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Reviews of scientific publications in the area of tourism continuously find commonalities and repeatedly applied criteria in conceptualizing destination image. Much emphasis has been placed on investigating the image components (dimensions) and potential impacts as perceived by the consumer. Publications on the image formation and change, however, do not disclose many details on the process and impacts of change agents. Hence, this study aims to look into the initial stages of destination image planning and how these plans are implemented through projecting onto the official destination websites.

The text-based content analysis builds on a random sample of one-third of the tourism development plans and the respective official tourism websites of sub-provincial prefectures in China. Terms (originally phrased in Chinese language only) were extracted that could be classified as image components considering the context where they appeared.

Results exhibit a sparse application of varied and imaginative image elements in both the tourism development plans and the official websites. Deviations between intended and projected destinations are substantial. An overwhelming majority of prefectural destination management organizations (DMOs) appears to be distant from a professional implementation of an image positioning strategy.

Due to resource limitations, only a random sample of one-third of the 365 sub-provincial prefectures in China could have been screened. To draw a complete, though structurally most probably not very different, picture on the scope and variety of image elements, a complete investigation would be necessary. The projection of image items on the official websites represents a restricted view on possible image formation agents. For a more comprehensive understanding, other information channels (e.g. printed advertising material, travel catalogues and guide books) would complement the perspective on induced image agents.

From a managerial perspective, it appears to be an easy job to position a destination along one or two main appeal characteristics, particularly when the majority of these are factual aspects of geography, landscape, history or culture. Whether such a positioning strategy materializes in view of an increasing competition among destinations is questionable. The condensed image profiles identified in this study can act as blueprints for developing more pronounced positioning profiles. The variation across groups of destination image profiles and the composition of prefectures for each group reflects the potential competitive pressure that prefectures may excel unless the DMO representatives decide to go for an adapted target position.

This study is a rare attempt to analyse the intended/planned and projected image elements of a multitude of tourism destinations simultaneously. Such a comparison is usually done on a case-by-case basis only. Thus, the insights of this study go beyond the limits of an individual destination enabling structural comparisons across neighbouring and nation-wide regions.

This study examines the determinants of medical crowdfunding performance. Drawing on signaling theory, the authors investigate how funding-related signals (funding goal and duration), story-related signals (text length, text sentiment, and use of first-person pronouns), and donor-related signals (donor identity disclosure) affect medical crowdfunding performance.

This study analyzed the data of 754 medical crowdfunding projects collected from the Qingsongchou platform in China to test the proposed model.

The empirical findings reveal that both funding goal and funding duration exhibit a U-shaped relationship with crowdfunding performance. Additionally, the authors find evidence that story text length and donor identity disclosure are positively related to crowdfunding performance, whereas the use of first-person pronouns is negatively related to crowdfunding performance.

This study extends the understanding of the determinants of medical crowdfunding performance through the signaling theory. Specifically, this study provides new insights into the roles of funding goal and funding duration in predicting medical crowdfunding performance and identifies several new predictors of crowdfunding performance, including the use of first-person pronouns in project story text and donor identity disclosure.

Online reviews in online health communities (OHCs) have been a vital information source for patients. The extant literature on the bias effects of helpful reviews mainly concentrates on traditional e-commerce, whereas research on OHCs is still rare. Thus, based on the heuristic-systematic model (HSM), this research explores how two unique reviewer characteristics in OHCs, which may induce attribution bias and confirmation bias, affect review helpfulness and how review length moderates these relationships.

This research analyzed 130,279 reviews collected from haodf.com (one of the representative OHCs in China) by adopting the negative binomial regression to test our research model.

The results indicate that reviewer cured status positively influences review helpfulness, whereas reviewer recommendation source negatively affects review helpfulness. Moreover, the effects of the two reviewer cues on review helpfulness will be weaker for longer reviews.

First, as one of the initial attempts, the current study investigates the effects of confirmation bias and attribution bias of online reviews in OHCs by exploring the effects of two unique reviewer characteristics on review helpfulness. Second, the weakening moderating effects of review length on the two bias effects provide empirical support for the theoretical arguments of the HSM in OHCs.

The purpose of the study is to address the problems of low accuracy and missed detection of occluded pedestrians and small target pedestrians when using the YOLOX general object detection algorithm for pedestrian detection. This study proposes a multi-level fine-grained YOLOX pedestrian detection algorithm.

First, to address the problem of the original YOLOX algorithm in obtaining a single perceptual field for the feature map before feature fusion, this study improves the PAFPN structure by adding the ResCoT module to increase the diversity of the perceptual field of the feature map and divides the pedestrian multi-scale features into finer granularity. Second, for the CSPLayer of the PAFPN, a weight gain-based normalization-based attention module (NAM) is proposed to make the model pay more attention to the context information when extracting pedestrian features and highlight the salient features of pedestrians. Finally, the authors experimentally determined the optimal values for the confidence loss function.

The experimental results show that, compared with the original YOLOX algorithm, the AP of the improved algorithm increased by 2.90%, the Recall increased by 3.57%, and F1 increased by 2% on the pedestrian dataset.

The multi-level fine-grained YOLOX pedestrian detection algorithm can effectively improve the detection of occluded pedestrians and small target pedestrians.

The authors introduce a multi-level fine-grained ResCoT module and a weight gain-based NAM attention module.

This study aims to explore how specific cues with new manifestations (i.e. herding message and price discount information) and customer cognitive style influence attention allocation and purchase intention.

To empirically validate the research hypotheses, an eye-tracking experiment with a 2 × 2 × 2 mixed design was conducted on a sample of 44 participants recruited from a university in China. Repeated measures analysis of variance was employed for data analysis.

The results show that herding message and price discount information play different roles in viewers' attention and have an interactive effect on attention. Moreover, individual cognitive styles moderate the impact of herding message on attention allocation. Still, two cues positively affect customer purchase intention.

This study guides future research by applying cue utilization theory to investigate the effects of two cues in live streaming. Findings offer practical implications for how live streaming cues affect viewers' attention allocation and purchase intention.

Numerous electronic word-of-mouth (eWOM) studies have been conducted to examine the effectiveness of persuasive eWOM messages. Despite the impact of eWOM messages in decision-making processes, few researches have directly tested potential antecedents of persuasive eWOM messages among message recipients in social media context. The purpose of this paper is to critically discuss and examine the determinants of persuasive eWOM messages when message recipients intend to accept and use eWOM messages.

The authors reviewed extant literature of eWOM and proposed hypotheses regarding persuasive eWOM messages in social media context. A survey of 78 respondents was conducted and the data were analysed using SmartPLS.

This study found that argument quality, source credibility, source attractiveness, source perception and source style are critical antecedents of persuasive eWOM messages. The PLS results suggested that source credibility (trustworthiness), source perception (usefulness, social ties) and source style (visual cues, number) are main characteristics of credible eWOM messages in relation to users’ intention to accept and use online reviews. The variance of information acceptance and intention to use were also explained in the findings.

This paper identified critical antecedents of persuasive eWOM messages and suggested eWOM messages as a credible source. An integrated conceptual framework was developed to illustrate comprehensive antecedents of persuasive eWOM messages, and the relationships between these messages, information acceptance and intention to use.

The significance of the study is to identify the effectiveness of eWOM messages and its impact on intention to accept and use these messages. Moreover, this study will provide insightful guidelines for marketers with practical implications in approaching emerging markets via eWOM initiatives.

Drawing on the technology-organization-environment (TOE) framework, this study aims to investigate determinants of performance of artificial intelligence (AI) adoption in hospitality industry during COVID-19 and identifies the relative importance of each determinant.

A two-stage approach that integrates partial least squares structural equation modeling (PLS-SEM) with artificial neural network (ANN) is used to analyze survey data from 290 managers in the hospitality industry.

The empirical results reveal that perceived AI risk, management support, innovativeness, competitive pressure and regulatory support significantly influence the performance of AI adoption. Additionally, the ANN results show that competitive pressure and management support are two of the strongest determinants.

This research offers guidelines for hospitality managers to enhance the performance of AI adoption and presents policy-making insights to promote and support organizations to benefit from the adoption of AI technology.

This study conceptualizes the performance of AI adoption from both process and firm levels and examines its determinants based on the TOE framework. By adopting an innovative approach combining PLS-SEM and ANN, the authors not only identify the essential performance determinants of AI adoption but also determine their relative importance.

In the digital economy, digital capability has become an important dynamic capability of enterprises and plays an essential role in enhancing firm resilience. This study aims to investigate the relationships among digital capability, knowledge search, coopetition behavior and firm resilience based on knowledge-based view and resource-based view.

This study uses the hierarchical regression and bootstrapping methods to test the theoretical framework and research hypotheses. The survey data were collected from 241 Chinese enterprises.

Digital capability has significantly positive effects on knowledge search and firm resilience. Knowledge search positively affects firm resilience and partially mediates the relationship between digital capability and firm resilience. Coopetition behavior weakens the relationship between digital capability and knowledge search, and the mediating effect of knowledge search in the relationship between digital capability and firm resilience. The moderating effect of coopetition behavior on the relationship between digital capability and firm resilience is insignificant.

This study clarifies the effect of digital capability on firm resilience and uncovers the “black box” from digital capability to firm resilience. In addition, this research enriches the literature on digital capability and firm resilience and expands the application of knowledge-based view and resource-based view in the digital context.

The purpose of this paper is to examine how the usage of enterprise social media (ESM) affects eventual employee turnover.

This study developed a theoretical model based on the proposition that different ESM usage behaviors (utilitarian use, hedonic use and social use) have different effects on employee turnover, and job type and job level can moderate the effect of ESM usage on turnover. The model was examined empirically using 1,791 employee samples from a large high-tech manufacturing enterprise deploying ESM.

The results indicate that the utilitarian and social use of ESM has negative effects on turnover, but the hedonic use of ESM has positive effects on turnover. Furthermore, for employees working in different job types and job levels, there are significant differences concerning the effect of ESM usage on their turnover.

ESM managers should encourage employees to use ESM for utilitarian needs and social support but restrict excessive use of ESM for leisure. In addition, different ESM use policies depending upon job types and job levels could be adopted to retain valuable employees.

Few studies have focused on how usage of ESM affects eventual employee turnover. Given the lack of theoretical research and empirical evidence, the authors developed a theoretical model and conducted an empirical study to fill the research gap.

Prefabricated buildings (PBs) have proven to effectively mitigate carbon emissions in the construction industry. Existing studies have analyzed the environmental performance of PBs considering the shift in construction methods, ignoring the emissions abatement effects of the low-carbon practices adopted by participants in the prefabricated building supply chain (PBSC). Thus, it is challenging to exploit the environmental advantages of PBs. To further reveal the carbon reduction potential of PBs and assist participants in making low-carbon practice strategy decisions, this paper constructs a system dynamics (SD) model to explore the performance of PBSC in low-carbon practices.

This study adopts the SD approach to integrate the complex dynamic relationship between variables and explicitly considers the environmental and economic impacts of PBSC to explore the carbon emission reduction effects of low-carbon practices by enterprises under environmental policies from the supply chain perspective.

Results show that with the advance of prefabrication level, the carbon emissions from production and transportation processes increase, and the total carbon emissions of PBSC show an upward trend. Low-carbon practices of rational transportation route planning and carbon-reduction energy investment can effectively reduce carbon emissions with negative economic impacts on transportation enterprises. The application of sustainable materials in low-carbon practices is both economically and environmentally friendly. In addition, carbon tax does not always promote the implementation of low-carbon practices, and the improvement of enterprises' environmental awareness can further strengthen the effect of low-carbon practices.

This study dynamically assesses the carbon reduction effects of low-carbon practices in PBSC, informing the low-carbon decision-making of participants in building construction projects and guiding the government to formulate environmental policies.