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This paper aims to examine the management status quo of archaeological heritage in Vietnam seen in the case of Vuon Chuoi, a complex of Bronze Age sites located in Central Hanoi, which has been believed to be Hanoi’s first human settlement. Like other archaeological sites located in urban areas, this site has been under threat of destruction caused by land encroachment pressure. Although researchers have long waged a campaign for preservation, the dissensus among key stakeholders and the dispute over responsibility have left this site at the heart of an interminable polemic over legislation.

This research utilises a qualitative approach, and the primary data were collected throughout multiple field trips in 2019 and 2020. Several open-ended interviews were conducted with various state and nonstate actors involved in the Vuon Chuoi Complex’s management process. The discussion was also supported by analysing related legal documents retrieved from national archives and official online directories.

This paper dissects the current legislative and administrative framework applied in governing heritage in general and archaeological sites in Vietnam, in particular. The results indicate that existing flaws in Vietnam’s legal system are detectable, and the unsystematic organisation has led to deferment of the decision-making processes. Also, there is an apparent difference found in the attitude of the bodies in charge toward the treatment of listed and unlisted sites.

This research outlines that in the wake of urbanisation and industrialisation in Vietnam, a consensus among key stakeholders and an inclusive legal system are required to help preserve archaeological sites in urgent need of attention. Although several Vietnamese laws and regulations have been put into practice, they have shown critical barriers and gaps in conserving Vietnamese cultural heritage.

The phenomenon of nonposting behavior, known as lurking, has become increasingly prevalent on social networking sites (SNS). This study aims to understand why certain users are inclined to lurk on SNS by proposing a theoretical framework that integrates self-presentational concerns, SNS fatigue and social presence.

Building upon the theoretical framework, a moderated mediation model is established to illustrate the mechanisms of lurking on SNS. Survey data were collected from 616 SNS users through an online survey and analyzed using the SPSS macro PROCESS.

The findings show that self-presentational concerns have positive and direct effects on lurking. Moreover, the relationship between self-presentational concerns and lurking is partially mediated by SNS fatigue. Furthermore, both the direct effect and the mediating effect are moderated by social presence.

This study offers a novel theoretical perspective on lurking behavior by introducing a moderated mediation model. The findings reveal intricate mechanisms underlying this specific SNS usage behavior and its connections to both self-presentational concerns and SNS fatigue, thereby enriching the existing literature on user engagement and inactivity on SNS. Furthermore, this research highlights the pivotal role of social presence in moderating the effects of self-presentational concerns, offering new insights into the dynamics of online social interactions.

This paper aims to explore the underlying mechanism whereby information-induced stress, resulting from the burdensome nature, questionable information reliability, misleading content and diffuse characteristics of infodemic pressure, impacts individuals’ online information-related behavior and health-related preventive behavior.

We conducted a cross-sectional survey organized with social media users during the post-pandemic period. Based on the 342 valid responses, structural equation modeling was employed to validate the research model.

The results substantiate our multidimensional view of infodemic stress, which encompasses dimensions including information overload, uncertainty, diffusivity and insecurity. We found that the infodemic stress contributes to pandemic fatigue, then engenders both negative information behavior and reduced engagement in preventive behavior. Furthermore, infodemic stress has also been found to have a direct positive effect on individuals’ engagement in preventive behavior.

This study introduces the concept of “infodemic stress” and presents a comprehensive framework to capture its various dimensions. This study offers novel insights into the psychological and behavioral repercussions of the infodemic stress transmitted through social media.

Lead users are essential participants in crowdsourcing innovation events; their continuance intention significantly affects the success of the crowdsourcing innovation community (CIC). Although researchers have acknowledged the influences of network externalities on users' sustained participation in general information systems, limited work has been conducted to probe these relationships in the CIC context; particularly, the predictors of lead users' continued usage intention in such context are still unclear. Hence, this paper aims to explore the precursors of lead users' continuance intention from a network externalities perspective in CIC.

This work ranked users' leading-edge status to recognize lead users in the CIC. And then, the authors proposed a research model based on the network externalities theory, which was examined utilizing the partial least squares (PLS) technique. The research data were collected from an online survey of lead users (n = 229) of a CIC hosted by a China handset manufacturer.

Results revealed that the number of peers, perceived complementarity and perceived compatibility significantly influence lead users' continuance intention through identification and perceived usefulness.

This work contributes to the crowdsourcing innovation research and provides views regarding how lead users' sustained participation can be developed in the CICs. This work also offers an alternative theoretical framework for further research on users' continued intention in open innovation activities.

Social group system and social tagging system, which leverage the psychological mechanisms underlying group influence and social tags to drive consumer behaviors, have been prevalent in the social commerce platform. However, limited studies have examined how the affordances of social group system and social tagging system influence consumers’ social shopping behavior. The purpose of this study is to examine the formation of social shopping behavior in the social commerce platform.

Combining affordance theory with dual-congruity theory, we develop a model to examine how the affordances of social group system and social tagging system influence consumers’ social shopping behavior through the underlying self-congruity and functional-congruity processes. We empirically validate the research model using a multimethod approach, including an instrument development study and a field survey study.

Our empirical findings show that social support positively influences relational identity, while it has a nonsignificant effect on social identity. Social interactivity positively influences relational identity and social identity. Furthermore, social tagging quality and social endorser credibility positively affect perceived diagnosticity and perceived serendipity. Finally, relational identity, social identity, perceived diagnosticity and perceived serendipity collectively determine consumers’ social shopping intention.

This study contributes to the theoretical understanding of social shopping in social commerce and offers practical implications for designing an effective social group system and social tagging system to boost product sales.

Institutional pressures, increased competition and environmental changes demand sustainable business performance. Using the lens of stakeholder theory, this study aims to explore the simultaneous relationships of corporate social responsibility (CSR), green practices and perceived organizational politics (POP) with sustainable business performance, incorporating employee pro-environmental behavior (EPB) as a moderator.

Using a cross-sectional research design, data were collected via a survey of employees (n = 422) from across industries.

Based on hierarchical regression analyses, the findings support stakeholder theory by showing that CSR and green practices positively affect sustainable business performance. The findings also extend stakeholder theory by showing that the CSR-sustainable business performance relationship is moderated by EPB.

The study has practical implications for leaders, managers and supervisors in managing CSR and green practices for sustainable business performance and managing EPB to capitalize on the benefits of CSR.

This study assesses the previously untested simultaneous effects of CSR, green practices and POP on sustainable company performance and the moderating effect of EPB.

The metaverse has garnered increasing attention from researchers and practitioners, yet numerous firms remain hesitant to invest in it due to ongoing debates about its potential financial benefits. Therefore, it is crucial to analyze how the implementation of metaverse initiatives affects firms’ stock market value – an area that remains underexplored in the existing literature. Additionally, there is a significant lack of research on the contingency factors that shape the stock market reaction, leaving a noticeable gap in managerial guidance on the timing and benefits of investments in the metaverse. To narrow these gaps, we examine whether and when the implementation of metaverse initiatives enhances firms’ stock market value.

Based on 73 metaverse implementation announcements disclosed by Chinese listed firms during January 2021–August 2023, we employ an event study approach to test the hypotheses.

We find that metaverse implementation announcements elicit a positive stock market reaction. Moreover, the stock market reaction is stronger for technology-focused announcements and smaller firms, or when public attention to the metaverse is higher. Nevertheless, firms’ growth prospects do not significantly alter the stock market reaction.

This study extends the nascent literature on the metaverse by applying signaling theory to offer novel insights into the signaling effect of metaverse implementation announcements on stock market value and the boundary conditions under which the effectiveness of the signal varies. Besides, it provides managers with important implications regarding how to tailor the investment and information disclosure strategies of the metaverse to more effectively enhance firms’ stock market value.

As businesses keep investing substantial resources in developing business analytics (BA) capabilities, it is unclear how the performance improvement transpires as BA affects performance in many different ways. This paper aims to analyze how BA capabilities affect firms’ agility through resources like information quality and innovative capacity considering industry dynamism and the resulting impact on firm performance.

This paper tested the research hypothesis using primary data collected from 192 companies operating in Bangladesh. The data were analyzed using partial least squares-based structural equation modeling.

The results indicate that BA capabilities improve business resources like information quality and innovative capacity, which, in turn, significantly impact a firm’s agility. This paper also found out that industry dynamism moderates the firms’ agility and, ultimately, firms’ performance.

The contribution of this work provides insight regarding the role of business analytics capabilities in increasing organizational agility and performance under the moderating effects of industry dynamism.

The present research is to the best of the authors’ knowledge among the first studies considering a firm’s agility to explore the impact of BA on a firm’s performance in a dynamic environment. While previous researchers discussed resources like information quality and innovative capability, current research theoretically argues that these items are a leveraging point in a BA context to increase firm agility.

This study aims to solve the problem that both the speed and the required driving power of electric vehicles (EVs) will change during the dynamic wireless charging (DWC) process, making it difficult to charge EVs with a constant power considering the overall efficiency of DWC system, the numbers of EVs and the power supply capacity. Therefore, this paper proposes the power control and efficiency optimization strategies for multiple EVs.

The wireless power charging system for multiple loads with a structure of double-sided LCC compensation topology is established. The expressions of optimal transmission efficiency and optimal equivalent impedance are derived. Taking the Tesla Model 3 as an example, a method to determine the number of EVs allowed by one transmitter coil and the overall charging power is proposed considering EV speed, power supply capacity, safe braking distance and overall efficiency. Then, the power control strategy, which can adapt to the changes of EV speed and the efficiency optimization strategy under different numbers of EVs are proposed.

In this paper, a method to determine the numbers of EVs allowed by one transmitter coil and the overall charging power is proposed considering EVs speed, power supply capacity, safe braking distance and overall efficiency. The accuracy of the charging power is good enough and the overall efficiency reaches a maximum of 91.79% when the load resistance changes from 5Ω to 20Ω.

In this paper, the power control and efficiency optimization strategy of DWC system for multiple EVs are proposed. Specifically, a method of designing the number of EVs and charging power allowed by one transmitter coil considering the factors of EV speed, power supply capacity, safe braking distance and overall efficiency is designed. The overall efficiency of the experiment reaches a maximum of 91.79% after adopting the optimization strategy.

Fine-tuning pre-trained language models (PLMs), e.g. SciBERT, generally require large numbers of annotated data to achieve state-of-the-art performance on a range of NLP tasks in the scientific domain. However, obtaining fine-tuning data for scientific NLP tasks is still challenging and expensive. In this paper, the authors propose the mix prompt tuning (MPT), which is a semi-supervised method aiming to alleviate the dependence on annotated data and improve the performance of multi-granularity academic function recognition tasks.

Specifically, the proposed method provides multi-perspective representations by combining manually designed prompt templates with automatically learned continuous prompt templates to help the given academic function recognition task take full advantage of knowledge in PLMs. Based on these prompt templates and the fine-tuned PLM, a large number of pseudo labels are assigned to the unlabelled examples. Finally, the authors further fine-tune the PLM using the pseudo training set. The authors evaluate the method on three academic function recognition tasks of different granularity including the citation function, the abstract sentence function and the keyword function, with data sets from the computer science domain and the biomedical domain.

Extensive experiments demonstrate the effectiveness of the method and statistically significant improvements against strong baselines. In particular, it achieves an average increase of 5% in Macro-F1 score compared with fine-tuning, and 6% in Macro-F1 score compared with other semi-supervised methods under low-resource settings.

In addition, MPT is a general method that can be easily applied to other low-resource scientific classification tasks.