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An automatic text annotation system (ATAS) that can collect resources from different databases through Linked Data (LD) for automatically annotating ancient texts was developed in this study to support digital humanities research. It allows the humanists referring to resources from diverse databases when interpreting ancient texts as well as provides a friendly text annotation reader for humanists interpreting ancient text through reading. The paper aims to discuss whether the ATAS is helpful to support digital humanities research or not.

Based on the quasi-experimental design, the ATAS developed in this study and MARKUS semi-ATAS were compared whether the significant differences in the reading effectiveness and technology acceptance for supporting humanists interpreting ancient text of the Ming dynasty’s collections existed or not. Additionally, lag sequential analysis was also used to analyze users’ operation behaviors on the ATAS. A semi-structured in-depth interview was also applied to understand users’ opinions and perception of using the ATAS to interpret ancient texts through reading.

The experimental results reveal that the ATAS has higher reading effectiveness than MARKUS semi-ATAS, but not reaching the statistically significant difference. The technology acceptance of the ATAS is significantly higher than that of MARKUS semi-ATAS. Particularly, the function comparison of the two systems shows that the ATAS presents more perceived ease of use on the functions of term search, connection to source websites and adding annotation than MARKUS semi-ATAS. Furthermore, the reading interface of ATAS is simple and understandable and is more suitable for reading than MARKUS semi-ATAS. Among all the considered LD sources, Moedict, which is an online Chinese dictionary, was confirmed as the most helpful one.

This study adopted Jieba Chinese parser to perform the word segmentation process based on a parser lexicon for the Chinese ancient texts of the Ming dynasty’s collections. The accuracy of word segmentation to a lexicon-based Chinese parser is limited due to ignoring the grammar and semantics of ancient texts. Moreover, the original parser lexicon used in Jieba Chinese parser only contains the modern words. This will reduce the accuracy of word segmentation for Chinese ancient texts. The two limitations that affect Jieba Chinese parser to correctly perform the word segmentation process for Chinese ancient texts will significantly affect the effectiveness of using ATAS to support digital humanities research. This study thus proposed a practicable scheme by adding new terms into the parser lexicon based on humanists’ self-judgment to improve the accuracy of word segmentation of Jieba Chinese parser.

Although some digital humanities platforms have been successfully developed to support digital humanities research for humanists, most of them have still not provided a friendly digital reading environment to support humanists on interpreting texts. For this reason, this study developed an ATAS that can automatically retrieve LD sources from different databases on the Internet to supply rich annotation information on reading texts to help humanists interpret texts. This study brings digital humanities research to a new ground.

This study proposed a novel ATAS that can automatically annotate useful information on an ancient text to increase the readability of the ancient text based on LD sources from different databases, thus helping humanists obtain a deeper and broader understanding in the ancient text. Currently, there is no this kind of tool developed for humanists to support digital humanities research.

In prior literature, online endorsement system allowing the users to “like” or “dislike” shared information is found very useful in information filtering and trust elicitation in most social networks. This paper shows that such systems could fail in the context of investment communities due to several psychological biases.

This study develops a series of regression analyses to model the “like”/“dislike” voting process and whether or not such endorsement distinguishes between valuable information and noise. Trading simulations are also used to validate the practical implications of the findings.

The main findings of this research are twofold: (1) in the context of investment communities, online endorsement system fails to signify value-relevant information and (2) bullish information and “wisdom over the past event” information receive more “likes” and fewer “dislikes” on average, but they underperform in stock market price discovery.

This study demonstrates that biased endorsement may lead to the failure of the online endorsement system as information gatekeeper in investment communities. Two underlying mechanisms are proposed and tested. This study opens up new research opportunities to investigate the causes of biased endorsement in online environment and motivates the development of alternative information filtering systems.

Digital humanities aim to use a digital-based revolutionary new way to carry out enhanced forms of humanities research more effectively and efficiently. This study develops a character social network relationship map tool (CSNRMT) that can semi-automatically assist digital humanists through human-computer interaction to more efficiently and accurately explore the character social network relationships from Chinese ancient texts for useful research findings.

With a counterbalanced design, semi-structured in-depth interview, and lag sequential analysis, a total of 21 research subjects participated in an experiment to examine the system effectiveness and technology acceptance of adopting the ancient book digital humanities research platform with and without the CSNRMT to interpret the characters and character social network relationships.

The experimental results reveal that the experimental group with the CSNRMT support appears higher system effectiveness on the interpretation of characters and character social network relationships than the control group without the CSNRMT, but does not achieve a statistically significant difference. Encouragingly, the experimental group with the CSNRMT support presents remarkably higher technology acceptance than the control group without the CSNRMT. Furthermore, use behaviors analyzed by lag sequential analysis reveal that the CSNRMT could assist digital humanists in the interpretation of character social network relationships. The results of the interview present positive opinions on the integration of system interface, smoothness of operation, and external search function.

Currently, the system effectiveness of exploring the character social network relationships from texts for useful research findings by using the CSNRMT developed in this study will be significantly affected by the accuracy of recognizing character names and character social network relationships from Chinese ancient texts. The developed CSNRMT will be more practical when the offered information about character names and character social network relationships is more accurate and broad.

This study develops an ancient book digital humanities research platform with an emerging CSNRMT that provides an easy-to-use real-time interaction interface to semi-automatically support digital humanists to perform digital humanities research with the need of exploring character social network relationships.

At present, a real-time social network analysis tool to provide a friendly interaction interface and effectively assist digital humanists in the digital humanities research with character social networks analysis is still lacked. This study thus presents the CSNRMT that can semi-automatically identify character names from Chinese ancient texts and provide an easy-to-use real-time interaction interface for supporting digital humanities research so that digital humanists could more efficiently and accurately establish character social network relationships from the analyzed texts to explore complicated character social networks relationship and find out useful research findings.

The international marketing field has witnessed many studies related to “country of origin” (COO) effects or the “made in” concept over the past few decades. Yet COO research is deeply rooted in the so-called “production-related” approach, which mainly accounts for production- or technology-based factors. Barely considered is the “consumption-related” perspective, which reflects consumers' proclivity to base their buying decisions on foreigners' product choice. In this paper, we propose the “country of reference” (COR) concept, in which consumers deliberately imitate the product choices of consumers from another country, to whom the former (i.e. the imitators) attribute superior or more prestigious personas.

Unlike the made in concept, which emphasizes favored product qualities from superior manufacturing countries, we believe product preferences may arise from cross-border benchmarking or “cross-country referencing.” Pivoting on the optimal distinctiveness theory, this paper suggests a COR framework that incorporates the system justification theory and the self-discrepancy concept, along with decision heuristics and mental simulation effects. The proposed framework aims to explain consumers' inclination to “buy what certain foreigners buy.”

We suggest critical propositions related to the COR concept, discuss its marketing implications, and pinpoint further research issues.

COR may become a coping strategy through which low-status consumers perceiving themselves as less privileged than their high-status counterparts can narrow this gap by means of decision mimicking.

Previous research has shown that consumers prefer a bonus pack to a price discount for virtue foods, whereas they prefer a price discount to a bonus pack for vice foods. Acting as a guilt-mitigating mechanism, a price discount justifies consumers' purchasing behavior, allowing them to save money and consume less vice foods. However, for virtue foods, neither the anticipated post-consumption guilt nor the resulting need for justification lead consumers to prefer a bonus pack to a price discount. This study investigates whether product promotions remain effective with other moderating variables.

The authors use pricing tactic persuasion knowledge (PTPK), which refers to the consumer persuasion knowledge of marketers' pricing tactics, as a lens to understand whether the power of these promotions could be enhanced or mitigated. The authors inferred that increasing the frequency of exposure to these foods could positively influence consumers' purchasing choices. They conducted three studies to examine these effects. In Study 1, using pearl milk tea (vice food) and sugar-free tea (virtue food), the authors contended that consumers would prefer a price discount when purchasing pearl milk tea, but a bonus pack when purchasing sugar-free tea. In Studies 2 and 3, the authors varied the participants' frequency of exposure to photographs of people in everyday situations with vice (virtue) foods.

In Study 1, PTPK was shown to be more predictive of consumer choices regarding price discounts and bonus packs. In Studies 2 and 3, the authors contended that increased exposure to vice (virtue) foods increases the selection of vice (virtue) foods by participants who were unaware of having been exposed to vice (virtue) foods.

This research has not only made quite managerial and policy implications for marketing but also brought the theoretical contributions for marketing researches. This research demonstrates that either for vice foods or virtue foods, a price discount is preferred to a bonus pack.

Opaque selling has become popular among service providers in recent years. Although many researchers have investigated the optimality of opaque selling for service providers focusing on heterogeneous consumers, one question remaining unexplored is how the service providers’ optimal decisions are impacted by competitive intensity in a heterogeneous market. This paper aims to determine the conditions under which opaque selling is optimal for competing service providers.

The paper takes a Hotelling model to characterize the competition between two service providers. The authors also consider the interaction between the service providers and intermediary. Service providers act as game leaders and determine whether they should cooperate with the intermediary to introduce the opaque service.

The authors find that two competing service providers do not always benefit from opaque selling in a heterogeneous market consisting of leisure and business consumers, and the competitive intensity plays a significant role in the service providers’ decision optimization. Opaque selling allows service providers to acquire more profit in a highly competitive market or when the market contains a large proportion of leisure consumers. Otherwise, it is optimal for service providers without introducing the opaque selling.

The paper examines the optimality of opaque selling for competing service providers, and provides the suggestions to optimize the service providers’ decisions.

The paper investigates how the service providers’ optimal decisions are impacted by competitive intensity, considering the interaction between the service providers and intermediary.

This study aims to develop an assessment scale for university service quality (university SQ) and examine University Service Quality assessment model (UNIQUAL) of higher education during the pandemic.

Two studies applied a mixed-method design to develop and validate the UNIQUAL scale. In-depth interviews and literature reviews were conducted to refine the initial dimensions and items of UNIQUAL in Study 1. Item analysis, EFA and CFA were then conducted to purify item refinement, scale refinement, purification and validation in Study 2. Finally, a confirmed UNIQUAL model was analyzed via partial least squares structural equation modeling (PLS-SEM) using Smart-PLS 4.0.

The research confirms the four-factor structure of UNIQUAL, with a total of 16 items, to be a valid and reliable scale for the assessment of the service quality (SQ) of universities. Having adopted the bias-corrected and accelerated (BCa) bootstrap approach to study 5,000 subsamples from 27 countries, the authors found “responsiveness” and “empathy” to be significantly associated and have positive relationships with students' satisfaction with university SQ. Furthermore, university SQ and satisfaction were mediated by “health and safety” concerns.

The newly developed UNIQUAL scale would be of value to educators and authorities of higher education to assess the SQ of their universities to enhance the effectiveness of student learning. The improvement in satisfaction with higher education's SQ ultimately helps in retaining both international and local students amidst concerns about traveling and studying during the pandemic.

COVID-19 has affected the private and public sectors worldwide. Millions of students have been affected by schools being shut down and substituted with distance-learning programs. Thus, the assessment of the quality of university services has become an important support mechanism for retaining the sustainability of higher education.

The UNIQUAL scale provides a conceptual model and validates an assessment tool. The research hypotheses confirm the relationship between university SQ and satisfaction from the perspective of international students.

Eco-innovation products, which means achieving more efficient and responsible use of resources and reducing the detrimental impact on the environment, can win a competitive advantage for the enterprises. But it is not easy to implement due to the high cost of eco-innovative technologies development, the uncertainty of market needs and return risk of investment. Many enterprises seek collaborations from their upstream suppliers to jointly carry out eco-innovation, such as Apple, IBM and Nike. A unique feature of collaboration is that efforts by one party enhance the marginal value of the other party's efforts. However, the collaboration will make the partner know the eco-innovation technology and prompt the partner to encroach the market to sell competitive products by herself. Motivated by this observation, this paper considers the optimal collaboration strategy on eco-innovation between upstream and downstream supply chain member and the optimal encroachment strategy of upstream supplier in a supply chain.

This paper models a supply chain wherein a supplier provides products or materials for her manufacturer and cooperates with her manufacturer in eco-innovation. Also, the supplier could encroach on the market to sell similar products by herself. Then this paper uses game theory and mathematical modeling to do relative analysis.

The analysis reveals several interesting insights. First, eco-innovation collaboration makes supplier encroachment no longer only rely on the encroachment cost. The delayed realized eco-innovation efficiency information also plays a vital role. Second, different from previous research, the authors find the manufacturer's preference for supplier encroachment depends on the uncertainty of eco-innovation efficiency and potential market demand. Third, both partial and full encroachment strategies of the supplier can effectively improve the eco-innovation level.

The paper is the first to take the interplay between collaboration and encroachment into account in a supply chain. The results caution enterprises and policymakers to take vertical collaboration and delayed realized information into account in the competitive supply chain before making any operational decisions. Furthermore, the authors propose that governmental intervention aimed at stimulating supplier encroachment in appropriate circumstances can contribute to the improved environmental performance of products.

The advantages of blockchain technology are being widely discussed by academics, the business community and government, because blockchain can promote data sharing, optimise business processes, reduce operation costs, improve collaborative efficiency and build credible systems. The supply chain is becoming a key area for the application of blockchain technology. However, few studies have discussed the effect of such emerging technologies on the supply chain in depth. Therefore, this paper aims to analyse how blockchain empowers supply chain and promotes supply chain management.

Based on a review of relevant literature and blockchain applications in practice, this paper analyses the development and research status of blockchain technologies. In addition, considering the different operational processes within the supply chain, the authors discuss the opportunities and challenges of blockchain technologies, such as the transparency of supply, intelligent manufacturing, the security of logistics, the platformisation of sales and the ecology of governance.

The authors find that information sharing, information traceability and trust establishment are the key categories of research achievements and applications of supply chain management. The central issues for blockchain researchers are the authenticity of transaction data, the traceability of long supply chains and the establishment of trust for all participants.

From the practical and theoretical perspectives, this paper shows the development of blockchain technologies to clarify the challenges, opportunities and prospects. This paper elucidates and facilitates the development of emerging interdisciplinary research and the practice of supply chain.

This paper aims to present a novel approach of image super-resolution based on deep–shallow cascaded convolutional neural networks for reconstructing a clear and high-resolution (HR) remote sensing image from a low-resolution (LR) input.

The proposed approach directly learns the residuals and mapping between simulated LR and their corresponding HR remote sensing images based on deep and shallow end-to-end convolutional networks instead of assuming any specific restored models. Extra max-pooling and up-sampling are used to achieve a multiscale space by concatenating low- and high-level feature maps, and an HR image is generated by combining LR input and the residual image. This model ensures a strong response to spatially local input patterns by using a large filter and cascaded small filters. The authors adopt a strategy based on epochs to update the learning rate for boosting convergence speed.

The proposed deep network is trained to reconstruct high-quality images for low-quality inputs through a simulated dataset, which is generated with Set5, Set14, Berkeley Segmentation Data set and remote sensing images. Experimental results demonstrate that this model considerably enhances remote sensing images in terms of spatial detail and spectral fidelity and outperforms state-of-the-art SR methods in terms of peak signal-to-noise ratio, structural similarity and visual assessment.

The proposed method can reconstruct an HR remote sensing image from an LR input and significantly improve the quality of remote sensing images in terms of spatial detail and fidelity.