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Based on the textual-analyzed data covering 2148 IPO firms in China’s stock market during the 2007–2018 period, the authors’ purpose is to examine the influence of anti-takeover provision (ATP) adoption on initial public offerings (IPO) underpricing and identify the reducing effect of the former.

The authors examine the sample consisting of Chinese A-share listed IPO firms between 2007 and 2018 from China Stock Market Accounting Research and Chinese Research Data Services, with ATP data collected from the IPO firm chapters. Specifically, the authors use text analysis to identify whether there are ATPs in the IPO firm chapters, as well as the number of ATPs. H1: IPO underpricing is less severe for firms adopting ATPs. H2: The effect of ATP adoption on IPO underpricing is more salient for firms in worse information environments.

The authors examine the influence of ATP adoption on IPO underpricing and identify the reducing effect of the former. This effect can be explained by the fact that adopting ATPs in IPO firm chapters can reduce information asymmetry to a large extent by helping external investors obtain more private information, which alleviates IPO underpricing. The authors also find that the reducing effect is more significant in the worsened information environment. Furthermore, the authors explore the influence of adopting ATPs on other IPO characteristics and find positive effects on IPO over-subscription, funds raised and trading activity and negative effects on listing fees.

This study mainly contributes to the literature from the following two aspects. First, the study enriches the literature about the influencing factors of IPO underpricing. Second, the study also enriches the literature about the economic consequences of ATP adoption. This study also has important policy implications. With the coming of the era of decentralized ownership in China’s capital market, ATP adoption has become more important and attracted more attention. Also, investors focus more on pricing efficiency. The findings in this paper provide a more comprehensive understanding of the relationship between ATP adoption and IPO underpricing.

This study aims to investigate the perception of smart tourism technologies (STTs) in creating museum tourism experiences and assess visitors’ intentions to test the impact, the extent and role that STTs play in museum service.

The investigation was conducted through an online and on-site survey; data were collected in four different museums with 365 museum tourists in four countries. SmartPLS (version 3.3.3) was used to access the measurement and structural model based on path modelling and bootstrapping.

STTs have a positive impact on creating a memorable tourism experience for museum visitors and are beneficial to revisit intention and positive recommendations. The impact is stronger than traditional services such as museum setting and staff service. STTs can be considered an independent new service to museums rather than complementing existing services. STTs negatively affect tourism’s experience at the communication stage.

Previous studies have noted the growing attention to the museum visitor experience, but the combined impact of new elements, such as STTs, remains an unexplored area. This study addressed the research gap through an empirical investigation and offers guidelines to test museum visitors’ acceptance and fitness for the services provided by STTs. In addition, this study presents a new point of view on the roles that STTs take in museum service, which can be useful for further investigations.

本研究旨在调查智慧旅游技术 (STT) 在创造博物馆旅游体验和评估游客意图方面的感知。测试智能旅游技术在博物馆服务中的影响、程度和作用。

研究调查是通过在线和现场问卷调查进行的; 数据是在四个国家的四个不同博物馆中收集的, 共收集了 365 名博物馆游客的数据。 SmartPLS（版本 3.3.3）用于访问基于路径建模和引导的测量模型和结构模型。

智慧旅游技术对为博物馆参观者创造难忘的旅游体验具有积极影响, 并有利于重访意向和积极推荐。这种影响比博物馆的环境设置和员工服务等传统服务更强。智慧旅游技术可以被视为对博物馆的一项独立的新服务, 而不是对现有服务的补充。智慧旅游技术对博物馆游客在传播阶段的体验产生负面影响。

以前的研究已经注意到学界对博物馆参观体验的关注日益增加, 但 STT 等新元素的综合影响仍然是一个未探索的领域。 本研究通过实证调查补充了研究缺口, 并提供了测试博物馆参观者对 STT 提供的服务的接受度和适应性的指南。此外, 本研究提出了 STT 在博物馆服务中的作用的新观点, 这对进一步的研究很有帮助。

Resumen

El objetivo de este estudio es investigar la percepción de las Tecnologías Turísticas Inteligentes (TTIs) en la creación de experiencias turísticas en los museos y evaluar las intenciones de los visitantes para probar el impacto, el alcance y el papel que desempeñan las TTIs en el servicio de los museos.

La investigación se llevó a cabo a través de una encuesta online y presencial; los datos se recogieron de 365 turistas en cuatro museos diferentes de cuatro países. Se utilizó SmartPLS (versión 3.3.3) para evaluar el modelo de medida y el modelo estructural basado en la modelización de trayectorias y el bootstrapping.

Las TTIs tienen un impacto positivo en la creación de una experiencia turística memorable para los visitantes de los museos y son beneficiosas para la intención de volver a visitarlos y las recomendaciones positivas. El impacto es mayor que el de los servicios tradicionales, como la ambientación del museo y el servicio del personal. Las TTIs pueden considerarse un nuevo servicio independiente para los museos, más que un complemento de los servicios existentes. Las TTIs afectan negativamente a la experiencia del turismo en la fase de comunicación.

En estudios previos se ha observado la creciente atención que se presta a la experiencia de los visitantes de los museos, pero el impacto combinado de nuevos elementos, como las TTIs, sigue siendo un área inexplorada. Este estudio aborda esta laguna de la investigación mediante una investigación empírica y ofrece directrices para comprobar la aceptación y la adecuación de los visitantes de los museos a los servicios prestados por las TTIs. Además, este estudio presenta un nuevo punto de vista sobre las funciones que asumen las TTIs en el servicio de los museos, que puede ser útil para futuras investigaciones.

Purpose: The main aim of the study is to provide a tool for non-financial information in decision-making. We analysed the non-financial data in the annual reports in order to show the usage of this information in financial decision processes.

Need for the Study: Main financial reports such as balance sheets and income statements can be analysed by statistical methods. However, an expanded financial reporting framework needs new analysing methods due to unstructured and big data. The study offers a solution to the analysis problem that comes with non-financial reporting, which is an essential communication tool in corporate reporting.

Methodology: Text mining analysis of annual reports is conducted using software named R. To simplify the problem, we try to predict the companies’ corporate governance qualifications using text mining. K Nearest Neighbor, Naive Bayes and Decision Tree machine learning algorithms were used.

Findings: Our analysis illustrates that K Nearest Neighbor has classified the highest number of correct classifications by 85%, compared to 50% for the random walk. The empirical evidence suggests that text mining can be used by all stakeholders as a financial analysis method.

Practical Implications: Combining financial statement analyses with financial reporting analyses will decrease the information asymmetry between the company and stakeholders. So stakeholders can make more accurate decisions. Analysis of non-financial data with text mining will provide a decisive competitive advantage, especially for investors to make the right decisions. This method will lead to allocating scarce resources more effectively. Another contribution of the study is that stakeholders can predict the corporate governance qualification of the company from the annual reports even if it does not include in the Corporate Governance Index (CGI).

A composite sentiment index (CSI) from quantitative proxy sentiment indicators is likely to be a lag sentiment measure as it reflects only the information absorbed in the market. Information theories and behavioral finance research suggest that market prices may not adjust to all the available information at a point in time. This study hypothesizes that the sentiment from the unincorporated information may provide possible market leads. Thus, this paper aims to discuss a method to identify the un-incorporated qualitative Sentiment from information unadjusted in the market price to test whether sentiment polarity from the information can impact stock returns. Factoring market sentiment extracted from unincorporated information (residual sentiment or sentiment backlog) in CSI is an essential step for developing an integrated sentiment index to explain deviation in asset prices from their intrinsic value. Identifying the unincorporated Sentiment also helps in text analytics to distinguish between current and future market sentiment.

Initially, this study collects the news from various textual sources and runs the NVivo tool to compute the corpus data’s sentiment polarity. Subsequently, using the predictability horizon technique, this paper mines the unincorporated component of the news’s sentiment polarity. This study regresses three months’ sentiment polarity (the current period and its lags for two months) on the NIFTY50 index of the National Stock Exchange of India. If the three-month lags are significant, it indicates that news sentiment from the three months is unabsorbed and is likely to impact the future NIFTY50 index. The sentiment is also conditionally tested for firm size, volatility and specific industry sector-dependence. This paper discusses the implications of the results.

Based on information theories and empirical findings, the paper demonstrates that it is possible to identify unincorporated information and extract the sentiment polarity to predict future market direction. The sentiment polarity variables are significant for the current period and two-month lags. The magnitude of the sentiment polarity coefficient has decreased from the current period to lag one and lag two. This study finds that the unabsorbed component or backlog of news consisted of mainly negative market news or unconfirmed news of the previous period, as illustrated in Tables 1 and 2 and Figure 2. The findings on unadjusted news effects vary with firm size, volatility and sectoral indices as depicted in Figures 3, 4, 5 and 6.

The related literature on sentiment index describes top-down/ bottom-up models using quantitative proxy sentiment indicators and natural language processing (NLP)/machine learning approaches to compute the sentiment from qualitative information to explain variance in market returns. NLP approaches use current period sentiment to understand market trends ignoring the unadjusted sentiment carried from the previous period. The underlying assumption here is that the market adjusts to all available information instantly, which is proved false in various empirical studies backed by information theories. The paper discusses a novel approach to identify and extract sentiment from unincorporated information, which is a critical sentiment measure for developing a holistic sentiment index, both in text analytics and in top-down quantitative models. Practitioners may use the methodology in the algorithmic trading models and conduct stock market research.

The purpose of this study is to build a theory on management investigating the relationship between organizational culture and strategic flexibility in firms, and how this relationship is affected through industry 4.0 capabilities and the market orientation of firms.

As a methodological approach, this paper uses a rich combination of literature review and exploratory interviews with managers and academics. Data were collected from 379 industrial managers; confirmatory factor analysis (CFA) and finally structural equation modeling (SEM) were performed to validate the data and examine the hypothesized relationships.

The results show that organizational culture drives firms to strategic flexibility, but the introduction of industry 4.0 capabilities and market orientation fully mediate this relationship, revealing their significance to strategic flexibility.

All the respondents of the study's dataset were from Greek firms; the role of national culture should be considered. Moreover, the comprehension of I4.0 is a quite recent concept that is still being formulated, this feature may modify the results of future studies.

Managers should allocate resources for the concurrent adoption of digital technologies capabilities and suitable market-oriented strategies in order for them to be key drivers for enhanced strategic flexibility.

The current state of knowledge of both theory and practise for critical organizational factors such as organizational culture, strategic flexibility, industry 4.0 capabilities and market orientation will be extended.

This study intends to explore the opportunities and roles of Macao's universities in the education and training development of Hengqin under the General Plan of the Development of the Guangdong–Macao In-Depth Cooperation Zone in Hengqin promulgated in August 2021.

This study is intended to analyze the role of Macao’ universities in Hengqin development, based on data collected through interviewing with dozens of scholars and government officials from Macao and Hengqin, and government reports, news reports and other relevant data.

The General Plan provides Macao's universities with opportunities to develop education and training in Hengqin. On the one hand, Hengqin can be the locus of Macao's tertiary education diversification; Macao's universities can jointly establish technological research institutions and set up postdoctoral research centers and university branches to enroll master's and doctoral students from Mainland China. On the other hand, Macao's universities can set up a branch of professional training and open training courses in Hengqin. These efforts are designed to promote technological innovation and cultural diffusion in Macao.

This study analyses the potential role of Macao's universities in developing education and training in Hengqin since the central policy was announced last August. This study will be of interest to scholars as well as policymakers.

Drawing on the theory of memory-dominant logic, this study aims to examine how the substantive staging of the servicescape, experience co-creation, experiential satisfaction and experience intensification affect experience memorability and hedonic well-being in the case of unmanned smart hotels.

An online survey was used, with the target respondents being hotel guests people aged 18 years and older who had been recent guests of the FlyZoo Hotel in Hangzhou, China. Data were collected online from 429 guests who had stayed in the hotel between April and June 2023. Data analysis was undertaken using structural equation modelling.

The results suggest that all the proposed four constructs are positive drivers of a memorable unmanned smart hotel experience. The relationship between the memorability of the hotel experience and hedonic well-being was found to be significant and positive.

Unmanned smart hotels should ensure that all smart technologies function effectively and dependably and offer highly personalised services to guests, allowing them to co-create their experiences. This will lead to the guest receiving a satisfying and memorable experience. To enable experience co-creation using smart technologies, unmanned smart hotels could provide short instructional videos for guests, as well as work closely with manufacturers and suppliers to ensure that smart technology systems are regularly updated.

This study investigates the antecedents and outcomes of a novel phenomenon and extends the concept of memorable tourism experiences to the context of unmanned smart hotels.

Traditional Chinese medicine (TCM) prescriptions have always relied on the experience of TCM doctors, and machine learning(ML) provides a technical means for learning these experiences and intelligently assists in prescribing. However, in TCM prescription, there are the main (Jun) herb and the auxiliary (Chen, Zuo and Shi) herb collocations. In a prescription, the types of auxiliary herbs are often more than the main herb and the auxiliary herbs often appear in other prescriptions. This leads to different frequencies of different herbs in prescriptions, namely, imbalanced labels (herbs). As a result, the existing ML algorithms are biased, and it is difficult to predict the main herb with less frequency in the actual prediction and poor performance. In order to solve the impact of this problem, this paper proposes a framework for multi-label traditional Chinese medicine (ML-TCM) based on multi-label resampling.

In this work, a multi-label learning framework is proposed that adopts and compares the multi-label random resampling (MLROS), multi-label synthesized resampling (MLSMOTE) and multi-label synthesized resampling based on local label imbalance (MLSOL), three multi-label oversampling techniques to rebalance the TCM data.

The experimental results show that after resampling, the less frequent but important herbs can be predicted more accurately. The MLSOL method is shown to be the best with over 10% improvements on average because it balances the data by considering both features and labels when resampling.

The authors first systematically analyzed the label imbalance problem of different sampling methods in the field of TCM and provide a solution. And through the experimental results analysis, the authors proved the feasibility of this method, which can improve the performance by 10%−30% compared with the state-of-the-art methods.

This paper aims to explore factors affecting young consumers’ purchasing of new smart products under the influence of social media.

Twenty semi-structured, face-to-face, in-depth interviews were conducted with young consumers who used new media, and five extended interviews were conducted with popular smartwatch retailers in Macau.

The findings reveal that they tend to garner product information from multiple channels of communication. Social media exert the greatest influence. Reliable information, strong branding and interactions with vendors are also influential, although new product pre-announcements may be boring and difficult to understand.

This study presents new insights into diffusion of innovation theory and provides retailers launching smart products with a better understanding of their target young customers’ purchasing behavior.

This study aims to develop a conceptual model to understand how customer knowledge management (CKM) affects fitness club membership renewal through the mediation of relationship quality.

Data were collected outside of fitness clubs using a systematic sampling method. A total of 224 valid responses were collected. Structural equation modelling was used to evaluate the relationship between the constructs of the research model.

The results indicate that both knowledge from customers and knowledge for customers have a positive influence on customer satisfaction and customer trust. Among them, knowledge for customers has a stronger influence on customer satisfaction while knowledge from customers has a greater influence on customer trust. Additionally, three dimensions of relationship quality (customer satisfaction, customer trust and customer commitment) positively influence membership renewal intention with customer commitment exhibiting the greatest influence on it.

This study combines the theories of CKM and relationship quality management to explain why members will renew their service contracts. By using fitness clubs as an example, this research extends the authors' understanding of how knowledge from and for customers can influence customers' attitudes and behavioural intentions towards service companies.