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There is limited research on the behavior of different categories of space tourists as identified by different types of space tourism. To address this deficiency, the purpose of this study is to examine what factors make consumers participate in orbital and/or suborbital space tourism, along with three dimensions of motivation, constraint and artificial intelligence. To achieve this study’s goals, a comprehensive research model was developed that included three dimensions of intrinsic and extrinsic motivation, intrapersonal and interpersonal constraint and awareness of and trust in artificial intelligence, in comparing orbital and suborbital space tourism groups.

A questionnaire was carried out with respondents who wanted to participate in orbital (n = 332) and suborbital (n = 332) space tourism in the future. Partial least squares-structural equation modeling, fuzzy-set qualitative comparative analysis, multi-group analysis and deep learning were used to understand potential space tourist behavior.

Extrinsic motivation has the greatest positive impact on behavioral intention, followed by awareness of and trust in artificial intelligence, while intrapersonal constraint strongly negatively affects behavioral intention. Surprisingly, interpersonal constraint is insignificant by partial least squares-structural equation modeling but is still one of sufficient causal configurations by fuzzy-set qualitative comparative analysis. Interestingly, the two types of space tourism have very distinct characteristics.

This study created a comprehensive integrated research model with three dimensions of motivation, constraint and artificial intelligence, along with potential orbital and suborbital space tourist groups, to identify future consumer behavior. Importantly, this study used multi-analysis methods using four different approaches to better shed light on potential orbital and suborbital space tourists.

对不同类型太空旅游所识别的不同类别太空游客行为的研究有限。 为了解决这一缺陷, 这项工作研究了哪些因素使消费者参与轨道和/或亚轨道太空旅游, 以及动机、约束和人工智能三个维度。 为了实现研究目标, 在比较轨道和亚轨道太空旅游群体时, 开发了一个综合研究模型, 包括内在和外在动机、内在和人际约束以及对人工智能的认识和信任三个维度。

对希望在未来参与轨道 (n = 332) 和亚轨道 (n = 332) 太空旅游的受访者进行了问卷调查。 利用偏最小二乘法 (PLS)-结构方程模型 (SEM)、模糊集定性比较分析 (fsQCA)、多组分析和深度学习来了解潜在的太空游客行为。

外在动机对行为意图的积极影响最大, 其次是对人工智能的认识和信任, 而内在约束对行为意图有强烈的负面影响。 令人惊讶的是, 人际约束对于 PLS-SEM 来说是微不足道的, 但对于 fsQCA 来说仍然是充分的因果配置之一。 有趣的是, 这两类太空旅游具有非常鲜明的特点。

这项工作创建了一个全面的综合研究模型, 具有动机、约束和人工智能三个维度, 以及潜在的轨道和亚轨道太空旅游群体, 以确定未来的消费者行为。 重要的是, 这项研究采用了多种分析方法, 使用四种不同的方法来更好地揭示潜在的轨道和亚轨道太空游客。

existe una investigación limitada sobre el comportamiento de las diferentes categorías de turistas espaciales identificados por diferentes tipos de turismo espacial. Para abordar esta deficiencia, este trabajo examina qué factores hacen que los consumidores participen en el turismo espacial orbital y/o suborbital, junto con tres dimensiones de motivación, restricción e inteligencia artificial. Para lograr los objetivos del estudio, se desarrolló un modelo de investigación integral que incluía tres dimensiones de motivación intrínseca y extrínseca, restricción intrapersonal e interpersonal, y conocimiento y confianza en la inteligencia artificial, al comparar grupos de turismo espacial orbital y suborbital.

se realizó un cuestionario con los encuestados que querían participar en el turismo espacial orbital (n = 332) y suborbital (n = 332) en el futuro. Se utilizaron modelos de ecuaciones estructurales (SEM) de mínimos cuadrados parciales (PLS), análisis comparativo cualitativo de conjuntos borrosos (fsQCA), análisis multigrupo y aprendizaje profundo para comprender el comportamiento potencial del turista espacial.

la motivación extrínseca tiene el mayor impacto positivo en la intención de comportamiento, seguida de la conciencia y la confianza en la inteligencia artificial, mientras que la restricción intrapersonal afecta negativamente la intención de comportamiento. Sorprendentemente, la restricción interpersonal es insignificante por PLS-SEM, pero sigue siendo una de las configuraciones causales suficientes por fsQCA. Curiosamente, los dos tipos de turismo espacial tienen características muy distintas.

este trabajo creó un modelo de investigación integral integral con tres dimensiones de motivación, restricción e inteligencia artificial, junto con posibles grupos de turistas espaciales orbitales y suborbitales para identificar el comportamiento futuro del consumidor. Es importante destacar que este estudio empleó métodos de análisis múltiple utilizando cuatro enfoques diferentes para arrojar mejor luz sobre los posibles turistas espaciales orbitales y suborbitales.

The current study investigates the impact on perceived review helpfulness of the simultaneous processing of information from multiple cues with various central and peripheral cue combinations based on the elaboration likelihood model (ELM). Thus, the current study develops and tests hypotheses by analyzing real-world review data with a text mining approach in e-commerce to investigate how information consistency (rating inconsistency, review consistency and text similarity) influences perceived helpfulness. Moreover, the role of product type is examined in online consumer reviews of perceived helpfulness.

The current study collected 61,900 online reviews, including 600 products in six categories, from Amazon.com. Additionally, 51,927 reviews were filtered that received helpfulness votes, and then text mining and negative binomial regression were applied.

The current study found that rating inconsistency and text similarity negatively affect perceived helpfulness and that review consistency positively affects perceived helpfulness. Moreover, peripheral cues (rating inconsistency) positively affect perceived helpfulness in reviews of experience goods rather than search goods. However, there is a lack of evidence to demonstrate the hypothesis that product types moderate the effectiveness of central cues (review consistency and text similarity) on perceived helpfulness.

Previous studies have mainly focused on numerical and textual factors to investigate the effect on perceived helpfulness. Additionally, previous studies have independently confirmed the factors that affect perceived helpfulness. The current study investigated how information consistency affects perceived helpfulness and found that various combinations of cues significantly affect perceived helpfulness. This result contributes to the review helpfulness and ELM literature by identifying the impact on perceived helpfulness from a comprehensive perspective of consumer review and information consistency.

The next generation of mobility is arising, and various challenging mobilities have entered the limelight. One of the most exciting of these is urban air mobility (UAM), and one of its challenges is constructing effective and efficient UAM service network. This study took a quantitative approach to the problem in an effort to support and facilitate the UAM service industry.

This study derived a multi-objective and multi-period (MOMP) location optimization model to support strategic UAM service network design. The model, based on its long-term service plan, determines where and when to open UAM airports. In addition, this study applied a modified e-constraint algorithm to derive managerial decisions on the Pareto relationship in consideration of multiple objectives and multiple periods.

Each Pareto solution represents a different UAM service network configuration. Thus, the model can analyze the trade-offs between Pareto decisions for the UAM service network. A case study of UAM service network design in South Korea demonstrates the validity of the proposed mathematical model and algorithm.

The design of a UAM service network should consider various aspects. Its construction and operation would require significant investments of time, capital and people, which would redound to society over a significant span of time. The results of this study provide quantitative guidelines for derivation and analysis of various UAM service network configurations in consideration of multiple objectives and multiple periods.

This paper proposes MOMP optimization, which approach is suitable to the fundamental characteristics of expanding UAM service networks and their design. It is expected that the present study will make significant contributions to the efforts of those deriving and analyzing future UAM service networks.

Textual information about restaurants, such as online reviews and food categories, is essential for consumer purchase decisions. However, previous restaurant recommendation studies have failed to use textual information containing essential information for predicting consumer preferences effectively. This study aims to propose a novel restaurant recommendation model to effectively estimate the assessment behaviors of consumers for multiple restaurant attributes.

The authors collected 1,206,587 reviews from 25,369 consumers of 46,613 restaurants from Yelp.com. Using these data, the authors generated a consumer preference vector by combining consumer identity and online consumer reviews. Thereafter, the authors combined the restaurant identity and food categories to generate a restaurant information vector. Finally, the nonlinear interaction between the consumer preference and restaurant information vectors was learned by considering the restaurant attribute vector.

This study found that the proposed recommendation model exhibited excellent performance compared with state-of-the-art models, suggesting that combining various textual information on consumers and restaurants is a fundamental factor in determining consumer preference predictions.

To the best of the authors’ knowledge, this is the first study to develop a personalized restaurant recommendation model using textual information from real-world online restaurant platforms. This study also presents deep learning mechanisms that outperform the recommendation performance of state-of-the-art models. The results of this study can reduce the cost of exploring consumers and support effective purchasing decisions.

关于餐厅的文本信息, 如在线评论和食品分类, 对于消费者的购买决策产生至关重要。然而, 先前的餐厅推荐研究未能有效利这些文本信息去预测消费者喜好。本研究提出了一种新颖的餐厅推荐模型, 以有效估计消费者对多个餐厅属性的评估行为。

我们从 Yelp.com 收集了来自25,369名消费者对 46,613 家餐厅的 1,206,587 条评论。利用这些数据, 我们通过结合消费者身份和在线消费者评论生成了消费者偏好向量。然后, 我们结合了餐厅身份和食品分类来生成餐厅信息向量。最后, 考虑到餐厅属性向量, 本研究调查了消费者偏好和餐厅信息向量之间的非线性交互关系。

我们发现, 所提出的推荐模型相比于之前最先进的模型表现出更优秀的性能, 这表明结合消费者和餐厅的各种文本信息是预测消费者喜好的基本因素。

据我们所知, 这是第一项利用来自真实在线餐厅平台的文本信息开发个性化餐厅推荐模型的研究。本研究还提出了胜过最先进模型的深度学习机制。本研究的结果可以降低探索消费者行为的成本并支持有效的购买决策。

This paper investigates the interrelationships of destination social responsibility (DSR), emotional attachment, self-congruity, experiential satisfaction and environmentally responsible behavior (ERB).

Using a sample of 294 visitors to the Guangzhou Zoo in China, this study tested the aforementioned relationships via structural equation modeling.

Emotional attachment mediates the effect of DSR on experiential satisfaction, while emotional attachment and experiential satisfaction mediate the effect of DSR on ERB sequentially. Moreover, self-congruity moderates the relationship between DSR and emotional attachment.

The management of zoos should use DSR communication strategies more proactively to make visitors become well-aware of their economic, philanthropic, environmental and social activities in the host community. This will result in many positive consequences, including visitors’ ERBs.

The study adds to the DSR literature by introducing multiple mediation mechanisms and paths that lead to visitors’ ERBs.

我们的论文调查了目的地社会责任 (DSR) 情感依恋、自我一致性、体验满意度和对环境负责的行为 (ERB) 之间的相互关系。

我们的研究以中国广州动物园的 294 名游客为样本, 通过结构方程模型测试了上述关系。

情感依恋介导 DSR 对体验满意度的影响, 而情感依恋和体验满意度依次介导 DSR 对 ERB 的影响。 此外, 自我一致性调节 DSR 与情感依恋之间的关系。

动物园的管理层应该更积极地使用 DSR 沟通策略, 让游客充分了解他们在东道社区的经济、慈善、环境和社会活动。 这将带来许多积极的后果, 包括访客的 ERB。

该研究通过引入多种调解机制和导致访客 ERB 的路径增加了 DSR 文献。

nuestro artículo investiga las interrelaciones de la responsabilidad social del destino (DSR), el apego emocional, la autocongruencia, la satisfacción experiencial y el comportamiento ambientalmente responsable (ERB).

utilizando una muestra de 294 visitantes del zoológico de Guangzhou en China, nuestro estudio probó las relaciones antes mencionadas a través del modelo de ecuaciones estructurales.

el apego emocional media el efecto de DSR en la satisfacción experiencial, mientras que el apego emocional y la satisfacción experiencial median el efecto de DSR en ERB secuencialmente. Además, la autocongruencia modera la relación entre DSR y apego emocional.

la administración de los zoológicos debe utilizar estrategias de comunicación de DSR de manera más proactiva para que los visitantes estén bien informados sobre sus actividades económicas, filantrópicas, ambientales y sociales en la comunidad anfitriona. Esto tendrá muchas consecuencias positivas, incluidos los ERB de los visitantes.

el estudio se suma a la literatura de DSR al presentar múltiples mecanismos de mediación y caminos que conducen a los ERB de los visitantes.

This paper aims to examine the impact of metaverse experiences on customers’ offline behavioral intentions: How do customers’ visits to a hospitality business’s virtual property in the metaverse affect their intentions to visit the physical property in the real world?

Based on the general learning model and social cognitive theory, this research hypothesizes the positive impact of metaverse experiences on customers’ visit intentions and explores two boundary conditions for positive impact: user–avatar resemblance and servicescape similarity. Two experimental studies were conducted.

Metaverse experience has a significant impact on customers’ visit intentions, and this impact is moderated by user–avatar resemblance and servicescape similarity.

This research addresses the call for empirical studies regarding the effects of metaverse experience on people’s behavioral intentions.

As one of the earliest empirical studies on the marketing effects of the metaverse, this research provides a basis for future metaverse studies in the hospitality field.

The non-fungible token (NFT) market has been multiplying in recent years. NFTs are tokens stored on a blockchain network based on smart contract technology that can be used to represent ownership of digital assets and cannot be changed like-for-like. With NFTs, all recorded digital properties can be freely traded and stored with values, making them possible to increase content transactions' privacy and security. In addition, NFTs engender new ways to organize, consume, share and store digital content. Despite the rapid growth of the NFT market, related consumer behaviors have yet to be well-known and relevant academic research results are very scarce. This study aims to explain how NFT fits with blockchain and cryptocurrency and how consumers accept it. This paper also develops a structured causal model with multiple paths to explain the antecedents and attitude variables for NFT acceptance.

The data collection was conducted from 542 young consumers in Korea via an online survey. The structural equation modeling method was used to analyze the hypotheses.

Attitudes toward technology and assets positively affect NFT purchase behavioral intentions. Additionally, symbolic driver affects behavioral intention directly.

The results expanded the understanding of the NFT market and consumers, which are still in their early stages. They also provide valuable insights for establishing future market strategies for NFT.

The purpose of the current study is to examine a social network site (SNS) users' overall satisfaction with SNS use in conjunction with their fan page visiting activities. We examined overall satisfaction with SNS usage from the lens of people's perceptions acquired from the use of sub-components of SNS.

The current study employed uncertainty reduction theory (URT) and general systems theory (GST) to examine antecedents affecting overall satisfaction with SNS use. Five constructs were adopted: interactive and passive uncertainty reduction strategies, perceived usefulness and continuance visiting behavior, satisfaction, and perceived functional benefits. Using a web-based survey, we analyzed 200 SNS users who follow at least one company's fan page, utilizing seemingly unrelated regression models to test hypotheses empirically.

Research findings reveal that uncertainty reduction strategies supported by URT are significantly associated with the perceived usefulness of a company's fan page. In turn, we found that perceived usefulness becomes a strong motivator to continuance visits to the fan page. The frequency of return visiting behaviors eventually accounts for overall satisfaction with SNS. Perceived functional benefits moderates the relationship between perceived usefulness and visiting behaviors significantly.

The current study contributes to information systems (IS), electronic communication, and their adjacent academic disciplines in providing evidence, including (1) the impact of uncertainty reduction strategies on continuance visiting behaviors in the SNS context, (2) SNS functionalities influencing the relationship between people's belief and behavior, and (3) theoretical significant perceptional link between a sub-component and a whole.

This study examines the impact of outdoor environments in public rental housing complexes on residents’ psychological restoration, taking into account the interconnectedness of physical and psychological factors in human health. Drawing on Kaplan and Kaplan’s Attention Restoration Theory and Ulrich’s Supportive Design Theory, the research investigates the factors influencing residents’ psychological restoration within these outdoor spaces.

The Perceived Restorativeness Scale (PRS), which is based on the Attention Restoration Theory and the Zuckerman Inventory of Personal Reactions (ZIPERS) are used to assess residents’ restorative experiences. Field research was conducted to collect data on the outdoor environments, and surveys were administered to the residents. The study analyzes the data using SPSS, including both factor and correlation analyses, to explore the relationship between the restorative effect and emotional factors.

The study verified a significant influence of positive emotions in ZIPERS on PRS’ overall restorative effect, thus supporting the utilization of both PRS and ZIPERS factors together to assess comprehensively the impact of outdoor environments on residents’ psychological restoration.

By employing a multidimensional approach involving residents’ experiences and emotions, this study quantified emotional and psychological data, which were hard to quantify. These results provide a basis for developing more objective restoration environment design guidelines and programs in the future.

This paper aims to investigate the impact of corporate dividend policy on the capital structure in the Korean stock market. To distinctly discern the voluntariness of changes in corporate dividend policy, we analyze companies that, following a substantial increase, do not reduce dividends for the subsequent two years or, after a significant decrease, do not raise dividends for the following two years. Our empirical findings indicate that companies that increase dividends experience a significant decrease in both book and market leverage, even after controlling for variables such as target leverage ratios. This result suggests that a large increase in dividends can effectively reduce information asymmetry, leading to a lower cost of equity. On the contrary, after a decrease in dividends, both book leverage and market leverage significantly increase, revealing a symmetric relationship between dividend policy and capital structure. In conclusion, large dividend increases in Korean companies not only reduce information asymmetry but also lower the cost of equity capital, resulting in observable changes in the leverage ratio.