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While robots have increasingly threatened frontline employees’ (FLEs) future employment by taking over more mechanical and analytical intelligence tasks, they are still unable to “experience” and “feel” to occupy empathetic intelligence tasks that can be handled better by FLEs. This study, therefore, aims to empirically develop and validate a scale measuring the new so-called empathetic creativity as being creative in practicing and performing empathetically intelligent skills during service encounters.

This study adopts a multistage design to develop the scale. Phase 1 combines a literature review with text mining from 3,737 service robots-related YouTube comments to generate 16 items capturing this new construct. Phase 2 assesses both face and content validity of those items, while Phase 3 recruits Prolific FLEs sample to evaluate construct validity. Phase 4 checks this construct’s nomological validity using PLS-SEM and Phase 5 experiments dedicated effort (vs natural talent) as an effective approach to foster FLEs’ perceived empathetic creativity.

The final scale is comprised of 13 refined items that capture three dimensions (social, interactive and emotional) of empathetic creativity. This research provides timely implications to help FLEs in high-contact services stay competitive.

This study introduces the new construct of empathetic creativity, which goes beyond the traditional definition of creativity in services and highlights the importance of empathetic intelligence for FLEs in future employment. This study also develops a multi-item scale to measure this construct, which can be applied to future service management research.

Automation poses to change how service work is organized. However, there is a lack of understanding of how automation influences specific sectors, including specific hospitality jobs. Addressing this gap, this paper looks at the relative automatability of jobs and tasks which fall within one specific hospitality context: frontline food service.

Study 1 analyzes the UK Office for National Statistics' Standard Occupational Classification (2020) data to determine the degree to which frontline food service jobs consist of tasks requiring mechanical, analytical, intuitive or empathetic intelligence. Study 2 contrasts these findings to current state of intelligent automation technology development through interviews and a focus group with food service technology experts (n = 13).

Of all the tasks listed under food service in the ONS SOC 2020, 58.8% are found to require mechanical, 26.8% analytical, 11.3% intuitive and 3.1% empathetic intelligence. Further, the automatability of these tasks is found to be driven by three streams of technology development in particular: (1) autonomous navigation, (2) object manipulation and (3) natural language processing.

Hospitality management literature has started to conceptualize a move from mechanical and analytical service tasks to tasks centered around intuition and empathy. While previous studies have adopted a general view to what this might mean for hospitality jobs, this paper develops a novel, task-centric framework for Actioning Intelligent Automation in Frontline Food Service.

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.

Algorithms, artificial intelligence (AI), machines, and all emerging digital technologies disrupt traditional auditing, raising many questions and debates. One of the central issues of this debate is the human-algorithms complex duality, which focuses on this investigation. This study aims to investigate the algorithms’ penetration in auditing activities, with a specific focus of a future scenario on the human-algorithms interaction in performing audits as intelligent teams.

The research uses a qualitative reflexive thematic analysis, taking into consideration the academic literature, as well as professional reports and websites of the “Big Four” audit firms and internationally recognized accounting bodies.

The results debate the complex duality between algorithms and human-based actions in the institutional settings of auditing activities by highlighting the actual stage of algorithms, machines and AI emergence in audit and providing real-life examples of their use in the audit. Furthermore, they emphasize the strengths and weaknesses of algorithms compared to human beings. Based on the results, a discussion on the human-algorithms interaction from the lens of the Human-in-the-Loop (HITL) approach concludes that the Auditor-Governing-the-Loop may be a possible scenario for the future of the auditing profession.

This study is exploratory, investigating academia and practitioners’ written debates, analyzes and reports, limiting its applicability. Nonetheless, the paper adds to the ongoing discussion on emerging technologies and auditing research. Finally, the authors address some potential biases associated with the extended use of algorithms and discuss future research implications. Future research should empirically test how the human-algorithms tandem is working and how AI and other emerging technologies will affect auditing activities and the auditing profession.

The study provides valuable insights for audit firms, auditors, professional organizations and standard-setters, and regulators revealing the implication of algorithms’ penetration in auditing activities from the human-algorithms complex duality perspective. Moreover, the academic education and research implications are highlighted, in terms of updating the educational curriculum by including the new technologies issues, as well as the need for further research investigations concerning the human-algorithms interactions issues as, for example, trust, legal restrictions, ethical concerns, security and responsibility.

The research uses HITL as a novel paradigm for responsible AI development in auditing. The study points to the strategic value of a HITL pattern for organizational reflexivity that, according to the study, ensures that the algorithm’s output meets the audit organization’s requirements and changes in the environment.

Within the scope of the research, articles about service robots were examined by the systematic review method.

The research aims to evaluate the articles on service robots, an artificial intelligence (AI) application in restaurant businesses, using a systematic review method. In systematic reviews, the data obtained as a result of scanning databases to find an answer to a research question are synthesized and reported. The criterion sampling technique, one of the purposeful sampling methods, was used for the sample of the research. Inclusion and exclusion criteria were applied within the scope of screening.

The articles on service robots were carried out between 2018 and 2023. In terms of research methods, most of the articles are quantitative, while there are studies on mixed and qualitative methods. In studies, data were generally collected by survey technique. The keywords of the studies on service robots are examined; the most commonly used words were service robot and AI, technology, restaurant, satisfaction, revisit intention, consumer behavior, intention, preference, hospitality and foods. The objectives of the articles pertinent to service robots are mostly to determine people's attitudes and acceptance toward these services focuses.

The studies seem to focus more on customer acceptance, trust, expectations, risks, adaptation, reasons for preference, impact on creative services, emotional and cognitive effects and human–robot interaction. Despite this, it is observed that there are fewer studies on topics such as the development of service robots in restaurant businesses, their reflections on the future, future opportunities and the quality of chef service robots. Based on this, it is recommended to consider studies that will serve as a reference for revealing innovative opportunities that can meet future expectations in order to increase the quality of service robots in restaurant businesses.

This paper aims to recall the attention on a key challenge for customer relationship management related to the role of human agents in the management of the “switch point” for ensuring the effectiveness and efficiency in a customer-machine conversation.

This study contributes to the discussion about the firms’ approach to artificial intelligence (AI) in frontline interactions under the conceptual umbrella provided by knowledge management studies.

This paper provides a theoretical model for clarifying the role of human intelligence (HI) in AI-based frontline interactions by highlighting the relevance of the actors’ subjectivity in the dynamics and perceptions of customer-machine conversations.

An AI-HI complementarity matrix is proposed in spite of the still dominant replacement view.

The digital transformation is dramatically changing the business-to-business (B2B) sales environment, challenging long-standing views regarding the critical competencies required of salespeople. This paper aims to explore the personal traits associated with sales performance in a digital selling environment.

Using template analysis, the researchers captured and coded over 21 h of in-depth, semi-structured interviews with senior sales leaders from various industry sectors, exploring their perceptions of the personal traits now required of B2B salespeople in the digital landscape.

The research identifies three high-level trait types critical to sales success within a digital selling environment: “analytical curiosity” – the natural motivation and ability to gather and synthesize sales-related knowledge, “empathetic citizenship” – the ability to establish initial rapport while building long-term trust and “disciplined drive” – the exertion of selling effort in a highly focused and methodical manner across all stages of the sales process.

The present data came from interviews with sales leaders in Canada. A more global sample may lead to additional insights. Moreover, the sample was drawn from long-cycle B2B sales environments; conclusions may differ for short-cycle or business-to-consumer markets.

This paper presents a framework for hiring and developing salespeople in the digital sales environment, identifying personal trait types that sales leaders should look for when hiring: analytical curiosity, empathetic citizenship and disciplined drive. The paper identifies how these trait types influence sales success, suggesting that sales leaders could coach and educate their teams to make the best use of them.

This paper presents a conceptual framework for hiring in the digital sales environment and introduces the trait of analytical curiosity not previously discussed in the literature.

This paper aims to provide researchers and practitioners with a consumer-focused alternative for considering the integration of artificial intelligence (AI) into services.

The paper reviews and critiques the most popular frameworks for addressing AI in service. It offers an alternative approach, one grounded in social psychology and leveraging influential concepts from management and human–computer interaction.

The frameworks that dominate discourse on this topic (e.g. Huang and Rust, 2018) are fixated on assessing technology-determined feasibility rather than consumer-granted permissibility (CGP). Proposed is an alternative framework consisting of three barriers to CGP (experiential, motivational and definitional) and three responses (communicate, motivate and recreate).

The implication of this research is that consistent with most modern marketing thought, researchers and practitioners should approach service design from the perspective of customer experience, and that the exercise of classifying service occupation tasks in terms of questionably conceived AI intelligences should be avoided.

Indicative of originality, this paper offers an approach to considering AI in services that is nearly the polar opposite of that widely advocated by e.g., Huang et al., (2019); Huang and Rust (2018, 2021a, 2021b, 2022b). Indicative of value is that their highly cited paradigm is optimized for predicting the rate at which AI will take over service tasks/occupations, a niche topic compared to the mainstream challenge of integrating AI into service offerings.

This empirical quantitative research study aimed to test whether historical texts could activate empathic concern and perspective taking in a US History classroom with adolescent students.

Eighth-grade participants (n = 227) were randomly assigned to read either a historical narrative text or a collection of primary documents, then participants self-reported a range of emotions and wrote a paragraph that was assessed for historical perspective taking.

Results indicated that for students randomly assigned to read the narrative text, empathic concern or compassion, was associated with higher historical perspective taking, even after controlling for literacy ability.

All participants attended a single predominantly. White upper middle class middle school, and read either one narrative text or one collection of primary documents. Findings cannot be generalized to all students or all texts. The study design did not assess for a causal relationship of empathic concern and historical perspective taking.

This study demonstrates that empathic concern, when activated through a certain narrative text, can be associated with greater achievement on cognitive academic tasks such as writing a paragraph assessed for historical perspective taking. Furthermore, this study provides evidence that empathic concern should be a target emotion for students rather than a similar emotional experience as the person they are empathizing with.

Adolescents today appear to have lower levels of empathy than in the past. Empathy may be crucial for moral behavior. Research indicates that historical texts could potentially provide effective empathic interventions for adolescents. However, there are no published empirical quantitative research studies related to activating empathy for adolescents through literacy in a history classroom.

This study investigates the determinants of effective human and artificial intelligence (AI) relationship-building strategies for brands. It explores the antecedents and consequences of consumers' interactant satisfaction with communication and identifies ways to enhance consumer purchase intention via AI chatbot promotion.

Microsoft Xiaoice served as the focal AI chatbot, and 331 valid samples were obtained. A two-stage structural equation modeling-artificial neural network approach was adopted to verify the proposed theoretical model.

Regarding the IQ (intelligence quotient) and EQ (emotional quotient) of AI chatbots, the multi-dimensional social support model helps explain consumers' interactant satisfaction with communication, which facilitates affective attachment and purchase intention. The results also show that chatbots should emphasize emotional and esteem social support more than informational support.

Brands should focus more on AI chatbots' emotional and empathetic responses than functional aspects when designing dialogue content for human–AI interactions. Well-designed AI chatbots can help marketers develop effective brand promotion strategies.

This research enriches the human–AI interaction literature by adopting a multi-dimensional social support theoretical lens that can enhance the interactant satisfaction with communication, affective attachment and purchase intention of AI chatbot users.