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This study aims to analyse online customer experience in the hospitality industry through dynamic topic modelling (DTM) and net promoter score (NPS). A novel model that was used for collecting, pre-processing and analysing online reviews was proposed to understand the hidden information in the corpus and gain customer experience.

A corpus with 259,470 customer comments in English was collected. The researchers experimented and selected the best K parameter (number of topics) by perplexity and coherence score measurements as the input parameter for the model. Finally, the team experimented on the corpus using the Latent Dirichlet allocation (LDA) model and DTM with K coefficient to explore latent topics and trends of topics in the corpus over time.

The results of the topic model show hidden topics with the top high-probability keywords that are concerned with customers and the trends of topics over time. In addition, this study also calculated and analysed the NPS from customer rating scores and presented it on an overview dashboard.

The data used in the experiment are only a part of all user comments; therefore, it may not reflect all of the current customer experience.

The management and business development of companies in the hotel industry can also benefit from the empirical findings from the topic model and NPS analytics, which will support decision-making to help businesses improve products and services, increase existing customer satisfaction and draw in new customers.

This study differs from previous works in that it attempts to fill a gap in research focused on online customer experience in the hospitality industry and uses text analytics and NPS to reach this goal.

本研究旨在通过动态主题建模和净推荐值分析酒店业的在线客户体验。 提出了一种用于收集、预处理和分析在线评论的新模型, 以了解语料库中的隐藏信息并获得客户体验。

收集了一个包含 259,470 条英文客户评论的语料库。 研究人员通过 Perplexity 和 Coherence Score 测量结果进行了实验, 并选择了最佳的 K 参数（主题数量）作为模型的输入参数。 最后, 团队使用 Latent Dirichlet allocation (LDA) 模型和具有 K 系数的 Dynamic Topic Model (DTM) 在语料库上进行实验, 以探索语料库中的潜在主题和主题随时间变化的趋势。

主题模型的结果显示了隐藏的主题, 其中包含与客户相关的顶级高概率关键字以及主题随时间的变化趋势。 此外, 该研究还根据客户评分计算和分析净推荐值 (NPS), 并将其显示在概览仪表板上。

实验中使用的数据只是所有用户评论的一部分; 因此, 它可能无法反映所有当前的客户体验。

酒店业公司的管理和业务发展也可以受益于主题模型和 NPS 分析的实证结果, 这将支持决策制定, 帮助企业改进产品和服务, 提高现有客户满意度, 并吸引新客户 .

本研究不同于以往的研究, 因为它试图填补以酒店业在线客户体验为重点的研究空白, 并使用文本分析和 NPS 来实现这一目标。

There is a limited understanding of experience revelation in tourism. This study aims to fill the gap by investigating the influence of review platforms’ characteristics, i.e. time-dimension and interactivity, on this issue to generate a holistic view of customer experience.

This study analysed data from Google Reviews, TripAdvisors and Twitter, consisting of 41,914 records within a three-year span, about Komodo National Park, Indonesia. An explanatory sequential mixed method was performed, adopting quantitative sentiment analysis with a naïve algorithm, opinion lexicon and Latent Dirichlet Allocation for topic modelling, followed by a qualitative analysis.

The findings support the proposed interaction between the characteristics of the platforms and the extent of customer experience shared through the platforms. Further elaboration of the data brought up five propositions on the relationship between the time dimension and interactivity characteristics of the review platforms and experience sharing on the platforms.

This study presents an original and initial effort to gather a holistic view on customer experience. It brings valuable implications to the theory and practice of customer experience management, especially in the tourism sector.

目前文献对旅游体验启示的认识有限。 本研究通过调查评论平台的特征（即时间维度和交互性）对此问题的影响来填补空白, 以生成客户体验的整体视图。

本研究分析了来自谷歌评论、TripAdvisors 和 Twitter 的数据, 包括三年内关于印度尼西亚科莫多国家公园的 41,914 条记录。 运用了解释性顺序混合方法, 采用朴素算法、意见词典和隐含狄利克雷分布进行主题建模的定量情感分析, 然后进行定性分析。

调查结果支持所提出的平台特征与通过平台共享的客户体验程度之间的相互作用。 对数据的进一步阐述, 提出了评论平台的时间维度和交互特征与平台经验分享之间关系的五个命题。

本研究通过一项原创和初步的努力收集了关于客户体验的整体观点。 它为客户体验管理的理论和实践带来了宝贵的启示, 尤其是在旅游领域。

In this era of smartphone applications, brands are actively developing applications to occupy the consumer’s mobile phone space, adding many practical functions to their applications to increase brand exposure or consumer interest in the brand. Augmented reality (AR) has evolved rapidly in the past decade because of technological breakthroughs, making AR no longer an untouchable technology, but one that can be easily used on almost every phone. Therefore, this study aims to combine extended customer experience with AR marketing activities to explain and predict usage and purchase intention.

The eight key factors integrated into the extended customer experience are used as environmental stimulation factors, and Wanna Kicks and FitGlasses are used as experimental environments. A total of 193 valid samples were collected from users with AR experience. The empirical data is processed and verified by partial least squares in this study.

Customer experience has received increasing attention in the field of marketing research. This study developed a model to evaluate the antecedents and consequences of AR marketing activities by systematically adding customer experience, continuance intention, purchase intention and customer engagement, and then linking them to the AR application environment. This study presents the academic and practical implications, which can provide future research directions and references for brand marketing strategies and AR applications.

This study adds interactivity, vividness, perceived usefulness and novelty to the extended concept of customer experience. Therefore, the authors proposed that the extended customer experience can be used to measure the perceptions related to AR applications. This study is expected to provide scholars and practitioners in AR fields with a better understanding of the causes and consequences of customer experience with innovative technologies and to suggest effective marketing recommendations.

在这个智能手机应用时代, 品牌积极发展能占据消费者的手机空间的应用软件, 在已有的应用软件上增加了许多实用性功能来增加品牌曝光或消费者对品牌的兴趣。增强现实 (AR) 在过去十年中由于技术突破从而发展迅速, 让AR不再触不可及, 而是几乎可以在每部手机上轻松使用的技术。因此, 本研究通过结合 AR 营销活动以及延展顾客体验, 来以解释和预测用户使用和购买意向。

本研究用延展顾客体验模型中的八项关键因素来作为环境刺激因素, Wanna Kicks 和 FitGlasses 用作实验环境。 本研究从具有 AR 经验的用户那里收集193个有效样本。数据通过偏最小二乘法来进行处理和验证。

客户体验在营销领域受到越来越多的关注。本研究通过系统地添加客户的 体验、持续意向、购买意向和客户参与度, 然后将它们链接到 AR 应用环境来评估AR 营销活动的前因和后果来建立了一项模型。本研究提出了学术和实践意义, 可以提供未来的研究方向品牌营销策略和AR应用提供参考。

这项研究对客户体验扩展概念增加了交互性、生动性、感知有用性、以及新颖性。因此, 我们建议延展客户体验模型可运用于衡量与 AR 应用相关的用户感知。本研究旨在为学者和AR领域的从业者关于创新技术的客户体验的因果提供更进一步的认知, 并提出有效的营销建议。

The increased utilization of artificial intelligence-enabled applications (AI-ETs) across the customer journey has transformed customer experience (CX), introducing entirely new forms of the concept. This paper aims to explore existing academic research on the AI-enabled customer experience (AICX), identifying gaps in literature and opportunities for future research in this domain.

A systematic literature review (SLR) was conducted in March 2022. Using 16 different keyword combinations, literature search was carried across five databases, where 98 articles were included and analysed. Descriptive analysis that made use of the Theory, Characteristics, Context, Methods (TCCM) framework was followed by content analysis.

This study provides an overview of available literature on the AICX, develops a typology for classifying the identified AI-ETs, identifies gaps in literature and puts forward opportunities for future research under five key emerging themes: definition and dynamics; implementation; outcomes and measurement; consumer perspectives; and contextual lenses.

This study establishes a fresh perspective on the interplay between AI and CX, introducing the AICX as a novel form of the experience construct. It also presents the AI-ETs as an integrated and holistic unit capturing the full range of AI technologies. Remarkably, it represents a pioneering review exclusively concentrating on the customer-facing dimension of AI applications.

随着人工智能应用程序 (AI-ET)在旅途中的使用不断增加, 消费者体验 (CX)得以转变, 引入了全新的概念形式。 本文旨在探索有关人工智能客户体验（AICX）的现有学术研究, 从中找出文献中的空白以及该领域未来研究的机会。

本系统性文献综述（SLR）于2022 年 3 月开工。基于16 个不同的关键词组合, 本综述统共收录并分析了来自 5 个数据库98 篇文献, 采用理论-特征-背景-方法 (TCCM) 框架先后进行描述性分析和内容分析。

该研究概述了 AICX 的现有文献, 开发了对已识别的 AI-ET 进行分类的类型学, 确定了现有文献中的空白, 并在 5 个关键新兴主题下提出了未来研究的机会：1. 定义和动态, 2 . 实施, 3. 结果和衡量, 4. 消费者视角, 5. 情境视角。

本研究建立了全新的视角看待 AI 和 CX 之间的相互作用, 引入了 AICX 这种新颖的体验构造形式, 还将 AI-ET 展示为一个集成了全方位人工智能技术的整体单元。 值得一提的是, 本文代表了一项专门关注人工智能应用面向客户维度的开创性综述。

La creciente utilización de aplicaciones habilitadas por inteligencia artificial (AI-ET) a lo largo del recorrido del cliente han transformado la experiencia del cliente (CX), introduciendo formas totalmente nuevas del concepto. Este artículo pretende explorar la investigación académica existente sobre la experiencia del cliente a través de la IA (AICX), identificando las lagunas en la literatura y las oportunidades para futuras investigaciones en este ámbito.

En marzo de 2022 se llevó a cabo una revisión bibliográfica sistemática (SLR). Utilizando 16 combinaciones diferentes de palabras clave, se realizó una búsqueda bibliográfica en 5 bases de datos en las que se incluyeron y analizaron 98 artículos. El análisis descriptivo que hizo uso del marco Teoría, Características, Contexto, Métodos (TCCM) fue seguido del análisis de contenido.

El estudio ofrece una visión general de la bibliografía disponible sobre la AICX, desarrolla una tipología para clasificar las AICX detectadas, identifica lagunas en la literatura y plantea oportunidades para futuras investigaciones bajo cinco temas emergentes claves: 1. Definición y dinámica, 2. Implementación, 3. Resultados y medición, 4. Perspectivas del consumidor, 5. Lentes contextuales.

El estudio establece una nueva perspectiva sobre la interacción entre la IA y la CX, introduciendo la AICX como una forma novedosa del constructo experiencia. También presenta las AICX como una unidad integrada y holística que capta toda la gama de tecnologías de la IA. Notablemente, representa una revisión pionera que se concentra exclusivamente en la dimensión orientada al cliente de las aplicaciones de la IA.

Based on relationship marketing theory, this study aims to test the effect of social customer relationship management (social CRM) on customer satisfaction (CS) and loyalty (CL).

To assess the proposed framework, structural equation modeling was performed on the data of 314 automotive customers surveyed online.

Social CRM dimensions [traditional CRM (TCRM) and social media (SM) technology use] have a direct and positive effect on CS. On the other hand, only TCRM has a direct and significant influence on CL, while the SM technology use effect seems to be indirect rather than direct. Indeed, the findings have provided empirical support for the contention that CS plays a mediating role between social CRM dimensions and CL.

In the automotive sector and developing countries in particular, companies’ managers could increase CS and CL and consequently enhance their competitiveness and market share by adopting an effective social CRM strategy. From this perspective, companies should focus their social CRM campaigns on the most SM used by customers, offer personalized choices and improve customer experience, interaction and value co-creation.

This paper enriches the understanding of how social CRM can affect CS and CL. The scales of social CRM, CS and CL were validated in the context of developing countries and the automotive sector. Furthermore, the direct and mediating effect of CS between social CRM (TCRM and SM) and CL was also confirmed.

Basándose en la teoría del marketing relacional, este estudio pretende comprobar el efecto de la gestión social de las relaciones con los clientes (CRM social) sobre la satisfacción y la fidelidad de los clientes.

Para evaluar el marco propuesto, se realizó un modelado de ecuaciones estructurales sobre los datos de 314 clientes de automoción encuestados online.

Las dimensiones del CRM social (CRM tradicional y uso de tecnología de medios sociales) tienen un efecto directo y positivo en la satisfacción del cliente. Por otro lado, solamente el CRM tradicional tiene una influencia directa y significativa en la fidelidad del cliente, mientras que el efecto del uso de la tecnología de medios sociales parece ser más indirecto que directo. De hecho, los resultados han proporcionado apoyo empírico a la afirmación de que la satisfacción del cliente desempeña un papel mediador entre las dimensiones del CRM social y la fidelidad del cliente.

Este artículo enriquece la comprensión de cómo el CRM social puede afectar a la satisfacción y la fidelidad de los clientes. Las escalas de CRM social, satisfacción del cliente y fidelidad del cliente se validaron en el contexto de países en vías de desarrollo y del sector automovilístico. Además, también se confirmó el efecto directo y mediador de la satisfacción del cliente entre el CRM social (CRM tradicional y medios sociales) y la fidelidad del cliente.

En el sector de la automoción y en los países en desarrollo en particular, los directivos de las empresas podrían aumentar la satisfacción y fidelidad de sus clientes y, en consecuencia, mejorar su competitividad y cuota de mercado adoptando una estrategia eficaz de CRM social. Desde esta perspectiva, las empresas deberían centrar sus campañas de CRM social en los medios más utilizados por los clientes, ofrecer opciones personalizadas y mejorar la experiencia del cliente, la interacción y la cocreación de valor.

基于关系营销理论, 本研究旨在检验社会化客户关系管理（social CRM）对客户满意度和忠诚度的影响。

为评估所提出的框架, 对 314 名汽车客户的在线调查数据进行了结构方程建模。

社交客户关系管理维度（传统客户关系管理和社交媒体技术使用）对客户满意度有直接的积极影响。另一方面, 只有传统客户关系管理对客户忠诚度有直接和显著的影响, 而社交媒体技术使用的影响似乎是间接而非直接的。事实上, 研究结果为客户满意度在社交客户关系管理维度和客户忠诚度之间发挥中介作用的论点提供了实证支持。

本文丰富了人们对社交客户关系管理如何影响客户满意度和忠诚度的认识。本文以发展中国家和汽车行业为背景, 对社会化客户关系管理、客户满意度和客户忠诚度的量表进行了验证。此外, 还证实了客户满意度在社会化客户关系管理（传统客户关系管理和社会化媒体）与客户忠诚度之间的直接和中介效应。

尤其是发展中国家, 企业管理者可以通过采取有效的社交客户关系管理战略, 提高客户满意度和忠诚度, 进而增强竞争力和市场份额。从这个角度来看, 企业应将社交客户关系管理活动的重点放在客户使用最多的社交媒体上, 提供个性化选择, 改善客户体验、互动和价值共创。

This study aims to examine technology adoption practices in Chinese theme parks by leveraging text mining and sentiment analysis approaches on actual theme park customers’ online reviews.

The study text mined a total of 65,518 reviews of 490 Chinese theme parks with the aid of the Python program. Further, it computed sentiment scores of the customer reviews associated with the ratings of each categorized technology practice applied in the theme parks.

The study identified two major categories of technology applications in theme parks: supporting and experiential technologies. Multiple statistical tests confirmed that supporting technologies consisted of three types: intelligent services, ticketing and in-park transportation. Experiential technologies further included five aspects of technologies according to Schmitt’s strategic experiential modules (SEMs): sense, feel, act, think and relate.

The study findings contribute to the current understanding of theme park visitors’ perceptions of technology adoption practices and provide insightful implications for theme park practitioners who intend to invest in high technology solutions to deliver a better customer experience.

通过对游客的在线评论进行文本挖掘和情感分析, 本研究论旨在探索在中国主题公园中科技采用的行为。

本研究运用Python 程序一共挖掘了来自490 中国主题公园的65,518 条评论。本研究进一步计算了在主题公园中与科技运用有关在线评论的情感指数。

本研究发现了在主题公园科技应用的两大主要分类：辅助和体验科技。辅助科技包括三种：智慧服务, 售票, 和园中运输。根据Schmitt 战略体验模块（SEMs): 体验科技进一步包括科技的五大方面：感官, 感觉, 思考, 和联系。

本研究对了解目前主题公园游客对科技使用行为的看法提供了见解, 以及对主题公园有意向投资科技来提高客户体验的从业人员提供了深远意义。

The purpose of this study is to assess the possibility of introducing a restaurant-menu curation (RMC) chatbot service to help consumers quickly and effectively decide on their restaurant or menu choices. To this end, it measures the characteristics of consumer chatbot experiences and analyzes their impact on future acceptance intentions through their attitudes toward the RMC chatbot service.

This study consists of three parts: developing a RMC chatbot prototype, testing the chatbot prototype and a customer survey based on experience. A convenience sample method was used to collect data from 368 adults who tried the RMC chatbot service before answering a self-administered questionnaire. Partial least squares structural equation modeling (PLS-SEM) was used to test the proposed structural model.

The results showed that all experience characteristics, except usable facets, had a significant positive impact on attitudes toward the chatbot. Three experience characteristics, “usable,” “usefulness” and “valuable,” revealed a significant positive effect on utilization intention. Attitudes toward chatbot services also significantly affected utilization intention.

The results of this study can offer practical and academic implications for establishing curation services in the restaurant industry that can increase customer acceptance and utilization intentions. Follow-up studies are required to explore and verify the various personal and psychological factors related to the intention to accept RMC chatbot services.

This study is meaningful because it makes it possible to evaluate the introduction of curation chatbot services in the restaurant sector, by developing and testing the dining-out curation service protocol to help customers’ smart choices in the information technology environment.

本研究的目的是评估引入餐厅菜单管理 (RMC) 聊天机器人服务以帮助消费者快速有效地决定他们的餐厅或菜单选择的可能性。为此, 本研究衡量了消费者聊天机器人体验的特征, 并通过他们对餐厅菜单管理聊天机器人服务的态度来分析它们对未来接受意图的影响。

研究由三部分组成; 开发餐厅菜单管理聊天机器人原型, 测试聊天机器人原型, 并根据经验进行客户调查。使用便利样本方法收集 368 名成年人的数据, 这些成年人在回答问卷之前尝试了 RMC 聊天机器人服务。 PLS-SEM 被用于测试提出的结构模型。

结果表明, 除可用方面外, 所有体验特征都对聊天机器人的态度产生了显着的积极影响。 “可用性”、“有用”和“有价值”三个体验特征对使用意愿有显着的正向影响。对聊天机器人服务的态度也显着影响了使用意愿。

本研究的结果可以为在餐饮业建立策展服务提供实践和学术意义, 从而提高客户的接受度和使用意图。需要进行后续研究, 以探索和验证与接受 RMC 聊天机器人服务的意图相关的各种个人和心理因素。

这项研究的重大意义在于它可以通过开发和测试外出就餐策展服务协议来评估在餐厅行业引入策展聊天机器人服务, 以帮助客户在信息技术 (IT) 方面做出明智的选择环境。

Collecting information from and interacting with customers through mobile platforms for personalization purposes have become a trend. While mobile-based value co-creation has attracted wide research attention, a noticeable gap exists regarding what might potentially affect the firm–customer interaction process through which value is co-created. This paper aims to explore how customers exchange information and communicate with firms through mobile applications for value co-creation purposes in a travel context.

Based on a constructivist research paradigm, this study adopted a qualitative research design. Data were collected through semi-structured interviews and were analyzed following narrative analysis approach.

The findings highlight the contextual factors (individual characteristics, trip characteristics and computer-mediated communication characteristics) that facilitate and inhibit the firm–customer interaction process. Practitioners are suggested to put more efforts on creating stimuli for interactions and managing customer expectation.

This study goes beyond technology adoption and focuses on customers’ post-adoption stage. The findings shed light on the important role of the service provider in facilitating effective interactions for value co-creation with customers.

This study focuses on the interaction process, rather than the antecedents and outcomes of mobile-based value co-creation. It contributes empirical evidence on how customers co-create value and why some situations present better opportunities for successful value co-creation.

基于移动资讯科技的价值共创：影响客户体验的情景因素

通过移动资讯科技平台收集客户信息并与客户互动从而提供个性化服务和体验已成为一种趋势。尽管基于移动资讯科技的价值共创已经吸引了广泛的研究关注, 但是什么潜在因素可能会影响企业与客户为了共创价值而进行的互动的过程仍未被确定。这项研究探讨客户如何在旅行环境中透过移动应用程式与服务提供商交换信息和进行交流, 以实现价值共创的目的。

本研究基于建构主义研究范式, 采用了定性研究设计。通过半结构化访谈收集数据, 并根据叙述分析方法进行数据分析。

调查结果突出了有助于和抑制企业与客户透过移动科技互动的情境因素（个人特征、行程特征和电脑媒介传播特征）, 建议业界人士在促进互动和管理客户期望方面付出更多的努力。

本研究关注企业和客户的互动过程, 而非着重于价值共创的前提和结果。它提供了经验性证据解释客户如何与企业共创价值, 以及为什么某些情况为成功实现价值共创提供更好的机会。

有别于过去着重探讨影响技术采用因素的研究, 本研究关注客户的采用后阶段。调查结果阐明了服务提供商在促进与客户进行有效互动从而与客户共创价值的重要角色。

El valor de la co-creación basado en dispositivos móviles: Factores contextuales sobre la experiencia de los clientes

Resumen

Basado en un paradigma de investigación constructivista, este estudio adopta un diseño de investigación cualitativa. Los datos se recopilaron a través de entrevistas semiestructuradas y se analizaron siguiendo un enfoque de análisis narrativo.

Recopilar información e interactuar con los clientes a través de plataformas móviles con fines de personalización se ha convertido en tendencia. Si bien la co-creación de valor basada en dispositivos móviles ha atraído una amplia atención en la investigación, existe una brecha notable con respecto a lo que podría afectar potencialmente en el proceso de interacción empresa-cliente a través del cual se co-crea valor. Este estudio tiene como objetivo explorar cómo los clientes intercambian información y se comunican con las empresas a través de aplicaciones móviles con fines de creación conjunta de valor en el contexto de los viajes.

Los resultados enfatizan los factores contextuales (características individuales, características del viaje y características de la comunicación mediada por ordenador) que facilitan e inhiben el proceso de interacción empresa-cliente. Se sugiere a los profesionales del turismo mayor esfuerzo para crear estímulos en las interacciones y gestionar las expectativas del cliente.

Este estudio va más allá de la adopción de tecnología y se centra en la etapa posterior a la adopción del uso de la tecnología por parte de los clientes. Los resultados proyectan luz sobre el importante papel del proveedor de servicios a la hora de facilitar interacciones efectivas para la creación conjunta de valor con los clientes.

Este estudio se centra en el proceso de interacción, más que en los antecedentes y resultados de la co-creación de valor basada en dispositivos móviles. Aporta evidencia empírica sobre cómo los clientes co-crean valor y por qué algunas situaciones presentan mejores oportunidades para la co-creación de valor exitosa.

This paper aims to find a way to personalize attraction recommendations for travelers. The research objective is to find a more accurate way to suggest new attractions to each traveler based on the opinions of other like-minded travelers and the traveler’s preferences.

To achieve the goal, developers have created a personalized system to generate attraction recommendations. The system considers an individual traveler’s preferences to construct a qualitative attraction ranking model. The new ranking model is the result of blending two processes: K-means clustering and the analytic hierarchy process (AHP).

The performance of the developed recommendation system has been assessed by measuring the accuracy and scalability of the ranking model of the system. The experimental results indicate that the ranking model always returns accurate results independent of the number of attractions and the number of travelers in each cluster. The ranking model has also proved to be scalable because the processing time is independent of the numbers of travelers. Additionally, the results reveal that the overall system usability is at a very satisfactory level.

The main theoretical implication is that integrating the processes of K-means and AHP techniques enables a new qualitative ranking model for personalized recommendations that deliver only high-quality attractions. However, the designed recommendation system has some limitations. First, it is necessary to manually update information about the new tourist attractions. Second, the overall response time depends on the internet bandwidth and latency.

This research contributes to the tourism business and individual travelers by introducing an accurate and scalable way to suggest new attractions to each traveler. The potential benefit includes possible increased revenue for travel agencies that offer personalized package tours and support individual travelers to make the final travel decisions. The designed system could also integrate with itinerary planning systems to plot out a journey that pinpoints what travelers will most enjoy.

This research proposes a design and implementation of a personalized recommendation system based on the qualitative attraction ranking model introduced in this article. The novel ranking model is designed and developed by integrating K-means and AHP techniques, which has proved to be accurate and scalable.

本研究主要探索如何建立个性化旅游胜地推荐模型。本研究通过分析旅游兴趣相似的游客意见和游客偏好选择, 建立一种更加准确推荐游客需要的旅游胜地方法。

为了达到研究目的, 本研究建立了一种个性化推荐旅游胜地的信息系统。其系统通过分析每个游客的旅游偏好来建设一种定性旅游胜地排名模型。这种新型模型主要通过结合以下两种分析算法：（1）K平均聚类算法（K-means clustering）（2）层次分析法（AHP）。

本研究建立的推荐信息系统经过了准确率和拓展性的测评。实验结果表明这种排名模型的准确率并不受旅游胜地多少和游客样本大小的影响。此外, 这种排名模型也具有拓展性, 因为算法时间并不受游客样本大小的影响。最后, 研究实验表明此排名模型客户体验性达到合格满意要求。

本研究的主要理论意义在于其结合了K平均聚类算法和层次分析法, 并建立了一种新型定性排名模型, 这种排名模型个性化地推荐更高质量的旅游胜地给游客。然而, 这种推荐信息系统有一些局限性。第一, 新旅游胜地的信息需要手动输入。第二, 整个系统的处理时间决定于网络带宽和延迟状况。

本研究的实践意义在于其建立了一种准确和具有拓展性的新型旅游胜地推荐模型。这种模型的潜在价值将有利于旅游机构提供定制化旅游套餐和帮助游客制定旅游计划。此外, 这种模型还可以结合旅游路线计划系统以制定更加使游客满意的旅游行程。

本研究推荐了一种基于定性旅游胜地排名模型的个性化旅游推荐模型。这种新型的排名模型结合K平均聚类算法和层次分析法, 实验证明这种模型更具准确性和拓展性。

This study aims to investigate Generation Z’s perception of and experience with restaurant service robots.

Established on the servicescape theoretical framework and following Zaltman Metaphoric Elicitation Technique, 34 in-depth interviews were conducted with Generation Z consumers.

The results showed that Generation Z consumers had a generally positive attitude toward the usage of service robots in restaurants. Research participants also indicated aspects that can be improved: the service robot’s appearance design and functionality, and the human service facilitating the robotic service process.

This paper provides a holistic understanding of the Generation Z’s mind maps about robot service restaurants presenting practical suggestions for restaurants.

This research offers an in-depth understanding of how the young consumption power perceives and expects the innovative service robots employed in restaurants. The research findings provide industry practitioners with timely guidelines to improve the usage of robotic services in restaurants to satisfy the emerging consumer group of Generation Z.

The current research contributes to the servicescape literature by extending this long-standing theory to the emerging robotic service contexts for updating discoveries. Particularly, the study focuses on the young consumers of Generation Z, shedding lights on the generational cohort research.

本研究调查了 Z 世代对餐厅服务机器人的看法和体验。

建立在服务场景理论框架之上, 并遵循 Zaltman 隐喻启发技术, 对 Z 世代消费者进行了 34 次深入访谈。

结果表明, Z 世代消费者对在餐厅使用服务机器人持普遍积极态度。 研究参与者还指出了可以改进的方面：服务机器人的外观设计和功能, 以及促进机器人服务过程的人工服务。

这项研究深入了解了年轻的消费力量如何看待和期望在餐厅使用的创新服务机器人。 研究结果为行业从业者提供了及时的指导方针, 以改善机器人服务在餐厅的使用, 以满足 Z 世代的新兴消费群体。

当前的研究通过将这一长期存在的理论扩展到新兴的机器人服务环境以更新发现, 从而为服务景观文献做出贡献。 特别是, 该研究侧重于 Z 世代的年轻消费者, 阐明了世代队列研究。