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Starting a relationship with AI! Exploring consumer's attitude towards digital human stylists
... Meeting these expectations increases technology acceptance. Attributes such as reliability, responsiveness, guarantees, interactivity and emotional conversations contribute to customer satisfaction (Song and Kim 2020;Hadwer et al. 2021;Lee, Kim, and Park 2022;Belagur et al. 2023;Le, Park, and Lee 2023;Bonetti et al. 2024). ...
Artificial intelligence (AI) is crucial in the fashion industry nowadays. It facilitates innovation in design, production, e‐commerce, personalisation and supply chain management, improving efficient operations and providing opportunities to support sustainability. The research aimed to identify the current use of artificial intelligence in the fashion industry and its arrangement and determine to what extent AI is used to support fashion sustainability. Data collection and selection were according to PRISMA guidelines and were retrieved from Scopus and WoS (January 2017–October 2024; n = 82 (from 234 identified items)). Data analysis was performed using six steps of thematic analysis, including topic modelling (in the MAXQDA 24 programme). The identified themes revealed the current effects of AI use in the fashion industry (RQ1): data‐centric design, forecasting using big data, and experience‐oriented services. AI technologies are predominantly utilised in fabric production and B2B distribution. Experience‐focused services are enhanced through precise image searches and chatbot support. Platforms like SaaS, generative fashion, and Science4Fashion enable the creation of new designs. Applications such as Style. Me serve as personal stylists, facilitating customised outfit selection and streamlining the purchasing process. The last theme (RQ2) allowed to establish that sustainable development requires innovation based on AI technology. Despite optimistic forecasts, available solutions are only used to a limited extent. The main barrier is that companies put economic goals above sustainable development goals. Models that combine commercial and environmental perspectives are essential in developing beneficial change strategies. Therefore, it is important to monitor progress in this area.
Artificial intelligence (AI) is likely to spawn revolutionary transformational effects on service organizations, including by impacting the ways in which firms engage with their customers. In parallel, customer engagement (CE), which reflects customer interactions with brands, offerings, or firms, has risen to the top of many managers' strategic wish-lists in the last decade. However, despite literature-based advances made in both areas, AI and CE are largely investigated in isolation to date, yielding a paucity of insight into their interface. In response to this gap, this Special Issue offers a pioneering exploration of CE in automated or AI-based service interactions. Our editorial first reviews AI's Industry 4.0 underpinnings, followed by an important AI typology that comprises robotic process automation (RPA), machine learning (ML), and deep learning (DL) applications. We then offer a high-level synopsis of existing CE research, followed by the development of a set of integrative Propositions of CE in automated service interactions. Next, we introduce the Special Issue papers, which feature particular RPA-, ML-, or DL-applications. We conclude with an overview of further research avenues in this growing area, which has the potential to develop into a powerful service research sub-stream in the coming years.
In a post-pandemic era marked by thriving digital payments and e-commerce transactions due to physical distancing norms, the growth of mobile payments or E-wallets is expected to expand in tandem with the global trend toward cashless payment solutions. However, it is unclear whether this momentum would be sustained for over-the-counter (OTC) retail payments, particularly QR-code E-wallets, that are more affordable and accessible to merchants and customers than NFC-based (near field communication) systems in emerging markets. This study aims to model the interaction effects of brand image in shaping consumers’ E-wallet usage intentions. Incorporating a consumer-brand relationship element (i.e., brand image) should improve the understanding of consumers’ digital service experiences in proximity-based retail encounters. The research model was empirically tested using 305 responses from QR-based E-wallet users in the Klang Valley, Malaysia. Statistical analysis was performed using structural equation modeling (SEM) to test the hypotheses. An empirical examination of the model revealed effort expectancy, social influence, hedonic motivation, and perceived value as significant positive predictors of consumer usage intention. Furthermore, brand image was found to significantly strengthen the positive effect of perceived value and weaken the positive impact of hedonic motivation on the outcome. The study’s key contributions include reaffirming the crucial contingent role of brand image in consumer technology adoption studies and investigating consumer perceptions of QR-based E-wallets, which are expected to gain traction, especially in emerging markets. E-wallet providers should reinforce their value propositions by providing seamless, engaging, and easy-to-use experiences that improve users' brand perceptions.
Digital technologies increasingly transform traditional business models, value chains and associated networks of emerging fashion designers in London. D-commerce becomes an additional dynamic capability contributing to their competitive advantage. An analysis of this phenomenon was conducted exploring data assembled from several online sources. The findings reveal that fashion designers use amalgamations of online and physical channels to develop a downstream value chain domestically and internationally. Although social media plays an increasing role in the marketing of designer products this does not necessarily translates into higher visibility of designers on the Internet, increased accumulation of value or their overall chances of survival and growth. Large retailers who excel at channel diversity remain the important part of the highly institutionalised and hierarchical fashion industry ecosystem. A typology of designers was developed based on the characteristics of their visibility on the Internet, their involvement in d-commerce and other retailing practices.
Advances in artificial intelligence (AI) are increasingly enabling firms to develop services that utilize autonomous vehicles (AVs). Yet, there are significant psychological barriers to adoption, and insights from extant literature are insufficient to understand customer emotions regarding AV services. To allow for a holistic exploration of customer perspectives, we synthesize multidisciplinary literature to develop the Customer Responses to Unmanned Intelligent-transport Services based on Emotions (CRUISE) framework, which lays the foundation for improved strategizing, targeting, and positioning of AV services. We subsequently provide empirical support for several propositions underpinning the CRUISE framework using representative multinational panel data ( N = 27,565) and an implicit association test ( N = 300). We discover four distinct customer segments based on their preferred degree of service autonomy and service risk. The segments also differ in terms of the valence and intensity of emotional responses to fully autonomous vehicle services. Additionally, exposure to positive information about AV services negatively correlates with the likelihood of membership in the two most resistant segments. Our contribution to service research is chiefly twofold; we provide: 1) a formal treatise of AV services, emphasizing their uniqueness and breadth of application, and 2) empirically validated managerial directions for effective strategizing based on the CRUISE framework.
This research draws upon the increasing usage of AI in service. It aims at understanding the extent to which AI systems have multiple intelligence types like humans and if these types arouse different emotions in consumers. To this end, the research uses a two-study approach: Study 1 builds and evaluates a scale for measuring different AI intelligence types. Study 2 evaluates consumers' emotional responses to the different AI intelligences. The findings provide a measurement scale for evaluating different types of artificial intelligence against human ones, thus showing that artificial intelligences are configurable, describable, and measurable (Study 1), and influence positive and negative consumers' emotions (Study 2). The findings also demonstrate that consumers display different emotions, in terms of happiness, excitement, enthusiasm, pride, inspiration, sadness, fear, anger, shame, and anxiety, and also emotional attachment, satisfaction, and usage intention when interacting with the different types of AI intelligences. Our scale builds upon human intelligence against AI intelligence characteristics while providing a guidance for future development of AI-based systems more similar to human intelligences.
Realistic looking humanoid love and sex dolls have been available on a somewhat secretive basis for at least three decades. But today the industry has gone mainstream with North American, European, and Asian producers using mass customization and competing on the bases of features, realism, price, and depth of product lines. As a result, realistic life size artificial companions are becoming more affordable to purchase and more feasible to patronize on a service basis. Sexual relations may be without equal when it comes to emotional intimacy. Yet, the increasingly vocal and interactive robotic versions of these dolls, feel nothing. They may nevertheless induce emotions in users that potentially surpass the pleasure of human-human sexual experiences. The most technologically advanced love and sex robots are forecast to sense human emotions and gear their performances of empathy, conversation, and sexual activity accordingly. I offer a model of how this might be done to provide a better service experience. I compare the nature of resulting “artificial emotions” by robots to natural emotions by humans. I explore the ethical issues entailed in offering love and sex robot services with artificial emotions and offer a conclusion and recommendations for service management and for further research.
Artificial intelligence (AI)-based voice assistants have become an essential part of our daily lives. Yet, little is known concerning what motivates students to use them in educational activities. Therefore, this research develops a theoretical model by extending the technology acceptance model (TAM) with subjective norm, enjoyment, facilitating conditions, trust, and security to examine students’ use of AI-based voice assistants for instructional purposes. The developed model was then validated based on data collected from 300 university students using the PLS-SEM technique. The results supported the role of enjoyment, trust, and perceived ease of use (PEOU) in affecting the perceived usefulness (PU) of voice assistants. The empirical results also showed that facilitating conditions and trust in technology strongly influence the PEOU. Contrary to the extant literature, the results indicated that subjective norm, facilitating conditions, and security did not impact PU. Similarly, subjective norm and enjoyment did not affect PEOU. This research is believed to add a holistic understanding of the key drivers affecting students’ use of voice assistants for educational purposes. It offers several theoretical contributions and practical implications on how to successfully employ these assistants.
Purpose-Service robots are now an integral part of our living and working environment, making them one of the hot topics for service researchers today. Against this background, this paper reviews the recent service robot literature following a Theory-Context-Characteristics-Methodology (TCCM) approach to capture the state-of-art of the field. In addition, building on qualitative input from researchers active in this field, we highlight where opportunities for further development and growth lie. Design/methodology/approach-This paper identifies and analyzes 88 manuscripts (featuring 173 individual studies) published in academic journals featured on the SERVSIG literature alert. In addition, qualitative input gathered from 79 researchers active in the service field and doing research on service robots is infused throughout the manuscript. Findings-The key research foci of the service robot literature to date include comparing service robots with humans, the role of service robots' look & feel, consumer attitudes toward service robots, and the role of service robot conversational skills & behaviors. From a TCCM view, we discern dominant theories (anthropomorphism theory), contexts (retail/healthcare, U.S. samples, B2C settings, and customer-focused), study characteristics (robot type: chatbots, not embodied, and text/voice-based; outcome: customer intentions), and methodologies (experimental, picture-based scenarios). Originality/value-This paper is the first to analyze the service robot literature from a TCCM perspective. Doing so, this study gives (1) a comprehensive picture of the field to date and (2) highlights key pathways to inspire future work.
COVID-19 has caused disruptions in the sharing economy for both platforms and owners, who are typically micro-businesses. Lower demand and ample supply means that users have a great deal of choice. Finding ways for properties to differentiate themselves has been a pressing need. Against this background, this paper pursued two objectives: firstly to explore the perceived functional and emotional value of smart accommodation and the factors contributing to this by adopting the Theory of Consumption Values, and secondly to examine the role of perceived value in driving intention to stay in smart accommodation in the future. 430 responses were collected to analyse the relationships among antecedents, value and intention. The results showed that the functional value of smart accommodation is associated with the perception that such accommodation represents good value for the price, smart devices are useful, they can enhance control of stay experiences, and there are resources and opportunities facilitating the use of technology. Emotional value is determined by the perception that staying in smart accommodation represents sustainable behaviour, the integration of smart home technologies offers control over the stay experience, improves the entertainment experience, aesthetics and playfulness of using technology. Emotional values are inhibited by the perception of surveillance in smart accommodation. Also, the study offers evidence of the correlation of intention with functional and emotional value. The evidence contributes to the literature by explaining the potential implications of innovative technologies for business recovery in the post-pandemic reality, exploring the applications of smart technologies in delivering tourism services, and identifying the factors in the adoption of smart homes in the hospitality sector. The findings provide practical implications for facilitating the applications of innovative technology and its adoption in home and non-home environments.
This study aims to understand the drivers behind the usage habits of voice assistants (VAs). To do so, we extend the Technology Acceptance Model in conjunction with the concept of privacy cynicism, a cognitive process that remains understudied in the academic literature. The model is validated using PLS analysis through Smart-PLS. Data gathered via MTurk includes 265 actual VAs users. It is observed that ease of use and perceived usefulness have a positive impact on attitude towards the usage of VAs, while privacy cynicism has a negative impact. Moreover, it is found that privacy cynicism has a positive impact on trust based on the usage of VAs. Interestingly, attitudes towards the usage of VAs does not fully explain the consumers' VA usage habits.
Digital assistant is a recent advancement benefited through data-driven innovation. Though digital assistants have become an integral member of user conversations, but there is no theory that relates user perception towards this AI powered technology. The purpose of the research is to investigate the role of technology attitude and AI attributes in enhancing purchase intention through digital assistants. A conceptual model is proposed after identifying three major AI factors namely, perceived anthropomorphism, perceived intelligence, and perceived animacy. To test the model, the study employed structural equation modeling using 440 sample. The results indicated that perceived anthropomorphism plays the most significant role in building a positive attitude and purchase intention through digital assistants. Though the study is built using technology-related variables, the hypotheses are proposed based on various psychology-related theories such as uncanny valley theory, the theory of mind, developmental psychology, and cognitive psychology theory. The study’s theoretical contributions are discussed within the scope of these theories. Besides the theoretical contribution, the study also offers illuminating practical implications for developers and marketers’ benefit.
The growth of artificial intelligence (AI) and its applications in business has proliferated in recent years. Businesses have started adopting various technology practices relevant to automation and AI, and research investigating this phenomenon is becoming increasingly important. Taking this as a cue, the present research investigates the effect of human‐to‐machine interaction and human‐to‐human interaction towards cognitive absorption and its subsequent effect on trust, experience, and continuation intention in the context of services. The study built a 3 × 3 factorial design with automated chatbots (machine interaction) and service executives (human interaction) used as a stimulus in the experiment. Data collected from 410 respondents were analyzed using structural equation modeling to test the proposed hypotheses. The findings indicated that human‐to‐machine interaction influences cognitive absorption more positively compared to human‐to‐human interactions. The study results also provide evidence for the role of the trust, experience, and technology continuation intention in a technology background rooted in human‐machine interactions. The present study adds a valuable contribution to the existing literature relevant to human‐to‐machine interaction, cognitive absorption, trust, experience, and continuation intention. The study also provides valuable inputs to technology and marketing managers.
An increasing number of firms introduce service robots, such as physical robots and virtual chatbots, to provide services to customers. While some firms use robots that resemble human beings by looking and acting humanlike to increase customers’ use intention of this technology, others employ machinelike robots to avoid uncanny valley effects, assuming that very humanlike robots may induce feelings of eeriness. There is no consensus in the service literature regarding whether customers’ anthropomorphism of robots facilitates or constrains their use intention. The present meta-analysis synthesizes data from 11,053 individuals interacting with service robots reported in 108 independent samples. The study synthesizes previous research to clarify this issue and enhance understanding of the construct. We develop a comprehensive model to investigate relationships between anthropomorphism and its antecedents and consequences. Customer traits and predispositions (e.g., computer anxiety), sociodemographics (e.g., gender), and robot design features (e.g., physical, nonphysical) are identified as triggers of anthropomorphism. Robot characteristics (e.g., intelligence) and functional characteristics (e.g., usefulness) are identified as important mediators, although relational characteristics (e.g., rapport) receive less support as mediators. The findings clarify contextual circumstances in which anthropomorphism impacts customer intention to use a robot. The moderator analysis indicates that the impact depends on robot type (i.e., robot gender) and service type (i.e., possession-processing service, mental stimulus-processing service). Based on these findings, we develop a comprehensive agenda for future research on service robots in marketing.
Technology acceptance in private spaces has not received much attention, although users' behaviour may be different due to the space in which usage takes place. To address this gap, the present study proposed a model exploring individuals' values, users' perception of technology performance and attitudinal beliefs in relation to use behaviour and satisfaction when using smart technologies in their homes. The study employed a sample of 422 participants in the USA. Structural equation modelling was utilised to test the proposed hypotheses. The model provided robust results explaining factors underpinning the use of pervasive technology in private settings. Specifically, the study showed that hedonic and utilitarian beliefs are critical for the perception of task fit, whereas privacy and financial factors were found to be not significant. The fit between tasks and technology demonstrated a significant role in predicting perceived usefulness, perceived ease of use, use behaviour, and satisfaction. Lastly, use behaviour showed a positive correlation with satisfaction.
Advances in artificial intelligence provide new tools of digital assistance that retailers can use to support consumers while shopping. The aim of this research is to examine how consumers react as a function of assistants’ appearance (human- vs. not human-like) and activation (automatic vs. human-initiated). We advance a model of sequential mediation whose empirical validation on 400 participants in two studies shows that non-anthropomorphic digital assistants lead to higher psychological reactance. In turn, reactance affects perceived choice difficulty, which positively reflects on choice certainty, perceived performance and—ultimately—satisfaction. Thus, although reactance might appear as a negative outcome, it eventually leads to higher satisfaction. Furthermore, initiation (system vs. user initiation) does not activate the chain of effects, but significantly interacts with anthropomorphism so that individuals exhibit lower reactance when confronted with human-like digital assistants activated by the consumer. Overall, reactance is highest for non-human like digital assistants that are computer-initiated.
Chatbots are mainly text-based conversational agents that simulate conversations with users. This study aims to investigate drivers of users’ satisfaction and continuance intention toward chatbot-based customer service. We propose an analytical framework combining the expectation-confirmation model (ECM), information system success (ISS) model, TAM, and the need for interaction with a service employee (NFI-SE). Analysis of data collected from 370 actual chatbot users reveals that information quality (IQ) and service quality (SQ) positively influence consumers’ satisfaction, and that perceived enjoyment (PE), perceived usefulness (PU), and perceived ease of use (PEOU) are significant predictors of continuance intention (CI). The need for interaction with an employee moderates the effects of PEOU and PU on satisfaction. The findings also revealed that satisfaction with chatbot e-service is a strong determinant and predictor of users’ CI toward chatbots. Thus, chatbots should enhance their information and service quality to increase users’ satisfaction. The findings imply that digital technologies services, such as chatbots, could be combined with human service employees to satisfy digital users.
This paper introduces a conceptual framework for understanding new and futuristic in-store technology infusions. First, we develop a 2 × 2 typology of different innovative and futuristic technologies focusing on their level of convenience and social presence for the consumer. Next, we offer a series of propositions based on the idea that convenience and social presence can trigger vividness by enhancing consumer involvement, imagery, and elaboration, which ultimately leads to enhanced sales. Finally, the paper then focuses on four moderating areas—consumer traits, product/service dimensions, mental models and social networks—to understand how they might impact the vividness experienced via the technology.
Anthropomorphic agents used in online-shopping need to be trusted by users so that users feel comfortable buying products. In this paper, we propose a model for designing trustworthy agents by assuming two factors of trust, that is, emotion and knowledgeableness perceived. Our hypothesis is that when a user feels happy and perceives an agent as being highly knowledgeable, a high level of trust results between the user and agent. We conducted four experiments with participants to verify this hypothesis by preparing transition operators utilizing emotional contagion and knowledgeable utterances. As a result, we verified that users' internal states transitioned as expected and that the two factors significantly influenced their trust states.
Purpose
The coronavirus disease 2019 (COVID-19) pandemic has had a big impact on organisations globally, leaving organisations with no choice but to adapt to the new reality of remote work to ensure business continuity. Such an unexpected reality created the conditions for testing new applications of smart home technology whilst working from home. Given the potential implications of such applications to improve the working environment, and a lack of research on that front, this paper pursued two objectives. First, the paper explored the impact of smart home applications by examining the factors that could contribute to perceived productivity and well-being whilst working from home. Second, the study investigated the role of productivity and well-being in motivating the intention of remote workers to use smart home technologies in a home-work environment in the future.
Design/methodology/approach
The study adopted a cross-sectional research design. For data collection, 528 smart home users working from home during the pandemic were recruited. Collected data were analysed using a structural equation modelling approach.
Findings
The results of the research confirmed that perceived productivity is dependent on service relevance, perceived usefulness, innovativeness, hedonic beliefs and control over environmental conditions. Perceived well-being correlates with task-technology fit, service relevance, perceived usefulness, perceived ease of use, attitude to smart homes, innovativeness, hedonic beliefs and control over environmental conditions. Intention to work from a smart home-office in the future is dependent on perceived well-being.
Originality/value
The findings of the research contribute to the organisational and smart home literature, by providing missing evidence about the implications of the application of smart home technologies for employees' perceived productivity and well-being. The paper considers the conditions that facilitate better outcomes during remote work and could potentially be used to improve the work environment in offices after the pandemic. Also, the findings inform smart home developers about the features of technology which could improve the developers' application in contexts beyond home settings.
In recent years, the extensive use of digital human avatar (DHA) endorsement has emerged. DHA endorsement often entails the use of real products, e.g., wearing real clothes, which raises the issue of the fit between a "fake" virtual image and a real product. Whether consumers feel a positive attitude toward this kind of fit is the basis of whether DHA endorsement can be successful. Accordingly, based on cue consistency theory, we infer that consumers generate positive attitudes toward DHA endorsement when there is a fit between a DHA and a real product. We therefore developed a DHA endorsement model to explain the antecedents and consequences of avatar-product fit, decomposing fit type into authenticity fit and association fit. Two tests were used to verify this model. Their results show that authenticity fit and association fit have significant positive impacts on consumer attitudes toward DHA endorsement. Furthermore, the influences of these two paths vary by product. For hedonic products, authenticity fit has a greater impact on attitudes; for utilitarian products, association fit is a more influential factor.
This study of anthropomorphic response to artificial intelligence begins with an extensive review of the literature and an identification of conceptual distinctions between anthropomorphism and anthropomorphic response. The authors develop an instrument for measuring how users form anthropomorphic response to interactions with AI chatbots. Amazon MTurk is used to recruit 120 users for a pilot study and 303 users for the main study. Participants respond to six scenarios depicting interactions with banking service chatbots of varying appearance and intelligence. Results show that anthropomorphic response depend on perceptions of agent appearance, cognitive intelligence, and emotional intelligence. Users perceive more humanness in highly intelligent but disembodied agents rather than in highly intelligent agents that have poorly designed appearances. And users who have strong tendencies to anthropomorphize non-sentient entities are less likely to form anthropomorphic response when interacting with agents with high cognitive intelligence. The study enhances understandings about human/AI interactions. It provides directions for future research regarding anthropomorphic response and provide directions for future research on designing and using artificial agents.
Owing to the development of anthropomorphic intelligent agent (IA) designs, users consider IAs as more than just inanimate tools. Previous studies have reported that anthropomorphic features can promote users' social feedback and aid in establishing intimate human–agent relationships. The present study examined the main and interaction effects of anthropomorphism level (a human-like IA vs. robot-like IA) and social role (servant vs. mentor) on emotional attachment, information disclosure tendency, and satisfaction in a smart home. The study participants were randomly assigned into four groups with balanced gender. The results indicate that high anthropomorphism and mentor role can positively predict users' emotional attachment. Additionally, users tend to disclose more personal information to the human-servant and robot-mentor IAs than the human-mentor and robot-servant IAs. Interestingly, social presence was determined to be a positive and significant mediator between anthropomorphic design and emotional attachment. The study findings highlight the importance of social role in anthropomorphic IA design and explain the mechanism of establishing effective human–agent relationships. Moreover, both theoretical and practical implications of these findings are analyzed.
Many retailers invest in artificial intelligence (AI) to improve operational efficiency or enhance customer experience. However, AI often disrupts employees' ways of working causing them to resist change, thus threatening the successful embedding and sustained usage of the technology. Using a longitudinal, multi-site ethnographic approach combining 74 stakeholder interviews and 14 on-site retail observations over a 5-year period, this article examines how employees' practices change when retailers invest in AI. Practice co-evolution is identified as the process that undergirds successful AI integration and enables retail employees' sustained usage of AI. Unlike product or practice diffusion, which may be organic or fortuitous, practice co-evolution is an orchestrated, collaborative process in which a practice is co-envisioned, co-adapted, and co-(re)aligned. To be sustained, practice co-evolution must be recursive and enabled via intentional knowledge transfers. This empirically-derived recursive phasic model provides a roadmap for successful retail AI embedding, and fruitful future research avenues.
Supplementary information:
The online version contains supplementary material available at 10.1007/s11747-022-00896-1.
With the contactless payment feature, QR code mobile payment systems (MPS) has been increasingly popular among consumers, particularly during the COVID-19 pandemic. These systems enable consumers to shop from their mobile devices and complete payment transactions quickly. This research aims to explain the user acceptance of QR code MPS and identify the causal relationships between the factors affecting the acceptance. An extended Technology Acceptance Model (TAM) was proposed to achieve this. The research data was obtained from 485 QR code MPS users in Turkey using an online survey.
The remarkable findings of the research showed that the most crucial determinant of the intention to use (IU) is perceived trust (PT), followed by perceived compatibility (PC) and perceived usefulness (PU). Although some of the variables included in the current study were also included in some past studies, no study in the area of QR code mobile payment adoption includes all our variables. In addition, the proposed model explains 65 % of the variance in intention to use QR code MPS (a higher value than the past studies). Therefore, it is expected that the results of this study will be useful in making inferences for countries with similar characteristics.
AI in service can be for routine mechanical tasks, analytical thinking tasks, or empathetic feeling tasks. We provide a conceptual framework for the customer, firm, and interactional use of AI for empathetic tasks at the micro-, meso-, and macro-levels. Emotions resulting from AI service interactions can include basic emotions (e.g., joy, sadness, and fear), self-conscious emotions (e.g., pride, guilt, embarrassment), and moral emotions (e.g., contempt, righteous anger, social disgust). These emotions are mostly likely to occur during frontline interactions in which both firms and customers use AI, a phenomenon called “AI as customer.” The analysis level of AI service and emotion can be at the macro-level in which AI is transforming the service economy into a feeling economy, at the meso-level in which firms can use “thoughtful AI” to make the employees’ and customers’ lives a little bit better by brightening their days, and at the micro-level in which customers can experience basic, self-conscious, and moral emotions from interactions with service AI.
In this study, we delve into the perceived quality of recommendations provided by AI-based virtual service assistants (VSAs). Specifically, the role of the social presence of VSAs in influencing recommendation perceptions is investigated. We also explore how the social presence of a VSA is formed and how perceived anthropomorphism plays a vital role in shaping social presence and eventually instilling trust in VSAs among consumers. These relationships are examined in the context of online government services. The results indicate that consumer interaction with VSAs - manifesting via perceived anthropomorphism, social presence, dialog length, and attitudes - improves recommendation quality perceptions, which further instills trust in VSA-based recommendations. Perceived anthropomorphism was found to strongly influence the formation of social presence, whereas trust and recommendation quality - the outcomes of social presence - were found to be partially conditional on the dialog length and the degree of positive attitudes toward VSAs. The findings additionally suggest that a VSA can be considered a social actor that possesses the capability to bring a “human touch” to online services, therefore improving the overall online service experience.
As shopper-facing retail technology (SFRT) increasingly replaces human interactions in retail environments, many businesses are considering how to make their retail technology more human-like. This paper identifies two methods of anthropomorphizing technology—visual and cognitive—and seeks to determine whether using these two types of anthropomorphism with a product/service is a better approach to interacting with consumers or whether a combination of visual and cognitive anthropomorphic features is less effective than one. This paper proposes that including one form of anthropomorphism in an SFRT may increase purchase intentions, while the addition of a second form of anthropomorphism will not lend an additional advantage. Specifically, the theory of social response is used to examine the process through which consumers view anthropomorphized SFRT. Three studies assess the proposed model in a mobile shopping application context and include the use of a functional app and 360° video experiments. Theoretical and practical implications are discussed.
Along with the popularity of service robots in various service settings, service robots are often gendered as either female or male. This study examines the role of service robots’ gender and level of anthropomorphism of service robots on pleasure and customer satisfaction at service encounters. A 2 gender of service robots (female/male) X 2 level of anthropomorphism (low/high) between-subject factorial design is employed to test hypotheses using a scenario-based experimental survey. Results of the proposed moderated mediation model suggests that female service robots generated more pleasure and higher satisfaction compared to that of male service robots, and its influence is amplified when the level of anthropomorphism is high rather than low. Findings highlight the benefit of female service robots in a hotel setting which is only effective when the service robot is humanized, which provides useful guidelines for hoteliers when applying service robots in their service settings.
Along with the development of artificial intelligence (AI), more IT applications based on AI are being created. A personal intelligent assistant is an AI application that provides information, education, consulting, or entertainment to users. Due to their high levels of cognitive and emotional capabilities, we assume that users can form humanlike relationships with intelligent assistants, therefore, we develop a research model based on the theory of love. Data were collected from users of intelligent assistants through a survey. The results indicate that users can develop intimacy and passion for an AI application similar to that experienced with human beings. These feelings are related to users’ commitment, promoting the usage of an intelligent assistant, influenced by AI factors (performance efficacy and emotional capability), and moderated by human trust disposition.
Digital assistants based on artificial intelligence (AI) have been increasingly used in contexts beyond home-oriented services to support individuals in carrying out work-related tasks. Given the lack of empirical evidence on this fast-developing area, this paper aims (1) to explore the factors which can lead to individuals' satisfaction with the use of technology, and (2) to examine the impact of satisfaction on productivity and job engagement. The model was tested using 536 responses from individuals who used digital assistants for work purposes. Results showed that performance expectancy, perceived enjoyment, intelligence, social presence and trust were positively related to satisfaction with digital assistants. Satisfaction with the digital assistants was found to correlate with productivity and engagement. The findings contribute to the literature focusing on the use of AI-based technology supporting and complementing work tasks. They also offer practical recommendations as to how digital assistants could be used in the workplace.
This paper draws on practice-informed, ethnographic research to develop an understanding of the novel social consequences and opportunities afforded from consumers' interactions with AI digital humans as part of the in-store shopping experience. We reveal and interrogate consumers’ experiences with AI digital humans in an exploratory study undertaken during the launch phase of an in-store kiosk digital store greeter in a flagship store of a large national technology and appliance chain. Our findings contribute to understanding the social significance of AI in retail on customer experience (CX) and the managerial implications of consumers interactions with AI digital humans are described and discussed.
This study aims to understand the extent to which a time of emergency, (e.g. the COVID-19 pandemic), impacts consumer behaviour in terms of risk and expectations. The methodology involves the systematic content analysis of 15,000 tweets collected from three countries (UK, Italy and Spain) in April 2020. The results suggest that the top-of-mind expectation by consumers deals with escaping from home and enjoying freedom, either by having a good meal (UK), drinking alcoholic beverages (Spain), or travelling (Italy). They also suggest that the high levels of risk individuals were exposed to during the pandemic will not influence behavior in the long-term post-lockdown. Instead, they suggest consumers are willing to restore their consumption levels especially of activities that contribute to the sense of escapism. Finally, results provide evidence of the cultural differences emerging from consumers from different countries during the pandemic. Implications for international marketers and retailers are provided.
We develop a conceptual framework for collaborative artificial intelligence (AI) in marketing, providing systematic guidance for how human marketers and consumers can team up with AI, which has profound implications for retailing, which is the interface between marketers and consumers. Drawing from the multiple intelligences view that AI advances from mechanical, to thinking, to feeling intelligence (based on how difficult for AI to mimic human intelligences), the framework posits that collaboration between AI and HI (human marketers and consumers) can be achieved by 1) recognizing the respective strengths of AI and HI, 2) having lower-level AI augmenting higher-level HI, and 3) moving HI to a higher intelligence level when AI automates the lower level. Implications for marketers, consumers, and researchers are derived. Marketers should optimize the mix and timing of AI-HI marketing team, consumers should understand the complementarity between AI and HI strengths for informed consumption decisions, and researchers can investigate innovative approaches to and boundary conditions of collaborative intelligence.
Conversational Artificial Intelligence (AI) backed Alexa, Siri and Google Assistants are examples of Voice-based digital assistants (VBDA) that are ubiquitously occupying our living spaces. While they gather an enormous amount of personal information to provide bespoke user experience, they also evoke serious privacy concerns regarding the collection, use and storage of personal data of the consumers. The objective of this research is to examine the perception of the consumers towards the privacy concerns and in turn its influence on the adoption of VBDA. We extend the celebrated UTAUT2 model with perceived privacy concerns, perceived privacy risk and perceived trust. With the assistance of survey data collected from tech-savvy respondents, we show that trust in technology and the service provider plays an important role in the adoption of VBDA. In addition, we notice that consumers showcase a trade-off between privacy risks and benefits associated with VBDA while adopting the VBDA such technologies, reiterating their calculus behaviour. Contrary to the extant literature, our results indicate that consumers' perceived privacy risk does not influence adoption intention directly. It is mediated through perceived privacy concerns and consumers’ trust. Then, we propose theoretical and managerial implications to conclude the paper.
The ongoing COVID-19 pandemic is disrupting the fashion industry and forcing fashion businesses to accelerate their digital transformation. The increased need for more sustainable fashion business operations, when coupled with the prospect that business might never be as usual again, calls for innovative e-commerce led practices. Recently, stakeholders have been experimenting with the idea of introducing digital humans for a more active role in fashion through the developments in artificial intelligence, virtual, augmented and mixed reality. As there is a lack of all-important empirical evidence on the consumer's propensity to interact with digital humans, we aim to quantitatively analyse consumer attitudes towards the propensity to interact with digital humans to uncover insights to help fashion businesses seeking to diversify their operations. The results reveal interesting, and statistically significant insights which can be useful for fashion business stakeholders for designing, developing, testing, and marketing digital human-based solutions. Besides, our findings contribute current insights to the existing literature on how consumers interact with digital humans, where research tends to be scarce.
Artificial intelligence (AI) is revolutionising the way customers interact with brands. There is a lack of empirical research into AI-enabled customer experiences. Hence, this study aims to analyse how the integration of AI in shopping can lead to an improved AI-enabled customer experience. We propose a theoretical model drawing on the trust-commitment theory and service quality model. An online survey was distributed to customers who have used an AI- enabled service offered by a beauty brand. A total of 434 responses were analysed using partial least squares-structural equation modelling. The findings indicate the significant role of trust and perceived sacrifice as factors mediating the effects of perceived convenience, personalisation and AI-enabled service quality. The findings also reveal the significant effect of relationship commitment on AI-enabled customer experience. This study contributes to the existing literature by revealing the mediating effects of trust and perceived sacrifice and the direct effect of relationship commitment on AI-enabled customer experience. In addition, the study has practical implications for retailers deploying AI in services offered to their customers.
With the rapidly dramatic environmental change and intensive competition, tourism organisations are required to adopt advanced marketing strategies and techniques. Recently, digital content marketing (DCM) has become one of the most prominent marketing tools that has substantial benefits and influences in different settings and domains. To this end, it is crucial to understand the effect of DCM on consumer behavior within the tourism context. Therefore, this research empirically examines an extended t echnology acceptance model (TAM) model to investigate and compare the influence of DCM on travel and tourism consumer behavior in two distinct countries in the Middle East and North Africa (MENA) region. A quantitative approach was adopted by collecting surveys from a convenience sample of 285 and 122 participants in Egypt and Oman, respectively. The findings of PLS‐structural equation modelling revealed that the TAM major constructs were good elucidating the attitude and behavior toward using of DCM for tourism purposes. It is also found that perceived enjoyment and perceived convenience are antecedents of customers' attitudes, which in turn, influence their intention and behavior of using DCM to buy or select a certain tourism product/service. The current study contributes to knowledge of DCM literature in the tourism field in general and within the MENA region in particular. It also adds to studies on TAM and digital technologies by extending two critical constructs related to tourism consumer behavior. The practical implications will greatly support tourism marketers and authorities to develop their tourism strategies and marketing activities. Future research can be expanded to study different target groups comprehending region differences.
Firms are deploying chatbots to automate customer service. However, miscommunication is a frequent occurrence in human-chatbot interaction. This study investigates the relationship between miscommunication and adoption for customer service chatbots. Anthropomorphism is tested as an account for the relationship. Two experiments compare the perceived humanness and adoption scores for (a) an error-free chatbot, (b) a chatbot seeking clarification regarding a consumer input and (c) a chatbot which fails to discern context. The results suggest that unresolved errors are sufficient to reduce anthropomorphism and adoption intent. However, there is no perceptual difference between an error-free chatbot and one which seeks clarification. The ability to resolve miscommunication (clarification) appears as effective as avoiding it (error-free). Furthermore, the higher a consumer’s need for human interaction, the stronger the anthropomorphism - adoption relationship. Thus, anthropomorphic chatbots may satisfy the social desires of consumers high in need for human interaction.
E-commerce is becoming a major contributor to the worldwide economic system, owing to its adaptability and ease of use for both customers and service providers. Recommender systems are embedded in most modern e-commerce websites, as efficient tools for guiding users to view additional items provided by e-commerce portals. These items are matched with customers' interests depending on their current activities, or on preferences stated in their profiles. As service providers are more concerned with the long-term behavior of customers, and specifically customer loyalty (which bears directly on the long-term success of e-commerce websites), most recommender systems have been developed to consider that aspect. This study investigates the major factors in the loyalty formation of female online shoppers through an e-commerce recommender agent. A new model is introduced, developed, and analyzed for helping to improve e-commerce customer loyalty via the recommender systems. Based on the implications of the results, we can understand research constructs and highlight research outcomes to help in managing recommender systems more effectively.
This article develops a strategic framework for using artificial intelligence (AI) to engage customers for different service benefits. This framework lays out guidelines of how to use different AIs to engage customers based on considerations of nature of service task, service offering, service strategy, and service process. AI develops from mechanical, to thinking, and to feeling. As AI advances to a higher intelligence level, more human service employees and human intelligence (HI) at the intelligence levels lower than that level should be used less. Thus, at the current level of AI development, mechanical service should be performed mostly by mechanical AI, thinking service by both thinking AI and HI, and feeling service mostly by HI. Mechanical AI should be used for standardization when service is routine and transactional, for cost leadership, and mostly at the service delivery stage. Thinking AI should be used for personalization when service is data-rich and utilitarian, for quality leadership, and mostly at the service creation stage. Feeling AI should be used for relationalization when service is relational and high touch, for relationship leadership, and mostly at the service interaction stage. We illustrate various AI applications for the three major AI benefits, providing managerial guidelines for service providers to leverage the advantages of AI as well as future research implications for service researchers to investigate AI in service from modeling, consumer, and policy perspectives.
Unprecedented competition and emergent technologies have posed a challenge to many traditional retailers in recent years. Yet within this competitive environment, emerging innovative business models have thrived and successfully disrupted the industry. We analyze the nature of disruptive business-model innovations and the ways they disrupt the fashion retail industry. To that end, we examine three disruptors in the industry: born-digital brands, AI-enabled demand forecasting and product design, and collaborative consumption. After introducing the concept of disruptive business-model innovation, we discuss the three disruptors’ effects on the fashion industry. We find that all of these models keenly answer fundamental needs unmet by current business models, such as offering quality products at a competitive price, curated services, and sustainable consumption. At the same time, all three disruptors suggest effective operation models for handling demand uncertainty, inventory management, and timely responses to the market, all of which are inherent issues for current push supply chains and forecast-based, inventory-driven systems. Based on this analysis, we discuss important implications for both academics and industry practitioners.
This paper investigates consumer's attitudes towards fashion product assortment in UK mid-market department stores. It aims to determine whether changes to assortment will increase purchase intention and help regain competitive advantage through aligning customer perceptions of product quality and fit with brand image. Our findings challenge the traditional role of the department store in curating fashion assortment. We find that increases in perceived quality, perceptions of brand portfolio and brand fit will increase the purchase intention of UK mid-market department store consumers, whilst reduced assortment sizes would lead to a decrease in purchase intent.
The rapid development of information communication technology has led towards the emergence of the “connected world” characterised by the pervasive embeddedness of smart technologies. Smart technologies have a transformative impact on different domains of life. The application of smart technologies redefines the way people live, interact and conduct business. To date, the attention of the scholarly community has been paid primarily to smart cities, smart manufacturing and smart homes. However, despite numerous studies discussing the benefits of advanced technologies in the workplace, there is a lack of research on smart offices and how they affect productivity and employee well-being. This opinion paper argues that office spaces constitute a distinctive type of space, and research on smart homes or manufacturing does not suffice to capture its essence. Therefore, the aim of this paper is to propose a research agenda that can advance the current literature on smart and information communication technologies in relation to workplace spaces and the potential implications these could have on productivity.
As mobile wallets are in constant demand and growing over the past few years, there is a need to identify views of different stakeholders involved in the process. Several studies have been done to investigate consumers' perspective intensively. On the other hand, review of perception and adoption of wallet services by other participants, in particular merchants, is often neglected. The present study aims to fill this gap. This study used an empirical model to measure merchant's intention to use a mobile wallet technology. The study includes the variables, perceived compatibility, perceived usefulness, awareness, perceived cost, perceived customer value addition and perceived trust, and aims to determine their influence on intention to use. Our study also tested the mediating effect of perceived trust on the influence of perceived usefulness to predict merchant's intention. The study includes results of the survey of 315 Indian merchants by an online survey method. We find the highest effect of perceived customer value addition on merchant's intention, followed by perceived usefulness of technology. The proposed mediation effect of perceived trust was small but significant on perceived usefulness. The results of the study can help mobile payment companies to understand factors that are relevant to increase adoption of technology in the context of merchants.
The capability of AI is currently expanding beyond mechanical and repetitive to analytical and thinking. A “Feeling Economy” is emerging, in which AI performs many of the analytical and thinking tasks, and human workers gravitate more toward interpersonal and empathetic tasks. Although these people-focused tasks have always been important to jobs, they are now becoming more important to an unprecedented degree. To manage more effectively in the Feeling Economy, managers must adapt the nature of jobs to compensate for the fact that many of the analytical and thinking tasks are increasingly being performed by AI, and, thus, human workers must place increased emphasis on the empathetic and emotional dimensions of their work.