Voice assistance (VA) has been widely adopted in the tourism and hospitality industry to interact and communicate with tourists and provide useful travel recommendations. Although more companies in tourism have adopted environmental sustainability practices and tourists have increased their sustainability awareness, lack of research has been conducted to investigate the effectiveness of VA in promoting sustainable consumption of tourism products or services. This study proposes that customising environmental sustainability messages for tourists with different cultural orientations and VA use purposes can enhance their engagement with the VA, which can affect their consumption decision. The results from a scenario-based experiment demonstrate that tourists with interdependent/independent cultural orientation have higher engagement with the environmental sustainability messages communicated by the VA when they are with a non-transactional/transactional purpose. Implications for tourism marketing strategies are discussed.

A ‘brand’ metaverse is a virtual space where customers experience the brand via digital avatars. With the advancement of augmented and virtual reality technologies, a brand metaverse is an important medium for communicating the brand with customers. In this study, we focus on the resemblance between a customer’s self and his/her avatar (i.e., self-avatar resemblance) in the brand metaverse and examine its influence on brand attitude. Prior studies examine self-avatar resemblance exclusively in non-brand-related virtual gaming platforms and test its effect on identity perception and immersion in the platforms. However, few studies probe the extent to which self-avatar resemblance influences customers' exploration in a brand metaverse and their attitude toward the brand. We fill this research gap by uncovering the positive effects of self-avatar resemblance on brand attitude and purchase intentions. Moreover, we proffer that the customers’ engagement toward the brand metaverse platform mediates the relationship between self-avatar resemblance and brand attitude. In addition, based on the interactive nature of metaverse, we hypothesize copresence―the number of avatars exploring the brand metaverse at the same time―to be a moderator, which strengthens the mediation. We conduct an experiment using a fashion brand’s virtual world positioned in a popular metaverse platform. In this experiment, participants create an avatar and freely roam around in the brand metaverse with their avatars. By reviewing the screen recording of each participant’s brand exploration in the metaverse, we measure self-avatar resemblance and other constructs. We also collect responses from questionnaires designed to measure attitudinal and behavioral variables. With the accumulated data, we test the hypotheses using partial least square structural equation model and find the results largely consistent with the hypotheses. With the findings, we provide important and interesting implications to marketing practitioners considering and doing ‘metaverse marketing.’

Imagination plays a critical role in travel decision-making. Given the intangible nature of tourism products, tourists cannot directly experience and evaluate the tourism resources in advance. Thus, tourists must first mentally predict and imagine the future travel experiences and scenarios in the destination based on the marketing information (e.g., travel photo, promo video) and prior knowledge, then form their subjective evaluation of the travel product. This future-thinking process is called “mental simulation”. Stacks of research have shown that mental simulations positively affect travel behavior (Le et al., 2019). However, given that travel is a kind of novelty-seeking activity, tourists are usually not familiar with the destination environment and activities. The lack of prior knowledge might inhibit their mental simulation process, even if destination photos and videos are provided. Thus, how to effectively arouse tourists’ mental simulation of destination experience is an important question for effective tourism marketing.

This study constructs a model to predict ad attitude when AI influencers act as ad endorsers. In the results, search products and rational ad appeal have more positive ad attitude, perceived empathy and perceived expertise as mediator. These three variables can be reinforced by the consistency of ad appeals and product categories.

With the popularity of live streaming commerce, the characteristics of streamers and products subtly influence consumer behavior through visual live streaming form. Based on dual-process theory, this paper develops a comprehensive theoretical model to examine how consumer perceived streamer characteristics and product characteristics influence streamer attractiveness and product attractiveness, and explore how consumer behavior inertia is affected by streamer attractiveness and product attractiveness. An online survey consisting of 300 participants was recruited to empirically examine the proposed research model. The results indicated that consumer perceived streamer characteristics and product characteristics are important factors affecting the streamer attractiveness and product attractiveness, which in turn positively affect consumer’s shopping experience memory, which further influence consumer behavior inertia. In addition, the moderating effects of mindfulness are also examined.

The present study choose to conduct consumer behavioral research in Metaverse situation, explores factors that influence consumer shopping enjoyment and purchase intention from product, service and technology perceptive. The research team gathered the primary data through questionnaire subjecting to Chinese consumers (n=300) who know about TaoBao future city which themes on virtual buying in Metaverse. In addition, structural equation modeling is employed to examine the hypothesized relationship among the variables. The result shows that all the driving factors positive effects consumer shopping enjoyment and then influence purchase intention positively. The finding is significantly fundamental to establish theoretical framework future about virtual shopping in Metaverse, and help marketers realize how to set virtual stores in Metaverse to enhance consumer shopping experience so that they could improve consumer purchase intention in the context.

In response to the increasing deployment of brand chatbots in the service sector, this study developed a conceptual framework to examine the psychological processes through which brand chatbots contribute to relationship building efforts. A 2 (interactivity: high vs. low) X 2 (anthropomorphic conversation style: warm vs. competent) between-subject design was conducted in the context of the coffee service business. The levels of interactivity in the chatbots were operationalized by altering the subdimensions of interactivity: modality (i.e., media richness, response latency), message (i.e., interdependency in message exchanges), and source (i.e., customization of content flow). Different linguistic elements (e.g., terms of address, vocabulary, punctuation, emoticons) were used to construct two sets of scripts that emphasized either warmth or competence of the chatbots. Based on the results of the pretests, four brand chatbots via Facebook Messenger were developed using the platform provided by GoSky AI Inc.

The extensive application of robots in hospitality and tourism service has transformed the original human-contact into contact-less, so it is necessary to understand the transformation of customers consumption behaviors under this new service mode. While studies have started investigating how service robots enhance the consumer autonomy, the impact of such technology on customers consumption behaviors remains largely unexplored and its underlying mechanism are still unclear. To address this issue, we explore how service robots shape customers autonomous behaviors in hospitality and tourism services. Drawing on the social impact theory, we presented an underlying process in terms of social discomfort, and reveal the boundary conditions.

Crowdfunding is an increasingly popular fundraising tool where project creators solicit capital from potential backers in return for monetary rewards or presales of products/services. Potential backers in crowdfunding are looking for potential cues to reduce uncertainty and predict new venture success when making their capital contributions. To increase their funding success, project creators strategically use project descriptions as a marketing tool to attract potential backers and funding.

The purpose of this paper is to explore underlying antecedents for inconspicuous luxury motivations by developing and testing a conceptual model under the self-concept mechanism with the influence of cultural capital. A questionnaire-based survey is used to collect data, and structural equation modeling is applied to analyze the data. Although growing interests in academia focus on the relationship between cultural capital and inconspicuous luxury, quantitative studies are scant. This research contributes to the literature by empirically investigating the impact of cultural capital in the relationship within the construct of consumers’ self-concept and inconspicuous luxury motivations.

Metaverse blends the physical and virtual worlds, transforming the customer's shopping experience. This study aims to identify the psychological mechanism in the metaverse environment and the relationship between metaverse experience and consumer happiness. To identify metaverse experiences, both behavioral and functional magnetic resonance imaging (fMRI) studies were conducted. In a behavioral study, we found that consumers' happiness increased when participants were in an immersive metaverse space. In the fMRI study, we found greater activation in the medial prefrontal cortex (MPFC) and lateral occipital cortex (LOC) regions in the high level of the immersive metaverse and found a positive relationship with consumers' happiness. This paper is the first attempt in marketing to provide an integrative brain map for the metaverse experience. This brain map helps marketers better understand the consumer experience. This study suggests that only in the immersive metaverse space where virtual and physical experiences interact can consumers become one with the virtual space and maximize customer experience values.

The economic recession and changes in purchasing habits of young adults (aged 18–25) has led to a decline in the sales of Japanese fashion apparel. This younger generation’s choice of fashion items is primarily based on price. They are not devoted to a favourite brand with a frequent purchase history. Thus, developing customer loyalty and strengthening brand value are essential for the fashion industry. This study explores the improvement in fashion companies’ financial performance (FP) through young generation’s behavioural brand loyalty (BBL) from two aspects: social media brand engagement (BE) and loyalty programmes (LPs). This study listed 14 popular Japanese fashion brands which belonged to 14 publicly traded companies in Japan. Further, we surveyed 183 consumers about their brand related behaviours. The findings reveal the positive and negative effects of the same variable (BE and LPs) on the outcome (short- and long-term FP), indicating that not all activities related to BE and LPs boost FP. FP is influenced by various combinations of these causal factors and complex situations, such as consumers’ demographics and shopping characteristics. The results deepen our understanding of brand loyalty formation and the linkage among BE, LPs, and FP in a realistic marketplace, and offer multiple practical solutions to achieve high levels of short- and long-term FP by targeting the right consumers based on their specific characteristics.

The GMT-Consortium Large Earth Finder (G-CLEF) is the first instrument for the Giant Magellan Telescope (GMT). G-CLEF is a fiber feed, optical band echelle spectrograph that is capable of extremely precise radial velocity measurement. G-CLEF Flexure Control Camera (FCC) is included as a part in G-CLEF Front End Assembly (GCFEA), which monitors the field images focused on a fiber mirror to control the flexure and the focus errors within GCFEA. FCC consists of an optical bench on which five optical components are installed. The order of the optical train is: a collimator, neutral density filters, a focus analyzer, a reimager and a detector (Andor iKon-L 936 CCD camera). The collimator consists of a triplet lens and receives the beam reflected by a fiber mirror. The neutral density filters make it possible a broad range star brightness as a target or a guide. The focus analyzer is used to measure a focus offset. The reimager focuses the beam from the collimator onto the CCD detector focal plane. The detector module includes a linear translator and a field de-rotator. We performed thermoelastic stress analysis for lenses and their mounts to confirm the physical safety of the lens materials. We also conducted the global structure analysis for various gravitational orientations to verify the image stability requirement during the operation of the telescope and the instrument. In this article, we present the opto-mechanical detailed design of G-CLEF FCC and describe the consequence of the numerical finite element analyses for the design.

The development of functional carbon materials using waste biomass as raw materials is one of the research hotspots of lithium-sulfur batteries in recent years. In this work, used a natural high-quality carbon source—coffee grounds, which contain more than 58% carbon and less than 1% ash. Honeycomb-like S and N dual-doped graded porous carbon (SNHPC) was successfully prepared by hydrothermal carbonization and chemical activation, and the amount of thiourea used in the activation process was investigated. The prepared SNHPC showed excellent electrochemical energy storage characteristics. For example, SNHPC-2 has a large pore volume (1.85 cm3·g− 1), a high mesoporous ratio (36.76%), and a synergistic effect (S, N interaction). As the cathode material of lithium-sulfur batteries, SNHPC-2/S (sulfur content is 71.61%) has the highest specific capacity. Its initial discharge-specific capacity at 0.2 C is 1106.7 mAh·g−1, and its discharge-specific capacity after 200 cycles is still as high as 636.5 mAh·g−1.

The present studies explored the possibility of immobilizing phosphocholine (PC) liposomes on the surface of graphene oxide (GO) which was pre-adsorbed with two kinds of enzymes, horseradish peroxidase and glucose oxidase. The transmission electron microscope images showed that the PC liposomes adsorbed onto the GO surface kept integrity. By using 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS)-encapsulated liposomes, a one-step colorimetric assay for glucose was developed. In the presence of glucose, the GO nanocomposites catalyzed the cascade enzymatic reaction producing colorimetric signals directly. Under the optimal conditions, the GO nanocomposites produced linearly increased colorimetric signal with increased concentrations of glucose ranging from 50 to 500 μM. The detection limit was 33 μM. The GO nanocomposites also exhibited good selectivity for the detection of glucose and were able to detect glucose in human serum.

In this study, we investigate the opportunity of using waste tire char as a cathode material for lithium-ion primary batteries (LPBs). The char obtained by carbonizing waste tires was washed with acid and thermally fluorinated to produce CFX. The structural and chemical properties of the char and CFX were analyzed to evaluate the effect of thermal fluorination. The carbon structure of the char was increasingly converted to CFX structure as the fluorination temperature increased. In addition, the manufactured CFX- based LPBs were evaluated through electrochemical analysis. The discharge capacity of the CFX reached a maximum of 800 mAh/g, which is comparable to that of CFX- based LPBs manufactured from other carbon sources. On the basis of these results, the use of waste tire char-based CFX as a cathode material for LPBs is presented as a new opportunity in the field of waste tire recycling.

Developing the high-performance semiconductor photocatalytic materials is an eternal topic under the background of the current energy and environment requirements. In recent years, single-atom photocatalysts (SAPCs) have been brought a lot of attention in energy conversion and environmental purification because of their unique characteristics and properties, including the unique coordination patterns, outstanding atomic utilization, quantum confinement effects, high catalytic activity, etc. Hence, this critical review focuses on the summarized various synthetic methods and the recent important applications of SAPCs, including photocatalytic H2 evolution (PHE) from water splitting, photocatalytic CO2 reduction, photodegradation of organic pollutants, etc. The prospects and challenges for future research topics of SAPCs with excellent activity and stability for various photocatalytic applications are prospected at the end of this review. We sincerely expect that this critical review can promote deep-level insight into the SAPCs subject for the future significant applications in other fields.

With about 80% of the global economy expected to shift to the global market by 2030, exports of reverse direct purchase products, in which foreign consumers purchase products from online shopping malls in Korea, are growing 55% annually. As of 2021, sales of reverse direct purchases in South Korea increased 50.6% from the previous year, surpassing 40 million. In order for domestic SMEs(Small and medium sized enterprises) to enter overseas markets, it is important to come up with export strategies based on various market analysis information, but for domestic small and medium-sized sellers, entry barriers are high, such as lack of information on overseas markets and difficulty in selecting local preferred products and determining competitive sales prices. This study develops an AI-based product recommendation and sales price estimation model to collect and analyze global shopping malls and product trends to provide marketing information that presents promising and appropriate product sales prices to small and medium-sized sellers who have difficulty collecting global market information. The product recommendation model is based on the LTR (Learning To Rank) methodology. As a result of comparing performance with nDCG, the Pair-wise-based XGBoost-LambdaMART Model was measured to be excellent. The sales price estimation model uses a regression algorithm. According to the R-Squared value, the Light Gradient Boosting Machine performs best in this model.

In the era of the 4th Industrial Revolution, Logistic 4.0 using data-based technologies such as IoT, Bigdata, and AI is a keystone to logistics intelligence. In particular, the AI technology such as prognostics and health management for the maintenance of logistics facilities is being in the spotlight. In order to ensure the reliability of the facilities, Time-Based Maintenance (TBM) can be performed in every certain period of time, but this causes excessive maintenance costs and has limitations in preventing sudden failures and accidents. On the other hand, the predictive maintenance using AI fault diagnosis model can do not only overcome the limitation of TBM by automatically detecting abnormalities in logistics facilities, but also offer more advantages by predicting future failures and allowing proactive measures to ensure stable and reliable system management. In order to train and predict with AI machine learning model, data needs to be collected, processed, and analyzed. In this study, we have develop a system that utilizes an AI detection model that can detect abnormalities of logistics rotational equipment and diagnose their fault types. In the discussion, we will explain the entire experimental processes : experimental design, data collection procedure, signal processing methods, feature analysis methods, and the model development.

Governments around the world are enacting laws mandating explainable traceability when using AI(Artificial Intelligence) to solve real-world problems. HAI(Human-Centric Artificial Intelligence) is an approach that induces human decision-making through Human-AI collaboration. This research presents a case study that implements the Human-AI collaboration to achieve explainable traceability in governmental data analysis. The Human-AI collaboration explored in this study performs AI inferences for generating labels, followed by AI interpretation to make results more explainable and traceable. The study utilized an example dataset from the Ministry of Oceans and Fisheries to reproduce the Human-AI collaboration process used in actual policy-making, in which the Ministry of Science and ICT utilized R&D PIE(R&D Platform for Investment and Evaluation) to build a government investment portfolio.