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This study aims to conduct a critical review of the research on the sharing economy to identify its key intellectual foundations and their evolution and offers thematic and methodological recommendations for future research to advance the domain.

A tri-method approach using bibliometric (co-citation) analysis, thematic content analysis and a quantitative systematic literature review was conducted on sharing economy research in hospitality and tourism journals, up to and including May 2020.

The findings from the three methods were coherent and provide a clear picture of the fact that while research on the sharing economy in hospitality and tourism has achieved significant depth, the breadth of the understanding of this area remains somewhat limited. Each of the three periods of research on the sharing economy identified in the study were marked by a focus on specific thematic areas, with largely Western-situated researchers demonstrating limited theoretical engagement and using a limited range of methods and perspectives (disciplinary, stakeholders and sectors) to examine these themes.

Recommendations for future thematic research opportunities are provided using a multi-level perspective. The present review does not include research that incorporates the impacts of COVID-19, which has significantly disrupted the hospitality and tourism industry and is the focus of current research in the field. However, the review represents the largest and most comprehensive assessment of the state of research on the sharing economy prior to COVID-19, and, as such, can serve as a valuable baseline for future reviews of sharing economy research during and after the pandemic.

In contrast with previous literature reviews, the present review is comprehensive in its scope, methodology and temporal coverage of sharing economy research. It also examines the evolution of research on the topic, enabling a more nuanced identification of gaps and future thematic and methodological research opportunities.

The aim of this study is to understand the knowledge-sharing structure and co-production of trip-related knowledge through online travel forums.

The travel forum threads were collected from TripAdvisor’s Sydney travel forum for the period from 2010 to 2014, which contains 115,847 threads from 8,346 conversations. The data analytical technique was based on a novel methodological approach – visual analytics, including semantic pattern generation and network analysis.

Findings indicate that the knowledge structure is created by community residents who camouflage as local experts and serve as ambassadors of a destination. The knowledge structure presents collective intelligence co-produced by community residents and tourists. Further findings reveal how these community residents associate with each other and form a knowledge repertoire with information covering various travel domain areas.

The study offers valuable insights to help destination-management organizations and tour operators identify existing and emerging tourism issues to achieve a competitive destination advantage.

This study highlights the process of social media mediated travel knowledge co-production. It also discovers how community residents engage in reaching out to tourists by camouflaging as ordinary users.

Taking a global perspective, this paper aims to examine the impact of COVID-19 on Airbnb booking activities through three critical perspectives – the initial Wuhan lockdown, local COVID-19 cases and local lockdowns.

Using Airbnb reviews and cancellations as proxies for Airbnb bookings on a global scale, econometrics was used to examine the impacts of the initial Wuhan lockdown, local COVID-19 cases and local lockdowns on Airbnb bookings.

The authors find that local lockdowns result in a 57.8% fall in global booking activities. Every doubling of newly infected cases is associated with a 4.16% fall in bookings. The sensitivity of bookings to COVID-19 decreases with geographic distance to Wuhan and increases with government stringency of lockdown policies and human mobility within a market.

The empirical evidence from this research can provide governments with insights into more accurate assessment of the financial loss of Airbnb hosts so that proper support can be offered based on the financial needs because of due to sudden lockdown.

This research contributes to new knowledge on peer-to-peer accommodation during a time of crisis and provides much needed global evidence to understand the impacts of COVID-19 on the accommodation industry.

This study aims to identify the key determinants of Airbnb rating scores.

This study is based on a sample of 127,257 listings across 43 cities. A total of 24 explanatory variables were identified, and they were further grouped into host verification information, communication, policy of renting, space, information about environment, price and experience of hosting. Both Tobit and ordered logit models were used to perform the analysis.

The results indicate that good communication, large space and provision of information about the listings’ environment have a positive effect on users’ satisfaction, whereas experience of hosting negatively influences users’ satisfaction.

This study contributes to the peer-to-peer accommodation literature by affording a more complete understanding about guest satisfaction and its determinants.

The study aims to provide a critical review of the origin, development and process of sentiment analysis (SA) and a demonstration for hospitality researchers and students on how to perform SA using a sample study.

A critical review and sample case demonstration approach was applied. The sample study used Leximancer to perform SA using TripAdvisor review data.

A critical evaluation of the most popular SA tools was provided, highlighting their advantages and disadvantages. A step-by-step demonstration with data provided makes it possible for readers to learn this technique at own pace.

By providing a critical review of SA supported with a demonstration case study, this study makes a timely contribution for broader awareness and understanding, as well as the application of SA in hospitality.

The purpose of this study is to explore the impact of mobile learning platforms on users' study efficiency and develop cognitive indicators to evaluate users' study efficiency on mobile learning platforms.

Layout style was the only independent factor that was investigated. A between-group experimental design was employed. Eye movement data were recorded during the experiment, following which participants were asked to complete an after-scenario questionnaire. This study evaluated the usability of the proposed new design using both subjective and objective data. The computer system usability questionnaire V3 (CSUQ) was used to measure subjective data. For the eye-tracking measure, gaze entropy, the proportion of fixation count and duration of each AOI were calculated. Gaze entropy reflects the complexity of information organization. Fixation counts and AOI duration represent the difficulty of information processing and attention distribution, respectively during the task.

The results indicated that interface layout presents significant effects on user's learning efficiency, usability and cognitive load. Sequential layout improved efficiency and satisfaction among participants and reduced information complexity. The results provided useful insights for designers whose goal is to improve user's learning efficiency under mobile learning scheme.

This study investigated the effects of interface layout on usability, user performance and cognitive load using subjective ratings and eye-tracking technology. Gaze entropy was used to measure the complexity of information organized by the interface design. Fixation count and duration proportion were used to identify the difficulty of information processing and distinguish users' distribution of cognitive resources. The results indicated that a vertical layout panel design was more efficient than a horizontal layout panel design. The design implications of the eye tracking indicators and research results were then summarized. This study is expected to encourage designers to optimize their design proposals using eye tracking testing.

By using an integrated theoretical analysis model and a practice analysis model, this study constructed an evaluation indicator system for public participation in post-disaster housing reconstruction.

This study investigated and summarised the public participation of earthquake victims in house building in Dujiangyan. A total of 16 analysis elements were proposed, including the subject and object for evaluation. A mixed qualitative and quantitative evaluation model was established.

The implementation results indicated that this evaluation index system was objective oriented and had public policy attributes. The index can effectively reflect the diverse needs of disaster victims, address multiple social aspects and evaluate and rate public participation.

This paper proposed a mixed research method that integrates analysis processes, such as theories and practical investigation, which are the pillars of public participation evaluation. The evaluation indicators had quantitative characteristics.

With the growing awareness about the environment and climate, sustainability has gained increased attention of investors. Many investors now factor in the long-term sustainability of successful and responsible companies when making their investment choices. The purpose of this paper is to investigate whether or not the sustainability performance of a company affects the informativeness of its earnings by exploring the mediating effect of sustainability performance on the association between stock returns and earnings changes.

Using a sample of firms for the period 2009–2016 with available sustainability data from TruValue Labs' database, the authors investigate how the sustainability performance of a firm mediates the relationship between stock returns and earnings (changes). The authors use ordinary least squares (OLS) regressions to test their hypotheses.

Consistent with the voluntary disclosure and environmental, social and governance (ESG) performance literature, the authors find that higher sustainability performance improves the stock price informativeness of earnings. The authors find evidence in support of increased earnings response coefficient with increased sustainability performance.

This study adds to the literature supporting the notion of sustainability investing indicating that sustainability performance of a firm affects the stock price informativeness and predictability of earnings (changes) of the firm.

This study has value for, both, investors and managers regarding the importance of sustainability performance of the firm. Sustainability performance of the firm sends signals to market participants, increasing the informational content of the reported earnings as well as predictability of future earnings.

The purpose of this paper is to investigate the effect of different soldering temperatures on the performance of chip-on-board (COB) light sources during vacuum reflow soldering.

First, the influence of the void ratio of the COB light source on the steady-state voltage, luminous flux, luminous efficiency and junction temperature has been explored at soldering temperatures of 250°C, 260°C, 270°C, 280°C and 290°C. The COB chip has also been tested for practical application and aging.

The results show that when the soldering temperature is 270°C, the void ratio of the soldering layer is only 5.1%, the junction temperature of the chip is only 76.52°C, and the luminous flux and luminous efficiency are the highest, and it has been observed that the luminous efficiency and average junction temperature of the chip are 107 lm/W and 72.3°C, respectively, which meets the requirements of street lights. After aging for 1,080 h, the light attenuation is 84.64% of the initial value, which indicates that it has higher reliability and longer life.

It can provide reference data for readers and people in this field and can be directly applied to practical engineering.

With the advent of the era of Big Data, the scale of knowledge graph (KG) in various domains is growing rapidly, which holds huge amount of knowledge surely benefiting the question answering (QA) research. However, the KG, which is always constituted of entities and relations, is structurally inconsistent with the natural language query. Thus, the QA system based on KG is still faced with difficulties. The purpose of this paper is to propose a method to answer the domain-specific questions based on KG, providing conveniences for the information query over domain KG.

The authors propose a method FactQA to answer the factual questions about specific domain. A series of logical rules are designed to transform the factual questions into the triples, in order to solve the structural inconsistency between the user’s question and the domain knowledge. Then, the query expansion strategies and filtering strategies are proposed from two levels (i.e. words and triples in the question). For matching the question with domain knowledge, not only the similarity values between the words in the question and the resources in the domain knowledge but also the tag information of these words is considered. And the tag information is obtained by parsing the question using Stanford CoreNLP. In this paper, the KG in metallic materials domain is used to illustrate the FactQA method.

The designed logical rules have time stability for transforming the factual questions into the triples. Additionally, after filtering the synonym expansion results of the words in the question, the expansion quality of the triple representation of the question is improved. The tag information of the words in the question is considered in the process of data matching, which could help to filter out the wrong matches.

Although the FactQA is proposed for domain-specific QA, it can also be applied to any other domain besides metallic materials domain. For a question that cannot be answered, FactQA would generate a new related question to answer, providing as much as possible the user with the information they probably need. The FactQA could facilitate the user’s information query based on the emerging KG.