship between HSR intellectual intelligence and perceived competence such that an effect is found for low but not for high HSR intelligence. When HSR intellectual intelligence is low, individuals with higher levels of speciesism (vs low) rate the HSR as less competent and display lower HSR acceptance (i.e. customers' decision to shop using retail robots as sales assistants).

This research responds to calls in research to adopt a human-like perspective to understand the compatibility between humans and robots and determine how personality traits, such as a person's level of speciesism, may affect the acceptance of AI technologies replicating human characteristics (Schmitt, 2019). To the best of the authors' knowledge, the present research is the first to examine the moderating role of speciesism on customer perceptions of non-human retail assistants (i.e. human-like and intelligent service robots). This study is the first to showcase that speciesism, normally considered a negative social behaviour, can positively influence individuals' decisions to engage with HSRs.

This commentary discusses the value of generative artificial intelligence (AI) for qualitative research in phygital settings to understand the customer experience.

The critical and logical analysis is based on current knowledge of generative AI.

Generative AI seems very useful for qualitative research in phygital settings to understand the customer experience and should be used in qualitative research projects. Generative AI can provide much-needed validation of the subjective nature of qualitative research and can also generate insights beyond human intuition.

The study is based on current technology, which changes fast. In the future, the skills of qualitative researchers may become outdated, relegating them to the role of prompt engineers.

Technology, and especially generative AI, will be a key tool for practitioners as they conduct practical research.

Qualitative researchers should overcome potential anti-technology speciesism and embrace the potential of generative AI.

This commentary provides insights into the role of generative AI for qualitative research in phygital settings.

Will consumers accept artificial intelligence (AI) products that evaluate them? New consumer products offer AI evaluations. However, previous research has never investigated how consumers feel about being evaluated by AI instead of by a human. Furthermore, why do consumers experience being evaluated by an AI algorithm or by a human differently? This research aims to offer answers to these questions.

Three laboratory experiments were conducted. Experiments 1 and 2 test the main effect of evaluator (AI and human) and evaluations received (positive, neutral and negative) on fairness perception of the evaluation. Experiment 3 replicates previous findings and tests the mediation effect.

Building on previous research on consumer biases and lack of transparency anxiety, the authors present converging evidence that consumers who got positive evaluations reported nonsignificant difference on the level of fairness perception on the evaluation regardless of the evaluator (human or AI). Contrarily, consumers who got negative evaluations reported lower fairness perception when the evaluation was given by AI. Further moderated mediation analysis showed that consumers who get a negative evaluation by AI experience higher levels of lack of transparency anxiety, which in turn is an underlying mechanism driving this effect.

To the best of the authors' knowledge, no previous research has investigated how consumers feel about being evaluated by AI instead of by a human. This consumer bias against AI evaluations is a phenomenon previously overlooked in the marketing literature, with many implications for the development and adoption of new AI products, as well as theoretical contributions to the nascent literature on consumer experience and AI.

The hospitality industry has recently witnessed explosive growth in robotization with the replacement of robots in many areas. Yet, a key consideration in this robotics wave is whether competition (i.e. robots take over all human tasks) or collaboration (i.e. humans collaborate closely with robots to perform work better) will define the future of the hospitality workspace. The purpose of this paper is to shed light on this controversial issue by taking a collaborative perspective to address the future human–robot relationship in hospitality workplace (i.e. cobotic team).

Drawing upon relevant theories and extant robotics literature, this paper will develop a critical reflection on the management of future cobotic team as a new phenomenon in hospitality industry.

The successful management of cobotics in hospitality lies in three interrelated key domains: feeling intelligence training for frontline employees, ethics governance for cobotics and trust building toward robot partners.

How to manage this cobotic team efficiently will be a focus for hospitality managers in the coming years. This paper offers several managerial insights for hospitality managers and practitioners regarding effectively managing the future collaboration between humans and robots within a dynamic work environment.

This study addresses cobotics as a critical yet unaddressed shift in the contemporary hospitality sector and proposes a framework highlighting three key domains for managing this cobotic team effectively. This framework also sets the direction to encourage more future empirical research exploring cobotic workforce in hospitality.