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This paper aims to help hosts or service providers of sharing economy-based accommodation (SEA) to attract new customers and retain existing customers by exploring the antecedents and outcomes of customers’ participation intention.

A questionnaire-based empirical study was conducted to explore the proposed relationships in SEA. Partial least squares modeling with SmartPLS was used to estimate the model and interpret the results.

The study shows that personal factors (utilitarian and hedonic motivation) positively influence customers’ participation intention. The relationship between environmental stimuli (perceived information fit-to-task and perceived visual appeal) and participation intention is negatively moderated by hedonic motivation. Furthermore, the results suggest a positive effect of participation intention on customer engagement behavior and the partial mediating role of experience evaluation.

This paper provides industry practitioners of SEA with valuable insights on attracting new customers and retaining regular customers. First, they can distinguish customers in terms of motivation and provide information based on their requirements. Second, they can encourage customers to evaluate their experience and provide feedback, which would help in promoting the accommodation and service and building a long-term and harmonious relationship with the customers.

This study first investigates the interaction effect of personal motivation and environmental stimuli on participation intention in SEA. It further examines the influence of participation intention on customer engagement behavior and the mediating role of experience evaluation.

本论文旨在帮助SEA(共享经济住宿)的民宿老板或服务提供者, 通过探索顾客参与和契合的影响因子和反应变量, 来吸引新顾客以及保留老顾客。

本论文采用问卷形式实际调研, 探索SEA中的各种假设关系。本论文采用SmartPLS软件, 使用PLS分析法来检验模型以及展示分析结果。

本论文结果表明, 个人因素（功利与享乐需求）正向影响顾客参与。环境因素（感知信息适合和感知视觉吸引）与参与意愿之间被享乐需求负向调节。此外, 结果还表明参与意愿对于顾客契合行为有着正向作用, 以及体验评价的部分中介效应。

本论文为SEA企业从业人员提供如何吸引新顾客以及留住老顾客的宝贵见解。首先, 顾客根据需求和基于他们的需求所提供的信息可进行区分。第二, 企业主应该鼓励顾客评价他们的体验和提供反馈, 从而帮助提高住宿服务以及建立与顾客长期和谐的关系。

本论文首先调研了个人需求和环境刺激对SEA参与意愿的相互作用。其次, 本论文还检验了参与意愿对于顾客契合行为的作用, 以及体验评价的中介效应。

This study aims to use a bottom-up, inductive approach to derive destination image attributes from large quantities of online consumer narratives and establish a destination classification system based on relationships among attributes and places.

Content and social network analyses were used to explore the consumer image structure for destinations based on online narratives. Cluster analysis was then used to group destinations by attributes, and ANOVA provided comparisons.

Twenty-two attributes were identified and combined into three groups (core, expected, latent). Destinations were classified into three clusters (comprehensive urban, scenic and lifestyle) based on their network centralities. Using data on Chinese tourism, the most mentioned (core) attributes were determined to be landscape, traffic within the destination, food and beverages and resource-based attractions. Social life was meaningful in consumer narratives but often overlooked by researchers.

Destinations should determine into which category they belong and then appeal to the real needs of tourists. Destination management organizations should provide the essential attributes while paying greater attention to highlighting the destinations’ social life atmosphere.

This research produced empirical work on Chinese tourism by combining a bottom-up, inductive research design with big data. It divided the 49 destinations into three categories and established a new system based on rich data to classify travel destinations.

本研究旨在使用自下而上的归纳方法从大量的在线消费者的叙述中总结出目的地形象的属性, 并根据目的地形象的属性和地点之间的关系建立一个目的地分类系统。

首先通过内容分析方法和社会网络分析方法分析在线消费者的叙述数据得出目的地的消费者形象结构, 然后采用聚类分析方法按照属性对目的地形象进行分组, 并通过方差分析进行比较。

结果显示总结出22种属性, 并将其组合为三组(核心、预期和潜在)。目的地根据其网络中心度被分为三个集群(综合城市、风景和生活方式)。最常被提及的(核心)属性是景观、目的地的交通、食品和饮料以及资源型景点。在消费者的叙述数据中表明社会生活是有意义的, 但常常被研究人员忽视。

首先本研究通过将自下而上的归纳研究设计与大数据相结合对中国旅游业进行了实证研究。其次通过将49个旅游目的地分为三类以及基于大数据建立了一个新的旅游目的地分类系统。

旅游目的地应该明确自己属于哪一类目的地类型然后迎合游客的真正需求。DMOs应该提供旅游目的地的基本属性, 注重提升旅游目的地的社会生活氛围。

Se realizó un análisis de contenido en redes sociales para explorar la estructura de la imagen de los destinos por parte de los consumidores basándose en las descripciones en línea. A continuación, se empleó el análisis de clusters para agrupar los destinos por atributos, estableciendo comparaciones mediante el análisis ANOVA.

Los propósitos de esta investigación eran utilizar un enfoque ascendente e inductivo para obtener atributos de imagen de los destinos a partir de grandes cantidades de descripciones de consumidores en línea, y establecer un sistema de clasificación de destinos basado en las relaciones entre atributos y lugares.

Se identificaron 22 atributos que luego se agruparon en tres grupos (principales, esperados, latentes). Los destinos se clasificaron en tres grupos (urbano integral, paisajístico y de estilo de vida) en función de sus centralidades de red. Utilizando datos sobre el turismo chino, se determinó que los atributos (centrales) más mencionados eran el paisaje, el tráfico dentro del destino, la comida y las bebidas, y las atracciones basadas en los recursos. La vida social era importante en los comentarios de los consumidores, pero a menudo los investigadores la pasaban por alto.

Los destinos deberían determinar a qué categoría pertenecen y luego apelar a las necesidades reales de los turistas. Los DMO deberían proporcionar los atributos esenciales prestando mayor atención a resaltar el entorno de vida social de los destinos.

Esta investigación elaboró un trabajo empírico sobre el turismo chino combinando un diseño de investigación inductiva ascendente con big data. Dividió los 49 destinos en tres categorías y estableció un nuevo sistema basado en los grandes datos para clasificar los destinos turísticos.

Various approaches have been made to alter the vibration sensing properties of zinc oxide (ZnO) films to achieve high sensitivity. This paper aims to report the experimental study of the fabrication of precursor molar ratio concentration varied ZnO nanostructures grown on rigid substrates using the refresh hydrothermal method. The effect of these fabricated ZnO nanostructures-based vibration sensors was experimentally investigated using a vibration sensing setup.

ZnO nanostructures have been grown using low temperature assisted refresh hydrothermal method with different precursor molar concentrations 0.025 M (R1), 0.075 M (R2) and 0.125 M (R3). Poly 3,4-ethylenedioxythiophene polystyrene sulfonate, a p-type material is spun coated on the grown ZnO nanostructures. Structural analysis reveals the increased intensity of the (002) plane and better c-axis orientation of the R2 and R3 sample comparatively. Morphological examination shows the changes in the grown nanostructures upon increasing the precursor molar concentration. The optical band gap value decreases from 3.11 eV to 3.08 eV as the precursor molar concentration is increased. Photoconductivity study confirms the formation of a p-n junction with less turn-on voltage for all the fabricated devices. A less internal resistance of 0.37 kΩ was obtained from Nyquist analysis for R2 compared with the other two fabricated samples. Vibration testing experimentation showed an improved output voltage of the R2 sample (2.61 V at 9 Hz resonant frequency and 2.90 V for 1 g acceleration) comparatively. This also gave an increased sensitivity of 4.68 V/g confirming its better performance when compared to the other fabricated two samples.

Photoconductivity study confirms the formation of a p-n junction with less turn-on voltage for all the fabricated devices. A less internal resistance of 0.37 kΩ was calculated from the Nyquist plot. Vibration testing experimentation proves an increased sensitivity of 4.68 V/g confirming its better performance when compared to the other fabricated two samples.

Vibration testing experimentation proves an increased sensitivity of 4.68 V/g for R2 confirming its better performance when compared to the other fabricated two samples.

Sargassum fusiforme is a popular edible seaweed in coastal cities of China that contains diverse nutrients including iodine. Cooking is an effective way to improve food safety, but it can alter both the contents of elements along with speciation and bioavailability. Three common cooking methods, the soaking, steaming and boiling, were evaluated for their effects on the protein structures, protein digestibility, iodine content and iodine bioavailability of S. fusiforme.

Fourier transform infrared spectroscopy was used to study the structural changes of protein, and an in vitro digestion/Caco-2 cell culture system was used to evaluate the digestibility of protein, bioaccessibility and bioavailability of iodine.

Boiling and steaming altered the protein secondary structure demonstrated by increased a-helix and random coil and decreased β-sheet, which improved the in vitro protein digestibility. Iodine content was reduced by cooking, with the highest loss observed after boiling, followed by soaking and steaming, while it was found that both bioaccessibility and cellular uptake of iodine were significantly elevated by boiling and steaming using an in vitro digestion/Caco-2 cell culture system. The presence of ascorbic acid, citric acid or tyrosine was beneficial for the iodine absorption, while oxalic acid and phytic acid hindered the iodine bioavailability.

The present finding suggested that cooking was conducive to the digestion and absorption of iodine in S. fusiforme. In addition, different dietary factors could have a certain impact on the absorption of iodine. Results of the study are essential for improving the application value of S. fusiforme to ensure reasonable consumption of seaweeds.

Improving the performance of recommender systems (RSs) has always been a major challenge in the area of e-commerce because the systems face issues such as cold start, sparsity, scalability and interest drift that affect their performance. Despite the efforts made to solve these problems, there is still no RS that can solve or reduce all the problems simultaneously. Therefore, the purpose of this study is to provide an effective and comprehensive RS to solve or reduce all of the above issues, which uses a combination of basic customer information as well as big data techniques.

The most important steps in the proposed RS are: (1) collecting demographic and behavioral data of customers from an e-clothing store; (2) assessing customer personality traits; (3) creating a new user-item matrix based on customer/user interest; (4) calculating the similarity between customers with efficient k-nearest neighbor (EKNN) algorithm based on locality-sensitive hashing (LSH) approach and (5) defining a new similarity function based on a combination of personality traits, demographic characteristics and time-based purchasing behavior that are the key incentives for customers' purchases.

The proposed method was compared with different baselines (matrix factorization and ensemble). The results showed that the proposed method in terms of all evaluation measures led to a significant improvement in traditional collaborative filtering (CF) performance, and with a significant difference (more than 40%), performed better than all baselines. According to the results, we find that our proposed method, which uses a combination of personality information and demographics, as well as tracking the recent interests and needs of the customer with the LSH approach, helps to improve the effectiveness of the recommendations more than the baselines. This is due to the fact that this method, which uses the above information in conjunction with the LSH technique, is more effective and more accurate in solving problems of cold start, scalability, sparsity and interest drift.

The research data were limited to only one e-clothing store.

In order to achieve an accurate and real-time RS in e-commerce, it is essential to use a combination of customer information with efficient techniques. In this regard, according to the results of the research, the use of personality traits and demographic characteristics lead to a more accurate knowledge of customers' interests and thus better identification of similar customers. Therefore, this information should be considered as a solution to reduce the problems of cold start and sparsity. Also, a better judgment can be made about customers' interests by considering their recent purchases; therefore, in order to solve the problems of interest drifts, different weights should be assigned to purchases and launch time of products/items at different times (the more recent, the more weight). Finally, the LSH technique is used to increase the RS scalability in e-commerce. In total, a combination of personality traits, demographics and customer purchasing behavior over time with the LSH technique should be used to achieve an ideal RS. Using the RS proposed in this research, it is possible to create a comfortable and enjoyable shopping experience for customers by providing real-time recommendations that match customers' preferences and can result in an increase in the profitability of e-shops.

In this study, by considering a combination of personality traits, demographic characteristics and time-based purchasing behavior of customers along with the LSH technique, we were able for the first time to simultaneously solve the basic problems of CF, namely cold start, scalability, sparsity and interest drift, which led to a decrease in significant errors of recommendations and an increase in the accuracy of CF. The average errors of the recommendations provided to users based on the proposed model is only about 13%, and the accuracy and compliance of these recommendations with the interests of customers is about 92%. In addition, a 40% difference between the accuracy of the proposed method and the traditional CF method has been observed. This level of accuracy in RSs is very significant and special, which is certainly welcomed by e-business owners. This is also a new scientific finding that is very useful for programmers, users and researchers. In general, the main contributions of this research are: 1) proposing an accurate RS using personality traits, demographic characteristics and time-based purchasing behavior; 2) proposing an effective and comprehensive RS for a “clothing” online store; 3) improving the RS performance by solving the cold start issue using personality traits and demographic characteristics; 4) improving the scalability issue in RS through efficient k-nearest neighbors; 5) Mitigating the sparsity issue by using personality traits and demographic characteristics and also by densifying the user-item matrix and 6) improving the RS accuracy by solving the interest drift issue through developing a time-based user-item matrix.

The purpose of this study is to reveal the research trends in guest experiences of service robots in the hospitality industry.

In this study, a review was carried out on the Web of Science (WoS) database with the assistance of bibliometric analysis techniques. Cluster analysis was also employed for this to group important data to determine the relationships and to visualize the areas in which the studies are concentrated. The thematic content analysis method was used to reveal on which customer experiences and on which methods the focuses were.

On the subject of experiences of service robots, the greatest number of publications was in 2021. In terms of country, China has come to the fore in the distribution of publications. As a result of thematic content analysis, it was determined that the leading factor was the main dimension of emotional experience. In terms of sub-dimensions, social interactions attracted more attention. Most of the studies discussed were not based on any theory. Apart from these, the Technology Acceptance Model (TAM), the Service Quality Model (SERVQUAL) and Perceived Value Theory (PVT) were featured more prominently among other studies.

In this study, only the WoS database was reviewed. In future studies, it would be possible to make contextual comparisons by scanning other databases. In addition to quantitative research designs, social dimensions may be examined in depth following qualitative research methods. Thus, various comparisons can be made on the subject with mixed-method research designs. Experimental research designs can also be applied to where customers have experienced human-robot interactions (HRIs).

In the hospitality industry, it is critical to uncover every dimension of guests' robot acceptance. This study, which presents the current situation on this basis, guides future projections for the development of guest experiences regarding service robots in the hospitality industry.