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Artificial intelligence (AI) technology has revolutionized customers' interactive marketing experience. Although there have been a substantial number of studies exploring the application of AI in interactive marketing, personalization as an important concept remains underexplored in AI marketing research and practices. This study aims to introduce the concept of AI-enabled personalization (AIP), understand the applications of AIP throughout the customer journey and draw up a future research agenda for AIP.

Drawing upon Lemon and Verhoef's customer journey, the authors explore relevant literature and industry observations on AIP applications in interactive marketing. The authors identify the dilemmas of AIP practices in different stages of customer journeys and make important managerial recommendations in response to such dilemmas.

AIP manifests itself as personalized profiling, navigation, nudges and retention in the five stages of the customer journey. In response to the dilemmas throughout the customer journey, the authors developed a series of managerial recommendations. The paper is concluded by highlighting the future research directions of AIP, from the perspectives of conceptualization, contextualization, application, implication and consumer interactions.

New conceptual ideas are presented in respect of how to harness AIP in the interactive marketing field. This study highlights the tensions in personalization research in the digital age and sets future research agenda.

This paper reveals the dilemmas in the practices of personalization marketing and proposes managerial implications to address such dilemmas from both the managerial and technological perspectives.

This is one of the first research papers dedicated to the application of AI in interactive marketing through the lenses of personalization. This paper pushes the boundaries of AI research in the marketing field. Drawing upon AIP research and managerial issues, the authors specify the AI–customer interactions along the touch points in the customer journey in order to inform and inspire future AIP research and practices.

This study aims to empirically investigate the relationship between artificial intelligence (AI) in marketing (AIM) and business performance from the resource-based view (RBV) perspective.

A survey strategy was used in this study to collect data from 225 small and medium enterprises (SMEs) respondents who were on the registered list of the Ghana Enterprise Agency in the Eastern Region of Ghana. Structural equation modeling – path analysis was used to estimate the impact of AIM on the performance of SMEs.

The analyzed data shows that AIM has significant impact on the financial performance, customer performance, internal business process performance and learning and growth performance in the case of SMEs in Ghana. This study establishes the significance of AIM approach in achieving financial performance, customer performance, internal business process performance and learning and growth performance through the application of AIM determinants including, Internet of Things (IoT), collaborative decision-making systems (CDMS), virtual and augmented reality (VAR) and personalization.

Aside the aforementioned significance of this research study, this study has limitations. The sample size of this research study can be expanded to include SME respondents in other geographical areas that were not considered in this study. Future research studies should concentrate on how AIM can analyze customer communications and information such as posts on social media to develop future communications that may enhance customer engagement.

The practical implications comprise of two key items. First, this research study encourages SME owners and managers to develop an AIM method as a fundamental strategic goal in their pursuit to improve SME performance. Second, SME owners and managers should increasingly implement the four determinants of AIM indicated in this research study (i.e., IOT, CDMS, VAR and personalization) to develop essential resources for effective application of AIM to improve their performance.

The results of this study provide a strong support to RBV theory and the proposition that AIM and its determinants (i.e., IOT, CDMS, VAR and personalization) should be recognized as an essential strategic resource for improving the performance (i.e., financial performance, customer performance, internal business process performance and learning and growth performance) of SMEs. This study also contributes to the current body of knowledge on AIM and management, particularly in the context of an emerging economy.

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.

The hospitality industry has witnessed numerous changes to enhance the stay experience of guests. To offer a memorable stay experience, the industry has started deploying intelligent robots. Therefore, this case study aims to examine and explore artificial intelligence (AI) enabled robots in hospitality industry in order to enhance guest experience in a smart city.

Semistructured interviews have been conducted at Novotel Ambassador Seoul Dongdaemun Hotels and Residences, Seoul, South Korea, to understand the stay experience of guests regarding services offered by AI enabled robots. The authors have selected employees for interviews since employees listen and witness the guest experience directly. Out of 214 employees in the hotel with varied experience and background, 26 interviews are conducted.

Through a systematic approach of coding, the authors have identified that deploying AI enabled robots facilitates the automation, information gathering, personalization and seamless service in the hospitality industry of a smart city. Further, with a back-and-forth mapping mechanism based on epistemological principles, the authors made four propositions that lead to the development of a research framework.

The practicing managers of hospitality industry can employ AI enabled robots within the scope of improving and automating the processes that can also offer increased personalization to enhance the stay experience, which is expected in a smart city.

The study offers a unique contribution to literature, since it is a live case study, and the information is from the practicing employees of a well-known organization in a hospitality sector from a smart city (Novotel Ambassador Seoul Dongdaemun Hotels and Residences, Seoul, South Korea).

Extending the technology acceptance model (TAM) to a new context, the purpose of this paper is to propose an integrative model of the brand of artificial intelligence-enabled voice assistants (AI-EVA) and customers’ perceptions and behavioral intentions of using AI-EVA in hotels. Moderating effects of construal levels and hotel scales were examined.

This paper adopted a mixed method approach. A qualitative and phenomenological methodology was adopted in Study 1 to explore hotel customers’ experience with AI-EVA. Study 2 applied experimental design to investigate the effects of the brand of AI-EVA and construal level on customers’ perceptions and behavioral intentions of using AI-EVA. Based on Studies 1 and 2 results, Study 3 examined how the brand of AI-EVA and hotel scale affect customers’ perceptions and behavioral intentions of using AI-EVA during hotel stays.

This research found that customers perceive a higher level of perceived usefulness, perceived ease of use and anthropomorphism when AI-EVA is branded (vs off-brand). Perceived usefulness positively affects customers’ intention to use and to spread positive word-of-mouth. Anxiety of using AI-enabled devices and privacy concerns inhibit customers’ intention to use AI-EVA. Anthropomorphism increases customers’ willingness to spread positive word-of-mouth. Construal level moderates the effect of the brand of AI-EVAs on perceived ease of use and anthropomorphism. Hotel scale moderates the effect of brand on perceived usefulness.

This paper is one of the first attempts to uncover and integrate different factors underlying customers’ perceptions of using AI-EVA in an extended TAM in hotel settings. This paper provides an integrative model extending the TAM to a new context by deploying a mixed-method approach across three studies.

India is a rapidly developing economy with a rapidly expending internet infrastructure and among the largest Generation Z population. This generation is tech savvy and the access to technology and network creates a conducive environment for such usage. Internet banking for the same reasons is growing leaps and bounds. The introduction of artificial intelligence (AI) has created disruptions in the traditional banking also. This paper aims to analyze the comfort level and usage of AI-enabled banking services by Generation Z.

The data is collected from 272 Generation Z members. The differential aspects, that is, the relationship of independent variables with dependent variables (AI-enabled internet banking), were analyzed using the structural equation modeling approach.

Defining factors for AI-enabled internet banking were identified. The results of factors were consistent with previous studies. It was found that the usage of AI-enabled internet banking services is insignificant, indicating that Generation Z does not perceive any advantage in using AI-enabled internet banking services.

This paper does not incorporate age groups other than Generation Z. Further research could throw light on the difference based on age groups. Further research is required to deeply understand why Generation Z does not perceive AI-enabled internet services as very important.

It has been observed that internet banking is important for Generation Z, but they also place greater importance on interpersonal communication. Banks need to consider this in designing their internet banking services.

This paper addresses the gap between comfort with and usage of AI-enabled internet banking services, by Generation Z. This paper indicates that the comfort with AI-enabled internet banking services does not translate to usage.

In the uncertain business environment, the supply chains are under pressure to balance routine operations and prepare for adverse events. Consequently, this research investigates how artificial intelligence is used to enable resilience among supply chains.

This study first analyzed the relationship among different characteristics of AI-enabled supply chain and how these elements take it towards resilience by collecting the responses from 27 supply chain professionals. Furthermore, to validate the results, an empirical analysis is conducted where the responses from 231 supply chain professionals are collected.

Findings indicate that the disruption impact of an event depends on the degree of transparency kept and provided to all supply chain partners. This is further validated through empirical study, where the impact of transparency facilitates the mass customization of the procurement strategy to Last Mile Delivery to reduce the impact of disruption. Hence, AI facilitates resilience in the supply chain.

This study adds to the domain of supply chain and information systems management by identifying the driving and dependent elements that AI facilitates and further validating the findings and structure of the elements through empirical analysis. The research also provides meaningful implications for theory and practice.

Various consumer-facing artificial intelligence (AI) applications are used to interact with consumers at all purchase stages, and related research has sharply increased. This study aims to synthesize the literature related to consumer–AI interaction using the customer journey framework, identify the factors affecting AI's effectiveness in interactive marketing and offer an agenda for future research.

This study undertakes a framework-based systematic review of 239 articles on AI in marketing from the consumer perspective published in peer-reviewed journals from 2007 to 2021.

This review identifies the roles of AI touch points and factors affecting the acceptance and effectiveness of consumer–AI interaction in each stage of the customer journey.

This study is the first to review the existing literature using a customer journey framework to identify the factors that influence customer interactions with AI touch points at each purchase stage and pave the way for future research.

Internet emergence has evolved the business of tourism companies, and every tourism company is available online and provides abundant choices, which causes consumers' confusion. The current study aims to investigate the interaction effect of demographic variables on confusion, decision postponement as a negative consequence of confusion and self-efficacy as a moderator in light of the stimulus organism response (SOR) model. Also, an attempt has been made to integrate the smart technology-based solution to deal with consumers' confusion.

Three-way ANOVA, Andrew Hayes process, structural equation modelling and random forest algorithm were used to assess the 507 self-filled questionnaire responses for examining consumers' confusion.

The results reveal that confusion decreases with increased education levels for each gender and age group. Also, for every gender and education level, consumer confusion increases and then decreases. Further, it was observed that self-efficacy effectively moderates the influence of different confusions on decision postponement. An artificial intelligence-enabled predictive model with an accuracy of 71.49% was established, which forecasts the possibility of consumers' decision postponement.

This study offers the comprehension of how the travel site administrators and stakeholders have used artificial intelligence-enabled solution for consumers' confusion and to reduce decision postponement.

To the best of the authors' awareness, this is one of the first studies to unveil the interaction effect of demographic factors on confusion, assess the role of self-efficacy as a moderator and provide artificial intelligence-enabled solutions for consumers' confusion.

Active and transformative learning (ATL) challenges the new era of teaching and learning in higher education. In this chapter a systematic approach to ATL theories and methodologies is provided. Authors provide a unique methodological framework for the integration of active learning practices in modern higher education curricula. This chapter also provides an integrated context for the overall discussion of the phenomenon in the volume. The recent arrivals of new disruptive artificial intelligence-enabled technologies such as OpenAI, ChatGPT, DeepAI and others are only few aspects of a new challenging era for the teaching, learning, innovation and sustainability in the higher education.