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The dark side of artificial intelligence in service: The “watching-eye” effect and privacy concerns
... While the virtual co-host only observes speaking times, we chose to not have it tell meeting participants what information it collected. We left the phrase "observe conversational dynamics" vague because we wanted to understand whether participants would feel a "watching-eye" effect [44] when being observed by the co-host if they did not know what info it was collecting (e.g., tone, sentiment, interruption patterns, etc.), and whether adding the Ask phase would mitigate or exacerbate those concerns. ...
... U6 said "my focus wasn't entirely on it. " Our results connect to prior work that the experience of a "watching-eye" effect is context-dependent [44], and show that AI agents may not have this negative effect in virtual meetings even if they are visually presented as attendees with their own user tiles. ...
... (We do not focus on Observe given the extensive and high-quality prior work on AI observation and inference-e.g. [44,54,64,71].) We then highlight implications for design to mitigate inequities in meetings. ...
Video conferencing meetings are more effective when they are inclusive, but inclusion often hinges on meeting leaders' and/or co-facilitators' practices. AI systems can be designed to improve meeting inclusion at scale by moderating negative meeting behaviors and supporting meeting leaders. We explored this design space by conducting 9 user-centered ideation sessions, instantiating design insights in a prototype ``virtual co-host'' system, and testing the system in a formative exploratory lab study (n=68 across 12 groups, 18 interviews). We found that ideation session participants wanted AI agents to ask questions before intervening, which we formalized as the ``Observe, Ask, Intervene'' (OAI) framework. Participants who used our prototype preferred OAI over fully autonomous intervention, but rationalized away the virtual co-host's critical feedback. From these findings, we derive guidelines for designing AI agents to influence behavior and mediate group work. We also contribute methodological and design guidelines specific to mitigating inequitable meeting participation.
... Despite the urgency, strategizing to alleviate travelers' privacy concerns in tourism personalized recommendations is still riddled with challenges. Unlike other sectors, where privacy may be more straightforward to manage, tourism services require handling a considerable amount of sensitive personal information from travelers (e.g., passport), thereby intensifying their concerns over privacy (Hu & Min, 2023;Ioannou et al., 2021). Previous research in tourism has explored various factors affecting privacy concerns, including privacy and data protection policies (C. ...
... Interestingly, these effects do not hold for servant communication styles. These findings contribute to the tourism literature in several ways: Firstly, our study identifies hedge words in personalized recommendation labels as a new factor that reduces travelers' privacy concerns, beyond previously known factors such as service providers' appearances (Hu & Min, 2023) and types (D'Acunto et al., 2021). Secondly, while previous research highlights the impact of personalized tourism recommendation information on travelers' responses, including issues of poor quality, unethical recommendations, and information quality (J. ...
... Different from the other sectors, tourism deals with substantial sensitive/private information (e.g., ID). This sensitivity is magnified by the typically brief nature of traveler interactions, escalating privacy concerns amidst the sector's high susceptibility to privacy breaches (Hu & Min, 2023;Ioannou et al., 2021;Song et al., 2023). Against this backdrop, existing studies have explored a range of factors influencing travelers' privacy concerns, including but not limited to privacy and data protection policies, the role of service providers, interactions with AI, and travelers' characteristics (please see Supplemental Table A2). ...
Personalized recommendations based on personal information enhance travelers’ experiences but raise privacy concerns. The inherent uncertainty in tourism, where travelers cannot fully visualize their destination choices and frequently alter their plans, leads to highly variable and complex travel decisions. This complexity poses a challenge for intelligent systems attempting to predict traveler preferences accurately. Previous research has seldom examined the role of uncertain expressions in predicting traveler preferences within personalized recommendation systems. Through a field study on Facebook and three experimental studies, we find that travelers exhibit fewer privacy concerns and more positive attitudes toward personalized advertising in the presence (vs. absence) of hedge words—a form of uncertain expression—in personalized recommendation labels. Meanwhile, this effect is mediated by travelers’ autonomy over their personal information and moderated by the communication styles used in recommendation labels. These findings contribute to the tourism literature on privacy concerns and personalized recommendations.
... Throughout consumers' journeys, every interaction decision-such as searching for hotels or flights or making bookings-involves some form of AI assistance (Melzner et al., 2023). While the information they provide is valuable for companies (Puntoni et al., 2021) it also raises privacy concerns (e.g., Cai et al., 2024;Hu and Min, 2023). However, the factors influencing these privacy concerns towards AI remain unclear. ...
... Furthermore, the literature provides mixed evidence on privacy concerns. While some studies suggest that privacy concerns are shaped by human-like characteristics (Hu and Min, 2023;Xie and Lei, 2022). Others suggest consumers are willing to disclose sensitive data with AI (vs. ...
... Recent research suggests that privacy concerns towards AI have mainly focused on factors such as service robots (Song et al., 2023) or AI characteristics, such as anthropomorphism (Cai et al., 2022;Hu and Min, 2023). In this paper, instead of examining factors beyond those associated with AI features, such as human-like characteristics, we shift our focus to how AI power shapes privacy concerns. ...
... In addition, another key challenge of rapid integration of AI-driven banking is the growing concern over privacy and data security (Mogaji et al., 2021). While previous studies, such as Kathuria and Kathuria (2022) on smart speakers and Hu and Min (2023) on hospitality, have examined privacy concerns in AI-enabled services, these contexts differ significantly from financial services. In banking, consumers not only worry about personal data privacy but also about the security of sensitive financial transactions. ...
... Second, this study contributes to the literature on privacy concerns in AI-enabled services. While previous studies have highlighted privacy concerns in AI interactions (Hu & Min, 2023), the digital banking context introduces a heightened sense of privacy risk due to the nature of financial transactions. Our findings show that privacy concerns significantly moderate the impact of certain immersive drivers (e.g. ...
This study examines the role of smart banking chatbots (SBCs) in shaping consumer trust and engagement in digital banking, with a focus on exploring the moderating impact of privacy concerns. Grounded in the stimulus-organism-response (SOR) model, the research investigates how immersive experience impacts trustworthiness, which in turn influences consumer engagement, measured by intention to explore and cognitive absorption. Data collected from 287 banking customers using structured survey questionnaire. The findings of the study reveals that conversational agility and ubiquitous connectivity significantly enhance trustworthiness, whereas responsiveness, personalization, perceived control, and social presence do not. Furthermore, the study reveals that privacy concerns negatively moderate the impact of responsiveness, conversational agility, and social presence on trustworthiness. The study uniquely contributes to the literature on AI-human interactions in financial services. This study also offers strategic insights for banks on optimizing SBCs to enhance customer trust and engagement to provide better consumer experience while addressing privacy concerns.
... Al trabajar con grandes conjuntos de datos, es importante asegurar que se estén tomando las medidas necesarias para proteger la privacidad de las personas cuyos datos se están utilizando. También es importante garantizar la seguridad de los datos para evitar la filtración o el robo (Hu & Min, 2023). ...
... Finalmente, otro desafío es asegurar la ética en el uso de la inteligencia artificial, es importante asegurar que esta no esté discriminando a ningún grupo de personas y que se estén tomando las medidas necesarias para evitar cualquier tipo de daño o mal uso, como fue el caso de Google Photos (BBC News Mundo, 2015), que confundió a una pareja de afroamericanos con "gorilas" por cuanto el entrenamiento que esta recibió fue con imágenes de anglosajones. Esto indica la importancia de considerar y discutir estos desafíos a medida que se desarrollan e implementan este tipo de herramientas y plataformas en la redacción de documentos e investigaciones (Hu & Min, 2023). ...
This research seeks to present the production of academic and scientific papers using artificial intelligence for Chat GPT language modeling in different areas of higher education. To obtain the results, the quantitative approach was followed, the type of research is descriptive with a non-experimental design at the field level. The size of the population were the teachers of various careers at the Machala Metropolitan University. For the sample, a probabilistic sampling with simple random selection was used, calculating the sample size with a margin of error of 5% and a confidence level of 95%. The main results in the application of the instrument are, among others, the fact that none of the teachers could detect that the document they reviewed was created by artificial intelligence and they gave it an average score of 8.88/10 and that the platform anti plagiarism Compilatio also generated an average of 1% similarity, demonstrating that academic and research papers are currently indistinguishable from human-made work, neither in human review nor on anti-plagiarism platforms. plagiarism.
... Although a breach of privacy concerns both genders, however, women harbor higher risk perception and severity associated with it (Garbarino and Strahilevitz, 2004;Hoy and Milne, 2010). Hu and Min (2023) espoused that gender has a significant impact on the dark side effects of AI deployment and women have greater privacy concerns leading to uneasiness than men; and consumer gender has a moderating effect on the mediation of privacy concerns on the relationship between extent of anthropomorphizing (AI devices) and uneasiness. There is scarcity of research on impact of demographic characteristics on behavioral outcomes of consumers while interacting with AA. ...
Purpose
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.
Design/methodology/approach
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 a 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.
Findings
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.
Originality/value
The present research makes essential contributions to anthropomorphism literature. First, this study explored the 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 the impact of demographic variables, showing that gender played a significant role in moderating relationships in the proposed model.
... Parallelly, the deployment of AI models in privacy-sensitive applications has raised concerns about protecting sensitive information within AI systems [22,27]. In the context of LLMs, even those trained on public datasets can retain and expose fragments of their training data, leading to specific privacy-oriented attacks [7,26,49]. ...
The widespread adoption of Retrieval-Augmented Generation (RAG) systems in real-world applications has heightened concerns about the confidentiality and integrity of their proprietary knowledge bases. These knowledge bases, which play a critical role in enhancing the generative capabilities of Large Language Models (LLMs), are increasingly vulnerable to breaches that could compromise sensitive information. To address these challenges, this paper proposes an advanced encryption methodology designed to protect RAG systems from unauthorized access and data leakage. Our approach encrypts both textual content and its corresponding embeddings prior to storage, ensuring that all data remains securely encrypted. This mechanism restricts access to authorized entities with the appropriate decryption keys, thereby significantly reducing the risk of unintended data exposure. Furthermore, we demonstrate that our encryption strategy preserves the performance and functionality of RAG pipelines, ensuring compatibility across diverse domains and applications. To validate the robustness of our method, we provide comprehensive security proofs that highlight its resilience against potential threats and vulnerabilities. These proofs also reveal limitations in existing approaches, which often lack robustness, adaptability, or reliance on open-source models. Our findings suggest that integrating advanced encryption techniques into the design and deployment of RAG systems can effectively enhance privacy safeguards. This research contributes to the ongoing discourse on improving security measures for AI-driven services and advocates for stricter data protection standards within RAG architectures.
... However, advancements in VA technology have seen a surge in voice-enabled AI technology in service contexts, such as smart customer telephone service, shopping assistant, and hotel intelligent butler. Researchers in probing the advantages and shortcomings of VA services in service contexts tend to focus on continued usage intention [24], user satisfaction [25], brand loyalty [26], and privacy concerns [27]. Though the WOM and eWOM effects triggered by VAs have also drawn scholarly attention [10,19], investigations mostly emphasize solely the positive evaluations, acceptance, and recommendations of the technology itself. ...
Internet platforms and self-media have become vital online communities for promoting positive reputations for hotels. Previous studies have primarily focused on enhancing positive electronic word-of-mouth (eWOM) through improvements in hotel infrastructure and staff services. As hotels deepen their digital transformation, the application of various artificial intelligence technologies in hotel service encounters significantly impacts the service experience. This study explores the effects of voice assistant (VA) attributes on the online reputation of hotels. Specifically, it examines how the attributes of VAs (anytime connectivity, information association, and interactivity) influence positive customer evaluations in hotels. Utilizing a questionnaire survey method, we collected 529 valid questionnaires offline and employed structural equation modeling along with the PROCESS plugin in SPSS to conduct path analysis, as well as mediation and moderation effect analyses. The results indicate that perceived value and the existence of human–AI rapport mediate the impact of VA attributes on positive eWOM, although the direct effect of some attributes (information association) was not supported. Furthermore, anytime connectivity enhances the influence on human–AI rapport through social presence, while privacy concerns negatively affect the relationship between perceived value and intentions to engage in eWOM. These insights are critical for hotels seeking to maximize the benefits of digital transformation.
... Drawing on assumptions of "buffering" and "coping" in JD-R model, this study investigates and discusses the interactive dynamics between various combinations of polychronicity, multitasking and AIempowered task processing as job demands and resources, thereby unfolding their specific impact on employees' self-leadership. Moreover, it is noteworthy that the implementation of AI, while granting employees an enhanced level of autonomy in their tasks, may also engender detrimental consequences such as diminished perceived control and self-efficacy (Hu and Min, 2023;Kellogg et al., 2020;Petriglieri et al., 2019;Tang et al., 2023). Its impact on self-leadership for employee with different work states exhibits significant heterogeneity. ...
As AI becomes increasingly integrated into the workplace, understanding how prevailing multitasking practices interact with AI support to foster employee self-leadership is essential for enhancing organizational effectiveness. This study elucidates how the fit between multitasking and polychronicity among employees in organizations can synergistically influence their self-leadership within the context of AI empowerment. This study conducts two time-lagged survey studies using polynomial regression analysis, block variable analysis, and response surface methodology based on the “Fit Between Individuals, Tasks and Technology” (FITT) framework and the JD-R theoretical model. Study 1 examined the polychronicity-multitasking fit based on data collected from 116 employees at two time points in an AI company in China. Study 2 tested the mediating and moderating effect based on data of 188 employees from two other AI companies in China at three time points. The results show that congruence between polychronicity and multitasking predicts greater employee self-leadership compared to incongruence, and the higher the degree of congruence, the stronger the self-leadership. For incongruence, the “high-low” state promotes self-leadership better than the “low-high” state. We also reveal the mediating role of thriving at work and the moderating role of AI-empowered task processing between polychronicity-multitasking fit and self-leadership. For well-matched employees, AI serves as a facilitator of task processing, thereby enhancing employee self-leadership; whereas for mismatched ones, AI acts as an additional task burden or as a catalyst that exacerbates the existing imbalance, which impedes the motivation for self-leadership. These findings advance the understanding of self-leadership in multitasking contexts and provide valuable insights for organizations implementing AI tools. This study underscores the critical importance of aligning employees' work preferences with task demands to fully leverage the potential of AI empowerment.
... No obstante, la IA también plantea preocupaciones, como la privacidad (Hu & Min, 2023), la seguridad de la información (S. Lee et al., 2020), el sesgo y la fiabilidad de los sistemas de toma de decisiones (Qiu et al., 2022;Sun et al., 2022), aspectos discutidos desde distintas perspectivas críticas. Entre los desarrollos recientes de la IA destacan los chatbots, también conocidos como robots conversacionales, agentes o asistentes personalizados, que interactúan y "conversan mediante texto" con usuarios humanos. ...
La creciente integración educativa de la inteligencia artificial está reconfigurando la educación superior, especialmente a través del uso de chatbots y modelos de lenguaje generativo. Este artículo realiza una revisión de la literatura, aplicando las directrices PRISMA a 155 artículos revisados por pares, para examinar las ventajas, limitaciones y aplicaciones pedagógicas de la IA en comparación con la enseñanza humana. Se identificaron tres principales escenarios de impacto en las prácticas educativas: a) Pérdida de ciertos aspectos tradicionales de la enseñanza, como la transmisión exclusiva de información y tareas de reporte, b) Transformación de roles, incluyendo el control sobre contenidos educativos y el contrato didáctico, c) Emergencia de nuevos elementos, como la personalización del aprendizaje y enfoques innovadores en la evaluación. A pesar de su potencial para automatizar procesos y ahorrar tiempo, los chatbots no replican cualidades humanas esenciales como la empatía y la adaptabilidad. Por ello, su integración óptima requiere análisis pedagógicos profundos que equilibren innovación y efectividad educativa. Este trabajo es valioso para investigadores, docentes y diseñadores educativos interesados en entender cómo aprovechar la IA sin comprometer la calidad de la enseñanza. Representa un paso crucial hacia estrategias de incorporación de IA basadas en principios pedagógicos sólidos.
... There's a lack of transparency regarding the usage of user data, many users have no idea how their data is being leveraged behind the scenes. Surveillance and monitoring "the watching eye" effect (Hu and Min, 2023) raises serious ethical concerns especially when AI is conscripted and encrypted into monitoring users online behaviour and facial pattern recognition. It is therefore critical for music streaming platforms to get user consent when implementing these technologies in efforts to reduce risk (Samba, 2024). ...
The rapid adoption and evolving nature of artificial intelligence (AI) is playing a significant role in shaping the music streaming industry. AI has become a key player in transforming the digital music streaming industry, particularly in enhancing user experiences and driving subscription growth. Through AI automation, platforms personalize music recommendations, optimize subscription offerings, and improve customer support services. This article reviews the role of AI in driving consumer subscription behaviors on digital music streaming platforms (DMSP), with a focus on recommendation algorithms, dynamic pricing models, marketing automation, and the future of AI in the music industry. Potential challenges related to privacy, ethics, and algorithmic biases are also discussed, showcasing how AI is revolutionizing the music streaming industry.
... Growing consumer privacy concerns [22] can hinder the general deployment of these technologies. Chatbots and AI-pushed systems in hotels often save past purchase histories and travel information, so raising questions about data privacy and security [29,46]. ...
Artificial Intelligence (AI) innovation speaks to a sizeable possibility to transform superior encounters and operational efficiencies within the travel, tourism industry. This article offers a literature review that investigates the vital openings and demanding situations associated with AI utilisation in tourism. The opportunities contain enhancement of optimisation of business decisions, personalised marketing message, automation of customer service through chatbots, and enhancement of both indoor and outdoor service experiences. However, those benefits come with challenges, such as technical constraints, financial barriers, legal considerations, and socio-ethical issues. Findings discover that AI can significantly improve efficiency and customer satisfaction. Addressing the challenges is crucial for the successful adoption of AI. Future research is suggested to enhance AI reliability, reduce costs, ensure data privacy, and balance automation with human interaction. These insights aim to direct tourism organisations and technology companies to leverage AI to improve traveller experiences.
... Söderlund et al., (2021) have found that morality significantly influences perceived humanness in virtual agents service delivery and mediates the relationship between morality and customer satisfaction. Hu et al. (2023), through their experiment, found that the "watching eye" effect, the perception of a consumer that he is watched, impacts both genders but is more pronounced in the case of women. ...
... While AI improves service personalization and efficiency, concerns about issues such as privacy breaches and reduced human interaction remain critical [32,33]. Similarly, Hu and Min [34] examined the "watchful eye" effect, where the presence of AI creates discomfort among users due to feelings of constant surveillance, which may negatively impact their trust in technology and overall satisfaction with services. Despite the many benefits AI offers, Zhang et al. [35] and Verma and Yadav [36] stressed the importance of human oversight in AI systems to better adapt technology to user needs and maintain a balance between efficiency and ethics. ...
This study examines the level of implementation of artificial intelligence (AI) in the
personalization of hotel services and its impact on guest satisfaction through an analysis of tourists’ attitudes and behaviors The focus of the research is on how personalized recommendations for food and beverages, activities, and room services, delivered by trustworthy AI systems, digital experience,
and the perception of privacy and data security, influence overall guest satisfaction. The research was conducted in Serbia and Hungary, using structural models to assess and analyze direct and indirect effects. The results show that AI personalization significantly contributes to guest satisfaction, with
mediating variables such as trust in AI systems and technological experience playing a key role. A comparative analysis highlights differences between Hungary, a member of the European Union, and Serbia, a country in transition, shedding light on specific regulatory frameworks and cultural preferences in these countries.
... The interplay between GAI and fake online reviews raises significant concerns (14,18,25) , with consequent negative implications for GAI-generated syWOM. Sourced fake online reviews generated to deceive or manipulate consumers 74,75 can undermine the integrity and reliability of the information retrieved from GAI. This information poses a threat to decision-making, trust in online platforms, and overall market fairness. ...
... Researchers then shifted their focus to current topics such as perception, personality, big data analytics, sustainability, privacy, industry 4.0, and explainable ai (e.g. alsubhi et al., 2023;hoffman et al., 2022;hu & Min, 2023;Panța & Popescu, 2023;Rosário & Dias, 2022;Yigitcanlar et al., 2023). this shows that the focus of researchers in the fields of management, business, and applied psychology has gradually shifted to issues such as human cognition and behavioral ability in the application of ai in industry 4.0, as well as sustainable development, which is also the trend of ai research and application. ...
Although more and more researchers have paid attention to artificial intelligence research in organizations across different subdivided fields in recent years, there is still a lack of integrative and comprehensive research on AI in organizations. Building upon previous quantitative and qualitative studies in the artificial intelligence literature, this study presents a bibliometric analysis of articles on artificial intelligence in the fields of management, business, and applied psychology up to June 2nd, 2023. The research explores the landscape of artificial intelligence articles, highlighting key intellectual contributions and research constituents such as journals, authors, countries, institutions, and topics. Additionally, the study investigates the intellectual structure and overlay visualization of keywords to identify popular topics and trends in recent artificial intelligence research. The findings offer readers a systematic understanding of artificial intelligence development and provide new insights that expand upon existing knowledge in artificial intelligence within management, business, and applied psychology.
... Perceived Task Excellence refers to the belief that AI tools can enhance task performance, a concept supported by studies emphasizing the proven efficacy of AI in improving educational outcomes (Boubker, 2024;Wu & Yu, 2024). Finally, Perceived Privacy Concerns have been recognized as a barrier to technology adoption, particularly in AI applications where data privacy is a significant concern (Hu & Min, 2023;Kronemann et al., 2023). This approach allows us to address the determinants of AI tool adoption that have not been studied before, especially in the context of Indian Gen Z learners, thereby strengthening our research's theoretical contributions and novelty. ...
Artificial Intelligence, at the forefront of innovation and intelligence, is redefining the pace of life and work, notably within education. This study investigates the determinants influencing Gen Z's behavioral intentions (BI) to integrate AI-powered tools within Indian Higher Educational Institutions (HEIs) by extending the UTAUT2 model with four additional constructs: trustworthiness, personal innovativeness, perceived task excellence, and perceived privacy concern. The data gathered from 430 respondents within Indian HEIs through an online survey following purposive sampling was meticulously analyzed using the structural equation modeling approach in AMOS. The findings validate the applicability of the UTAUT2 model for understanding AI tool integration in the Indian context, with an explanatory power of 34.2%. The study highlights the beneficial impact of hedonic motivation, perceived task excellence, facilitating conditions, and performance expectancy on Gen Z's intention to integrate AI tools. Additionally, the study suggests recommendations for future research and outlines implications based on these findings.
... Job loss concerns arise from AI's potential to automate tasks, particularly in repetitive industries, raising fears of economic instability [19]. Privacy concerns with AI devices, especially those with built-in cameras and humanoids, impact both genders, with a more pronounced effect on women [20]. Ethical questions include biases in AI decision-making and accountability for AI-generated outcomes, necessitating responsible development practices [21,22]. ...
Attitudes towards artificial intelligence (AI) are influenced by individual intentions to use it and concerns about its implications, which can vary across different age groups and genders, highlighting the need for more nuanced design and communication strategies. This study explores how gender and age influence attitudes towards AI by examining intentions and concerns from a cross-cultural perspective. Using a sample of 562 participants from the UK (281) and the Arab Gulf Cooperation Council (GCC) (281), the research investigates demographic and cultural differences in AI use intentions and concerns. The study finds that gender and age significantly influence AI acceptance in the UK, whereas these factors have a less pronounced impact in the Arab context. The results highlight that women generally express more ethical and privacy concerns about AI than men, and older adults show more apprehension towards AI acceptance than younger individuals. By showing that cultural nuances play a role in shaping these attitudes, we also show the need for tailored strategies to address demographic-specific concerns to reduce fear towards AI and, at the same time, avoid over-acceptance. The combined influence of age and gender can enhance the effectiveness of AI strategies, emphasizing the importance of considering personal and cultural factors in design and policy-making, e.g., in aiding trust calibration and informed adoption of AI.
... According to SOR, as important factors of organisms, positive emotions positively influence customers' satisfaction, trust and behaviors, while negative affect, referring to emotional reactions triggered by other adverse experiences such as anxiety, tension and restlessness, may result in discomfort and worry (O. H. Chi et al., 2022;Gursoy, Chi, et al., 2019a;Hu & Min, 2023;Yoo et al., 2022). According to the analysis, we propose the following hypothesis. ...
... Similarly, their perception will be positive. However, the utilisation of AI is below expectations among students, unlike what it is used for in other areas like instruction, assessment, and instructional delivery (Ismail et al., 2022), and most students' perceptions are negative (Elliott & Soifer, 2022;Hu & Min, 2023;Saura et al., 2022). The utilisation of AI in research cannot be achieved where students, academic staff, and stakeholders in higher institutions of learning are not concerned with the creation and integration of AI at various levels of instruction (Langran et al., 2020;Qin et al., 2020). ...
Keywords Abstract Artificial intelligence; awareness; multigroup analysis; perception; structural equation modelling; utilisation. The era of AI has brought tremendous impact in academic research, and this has provided the impetus for students to leverage on novel tools in carrying out a lot of quality research works. Previous studies have relied so much on AI for instruction, classroom management and assessment and utilisation of AI tools for research has scarcely been examined. This study covered the gap by examining students' utilisation of AI tools based on their level of awareness and perception and finding out the difference based on gender and programme type in such prediction. A total of 5554 university students were used for the study. Exploratory factor analysis was first carried for dimensionality and other validity checks (convergent and discriminant) using Average Variance Extracted (AVE) and Fornel-Larcker criterion and methods. Population t-tests and multi-group analyses were performed using SPSS and Smart PLS 3. The study found that students have high level of awareness and positive perception of AI tools in research. Similarly, the level of utilisation of AI tools in research is high. Male and postgraduate students have a higher level of awareness and positive perception of AI tools in research, with female students stronger than male students in terms. Perception and awareness directly impacted on utilisation but perception mediates positively and significantly in the nexus between awareness and utilisation. The study findings provide useful insights into using AI tools among university students and also identify the rationale to consider variables like gender and programme type when developing curriculum that will meet the current technology needs in our higher institutions.
... Service robots implementation create new tasks and roles for employees (Tuomi et al., 2020), resulting in risks (Huang et al., 2022). For example, Hu and Min (2023) suggest that people may perceive these devices as privacy invaders. Hotel employees appraise these robots as a threatening risk to replace human labor . ...
Purpose
Service robots are increasingly prevalent in the hospitality industry. While studies have explored the concept of service robot risk awareness (SRRA) – an employee’s perception of service robots posing a threat to human labor – the impact of SRRA on robot abuse and its emotional mechanism through which it affects employees remains unclear. This research leverages emotional appraisal theory to investigate the mediating role of fear of robots in the relationship between SRRA and robot abuse. Additionally, considering the influential role of leadership in shaping emotional appraisal, this study aims to examine the moderating impact of transformational leadership.
Design/methodology/approach
To test the proposed model, time-lagged survey data were collected from 283 employees working under 54 leaders in 18 hotels in China. The model was analyzed using multilevel modeling in Mplus 7.3.
Findings
At the individual level, SRRA indirectly increases robot abuse through the mediation of fear of robots. However, there is a cross-level moderation: the indirect relationship is alleviated when leaders exhibit high levels of transformational leadership.
Originality/value
This study pioneers the concept of robot abuse in hospitality and tourism settings. It extends emotional appraisal theory by highlighting the significant mediating role played by fear of robots. Furthermore, demonstrating how transformational leadership can mitigate the effects of SRRA offers valuable insights for leadership selection and training to facilitate the successful implementation of service robots.
... This data, extending beyond academic performance to encompass sensitive personal details, enables predicting students at risk of falling behind, facilitating the development of targeted support and early intervention strategies. However, the advent of big data in education raises critical concerns regarding privacy and data protection, areas that remain underexplored in scholarly literature (Hu & Min, 2023). The transition from rule-based to more advanced NLP and machine-learning techniques in chatbot technology introduces additional complexities (Mahendran et al., 2021;Wu et al., 2023). ...
The growing integration of artificial intelligence (AI) dialogue systems within educational and research settings highlights the importance of learning aids. Despite examination of the ethical concerns associated with these technologies, there is a noticeable gap in investigations on how these ethical issues of AI contribute to students’ over-reliance on AI dialogue systems, and how such over-reliance affects students’ cognitive abilities. Overreliance on AI occurs when users accept AI-generated recommendations without question, leading to errors in task performance in the context of decision-making. This typically arises when individuals struggle to assess the reliability of AI or how much trust to place in its suggestions. This systematic review investigates how students’ over-reliance on AI dialogue systems, particularly those embedded with generative models for academic research and learning, affects their critical cognitive capabilities including decision-making, critical thinking, and analytical reasoning. By using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, our systematic review evaluated a body of literature addressing the contributing factors and effects of such over-reliance within educational and research contexts. The comprehensive literature review spanned 14 articles retrieved from four distinguished databases: ProQuest, IEEE Xplore, ScienceDirect, and Web of Science. Our findings indicate that over-reliance stemming from ethical issues of AI impacts cognitive abilities, as individuals increasingly favor fast and optimal solutions over slow ones constrained by practicality. This tendency explains why users prefer efficient cognitive shortcuts, or heuristics, even amidst the ethical issues presented by AI technologies.
... Recent works in tourism already addressed AI's potential dark side. For instance, Hu and Min (2023) focused on the impact of AI design features on consumers' privacy concerns. However, the present research focuses on what psychological mechanism could explain consumers' potential negative reactions. ...
Artificial intelligence service agent (AISA) anthropomorphism is increasingly applied in service situations to deliver consumers a more personalized service experience. Extensive research has explored it over the past decade. However, the body of knowledge about this domain remains fragmented due to the lack of a comprehensive review. This paper aims to understand the domain through a framework‐based systematic literature review approach. We conducted an in‐depth analysis of 149 peer‐reviewed articles retrieved on April 25, 2024. Significant results include (a) analyzing research profiling (i.e., publication, journal, citation, and country analysis), (b) synthesizing findings based on the theories‐contexts‐characteristics‐methods (TCCM) framework, and (c) proposing future research agendas based on the TCCM framework. The present review concludes that research in the domain is experiencing rapid growth, evidenced by an increasing number of citations, a wide range of publications in reputable journals, and a notable surge in international collaboration. Furthermore, AISA anthropomorphism affects outcomes related to the AISA, the product, the brand, and even the company via cognitive, affective, and social mediators. Service‐, AISA‐, and consumer‐related factors moderate these effects. Based on the gap analysis, we propose future research directions. Our review advances the research domain of AISA anthropomorphism affecting consumer responses. It offers valuable insights for practitioners on effectively deploying anthropomorphic AISAs to serve consumers in appropriate contexts.
Based on theory and practice it takes the student through the key issues focusing on the hospitality industry, particularly the hotel sector. It analyses environmental, economic and social value in the industry, and what can be done to maximise the good for all the stakeholders in the long term.
餐饮企业监督顾客实现“光盘”，是防止食品浪费的重要举措，但难免会引起消极的顾客响应。在数智化时代，使用服务机器人执行监督职能，是否可以缓和自助餐顾客因餐盘浪费监督而产生的消极响应？本研究基于心智知觉理论，通过一个预实验和情景实验，在自助餐厅监督顾客餐盘浪费的情境下，探究了服务提供者类型（服务机器人vs. 人类服务员）与监督力度（大 vs. 小）对顾客响应的影响及其心理机制。研究结果表明：（1）相较于较小的监督力度，较大的监督力度增加了消极的顾客响应，具体表现为顾客态度更消极、负面口碑行为意愿更高。（2）服务提供者类型与监督力度对顾客响应产生了交互效应：当监督力度较小时，服务机器人相较于服务员减轻了消极的顾客响应；当监督力度较大时，服务机器人减轻消极的顾客响应的效应消失。（3）感知限制性在服务提供者类型和监督力度对顾客响应的交互影响中发挥了中介作用。本研究不仅丰富了数智化技术助推顾客减少餐盘浪费的理论研究，同时也为自助餐企业实施餐盘浪费监督在服务提供者选择方面提供了实践启示。 Monitoring customers to finish their meals ("clean plate campaign") in catering enterprises is an important measure to prevent food waste, but it may inevitably provoke negative responses from customers. With the rise of intelligent dining services, service robots become increasingly prevalent. However, there is limited research examining whether the use of service robots can mitigate the adverse effects of monitoring customers’ plate waste in buffet settings. To address this gap in the literature, the present study draws upon theory of mind perception and investigates how buffet customers respond to different intensity of plate waste motitoring by service robots compared to human waitstaff. Through a pilot study and a scenario-based experiment, the current study reveals the following key findings: (1) Close monitoring, as opposed to loose monitoring, leads to more negative customer responses, including increased negative word-of-mouth behavioral intention and unfavorable customer attitude. (2) Service provider type interactives with monitoring intensity: when plate waste monitoring is loose, service robots reduce negative customer responses compared to human waitstaff; however, with increased monitoring intensity, the mitigating effect of service robots on negative customer responses diminishes. (3) Perceived restrictiveness serves as a mediator between the interactive effects of service provider type and monitoring intensity on customer responses. In conclusion, the current study not only enriches theoretical research by revealing the novel role of service robot in nudging customers to reduce plate waste, but also offers practical insights for catering enterprises aiming to optimize their plate waste monitoring strategies.
Collaboration between robots and humans is no longer science fiction; it is gradually becoming a reality and is poised to revolutionize the hospitality industry. However, many employees see robots as a threat to their job security and are reluctant to accept them as co-workers. The current study delves into this complex phenomenon by examining the interplay between drivers, barriers, and employees’ characteristics in determining employees’ intention to work with service robots in the restaurant setting. Fuzzy-set qualitative comparative analysis (fsQCA) was employed to analyze data from a survey of 102 staff members working in Malaysian restaurants that are expected to introduce service robots in the near future. The results identified four pathways that lead to a high intention among employees to work with service robots. The nuanced and informative insights yielded by this study’s findings will guide restaurateurs toward successful collaboration between human employees and robots.
This study examines the role of Artificial Intelligence (AI) and Business Intelligence (BI) in transforming the business operations of the hospitality and airline sectors. The analysis focuses on the impact of these technologies on business transformation through business optimization, effective resource management, and service personalization. Data were collected through an anonymous survey conducted among employees in hotels and the airline company Air Serbia. The results of the SEM analysis confirm that BI and AI significantly contribute to operational efficiency, sustainability, and the enhancement of customer experience, with service personalization having the greatest impact on business transformation. The findings provide insights into the implementation of modern technologies and their role in shaping future business practices.
The utilization of robotic technology in the service sector, particularly in restaurants and cafes, is an emerging technology that has not been widely implemented in Indonesia. Although Indonesia ranked third in technology capital, it is contrasting with the adaptive attitude (ranked 59th). Furthermore, studies that focus on factors influencing service robot utilization, especially from the Indonesian consumers’ perspectives, remain understudied. This exploratory study aims to delving into both, the driving factors and barriers, to utilize service robots in restaurant and cafes by employing behavioral reasoning theory model. Data were collected through semi-structured interviews with 24 informants who have (not) used service robots in restaurant or cafes. The study found (1) robot appearance, (2) unique services, (3) service reliability, (4) emotional satisfaction, and (5) hygiene to be the reasons for utilizing service robots. While the inhibiting factors, including (1) need for social presence, (2) privacy concerns, (3) lack of customer control, and (4) technological anxiety, are perceived as risks when being served by service robots. This study enhances understanding of consumers’ perspectives toward service robots and expands the reasoning theory literature as the study found noteworthy insights. The findings of this study are relevant for business practitioners who are willing to incorporate service robots into their services as it provides specific empirical data from customers’ points of view to optimize service robot utilization and improve their intention to visit the restaurant and cafes.
Hızla değişen perakende sektöründe işletmeler, hedef kitlelerine daha etkili bir şekilde ulaşmak için sürekli yenilikçi yollar aramaktadır. Yapay zekâ (AI) ve makine öğrenimi yazılımları, rekabette bir adım öne geçmek, müşteri deneyimlerini iyileştirmek ve operasyonlarını optimize etmek isteyen perakendeciler için paha biçilmez araçlar olarak ortaya çıkmıştır. AI, büyük miktarda veriyi analiz etme, pazarlama çabalarını kişiselleştirme, dinamik fiyatlandırma, otomasyon, talep tahmini ve gelişmiş müşteri hizmetlerini yönetimi yeteneği ile perakende sektöründe büyük bir değişim başlatmıştır. AI, bilgisayarların tipik olarak insan zekâsı gerektiren görevleri yerine getirmeleri için eğitilmesidir. AI, insan gibi düşünmek ve öğrenmek üzere programlanmış makinelerde insan zekâsının simülasyonudur. AI, perakendedeki geleneksel rolleri dönüştürerek çok sayıda rutin görevin yükünü ortadan kaldırmaktadır. Günümüzün dinamik perakende dünyasında AI, sektörün geleceğini yeniden şekillendirmektedir. Bu çalışma, perakende sektöründe AI’ın artan önemini ve etkilerini anlamaya odaklanmaktadır. Ayrıca çalışma, güncel istatistiklerle birlikte AI uygulamalarının faydaları, zorlukları ve stratejilerini de inceleyerek işletme yöneticilerine ilgili kararları alırken yardımcı olmayı amaçlamaktadır
In the rapidly changing retail sector, businesses are constantly looking for innovative ways to reach their target audiences more effectively. AI and machine learning software have emerged as invaluable tools for retailers looking to stay ahead of the competition, improve customer experiences and optimize operations. Artificial intelligence has initiated a major shift in the retail industry with the ability to analyze large amounts of data, personalize marketing efforts, dynamic pricing, automation, demand forecasting and improved customer service management. Artificial
intelligence is the training of computers to perform tasks that typically require human intelligence. AI is the simulation of human intelligence in machines programmed to think and learn like humans. Artificial intelligence is transforming
traditional roles in retail, removing the burden of many routine tasks. In today's dynamic retail world, AI is reshaping the future of the industry. This study focuses on understanding the growing importance and implications of AI in the retail industry. It also examines the benefits, challenges and strategies of AI applications along with up-to-date statistics to assist business managers in making relevant decisions
Purpose
With the disruptive evolution of artificial intelligence (AI), the roles of robotics in hospitality and tourism are shifting rapidly from automatic to emotional works. Although academics have focused on examining robotic applications in hospitality and tourism, concrete images of the different roles of robots are unclear. Thus, this study aims to systematically aggregate and evaluate existing robotic technology-related studies published in Social Science Citation Index-listed hospitality and tourism journals to link the fragmented knowledge and provide an up-to-date overview of robotic technology in hospitality and tourism.
Design/methodology/approach
This study retrieved 134 robotic technology-related articles and used descriptive and content analyses to analyze the retrieved papers thoroughly.
Findings
The top keyword identified was service robot. Robotic technologies are categorized into AI-supplemented, AI-generated, robotic technology anthropomorphism (RTA)-facilitated and RTA-mediated.
Research limitations/implications
Future studies can consider exploring service robots further from the perspectives of suppliers in tourism. A more comprehensive categorization of robotic technologies is also recommended.
Originality/value
This study contributes to the robotic research realm by providing a holistic view of robotic applications in hospitality and tourism research. This study also attempts to pin down the potential research directions to guide researchers in expanding future studies.
Objectives of the Study: The goal of this chapter is to find out the influence of AI on traveler conduct (Gopal et.al 2023), assess how tourism centered AI technology swap tourist aim, goals and ideas (Hoyer et al. 2020). Also to The research aims to recognize the influence of AI on tailored travel, ecological consciousness, and each aspect of the trip itself. Study Method/Design: The current study deploys secondary data and qualitative technique involve content analysis and in-depth interviews. Findings: From the study, it is, highlighted that AI impacts tourist conduct at various period of trip. Moreover, travel gathers abundance of memories, experiences collected by tourist, exploration of new places (Gretzel et al., 2015) On top of that inclusion of AI gives the benefit of smooth AI improves travel experience by making better ways, using less energy, and minimizing waste (Neidhardt et al ., 2015).
The presence of toxic chemicals in water, including heavy metals like mercury and lead, organic pollutants such as pesticides, and industrial chemicals from runoff and discharges, poses critical public health and environmental risks leading to severe health issues and ecosystem damage; education plays a crucial role in mitigating these effects by enhancing awareness, promoting sustainable practices, and integrating environmental science into curricula to empower individuals to address and advocate for effective solutions to water pollution. However, the educational transformation should be accompanied with a technical process which can be eventually transferred to society to empower environmental education. In this study, carbonaceous material derived from Haematoxylum campechianum (CM-HC) was utilized for removing 3-nitrophenol (3-Nph) from aqueous solutions. The novelty of this research utilizes Haematoxylum campechianum bark and coconut shell, abundant agricultural wastes in Campeche, Mexico, for toxin removal, enhancing the adsorption process through artificial neural networks and genetic algorithms to optimize conditions and maximize the absorption efficiency. CM-HC’s surface morphology was analyzed using scanning electron microscopy (SEM/EDS), BET method, X-ray powder diffraction (XRD), and pHpzc. Kinetic models including pseudo-first-order (PFO), pseudo-second-order (PSO), and Elovich were applied to fit the data. Adsorption isotherms were determined at varying pH (3–8), adsorbent dosages (2–10 g/L), and temperatures (300.15–330.15 K), employing Langmuir, Freundlich, Temkin, and Redlich–Peterson models. PSO kinetics demonstrated a good fit (R² > 0.98) for Ci = 50–100 mg/L, indicating a chemical adsorption mechanism. The Langmuir isotherm model exhibited the best fit, confirming chemical adsorption, with a maximum adsorption capacity (Qm) of 236.156 mg/g at T = 300.15 K, pH = 6, contact time = 3 h, and 2 g/L adsorbent dosage. Lower temperatures favored exothermic adsorption. Artificial neural networks (ANNs) were employed for deep learning, optimizing the predictive model for removal percentage. Correlation heat maps highlighted positive correlations between time, dosage, and removal percentage, emphasizing the impact of initial concentration on efficiency. ANN modeling, incorporating iterative optimization, yielded highly accurate predictions, aligned closely with experimental results. The study showcases the success of deep learning in optimizing adsorption processes, emphasizing the importance of diverse correlation algorithms for comprehensive insights into competitive adsorption dynamics. The 5-14-14-1 deep learning architecture, fine-tuned over 228 epochs, demonstrated strong performance with mean squared error (MSE) values of 4.07, 18.406, and 6.2122 for training, testing, and total datasets, respectively, and high R-squared values. Graphical analysis showed a solid linear correlation between experimental and simulated removal percentages, emphasizing the need to consider more than just testing data for optimization. Experimental validation confirmed a 98.77% removal efficiency, illustrating the effectiveness of combining deep learning with genetic algorithms, and highlighting the necessity of experimental trials to verify computational predictions. It is concluded that the carbonaceous material from Haematoxylum campechianum waste (CM-HC) is an effective, low-cost adsorbent for removing 3-nitrophenol from aqueous solutions, achieving optimal removal at pH 6 and 300.15 K with a maximum adsorption capacity of 236.156 mg/g, following Langmuir model and pseudo-second order kinetics. The validated ANN model offers a reliable tool for practical applications in environmental remediation, advancing both environmental science and educational innovation by integrating artificial neural networks and data science methodologies into student learning experiences.
This study explores the impact of artificial intelligence (AI) on the growth and competitiveness of regional tourism and hospitality destinations. AI's application has both positive and negative effects on sustainable tourism. On the positive side, AI enhances accessibility to tourist sites, provides detailed information about attractions, simplifies understanding of costs and amenities, boosts local economies, and increases federal income. A notable downside is the replacement of human workers by machine learning technologies, which may necessitate remedial measures such as training. This review examines AI's foundational IT principles, current applications, and systems in the industry, focusing on the hotel sector. It concludes with an overview of AI’s challenges in this field, proposes a research agenda, and suggests future directions for AI development in tourism and hospitality.
By understanding how AI affects organizational operations, stakeholders can plan and profit from these improvements. This chapter summarizes AI-related hotel marketing trends. For competitiveness, artificial intelligence enhances internal processes by using data and information better. Worker capabilities are increased, and bulk customisation is possible. By measuring ROI, improving sustainability, and regulating data use laws and ethics, AI affects stakeholder relationships. AI focuses on and integrates commercial networks, changing distribution paradigms. AI makes product and service design and customer support smarter and more predictive. Product and service development is improved and predictive using AI. AI's impact on hospitality and tourist marketing is discussed. Academics and industry people discuss its research. To maximize income, deliver customized experiences, and increase operational efficiency, hospitality and tourism are using AI. AI-driven research and automation may help digital organizations stand out, create unique customer experiences, and grow sustainably.
The ongoing Fourth Industrial Revolution, which is driven by artificial intelligence (AI) and other disruptive technologies, has attracted a surge of AI studies. Previous reviews primarily offered a bird’s view of AI research and were somewhat one-sided focusing on AI’s bright sides. Reviews on the networks of resources, process dynamics, and divergent outcomes of this complex system are rare, if any. This review article aims to fill this research gap by deconstructing how value is formed in the AI-integrated service system. Guided by the SPAR-4-SLR protocol and interactive value formation framework, this study reviewed and analyzed state-of-the-art AI studies (i.e. 2019–2023) published in six selected top-tier hospitality and tourism journals. This review makes contributions by outlining the resource orchestration, resource integration practices, and divergent outcomes of value formation in the AI-integrated system in hospitality and tourism. Future research directions and implications were also provided to advance the field.
Understanding privacy needs has become paramount in the evolving landscape of social robots in the hospitality industry. Traditional privacy concerns, dominated by transactional interactions, fail to encapsulate the complexities of human-social robot interaction on service frontlines. Drawing from the communication management theory and the media equation theory, this study conceptualized "Social Robot Privacy Concerns" (SRPC) and discovered its multidimensionality. The SRPC construct is characterized by three key dimensions: (1) Information Privacy Concern, addressing data management and collection; (2) Interpersonal Privacy Concern, focusing on the robot's physical presence; and (3) Environmental Privacy Concern, encompassing broader contexts of government regulations, media influence, and surveillance mechanisms. The findings unveil that these dimensions are pivotal in navigating the challenges posed by service robots implemented in the hospitality industry, necessitating a nuanced approach to understanding and addressing emergent privacy needs. This study also empirically validated the scale to measure the SRPC construct, providing a foundational tool for future research.
The increasing usage of service robots in hotels has generated discussions about their positive impacts. However, little research has been done on the adverse aspects of robot usage from the perspective of the employees, and few studies have investigated the antecedents of employee robot risk awareness. This study posits that employees are aware of potential threats posed by robots; they observe the organization's investment in robot development and witness customers' satisfaction with robot usage. People with greater robot risk awareness engage in more withdrawal behaviors at work (employee sample in Study 1) and report lower intentions to work in the hospitality industry (student sample in Study 2). Those two relationships are further contingent upon employees' learning goal orientation. The proposed relationships were validated by a survey-based study (612 frontline employees) and two experiments (205 student participants). Based on the research results, hotels are encouraged to emphasize augmentation but not automation.
In the vision of future travel, advanced technology emerges as the linchpin, orchestrating a paradigm shift in planning, experiencing, and securing journeys. Central to this evolution is the concept of personalized itineraries, where algorithms craft unique travel experiences tailored to individual preferences, breaking away from conventional one-size-fits-all packages. Proactive addressing of travel challenges becomes paramount, with predictive analytics identifying and mitigating hurdles in real-time for a stress-free experience. Guided tours transform into dynamic experiences as AI-powered guides adapt narratives, enriching exploration with personalized insights. Virtual reality reconstructions transport travelers to historical landmarks, transcending temporal boundaries for accessible cultural exploration. Streamlined city navigation via mobile apps or wearables provides real-time information. Advanced security, driven by technology and AI, ensures a safe travel environment.
Motivations: Recent research has emerged on generally how to improve AI products’ Human-AI Interaction (HAI) User Experience (UX), but relatively little is known about HAI-UX inclusivity. For example, what kinds of users are supported, and who are left out? What product changes would make it more inclusive?
Objectives: To help fill this gap, we present an approach to measuring what kinds of diverse users an AI product leaves out and how to act upon that knowledge. To bring actionability to the results, the approach focuses on users’ problem-solving diversity. Thus, our specific objectives were: (1) to show how the measure can reveal which participants with diverse problem-solving styles were left behind in a set of AI products; and (2) to relate participants’ problem-solving diversity to their demographic diversity, specifically gender and age.
Methods: We performed 18 experiments, discarding two that failed manipulation checks. Each experiment was a 2x2 factorial experiment with online participants, comparing two AI products: one deliberately violating one of 18 HAI guideline and the other applying the same guideline. For our first objective, we used our measure to analyze how much each AI product gained/lost HAI-UX inclusivity compared to its counterpart, where inclusivity meant supportiveness to participants with particular problem-solving styles. For our second objective, we analyzed how participants’ problem-solving styles aligned with their gender identities and ages.
Results & Implications: Participants’ diverse problem-solving styles revealed six types of inclusivity results: (1) the AI products that followed an HAI guideline were almost always more inclusive across diversity of problem-solving styles than the products that did not follow that guideline—but “who” got most of the inclusivity varied widely by guideline and by problem-solving style; (2) when an AI product had risk implications, four variables’ values varied in tandem: participants’ feelings of control, their (lack of) suspicion, their trust in the product, and their certainty while using the product; (3) the more control an AI product offered users, the more inclusive it was; (4) whether an AI product was learning from “my” data or other people’s affected how inclusive that product was; (5) participants’ problem-solving styles skewed differently by gender and age group; and (6) almost all of the results suggested actions that HAI practitioners could take to improve their products’ inclusivity further. Together, these results suggest that a key to improving the demographic inclusivity of an AI product (e.g., across a wide range of genders, ages, etc.) can often be obtained by improving the product’s support of diverse problem-solving styles.
Artificial intelligence is rapidly reshaping various aspects of society, including the realm of education. The emergence of ChatGPT in November 2022 has ignited global discourse, prompting numerous studies to explore its potential benefits and drawbacks, particularly within higher education. ChatGPT offers numerous advantages for teaching and learning, such as promoting personalized education, assisting in research paper writing, refining grammar, and writing skills, and fostering critical thinking. However, alongside its benefits, concerns regarding ethical considerations and academic integrity have been raised. This paper delves into the role of ChatGPT as an AI tool, examining studies conducted between 2022 and 2023. It also addresses the ethical and academic dilemmas associated with its widespread use. Additionally, the paper discusses the challenges impeding the seamless integration of AI in education, particularly in developing countries like Jordan. Furthermore, it sheds light on Jordan's efforts to embrace artificial intelligence in education and underscores the necessity for an education-centric AI policy to cultivate skilled professionals capable of navigating the global AI revolution. Despite varying conclusions across studies, this review underscores the importance of prioritizing the education sector within Jordanian AI policies. Such prioritization is crucial given the sector's role in nurturing skilled individuals and implementing curricula effectively, thus facilitating national development. This paper holds significance for researchers, educational technology practitioners, and policymakers alike, offering insights into the multifaceted implications of AI integration in education and advocating for informed policy decisions to harness its potential effectively.
The impacts that AI and robotics systems can and will have on our everyday lives are already making themselves manifest. However, there is a lack of research on the ethical impacts and means for amelioration regarding AI and robotics within tourism and hospitality. Given the importance of designing technologies that cross national boundaries, and given that the tourism and hospitality industry is fundamentally predicated on multicultural interactions, this is an area of research and application that requires particular attention. Specifically, tourism and hospitality have a range of context-unique stakeholders that need to be accounted for in the salient design of AI systems is to be achieved. This paper adopts a stakeholder approach to develop the conceptual framework to centralize human values in designing and deploying AI and robotics systems in tourism and hospitality. The conceptual framework includes several layers-'Human-human-AI' interaction level, direct and indirect stakeholders, and the macroenvironment. The ethical issues on each layer are outlined as well as some possible solutions to them. Additionally, the paper develops a research agenda on the topic.
Voice-based artificial intelligence (AI) devices transform human-computer bidirectional interactions with new touchpoints. Despite the recent release of purpose-developed in-room voice assistants for hotels, they have not been widely deployed by hospitality companies. There is limited research on the phenomenon of voice-based digital assistants and a research gap in their adoption by hotels for automating workflows and enhancing guests’ experiences. This study analysed the role of voice devices for mediating interactions between hotels and guests from both the hospitality technology providers’ and guests’ perspectives. This was done by the means of inductive qualitative research using 28 semi-structured interviews. The findings revealed that benefits associated with the application of voice-based digital assistants in hospitality outweigh the drawbacks for both hotels and guests. The paper proposes a model which illustrates the essence of speech-based interactions between hotels and guests via voice assistants. This concept contributes to human-computer interactions in the hotel industry.
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.
This study examines tourists’ attitudes toward the use of artificially intelligent (AI) devices in either relatively more utilitarian or hedonic tourism services (airline and hospitality services, respectively). Findings suggest that tourists’ acceptance of the use of AI devices in both service contexts is influenced by social influence, hedonic motivation, anthropomorphism, performance and effort expectancy, and emotions toward the artificially intelligent devices. Findings further suggest that social influence is a stronger determinant in hospitality services compared to airline services. Tourists have higher performance expectancy from AI devices used to provide airline services compared to hospitality services. Tourists’ willingness to accept the use of AI devices for delivering hospitality services is lower than airline services. These results suggest that while the utilization of AI devices for delivering functional services is acceptable, the use of AI devices in the delivery of hedonic services may backfire.
Digital assistants (DA) perform routine tasks for users by interacting with the Internet of Things (IoT) devices and digital services. To do so, such assistants rely heavily on personal data, e.g. to provide personalized responses. This leads to privacy concerns for users and makes privacy features an important component of digital assistants.
This study examines user preferences for three attributes of the design of privacy features in digital assistants, namely (1) the amount of information on personal data that is shown to the user, (2) explainability of the DA’s decision, and (3) the degree of gamification of the user interface (UI). In addition, it estimates users’ willingness to pay (WTP) for different versions of privacy features.
The results for the full sample show that users prefer to understand the rationale behind the DA’s decisions based on the personal information involved, while being given information about the potential impacts of disclosing specific data. Further, the results indicate that users prefer to interact with the DA’s privacy features in a serious game. For this product, users are willing to pay €21.39 per month. In general, a playful design of privacy features is strongly preferred, as users are willing to pay 23.8% more compared to an option without any gamified elements. A detailed analysis identifies two customer clusters “Best Agers” and “DA Advocates”, which differ mainly in their average age and willingness to pay. Further, “DA Advocates” are mainly male and more privacy sensitive, whereas “Best Agers” show a higher affinity for a playful design of privacy features.
Against the backdrop of advancements in technology and its deployment by companies and governments to collect sensitive personal information, information privacy has become an issue of great interest for academics, practitioners, and the general public. The travel and tourism industry has been pioneering the collection and use of biometric data for identity verification. Yet, privacy research focusing on the travel context is scarce. This study developed a valid measurement of Travelers’ Online Privacy Concerns (TOPC) through a series of empirical studies: pilot (n=277) and cross-validation (n=287). TOPC was then assessed for its predictive validity in its relationships with trust, risk, and intention to disclose four types of personal data: biometric, identifiers, biographic, and behavioral data (n=685). Results highlight the role of trust in mitigating the relationship between travelers’ privacy concerns and data disclosure. This study provides valuable contribution to research and practice on data privacy in travel.
In light of mounting privacy concerns over the increasing collection and use of biometric and behavioral information for travel facilitation, this study examines travelers’ online privacy concerns (TOPC) and its impact on willingness to share data with travel providers. A proposed theoretical model explaining antecedents and outcomes of TOPC related to biometric and behavioral data sharing was tested using structural equation modeling with data collected from 685 travelers. The results extend the Antecedents – Privacy Concerns – Outcomes (APCO) framework by identifying a set of salient individual factors that shape TOPC. The findings provide empirical evidence confirming the context dependence of privacy preferences, showing that although travelers are concerned over their information privacy they are still willing to share their behavioral data; while in the case of biometric information, the disclosure decision is dependent upon expected benefits rather than privacy concerns. This study offers insights into privacy behavior of online consumers in the travel context and constitutes one of the few focusing on the social aspects of biometric authentication.
The emergence of camera-based assistive technologies has empowered people with visual impairments (VIP) to obtain independence in their daily lives. Popular services feature volunteers who answer questions about photos or videos (e.g., to identify a medical prescription). However, people with VIPs can (inadvertently) reveal sensitive information to these volunteers. To better understand the privacy concerns regarding the disclosure of background objects to different types of human assistants (friends, family, and others), we conducted an online survey with 155 visually impaired participants. In general, our participants had varying concerns depending on the type of assistants and the kind of information. We found that our participants were more concerned about the privacy of bystanders than their own when capturing people in images. We also found that participants were concerned about self-presentation and were more comfortable sharing embarrassing information with family than with their friends. Our findings suggest directions for future work in the development of human-assisted question-answering systems. Specifically, we discuss how humanizing these systems can give people a greater sense of personal security.
This paper provides an early evaluation of Artificial Intelligence (AI) against COVID-19. The main areas where AI can contribute to the fight against COVID-19 are discussed. It is concluded that AI has not yet been impactful against COVID-19. Its use is hampered by a lack of data, and by too much data. Overcoming these constraints will require a careful balance between data privacy and public health, and rigorous human-AI interaction. It is unlikely that these will be addressed in time to be of much help during the present pandemic. In the meantime, extensive gathering of diagnostic data on who is infectious will be essential to save lives, train AI, and limit economic damages.
Digitization makes it easier for firms to build their innovation and marketing efforts around consumers' personal data. In this research, we employ a privacy perspective based on contextual integrity to examine how such practices can trigger privacy concerns. We propose that small entrepreneurial firms are often at a particular disadvantage compared to large incumbent firms. At the same time, we also highlight that there are several strategies firms can use to mitigate privacy concerns and that in some circumstances, privacy concerns may also exert positive effects on data-driven marketing by stimulating privacy innovation and providing a source of competitive advantage.
Fueled by ever-growing amounts of (digital) data and advances in artificial intelligence, decision-making in contemporary societies is increasingly delegated to automated processes. Drawing from social science theories and from the emerging body of research about algorithmic appreciation and algorithmic perceptions, the current study explores the extent to which personal characteristics can be linked to perceptions of automated decision-making by AI, and the boundary conditions of these perceptions, namely the extent to which such perceptions differ across media, (public) health, and judicial contexts. Data from a scenario-based survey experiment with a national sample (N = 958) show that people are by and large concerned about risks and have mixed opinions about fairness and usefulness of automated decision-making at a societal level, with general attitudes influenced by individual characteristics. Interestingly, decisions taken automatically by AI were often evaluated on par or even better than human experts for specific decisions. Theoretical and societal implications about these findings are discussed.
If a robot sends a deceptive signal to a human user, is this always and everywhere an unethical act, or might it sometimes be ethically desirable? Building upon previous work in robot ethics, this article tries to clarify and refine our understanding of the ethics of robotic deception. It does so by making three arguments. First, it argues that we need to distinguish between three main forms of robotic deception (external state deception; superficial state deception; and hidden state deception) in order to think clearly about its ethics. Second, it argues that the second type of deception-superficial state deception-is not best thought of as a form of deception, even though it is frequently criticised as such. And third, it argues that the third type of deception is best understood as a form of betrayal because doing so captures the unique ethical harm to which it gives rise, and justifies special ethical protections against its use.
In the future, artificial intelligence (AI) is likely to substantially change both marketing strategies and customer behaviors. Building from not only extant research but also extensive interactions with practice, the authors propose a multidimensional framework for understanding the impact of AI involving intelligence levels, task types, and whether AI is embedded in a robot. Prior research typically addresses a subset of these dimensions; this paper integrates all three into a single framework. Next, the authors propose a research agenda that addresses not only how marketing strategies and customer behaviors will change in the future, but also highlights important policy questions relating to privacy, bias and ethics. Finally, the authors suggest AI will be more effective if it augments (rather than replaces) human managers.
The availability of always-on digital agents in hotel rooms, providing agency and surveillance cues, presents opportunities for behavioral interventions. This study tested the effectiveness of agency and social feedback on pro-environmental behavior intention of hotel consumers. A survey with scenario-based experimental design was distributed to US and UK travelers (N=621). Results suggest that no one type of agent was more effective than the other in influencing pro-environmental behavior intention. Social feedback was found effective when given by a virtual assistant. Perception of another agent being 'present' in the room, even when invisible, is sufficient to induce normative behavior. This enriches literature on surveillance cues and behavior change and contributes to finding new ways of leveraging emerging technologies to foster sustainability.
The link between information privacy concerns and privacy behaviours has been a focus of extensive investigation in various disciplines. However, little attention has been devoted to this issue in the tourism literature. Spurred by technological development and shaped by tourism-related environments, emerging privacy issues call for comprehensive yet context-specific studies to ensure tourists are making beneficial privacy choices. This paper first presents a comprehensive review of state-of-the-art research on privacy concerns and behaviours. Then, it suggests a list of overarching research priorities, merging social and technical aspects of privacy protection approaches as they apply to tourism. The priorities include research to measure tourists’ privacy concerns, explore specific biases in tourists’ privacy decisions, experiment with privacy nudges, and explore how to integrate privacy nudges in system design. Thus, this paper contributes to guiding the direction of future research on privacy protection in tourism.
This article compares problems and strategies cited in the services marketing literature with those reported by actual service suppliers in a study conducted by the authors. Discussion centers on several broad themes that emerge from this comparison and on guidelines for future work in services marketing.
Social robotics entertains a particular relationship with anthropomorphism, which it neither sees as a cognitive error, nor as a sign of immaturity. Rather it considers that this common human tendency, which is hypothesized to have evolved because it favored cooperation among early humans, can be used today to facilitate social interactions between humans and a new type of cooperative and interactive agents – social robots. This approach leads social robotics to focus research on the engineering of robots that activate anthropomorphic projections in users. The objective is to give robots “social presence” and “social behaviors” that are sufficiently credible for human users to engage in comfortable and potentially long-lasting relations with these machines. This choice of ‘applied anthropomorphism’ as a research methodology exposes the artifacts produced by social robotics to ethical condemnation: social robots are judged to be a “cheating” technology, as they generate in users the illusion of reciprocal social and affective relations. This article takes position in this debate, not only developing a series of arguments relevant to philosophy of mind, cognitive sciences, and robotic AI, but also asking what social robotics can teach us about anthropomorphism. On this basis, we propose a theoretical perspective that characterizes anthropomorphism as a basic mechanism of interaction, and rebuts the ethical reflections that a priori condemns “anthropomorphism-based” social robots. To address the relevant ethical issues, we promote a critical experimentally based ethical approach to social robotics, “synthetic ethics,” which aims at allowing humans to use social robots for two main goals: self-knowledge and moral growth.
Artificial intelligence (AI) is increasingly reshaping service by performing various tasks, constituting a major source of innovation, yet threatening human jobs. We develop a theory of AI job replacement to address this double-edged impact. The theory specifies four intelligences required for service tasks—mechanical, analytical, intuitive, and empathetic—and lays out the way firms should decide between humans and machines for accomplishing those tasks. AI is developing in a predictable order, with mechanical mostly preceding analytical, analytical mostly preceding intuitive, and intuitive mostly preceding empathetic intelligence. The theory asserts that AI job replacement occurs fundamentally at the task level, rather than the job level, and for “lower” (easier for AI) intelligence tasks first. AI first replaces some of a service job’s tasks, a transition stage seen as augmentation, and then progresses to replace human labor entirely when it has the ability to take over all of a job’s tasks. The progression of AI task replacement from lower to higher intelligences results in predictable shifts over time in the relative importance of the intelligences for service employees. An important implication from our theory is that analytical skills will become less important, as AI takes over more analytical tasks, giving the “softer” intuitive and empathetic skills even more importance for service employees. Eventually, AI will be capable of performing even the intuitive and empathetic tasks, which enables innovative ways of human–machine integration for providing service but also results in a fundamental threat for human employment.
Technology is rapidly changing the nature of service, customers’ service frontline experiences, and customers’ relationships with service providers. Based on the prediction that in the marketplace of 2025, technology (e.g., service-providing humanoid robots) will be melded into numerous service experiences, this article spotlights technology’s ability to engage customers on a social level as a critical advancement of technology infusions. Specifically, it introduces the novel concept of automated social presence (ASP; i.e., the extent to which technology makes customers feel the presence of another social entity) to the services literature. The authors develop a typology that highlights different combinations of automated and human social presence in organizational frontlines and indicates literature gaps, thereby emphasizing avenues for future research. Moreover, the article presents a conceptual framework that focuses on (a) how the relationship between ASP and several key service and customer outcomes is mediated by social cognition and perceptions of psychological ownership as well as (b) three customer-related factors that moderate the relationship between ASP and social cognition and psychological ownership (i.e., a customer’s relationship orientation, tendency to anthropomorphize, and technology readiness). Finally, propositions are presented that can be a catalyst for future work to enhance the understanding of how technology infusion, particularly service robots, influences customers’ frontline experiences in the future.
This paper captures the current state of privacy scholarship in marketing and related disciplines. We examine theoretical perspectives and empirical findings about data and information privacy grouped according to privacy?s role in society, the psychology of privacy, and the economics of privacy. Although a coherent subset of research themes provide deep understanding, theoretical and empirical findings show this narrow focus also has constrained our view of privacy to consumer, organizational, ethical, or legal silos. In response, we take a necessary step toward expanding the privacy domain across these borders, emphasizing the compelling synergies that span multiple interests. We conclude by highlighting future research themes that embody a multidimensional approach, which blends the many interconnected concerns that feature in contemporary privacy questions in marketing. Since internal and external stakeholders are affected in multiple and potentially unforeseen ways by data privacy issues, additional work in this space remains critical and needed.
In recent years, a growing number of researchers have examined the watching eyes phenomenon (i.e., increased prosocial and decreased antisocial behavior when subtle watching eyes are present in the environment). Somewhat surprisingly, the questions how and under what conditions subtle cues of being watched operate have been unanswered so far. The present contribution addresses this research gap. In two studies we document that (a) subtle cues of being watched induce a sense of being seen and (b) chronic public self-awareness moderates the watching eyes phenomenon in that specifically individuals with strong chronic public self-awareness show more prosocial behavior under conditions of watching eyes. The applicability of subtle cues of being watched in research on social presence is discussed.
Models indicate that opportunities for reputation formation can play an important role in sustaining cooperation and prosocial behavior. Results from experimental economic games support this conclusion, as manipulating reputational opportunities affects prosocial behavior. Noting that some prosocial behavior remains even in anonymous noniterated games, some investigators argue that humans possess a propensity for prosociality independent of reputation management. However, decision-making processes often employ both explicit propositional knowledge and intuitive or affective judgments elicited by tacit cues. Manipulating game parameters alters explicit information employed in overt strategizing but leaves intact cues that may affect intuitive judgments relevant to reputation formation. To explore how subtle cues of observability impact prosocial behavior, we conducted five dictator games, manipulating both auditory cues of the presence of others (via the use of sound-deadening earmuffs) and visual cues (via the presentation of stylized eyespots). Although earmuffs appeared to reduce generosity, this effect was not significant. However, as predicted, eyespots substantially increased generosity, despite no differences in actual anonymity; when using a computer displaying eyespots, almost twice as many participants gave money to their partners compared with the controls. Investigations of prosocial behavior must consider both overt information about game parameters and subtle cues influencing intuitive judgments.
Understanding the drivers of technology adoption remains an important organizational problem. Our research focused on a personality trait that is relevant to the adoption of technological innovation: personal innovativeness in IT (PIIT). We examined the causal pathways by which this trait affects behavioral intention by testing three alternative models based on innovation diffusion theory, the theory of planned behavior, and an integrative perspective that combines them. Data were collected from 196 hospital administrators in South Korea. The target innovation was an e-commerce purchasing system. Testing across all three models resulted in complete mediation, indicating that PIIT is a strong predictor of intended use of IT but it exerts its influence by altering the mediators. Our integrative perspective provides a more complete account of the causal mechanisms underlying the relationships as well as unique insights that cannot be obtained with a single theory driven model.
Previous research has shown that design features that support privacy are essential for new technologies looking to gain widespread adoption. As such, privacy-sensitive design will be important for the adoption of social robots, as they could introduce new types of privacy risks to users. In this paper, we report findings from our preliminary study on users' perceptions and attitudes toward privacy in human-robot interaction, based on interviews that we conducted about a workplace social robot.
The COVID-19 outbreak has accelerated the development of service robots. However, service robots in some hotels have been put aside despite successful adoption. This study thus focuses on hotel employees' inhibited continuous usage intention by examining the challenges of benefiting from service robots. A robot usage resistance model (RURM) has been proposed based on the results. In this model, lack of authentic anthropomorphous features and low usability as technological characteristics could influence employees' cognitions toward service robots, while robot-related excessive workloads, techno-insecurity, and techno-uncertainty as psychological stimuli could trigger negative emotional arousal, which in turn fosters employee resistance to service robot continuous usage. This study offers a more solid conceptual investigation into employee resistance to service robot continuous usage, thus allowing the development of strategies to better reap the rewards of hotel service robot usage.
This research reveals a novel downside effect of the emerging AI-powered voice assistant (vs. the prevalent touch panel) in the hotel in-room technology context. Building on the automated social presence theory, this research comprises two studies to explore whether, how, and why using voice assistant (vs. touch panel) affects consumers’ satisfaction levels and their behavioral intentions differently. This research documents that using AI-powered voice assistant (vs. touch panel) leads to a lower level of satisfaction due to a deficiency in perceived control, especially among consumers with independent self-construal tendency. Serial mediation tests confirm the downstream effects via the causal link of psychological mechanism (perceived control) to service evaluation (satisfaction) and to future behavioral intentions (consumption preference and WOM). This research contributes to the burgeoning literature on the AI-powered voice assistant by elucidating the phenomenon and investigating why consumers may respond differently to this emerging technology with practical implications.
The wide deployment of digital technologies for the management of the COVID-19 pandemic has triggered concerns about privacy and intrusion from government surveillance. This study investigates individual privacy and surveillance attitudes by developing a theoretical model to explain acceptance of government surveillance and privacy protection behaviours during health-crises, such as the COVID-19 pandemic. Results from a US sample reveal that people are concerned about the collection and use of their personal information via mobile applications and the monitoring of their online activities by authorities. Findings reveal the important roles of political trust and belief that governments' need to be proactive in protecting peoples' welfare during a crisis that can increase acceptance of surveillance and thus assist in the management of the health crisis. Implications for research and practice are discussed.
That artificial intelligence (AI) has the potential to provide significant benefits is generally accepted by both practitioners and scholars. However, the dark side of AI is less discussed, and less understood. In this paper, the authors first classify the wellspring of AI benefits in both B2C and B2B settings. In B2C settings AI benefits are primarily via customized experiences, while B2B AI benefits are manifested via business efficiencies. Next, guided by the relationship marketing literature, the authors identify the drivers of the dark side of AI - lack of trust and power asymmetries, with lack of trust being a stronger factor in B2C settings and power asymmetries being a stronger factor in B2B settings. Finally, the authors provide an organizing framework for understanding both the bright side and the dark side of AI, in both B2C settings and B2B settings. This paper is differentiated from prior work by its focus on B2B settings (most focus on B2C settings), and by its focus on the dark side of AI (most focus on the bright side of AI).
Previous studies provide inconsistent evidence regarding the effect of anthropomorphism on customers’ willingness to use AI service agents. This paper explains the reason by introducing service context. Two situational experiments are used to demonstrate that under the context of high perceived control, customers expect AI service agents with more anthropomorphic designs to perform better and prefer highly human-like AI service agents. However, under the context of low perceived control, customers perceive stronger threat in facing AI service agents with more anthropomorphic designs and prefer less human-like AI service agents. Moreover, we find that this effect is significant only in social scenarios. These findings provide new insights into previous inconsistent evidence regarding anthropomorphic design’s influence on customers’ willingness to use AI service agents. Our findings also have important implications for AI service agents design in different service contexts and advance the literature on human–robot interaction and marketing.
This study examined the effects of service recovery entities (i.e., robot-generated text, robot-generated voice, and human service employee) on recovery satisfaction through the mediation of perceived sincerity. This study also tested the moderating role of the need for human interaction. The results of this study suggest that people perceive service recovery provided by a human to be more sincere than that provided by a service robot, thereby leading to higher levels of satisfaction. Moreover, this effect is stronger for those with a stronger need for human interaction. However, differences in perceived sincerity and recovery satisfaction were not identified between the use of service robots generating either text or a voice to express an apology. This study contributes to the theoretical discussion on ways to enhance customer experiences in robot service environments and provides insights that can guide service management in the hospitality and tourism industry.
The accelerated deployment of humanoid robots in hospitality services precipitates the need to understand related consumer reactions. Four scenario-based experiments, building on social presence and social cognition theories, examine how humanoid robots (vs. self-service machines) shape consumer service perceptions and intentions vis-à-vis concurrent presence/absence of human staff. The influence of consumers’ need for human interaction and technology readiness is also examined. We find that anthropomorphizing service robots positively affects expected service quality, first-visit intention, and willingness to pay, as well as increasing warmth/competence inferences. These effects, however, are contingent on the absence of human frontline staff, which can be understood by viewing anthropomorphism as a relative concept. Humanoid robots also increase psychological risk, but this poses no threat to expected service quality when consumers’ need for human interaction is controlled for. Hence, humanoid robots can be a differentiating factor if higher service quality expectations are satisfied. Additionally, we show that a humanoid robot’s effect on expected service quality is positive for all but low levels of technology readiness. Further implications for theory/practice are discussed.
Purpose
The main purpose of this study is to investigate the impact of service robots on hotel visitors' behaviour and to verify the role of anthropomorphism(human likeness) in customer satisfaction with robots.
Design/methodology/approach
An online survey of 381 respondents was conducted, divided into three types of robots according to the level of anthropomorphism. The research model was thoroughly tested using the PLS-SEM method. Research model was tested thoroughly using the PLS-SEM method.
Findings
This study found that user satisfaction with service robots in a hotel had a positive impact on user satisfaction, attitude towards the hotel and room purchase intention. Moreover, our results showed that users were most likely to accept medium-human likeness robots and least likely to accept high–human likeness robots.
Originality/value
This study proposes influencing factors to be considered when researching hotel service robots, as well as practical suggestions for any hotel intending to use or currently using a service robot.
Robots are the next wave in service technology; however, this advanced technology is not perfect. This research examines how social perceptions regarding the warmth and competence of service robots influence consumer reactions to service failures and recovery efforts by robots. We argue that humanoid (vs. nonhumanoid) service robots are more strongly associated with warmth (whereas competence does not differ). This tendency to expect greater warmth from humanoid robots has important consequences for service firms: (i) consumers are more dissatisfied due to lack of warmth following a process failure caused by a humanoid (vs. nonhumanoid; Study 1); (ii) humanoids (but not nonhumanoids) can recover a service failure by themselves via sincere apology, restoring perceptions of warmth (Study 2A); (iii) humanoids (but not nonhumanoids) can also effectively provide explanations as a recovery tactic (Study 2B); and, importantly, (iv) human intervention can be used to mitigate dissatisfaction following inadequate recovery by a nonhumanoid robot (Study 3), supporting the notion of human-robot collaboration. Taken together, this research offers theoretical implications for robot anthropomorphism and practical implications for firms employing service robots.
Purpose
Facial recognition systems represent a viable solution to today’s hotels’ security and service challenges. The purpose of this study was to build and empirically validate a conceptual model that examined consumers’ willingness to create a profile based on biometric information disclosed via facial recognition systems.
Design/methodology/approach
Data were collected from 421 US general population consumers who stayed in hotels. The study used a confirmatory factor analysis to test the measurement model and a structural equation modeling approach to empirically validate the structural model.
Findings
It was found that the benefit of information disclosure was the strongest predictor of value of disclosure and that value of disclosure and privacy concerns influenced consumers’ willingness to disclose biometric information. In turn, consumers’ willingness to disclose biometric information and their desire to be loyal to hotels influenced consumers’ willingness to create a profile.
Originality/value
To the best of the author’s knowledge, this is the first study to examine profile creation and biometric information disclosure via facial recognition systems in hotels, a technology that is likely to disrupt the current authentication and service quality models in hotels. This study also advances the literature by expanding the scope of the privacy calculus by adding social rewards, and by elucidating the role of desires in service contexts.
In the 21st century, we live in a world packed with closed-circuit video cameras, facial recognition systems, radio frequency identification chips, electronic toll collectors, smartphones with location tracking, and widespread monitoring of our electronic communications. As deeply as the industrial revolution upended 20th century social norms and political structures, so too has modern information technology been a revolution, giving governments and large private corporations vast power to keep track of, manipulate, and potentially repress entire populations. China offers some examples in this area, but even democratically elected governments have shown a tendency to want to digitally profile and analyze their citizens without sufficient respect for individual privacy. And just as rampant industrialization had to be reined in to protect human rights and individual dignity, information technology and digital systems must be controlled to prevent abuse and exploitation. This “boundary setting” can come none too soon, as rapid advances in artificial intelligence allow an even greater ability to process and categorize the vast amounts of data generated by all our electronic devices.
The recent breakthroughs in Artificial Intelligence (AI) have allowed individuals to rely on automated systems for a variety of reasons. Some of these systems are the currently popular voice-enabled systems like Echo by Amazon and Home by Google that are also called as Intelligent Personal Assistants (IPAs). Though there are rising concerns about privacy and ethical implications, users of these IPAs seem to continue using these systems. We aim to investigate to what extent users are concerned about privacy and how they are handling these concerns while using the IPAs. By utilizing the reviews posted online along with the responses to a survey, this paper provides a set of insights about the detected markers related to user interests and privacy challenges. The insights suggest that users of these systems irrespective of their concerns about privacy, are generally positive in terms of utilizing IPAs in their everyday lives. However, there is a significant percentage of users who are concerned about privacy and take further actions to address related concerns. Some percentage of users expressed that they do not have any privacy concerns but when they learned about the "always listening" feature of these devices, their concern about privacy increased.
Social robots are designed to interact, collaborate, and work with people, engaging them at an interpersonal and socio-affective level. The goal directed model was used to study the role of socio-cognitive,
affective (anticipated emotions), and motivational (behavioral desire) factors of the intention to work with social robots in the near future. The effects of robots' perceived warmth and competence on components of the goal directed model are explored as well as the effects of social robots' design (machinelike vs. humanoid vs. humanlike). Results show that the goal directed model accounts for a
considerable amount of the explained variance of behavioral intention, underlining the importance of affective and motivational factors, in the intention to work with a social robot. The role of robots' perceived warmth and competence was also confirmed. Results for robot design were inconclusive
Purpose
To review recent work in the robotics literature and identify future opportunities for consumer/tourist experience research in human-robot interactions (HRI).
Design/methodology/approach
The paper begins by covering the framework of robotic agent presence and embodiment that is relevant for HRI. Next, the paper identifies future opportunities for hospitality and tourism scholars to undertake consumer/tourist experience research in HRI.
Findings
Potential directions to advance theoretical, methodological, and managerial implications for tourism experience research in HRI.
Research limitations/implications
Concepts from robotics research are diffusing into a range of disciplines, from engineering to social sciences. These advancements open many unique, yet urgent, opportunities for hospitality and tourism research.
Practical implications
This paper illustrates the speed by which robotics research is progressing. Moreover, the concepts reviewed in this research on robotic presence and embodiment are relevant for real-world applications in hospitality and tourism.
Originality/value
This research is one of the early papers in the field to review robotics research and provide innovative directions to broaden the interdisciplinary perspective for future hospitality and tourism research.
This research fills a gap in the retailing literature regarding the impact of shoppers’ perceptions of being watched while shopping for embarrassing products. Four studies consistently show that an employee watching a shopper can cause the shopper to either permanently or temporarily leave the shopping area as purchase intentions decrease. Reactance theory explains this relationship, which is mediated by consumers’ feelings of control over their own privacy. Essentially, when shoppers believe an employee is watching them, they feel less in control of their privacy, resulting in negative consequences for the retailer. This relationship is especially important for products that consumers may already feel some level of embarrassment over purchasing in the first place. The results have important theoretical implications for reactance theory by demonstrating that a consumer can regain control even when the original threat to behavior still exists. Additionally, increasing options that allow a consumer to regain control will reduce the overall reactance to the threat to privacy and will improve retailer outcomes. Practitioner recommendations present several techniques that allow the consumer to regain privacy control in spite of the sometimes necessary practice of watching in-store consumers.
Individuals communicate and form relationships through Internet social networking websites such as Facebook and MySpace. We study risk taking, trust, and privacy concerns with regard to social networking websites among 205 college students using both reliable scales and behavior. Individuals with profiles on social networking websites have greater risk taking attitudes than those who do not; greater risk taking attitudes exist among men than women. Facebook has a greater sense of trust than MySpace. General privacy concerns and identity information disclosure concerns are of greater concern to women than men. Greater percentages of men than women display their phone numbers and home addresses on social networking websites. Social networking websites should inform potential users that risk taking and privacy concerns are potentially relevant and important concerns before individuals sign-up and create social networking websites.
This study examines gender differences in young adults' privacy beliefs, their reactions to behavioral advertising, personal information-sharing behaviors, and privacy protection behaviors on social networks. This investigation uses a large-scale survey of college students based on a social networked sampling technique facilitated through a Facebook group. Results reveal several gender differences in these areas. Third-party data usage beyond the original purpose and behavioral advertising techniques are of concern to both genders but more to women. In addition, women engage in noticeably more proactive privacy protection behavior compared with a decade ago. The authors conclude with a discussion of implications for behavioral advertising. [ABSTRACT FROM AUTHOR] Copyright of Journal of Interactive Advertising is the property of Journal of Interactive Advertising and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
This study aims to investigate trust and privacy concerns related to the willingness to provide personal information online under the influence of cross-cultural effects. This study investigated the relationships among the content of online privacy statements, consumer trust, privacy concerns, and the moderating effect of different cultural backgrounds of the respondents. In specific, this study developed a proposed model based on Privacy–Trust–Behavioral Intention model. Further, a total of 500 participants participated in the survey, including 250 from Russia and 250 from Taiwan. The findings indicate a significant relationship between the content of privacy policies and privacy concern/trust; willingness to provide personal information and privacy concern/trust; privacy concern and trust. The cross-cultural effect on the relationships between the content of privacy policies and privacy concern/trust was also found significant.
This paper describes the results of an experimental study in which older adult participants interacted with three monitoring technologies designed to support their ability to age in place in their own home - a camera, a stationary robot, and a mobile robot. The aim of our study was to evaluate users' perceptions of privacy and their tendencies to engage in privacy enhancing behaviors (PEBs) by comparing the three conditions. We found that privacy concerns lead older adults to change their behavior in a home environment while being monitored by cameras or embodied robots. We expected participants to engage in more PEBs when they interacted with a mobile robot, which provided embodied cues of ongoing monitoring; surprisingly, we found the opposite to be true - the camera was the condition in which participants performed more PEBs. We describe the results of quantitative and qualitative analyses of our survey, interview, and observational data and discuss the implications of our study for human-robot interaction, the study of privacy and technology, and the design of assistive robots for monitoring older adults.
This study examines whether gender differences are apparent in attitudes and behaviors toward advertising and marketing practices involving information gathering and privacy on-line. As part of a larger study, 889 internet users nationwide were surveyed using electronic mail. Results indicated that women and men differed significantly in their attitudes toward several practices, with women generally appearing more concerned about the effect the practice would have on their personal privacy. Additionally, the study found that men were likely to adopt behaviors to protect their privacy when they became concerned; women, however, rarely adopted protective behaviors. Implications for web advertisers are provided. © 1999 John Wiley & Sons, Inc. and Direct Marketing Educational Foundation, Inc.
Organizational information practices can result in a variety of privacy problems that can increase consumers' concerns for information privacy. To explore the link between individuals and organizations regarding privacy, we study how institutional privacy assurances such as privacy policies and industry self-regulation can contribute to reducing individual privacy concerns. Drawing on Communication Privacy Management (CPM) theory, we develop a research model suggesting that an individual's privacy concerns form through a cognitive process involving perceived privacy risk, privacy control, and his or her disposition to value privacy. Furthermore, individuals' perceptions of institutional privacy assurances -- namely, perceived effectiveness of privacy policies and perceived effectiveness of industry privacy self-regulation -- are posited to affect the riskcontrol assessment from information disclosure, thus, being an essential component of privacy concerns. We empirically tested the research model through a survey that was administered to 823 users of four different types of websites: 1) electronic commerce sites, 2) social networking sites, 3) financial sites, and 4) healthcare sites. The results provide support for the majority of the hypothesized relationships. The study reported here is novel to the extent that existing empirical research has not explored the link between individuals' privacy perceptions and institutional privacy assurances. We discuss implications for theory and practice and provide suggestions for future research.
Survey data from 395 high school students revealed that girls perceive more privacy risks and have a higher level of privacy concerns than boys. Regarding privacy protection behaviors, boys tended to read unsolicited e-mail and register for Web sites while directly sending complaints in response to unsolicited e-mail. This study found girls to provide inaccurate information as their privacy concerns increased. Boys, however, refrained from registering to Web sites as their concerns increased.