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This paper proposes a self-supervised monocular depth estimation algorithm under multiple constraints, which can generate the corresponding depth map end-to-end based on RGB images. On this basis, based on the traditional visual simultaneous localisation and mapping (VSLAM) framework, a dynamic object detection framework based on deep learning is introduced, and dynamic objects in the scene are culled during mapping.

Typical SLAM algorithms or data sets assume a static environment and do not consider the potential consequences of accidentally adding dynamic objects to a 3D map. This shortcoming limits the applicability of VSLAM in many practical cases, such as long-term mapping. In light of the aforementioned considerations, this paper presents a self-supervised monocular depth estimation algorithm based on deep learning. Furthermore, this paper introduces the YOLOv5 dynamic detection framework into the traditional ORBSLAM2 algorithm for the purpose of removing dynamic objects.

Compared with Dyna-SLAM, the algorithm proposed in this paper reduces the error by about 13%, and compared with ORB-SLAM2 by about 54.9%. In addition, the algorithm in this paper can process a single frame of image at a speed of 15–20 FPS on GeForce RTX 2080s, far exceeding Dyna-SLAM in real-time performance.

This paper proposes a VSLAM algorithm that can be applied to dynamic environments. The algorithm consists of a self-supervised monocular depth estimation part under multiple constraints and the introduction of a dynamic object detection framework based on YOLOv5.

The purpose of this study is to examine the differential impact of qualitative and quantitative informational signals within the merger and acquisition (M&A) press releases on deal completion and duration. A significant percentage of deals by emerging market acquirers get abandoned before completion, and those that are completed have a longer duration. The limited information about the operations of acquirers from emerging markets creates suspicion among the stakeholders involved in deal resolution, hindering the completion of deals. Thus, using the signal-feedback paradigm, authors investigate how informational signals in the M&A press release impact the deal resolution.

The study employs content analysis on M&A press releases announced by firms from five emerging economies: Brazil, Russia, India, China and South Africa. The technique is applied based on the exploration-exploitation framework developed by March (1991) to categorize the announced deal motives (qualitative information). Next, the authors identify the percentage of relevant quantitative information disclosed in the press release, following which results are obtained using logistic and ordinary least square regressions.

The study reports that deals with declared exploratory motives take longer to complete. Additionally, deals disclosing higher percentage of quantitative disclosure exhibit lower completion rate and increased deal duration.

This is the first study to provide evidence that familiarity bias impacts deal duration as relative to exploitation deals that are familiar to the stakeholders; exploratory deals take longer to conclude. Further, our analysis indicates that a greater percentage of quantitative disclosure may not always reduce information risk but rather be interpreted negatively in the form of the acquirer’s overconfidence in the deal’s potential.

This paper seeks to address the following two research questions. RQ1: What are the influential user archetypes in the social question-answering (SQA) community? RQ2: To what extent does user feedback affect influential users in changing from one archetype to another?

Based on a sample of 13,840 influential users drawn from the Covid-19 community on Zhihu, the archetypes of influential users were derived from their ongoing participation behavior in the community using the Gaussian mixture model. Additionally, user feedback characteristics such as relevance and volume from 222,965 commenters who contributed 546,344 comments were analyzed using the multinomial logistic regression model to investigate the archetype change of influential users.

Findings suggest that influential users could be clustered into three distinctive archetypes: touch-and-go influential users, proactive influential users and super influential users. Moreover, feedback variables have various impacts on the influential user archetype change, including a shift toward creating higher-quality content and fostering increased interaction, a shift toward generating lower-quality content and decreased interaction but improved speed and having mixed effects due to differences in information processing among these archetypes.

This study expands the existing knowledge of influential users and proposes practical approaches to cultivate them further.

This study aims to contribute to social identity theory in tourism by exploring the reflections of Chinese tourists visiting North Korea and how they negotiate their intergroup identity in this similar-others destination.

Data were collected via online platforms and in-depth interviews. A qualitative approach, i.e. thematic analysis, was used to analyse the two sets of data.

Results showed that when encountering North Koreans perceived as similar others amid the social, economic and political environments in which they were embedded, Chinese tourists often categorised themselves as ordinary tourists, preferentially treated tourists and vicarious tourists based on intergroup similarities to North Koreans. They also performed intergroup comparison to boost their self-esteem at group and collective levels and developed corresponding strategies to generate distinct emotional group commitments.

It is recommended that destinations reinforce the friendly and unique relationship with their similar-others source markets to improve their attractiveness. Destinations should also strive to enhance the experience of tourists from their similar-others markets by arousing their positive collective-level affection, emotional resonance and nostalgic memories and avoiding negative emotions.

This study offers a theoretical framework analysing the features of tourists’ social identities while visiting a similar-others destination, contributing to our understanding of the interactive and contingency nature of social identity in tourism, and responding to the call for addressing the broader social contexts in which tourists’ group identity is embedded.

本文致力于探索赴朝中国游客的反思以及他们在这个“类似他者”目的地如何协商群际身份, 以对旅游领域的身份认同理论做出贡献。

本文运用主题分析方法; 资料来源于网络文本和深度访谈。

当中国游客面对被视为“类似他者”的朝鲜人时（具有相似的社会、经济和政治环境）, 他们会根据与朝鲜人的群际相似性, 将自我分类为“普通游客”、“受优待者”和“过来人”三类。此外, 他们还会通过群际比较来提高群体层面和集体层面的自尊, 并且采取对应策略以产生不同的群体承诺。

本文提出了一个分析游客访问“类似他者”目的地时的社会认同的理论框架, 对理解旅游中的社会认同的互动性和偶然性做出了贡献, 并响应了在考察游客群体认同时关注其所处的更广泛的社会背景的呼吁。

目的地应该强化它们与“类似他者”客源市场之间友好且独特的关系, 以提升自身吸引力。目的地还应该唤醒游客积极的集体情感和怀旧, 产生主客情感共鸣, 并避免负面情绪, 以尽力提升来自“类似他者”客源地的游客的体验。

Este estudio pretende contribuir a la teoría de la identidad social en turismo mediante la exploración de las reflexiones de los turistas chinos que visitan Corea del Norte y cómo negocian su identidad intergrupal en este destino similar a otros.

Los datos se recogieron mediante plataformas en línea y entrevistas en profundidad. Se utilizó un enfoque cualitativo a través de análisis temático para analizar los dos conjuntos de datos.

Los resultados mostraron que, al encontrarse con norcoreanos percibidos como otros similares en el entorno social, económico y político en el que estaban integrados, los turistas chinos a menudo se categorizaban a sí mismos como turistas ordinarios, turistas con trato preferente y turistas vicarios basándose en las similitudes intergrupales con los norcoreanos. También, realizaban comparaciones intergrupales para aumentar su autoestima a nivel grupal y colectivo, y desarrollaban las estrategias correspondientes para generar distintos compromisos emocionales grupales.

Se presenta un marco teórico en el que se analizan las características de las identidades sociales de los turistas que visitan un destino similar a otro, lo que contribuye a nuestra comprensión de la naturaleza interactiva y contingente de la identidad social en turismo y responde a la necesidad de abordar los contextos sociales más amplios en los que se inserta la identidad de grupo de los turistas.

Se recomienda que los destinos refuercen las relaciones amistosas y únicas con sus mercados emisores similares para mejorar su atractividad. Los destinos deberían esforzarse por mejorar la experiencia de los turistas de sus mercados emisores similares, estimulando su afecto positivo a nivel colectivo, su resonancia emocional y sus recuerdos nostálgicos, y evitando las emociones negativas.

Given that Chinese brands and products are widespread in the global market, this paper aims to examine the effect of Chinese brand origin salience (vs not) on brand evaluations by increasing global identity perceptions, and figuring out the moderating role of uncertainty avoidance.

Three experiments were conducted in different product categories to test the effect of Chinese brand origin salience. Study 1 and Study 2 collected data from different countries (i.e. Japan and the USA) with varying levels of uncertainty avoidance. In Study 3, the data were collected from the single-country sample, including participants from cultural backgrounds with high (Asian Americans) and low (Caucasian Americans) uncertainty avoidance.

Chinese brand origin salience positively influences brand evaluations via increased global identity perceptions. In addition, uncertainty avoidance plays a moderating role in the process. Specifically, the favorable effect of Chinese brand origin salience on brand evaluations will be attenuated among consumers with high (vs low) uncertainty avoidance.

This paper demonstrates that Chinese brand origin salience can enhance brand evaluations beyond prior work focusing on the negative stereotypes of Chinese brands and their imitation of Western brands. Importantly, Chinese brands have become an important part of the global community as Chinese brand origin salience can evoke consumers’ global identity.

Social media fatigue (SMF) has been widely recognized; however, previous studies have included various concepts into a single fatigue construct. Fatigue has typically been explored from the stressor-strain-outcome (SSO) or stimulus-organism-response (SOR) perspectives. To further investigate SMF, the authors split it into the two constructs of exhaustion and disinterest. Furthermore, the authors introduced the concept of emotional labor and identified rules that may affect surface and deep acting strategies.

The authors designed and conducted a survey to collect data from social networking platform users.

Results from 364 users of social networking platforms supported most of the authors' hypotheses. First, most of the display rules affect the choice of deep or surface acting. Second, both types of acting lead to exhaustion, but only surface acting leads to disinterest. Third, discontinuance intention is affected by both types of fatigue.

This study contributes to SMF research by adding more antecedents (deep and surface acting) based on the emotional labor perspective and showing the impacts of communication rules on emotional labor. In addition, this study also distinguishes disinterest-style fatigue from exhaustion.

This study aims at assessing entrepreneurial business performance (EBP) from dynamic capability (DC) and technology-organization-environment (TOE) framework perspectives, taking support from crowdfunding.

With the inputs from the literature, supported by TOE framework and the dynamic capability view (DCV), a model has been proposed. This model has been tested by the factor-based partial least squares structural equation modeling (PLS-SEM) technique through a survey and quantifying the responses of 406 respondents on a five-point Likert scale has been used.

The study has found that crowdfunding support (CFS) has an effective influence on the improvement of EBP. Also, the DC supports to improve the EBP. Environmental dynamism (END) has also a critical role in impacting business performance.

Crowdfunding involves investors who have a similar interest in the business, close friends, family members, venture capitalists, investment groups, etc. Thus, the proposed model can be used by these stakeholders for investment purposes as well as for improving EBP. This study is a cross sectional research work which has limitations. Moreover, the sample size of this project is limited and did not include global respondents, Thus, the findings of this study cannot be generalizable which is another limitation of this study.

Crowdfunding involves investors who have a similar interest in the business, such as close friends, family members, venture capitalists, investment groups, etc. Thus, the proposed model can be used by these stakeholders for investment purposes as well as for improving EBP. The study can help policymakers understand the importance of crowdfunding in promoting entrepreneurship activities in a region, which helps in the economic development of that region.

This research work enriches the extant literature in the fields of crowdfunding and investment, DC and entrepreneurship. Not many studies have dealt with the issues of CFS for the improvement of EBP. Hence, this study may be considered novel. Moreover, the proposed research framework related to crowdfunding possesses a high predictive power. This makes the study unique.

This paper aims to explore whether, how and when influencers’ value co-creation behavior in a post – influencers investing operant resources to enhance the value gained from using the product – shapes consumers’ responses toward an influencer’s recommendation. Based on value co-creation theory and consumer inference, this paper proposes that consumers infer influencers have more expertise with and more favorable attitude toward the product from value co-creation. Consequently, such behavior can foster consumer engagement and favorable attitude toward the recommended product.

The authors collected real-world data and conducted four sets of experimental studies. In the experimental studies, the presence of an influencer’s value co-creation behavior (i.e. co-creation vs. control) was manipulated to test the causal effect and mechanisms of co-creation on engagement and product attitude.

Results show that influencers’ value co-creation behavior positively affects consumers’ engagement and attitudes toward the product. These effects are driven by perceived influencer’s expertise with and favorability toward the product (Studies 1 and 2). Co-creating utilitarian value is more effective than co-creating hedonic value (Studies 3A and 3B). In addition, influencers with a larger number of followers benefit more from co-creation behavior than those with fewer followers (Study 4).

This research adds to the literature on value co-creation by investigating how, why and when influencers’ value co-creation enhances consumer responses to the post. Our investigation concretizes value co-creation behavior in the context of influencer marketing and demonstrates its signaling effect with nuanced role of value type in shaping the effect. The findings provide a novel cue that influencers can use to enhance consumer responses to their posts.

Brands should encourage influencers to focus on value-in-use co-creation when promoting their products. While emphasizing value co-creation in influencer partnership, brands should also carefully consider the type and size of influencers they collaborate with based on the nature of their products and target audience. The research findings also provide practical implications for influencers, offering guidance on how they can optimize their content creation strategies and enhance their effectiveness in influencer marketing collaborations.

This research enriches understanding of value co-creation in the context of influencer marketing and the role of content specifics in influencers’ posts. The findings provide a novel cue that influencers can use to enhance consumer responses to their posts.

To address the issues of low localization and mapping accuracy, as well as map ghosting and drift, in indoor degraded environments using light detection and ranging-simultaneous localization and mapping (LiDAR SLAM), a real-time localization and mapping system integrating filtering and graph optimization theory is proposed. By incorporating filtering algorithms, the system effectively reduces localization errors and environmental noise. In addition, leveraging graph optimization theory, it optimizes the poses and positions throughout the SLAM process, further enhancing map accuracy and consistency. The purpose of this study resolves common problems such as map ghosting and drift, thereby achieving more precise real-time localization and mapping results.

The system consists of three main components: point cloud data preprocessing, tightly coupled inertial odometry based on filtering and backend pose graph optimization. First, point cloud data preprocessing uses the random sample consensus algorithm to segment the ground and extract ground model parameters, which are then used to construct ground constraint factors in backend optimization. Second, the frontend tightly coupled inertial odometry uses iterative error-state Kalman filtering, where the LiDAR odometry serves as observations and the inertial measurement unit preintegration results as predictions. By constructing a joint function, filtering fusion yields a more accurate LiDAR-inertial odometry. Finally, the backend incorporates graph optimization theory, introducing loop closure factors, ground constraint factors and odometry factors from frame-to-frame matching as constraints. This forms a factor graph that optimizes the map’s poses. The loop closure factor uses an improved scan-text-based loop closure detection algorithm for position recognition, reducing the rate of environmental misidentification.

A SLAM system integrating filtering and graph optimization technique has been proposed, demonstrating improvements of 35.3%, 37.6% and 40.8% in localization and mapping accuracy compared to ALOAM, lightweight and ground optimized lidar odometry and mapping and LiDAR inertial odometry via smoothing and mapping, respectively. The system exhibits enhanced robustness in challenging environments.

This study introduces a frontend laser-inertial odometry tightly coupled filtering method and a backend graph optimization method improved by loop closure detection. This approach demonstrates superior robustness in indoor localization and mapping accuracy.

With the appearance of additional review functionality on e-commerce platforms emotional changes in composite reviews have become more diverse. How consumers process the emotional changes in composite reviews is an important concern for companies. This study investigates the impact of explores how changes in the emotional valence and emotional intensity of composite reviews on consumers' information adoption.

Based on emotion as social information theory, this study constructs a double mediation model of how the change in emotional valence of composite reviews affects consumers' adoption intention and examines the moderating effect of the dynamic change of emotional intensity. One field and three online experiments were conducted to test the proposed hypotheses.

Consumers were more likely to adopt positive–negative composite reviews than negative–positive composite reviews. Compared to negative–positive composite reviews, positive–negative composite reviews led to higher perceived empathy and lower motivational suspicion, which, in turn, led to higher information adoption. Moreover, dynamic changes in emotional intensity played a moderating role in this effect. Interestingly, the amount of attribute difference changed the differences in perceived empathy and motivated consumer suspicion generated by the composite review when considering the reviewer’s attribute difference description.

The findings have important theoretical contributions that deepen business and consumer understanding of the impact of composite reviews and have practical implications for improving the management of composite reviews by businesses.