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Customer perceptions toward hybrid human–robot teams remain largely unexplored. We focus on the impact of frontline robots’ (FLRs) automated social presence (ASP) on customers’ perceived teamwork quality, and ultimately frontline employees’ (FLEs) competence and warmth. We explore the role of interrogation as a relevant contingency. We complement the customer view with insights into the FLEs’ viewpoint.

We manipulate FLR’s ASP cues (speech and identity) in a hybrid team in four business-to-consumer (B2C) video-based experiments and collect data from online participants. We combine these with one business-to-business (B2B) field survey which collected data from FLEs working in hybrid teams.

When FLR’s ASP increases, customers more positively evaluate teamwork quality, ultimately affecting FLEs’ competence and warmth. FLEs who correct (interrogate) robotic mistakes strengthen the positive effect of FLRs’ ASP on teamwork quality. When FLRs correct FLEs, ASP’s effect on teamwork quality is also strengthened, while FLEs are not “punished” for erring. In contrast, FLEs themselves do perceive corrections as detrimental to teamwork quality. We term this the hybrid team evaluation paradox.

We recommend that firms deploy hybrid teams equipped with high-ASP FLRs (name and speech suffice). FLEs should be trained, and FLRs programmed, to appropriately use interrogation. Managers should pay attention to the paradox, given the conflicting perceptions toward interrogative behaviors.

We advance the hybrid teams literature by drawing on ASP, social cognition and collective mindfulness theories and behaviors that ameliorate customer perceptions. Our results support using FLRs to enhance FLEs’ capabilities.

To promote long-term sustainability and improve consumers’ quality of life, marketers can use artificial intelligence (AI) chatbots to initiate conversations about mindful consumption. Although anthropomorphic designs are integral for successful persuasion, there is scant research on how anthropomorphizing chatbots’ internal traits influences consumers. Integrating the Uncanny Valley Effect (UVE) and the Arousal-Biased Competition (ABC) theory, this study aims to investigate how a chatbot with a higher experiential mind shapes attitudes toward mindful consumption messages by examining people’s emotional responses (i.e. eeriness and amazement) and cognitive engagement.

Incorporating real-time interactions with a chatbot, this research adopted an online experiment with a one-factor, two-condition (a higher vs a lower experiential mind) design with eeriness and amazement as parallel mediators, leading to cognitive absorption and, consequently, attitudes toward mindful consumption messages in a serial manner.

This study found that a chatbot with a higher (vs lower) experiential mind simultaneously triggers higher levels of eeriness and amazement, leading to higher cognitive absorption and a more positive message attitude.

This study expands the current anthropomorphism literature by examining the effects of imbuing nonhuman agents with an experiential mind and emphasizing a nuanced view of emotional responses to anthropomorphized chatbots. The findings contribute to establishing a theoretical link between a chatbot’s experiential mind level and persuasion outcomes, offering strategic and ethical insights for anthropomorphized AI use in sustainability marketing.

Though originality is a requirement for copyright protection, the term is not defined, leaving room for differing interpretations. Over the years, there has been a shift in the yardstick for determining originality. The lack of definition gives room for ambiguity and creates hurdle for protection. The difficulty in assessment of originality is more felt in certain forms of work like music, which has its own theories and limitations. Absence of determining tests creates uncertainty for the authors to draw the line between inspiration and infringement. This paper aims to define originality as applicable to each subject matter.

The methodology used is a qualitative analytical approach and draws from theories and relevant case laws.

The requirement of “originality” as laid down in the Copyright Act has to be defined with precision. The requirement of originality for different categories of subject matter encompassed within copyright law will differ and so the term should be redefined with respect to each subject matter.

This paper is an original work and canvasses for a definition of the term originality in the Copyright Act with reference to the various subject matter entitled to protection.

Benefiting from the development and innovation of artificial intelligence and affective computing technology, social chatbots that integrate cognitive analysis and affective social services have flooded into the consumer market. For cognition and emotion-oriented tasks, social chatbots do not always receive positive consumer responses. In addition, consumers have a contradictory attitude toward the anthropomorphism of chatbots. Therefore, from the perspective of mind perception and the two dimensions of social judgment, this research explores the mechanism of consumer responses to anthropomorphic interaction styles when social chatbots complete different service tasks.

This paper utilizes three behavior experimental designs and survey methods to collect data and the ANOVA, t-test and bootstrap analysis methods to verify the assumed hypotheses.

The results indicate that when the service task type of a social chatbot is cognition-oriented, compared to a warm anthropomorphic interaction style, a competent anthropomorphic interaction style can improve consumer responses more effectively. During this process, agent-mind perception plays a mediating role. When the service task type of a social chatbot is emotion-oriented, compared with a competent anthropomorphic conversation style, a warm anthropomorphic conversation style can improve consumer responses. Experience-mind perception mediates this influencing relationship.

The research results theoretically enrich the relevant research on the anthropomorphism of social chatbots and expand the application of the theory of mind perception in the fields of artificial intelligence and interactive marketing. Our findings provide theoretical guidance for the anthropomorphic development and design of social chatbots and the practical management of service task scenarios.

Technological innovations are rapidly transforming service frontlines, resulting in increasingly complex service touchpoints. These touchpoints place greater demands on frontline employees (FLEs) to deliver a positive customer experience. Despite the considerable extant body of knowledge on FLE competencies, the literature on frameworks for managing the complexity of contemporary frontlines from the FLE’s perspective is sparse. This paper aims to fill this critical gap by developing a framework that enables FLEs to deliver positive moments of truth (MOTs) while ensuring the well-being of all actors involved.

This paper uses a conceptual approach rooted in the organizational mindfulness and individual mindfulness literature as the theoretical lens. This is complemented by a comprehensive review of the FLE skills literature supported by marketplace examples to illustrate the optimal use of the said skills.

This paper proposes a conceptual framework of mindfulness orientation which delineates how FLE competencies underpinned by a set of key skills can deliver positive MOTs and actor well-being.

The research is conceptual in nature and does not contain validation through empirical data.

This comprehensive skill set provides a clear roadmap for firms in both recruitment and developing training for their FLEs, thus contributing to practice.

Firstly, we present a conceptual framework of mindfulness, combining organizational mindfulness and individual mindfulness that will enable employees to help facilitate the creation of positive MOTs. Secondly, we develop a comprehensive set of employee skills that underpin the mindfulness orientation framework.

South Africa’s wool industry plays an important role in the agricultural sector. The wool industry provides a valuable source of income for farmers who practice sustainable farming practices. However, wool farmers face numerous challenges, such as wool contamination, dirty wool and producing good-quality wool. Good-quality wool is determined by fibre diameter, clean yield, vegetable matter and staple length. This study aims to address these challenges.

A multiple regression analysis of price (R/kg) of White wool and Merino wool was applied to four variables fibre diameter: vegetable matter, clean yield and staple length. The analysis was based on the data for the 2009–2019 data from Cape Wools auctions.

Fibre diameter, clean yield and staple length, with exception of vegetable matter, made a statistically significant contribution to the determination of wool price after all other independent variables were controlled for (p < 0.05). A one-unit (micron) increase in fibre diameter resulted in a 0.404-unit decrease in wool price (R/kg). A one-unit (mm) increase in staple length resulted in a 0.022-unit increase in wool price (R/kg). There was no statistically significant association between vegetable matter and wool price. A one-unit increase in clean yield was associated with a 0.111-unit increase in wool price (R/kg).

Since wool fleeces consist of the largest portion of wool shorn from sheep, it is important for wool farmers to focus on wool with low fibre diameter, high clean yield percentage, low percentage of vegetable matter content and good length of the wool.

Since wool fleeces consist of the largest portion of wool shorn from sheep, it is important for wool farmers to focus on wool with low fibre diameter, high clean yield percentage, low percentage of vegetable matter content and good length of the wool.

In a developing country such as South Africa, this study is important for the following reason. It is understanding the wool characteristics that have the most significance influence on the determination of wool price for Merino wool and White wool might effectively help the wool farmers to adapt their production systems to improve the wool characteristics that determine wool price.

This study identified a need for a study to be conducted on all wool classes.

This study aims to discover the impact of the interaction between learning performance (as behavioral realism) and realistic appearance (as form realism) of AI-powered software robots on user trust. The study also aims to reveal how and why the interaction happens, especially from the dual processing perspective of affective and cognitive human responses.

We adopted an experimental research methodology in a movie recommendation system environment where machine learning-based recommendations are widely used. We conducted a 3 × 2 factorial design experimentation based on the three levels of learning performance (low, mid and high) and two levels of realistic appearance (caricature avatar and digital human). We used ANCOVA and the PROCESS Macro to analyze our models.

Our results confirm that learning performance (intelligence) is a critical factor influencing user trust in software robots, and this intelligence–trust relationship is influenced by their realistic appearance. Our results further reveal that there are two significant intermediating mechanisms, i.e. affective and cognitive user responses, and that the intelligence–appearance interaction effect on trust is explained especially by the affective response mechanism.

This study provides valuable implications for creating optimal learning performance and realistic appearance that can lead to trust in various settings where AI-powered software robots are utilized.

This study has sveral contributions to the literature. First, in addition to the well-recognized factor of anthropomorphic characteristics, this study investigates another critical behavioral factor of AI-powered robots (learning performance as intelligence characteristics) and the intriguing interaction between the two realism factors. Second, drawing upon the mediated moderation perspective, the study proposes a novice perspective on how and why the two realism factors can build user trust (the underlying mechanisms).

As innovative household products, social home robots have a significant impact on the interactive consumer experience. However, prior research on consumer intentions to use such robots has rarely considered the configuration perspective. The present study examines how consumers balance the key benefits and risks created by these robots and explores how key influential factors jointly influence usage intention from a configuration perspective.

We adopted a hybrid research design. In Study 1, a thematic analysis was conducted to derive a conceptual framework reflecting the interplay of key factors influencing usage intention. In Study 2, a fuzzy set qualitative comparative analysis (fsQCA) was applied to reveal how these factors jointly shape usage intention.

Equifinal configurations of antecedent conditions (i.e. emotional and instrumental support beliefs, concerns about informational and relational privacy risks, self-construal and anthropomorphic design) led to usage intention. Additionally, four distinct benefit-risk trade-off patterns emerged across individuals.

This study highlights the need to examine robot adoption in interactive marketing, particularly in the service domain. It has implications in the context of commercializing social home robots, emphasizing the potential of leveraging social home robots to enhance interactive consumer experiences and foster close connections with consumers.

We developed a neoconfigurational model to obtain a comprehensive understanding of social home robot acceptance in domestic settings, highlighting its implications for consumer–robot interactions and advancing research in interactive marketing.

This study aims to delve into the cultural differences between developing and developed countries pertaining to the negative behavioral fallouts of adopting anthropomorphized humanoids or robots. The underlying motivation (for the study) lies in the fact that these countries are at the vanguard of artificial intelligence development and deployment, albeit with varying levels of development and acceptance.

The research framework used in this study is guided by the computers as social actors framework, expectancy disconfirmation theory and is supported by the uncanny valley theory. The data was collected in two contexts using probabilistic sampling technique, N= = 782 (n₁ = 393 respondents: developed country i.e. USA and n₂ = 389 respondents: developing country i.e. India). The partial least square analysis was carried out for the proposed model’s validation and hypotheses testing.

This study shows that in developed countries, the consumers have high preinteraction expectations while they express comparatively more dark behavior than respondents from developing countries. Consumers in developed countries focus on anthropomorphic knowledge and design cues, while in developing countries, they pay attention to utility and functionality. Finally, the results also suggest that female respondents from developed countries exhibit more resilience toward anthropomorphized agents in adopting and expressing dark behavior.

The present research makes essential contributions to anthropomorphism literature. First, this study explored impact of the interaction effect on the dark side, a rather under-explored domain in regret literature. Second, this study provides evidence for cross-cultural variations pertaining to the dark side impacts. Finally, this study adds to impact of demographic variables, showing that gender played a significant role in moderating relationships in the proposed model.

Influencer marketing has emerged as a key strategy for brands to promote green products. The effectiveness of virtual influencers – a newer type of endorser – for these products remains unclear. This study aims to compare the effectiveness of virtual and human influencers in promoting green products and to uncover the key mechanisms that drive their influence.

In this research, the hypotheses were examined across three experimental studies. Study 1 initially investigated the effectiveness of virtual influencers in endorsing green products. Study 2 identified perceived altruistic motivation and perceived congruence of product and influencer as key mechanisms. Study 3 explored the moderating effect of language type (rational vs emotional).

The findings indicate that virtual influencers are generally less effective than human influencers in promoting green products, largely due to lower perceived altruistic motivation and congruence. Yet, using rational language in promotional posts can enhance the effectiveness of virtual influencers in endorsing these products.

This study combines the mind perception theory with the match-up hypothesis to explore the intrinsic mechanisms of perceived altruistic motivation and congruence, specifically from an emotional perspective, based on the unique attributes of virtual influencers and green products. These insights guide brands in selecting suitable endorsers for green products in the context of increasing environmental awareness and digitalization.