Search results

Tracking trends in new technology funding patterns is essential for venture scaling. The emerging advanced digital technologies (ADT) such as virtual reality (VR), artificial intelligence (AI), blockchain and Internet-of-things (IoT) promote business innovation adaptations, and in turn, reshape the industrial landscape. To attract nascent funding for such prospective projects among the public, well-articulated project pitches that are equipped with effective marketing communication convey the projects' importance and marketability. Specifically, when the entrepreneurs and the crowdfunding platform users interact via different types of crowdfunding platforms, pitch framing, including the signaling of ADT terms, project location and fundraising goal, becomes imperative to help facilitate crowdfunding success.

Drawing on data collected from six leading US-based equity and reward-based crowdfunding platforms in 2020, an empirical study was performed. Using the text analysis approach, the authors examined the positive effects of incorporating technology orientation on crowdfunding success. While the effect between the project description's signaling of geographic location, fundraising goal and articulation style on fundraising success, while controlling for project and platform characteristics.

The results suggested that the technology-orientated projects are more likely to achieve better fundraising outcomes. Taking crowdfunding platform types, project locations, minimum fundraising goals and articulation with analytical and authentic into consideration, the results still hold.

Building on the theoretical framework of signaling theory, the authors consider the crowdfunding-specific contextual factors to enhance the understanding of the positivity impact of technology orientation. By such addition, it facilitates more effective strategic composition of entrepreneurs' fundraising conversations.

Industry 5.0 focuses on human–machine collaboration and sustainability with collaborative robots (CoBots) and artificial intelligence (AI) penetration via different technology-enabled devices. Such devices have gained increasing interest to facilitate more efficient, effective, and budget-friendly outcomes in major sectors, including healthcare. The healthcare sector has been evolving at an increasing speed across the globe. In this context, challenges and opportunities have arisen in terms of improving patient outcomes and improving the efficiency of healthcare practitioners’ work. Hence, the adoption of CoBots and AI-enabled devices in this sector is now crucial, and they have been implemented in several domains in healthcare, including diagnosis, medication development, and treatment. However, the successful implementation depends on the users’ attitudes toward the adoption. While extant studies have shown that such devices have significant practical advantages from the patients’ perspective, little is known about healthcare practitioners’ willingness to adopt tech-enabled devices. Thus, this study focuses on the adoption of CoBots and AI-enabled devices in the healthcare sector by examining Turkish medical doctors’ attitudes toward adopting them in their daily operations. The study supplements current literature on Industry 5.0 in healthcare, sheds light on real-life practices, and proposes future directions.

The missing travel time data for roads is a common problem encountered by traffic management departments. Tensor decomposition, as one of the most widely used method for completing missing traffic data, plays a significant role in the intelligent transportation system (ITS). However, existing methods of tensor decomposition focus on the global data structure, resulting in relatively low accuracy in fibrosis missing scenarios. Therefore, this paper aims to propose a novel tensor decomposition model which further considers the local spatiotemporal similarity for fibrosis missing to improve travel time completion accuracy.

The proposed model can aggregate road sections with similar physical attributes by spatial clustering, and then it calculates the temporal association of road sections by the dynamic longest common subsequence. A similarity relationship matrix in the temporal dimension is constructed and incorporated into the tensor completion model, which can enhance the local spatiotemporal relationship of the missing parts of the fibrosis type.

The experiment shows that this method is superior and robust. Compared with other baseline models, this method has the smallest error and maintains good completion results despite high missing rates.

This model has higher accuracy for the fibrosis missing and performs good convergence effects in the case of the high missing rate.

As the Chinese Government is planning to transform the economy from an export-oriented economy into a consumption-oriented economy, the impact of Chinese consumers to the economy will become more and more important. However, there is a lack of literature on Chinese consumers’ behavior and the critical factors of shopping malls in China. Hence, this study aims to determine the critical factors of a shopping mall in an urban complex in China from customers’ perspective, using Nanjing Wanda Plaza as an example for our case study.

This study carries out ranking analysis and factor analysis to determine the critical factors of the shopping mall. Then cluster analysis is applied to divide the customers into three segments, showing the importance of each factor to different customer segments. Furthermore, correspondence analysis is conducted to investigate the relationship between customer segments and customer characteristics (gender, occupation, age and income). This method can show how customer characteristics affect the critical factors of the shopping mall.

Sensual enjoyable shoppers consider the “soft factors” to be superior to the “hard factors”, whereas the pragmatic shoppers are just the opposite.

This study can serve as a useful reference for developers in designing shopping malls in urban complexes to attract more customers.

The purpose of this study is to investigate the recovery and resilience tourism strategies and possible future development of four main Chinese tourism cities.

The authors collected data from the official accounts of tourism administrations of these cities, tourist attractions and opinions from media and newspapers in Sina Weibo platform. The authors adopted an inductive approach in observing relevant social media posts and applied content analysis to identify main China’s tourism prevention and recovery strategies.

During the mass pandemic infection period, top-down prevention and control measures were implemented by the Chinese central and local governments, with feasible and regional recovery policies and protocols being adapted according to local situations. Measures related to tourism industrial re-employment, improvement of international images and governmental financial supports to re-boost local tourism in Chinese cities were paid great attention. Digitalization, close-to-nature and cultural heritages became important factors in the future development of China’s tourism. Dark tourism, as a potential tourism recovery strategy, also obtained huge emergence, for the memory of people deceased in the pandemic and for the inheritance of national patriotism.

This study enriches the current literature in urban tourism recovery studies analyzing the specific case of Chinese tourism cities and fulfill some voids of previous research mostly focused on the first wave of the pandemic and the recovery strategies mainly of Western cities. It also provides valuable suggestions to tourism practitioners, destinations and urban cities in dealing with regional tourism recession and finding possible solutions for the scenario associated to the COVID-19 and other similar health crisis.