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The results of this study have significant policy implications for charting a new course toward enhancing agricultural productivity among Chinese farmers.

By establishing a rural household decision-making model based on the transfer market of farmland operation rights, this paper systematically analyzes the effects of land transfer-in and land transfer-out on the productivity (per labor income) of rural households. The authors conducted basic regression analysis and robustness tests using propensity score-matching and proxy variable approaches based on the micro survey data from rural households in 30 counties in 21 provinces/municipalities/autonomous regions in 2013.

After the completion of land transfer, the total productivity of rural households transferring in lands will increase with an increase in the agricultural productivity; the total productivity of rural households transferring out land will increase due to a rise in non-agricultural productivity and the absolute total productivity of rural households not involved in land transfer will remain unchanged.

Unlike previous literature, this paper discusses the impacts of land transfer-in and transfer-out on total productivity, agricultural productivity and non-agricultural productivity among various rural households (i.e. those transferring in land, transferring out land or which are self-sufficient).

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.

This paper aims to illustrate the scope and challenges of using computer-aided content analysis in international marketing with the aim to capture consumer sentiments about COVID-19 from multi-lingual tweets.

The study is based on some 35 million original COVID-19-related tweets. The study methodology illustrates the use of supervised machine learning and artificial neural network techniques to conduct extensive information extraction.

The authors identified more than two million tweets from six countries and categorized them into PESTEL (i.e. Political, Economic, Social, Technological, Environmental and Legal) dimensions. The extracted consumer sentiments and associated emotions show substantial differences across countries. Our analyses highlight opportunities and challenges inherent in using multi-lingual online sentiment analysis in international marketing. Based on these insights, several future research directions are proposed.

First, the authors contribute to methodology development in international marketing by providing a “use-case” for computer-aided text mining in a multi-lingual context. Second, the authors add to the knowledge on differences in COVID-19-related consumer sentiments in different countries. Third, the authors provide avenues for future research on the analysis of unstructured multi-media posts.

Based on the theory of trust and cost-benefit perspective, this paper examines the relationship between citizens’ trust and their digital attitudes by considering the mediating effects of performance expectancy and perceived risk, as well as the moderating effect of media use.

The city digital transformation in Shanghai is chosen as the case in this study. 466 questionnaires were collected through a survey, with Structural Equation Modeling to test the hypotheses in AMOS.

Citizens’ trust of government and trust of technology has no significant direct effect on their digital attitudes. However, performance expectancy mediates between the trust of government and digital attitudes, and perceived risk mediates the effect of trust of technology on attitudes. The use of social media significantly moderates the association between trust of technology and citizens’ attitudes.

Exploring why citizens shape supportive attitudes toward digitalization is critical to achieving digital governance goals in developing countries, especially large cities where digital transformation is accelerating. The originality lies in using cost-benefit analysis as a perspective and media use as a moderator to examine the mechanisms of citizens’ trust and digital attitudes.

This study investigates the relationships among digital transformation, technological innovation, industry–university–research collaborations and labor income share in manufacturing firms.

The relationships are tested using an empirical method, constructing regression models, by collecting 1,240 manufacturing firms and 9,029 items listed on the A-share market in China from 2013 to 2020.

The results indicate that digital transformation has a positive effect on manufacturing companies’ labor income share. Technological innovation can mediate the effect of digital transformation on labor income share. Industry–university–research cooperation can positively moderate the promotion effect of digital transformation on labor income share but cannot moderate the mediating effect of technological innovation. Heterogeneity analysis also found that firms without service-based transformation and nonstate-owned firms are better able to increase their labor income share through digital transformation.

This study provides a new path to increase the labor income share of enterprises to achieve common prosperity, which is important for manufacturing enterprises to better transform and upgrade to achieve high-quality development.

This article examines the mechanisms through which big data analytics capabilities (BDAC) contribute to creating sustainable value and analyzes the mediating roles that supply chain management capabilities (SCMC), as well as circular economy practices (CEP), play through their impact on sustainable performance.

Following a literature review, a serial mediation model is presented. Hypotheses regarding direct and mediating relationships are tested to determine their potential for sustainability impact and circularity. Partial least squares structural equation modeling (PLS-SEM) has been applied for causal and predictive purposes.

The results indicate that big data analytics capabilities do not have a direct positive impact on sustainable performance but influence indirectly through SCMC and CEP.

Although some authors have addressed the associations between IT business value, supply chain (SC), and sustainability, this paper provides empirical evidence related to these relationships. Additionally, this study performs novel predictive analyses.

The smoothness of the high-speed railway (HSR) on the bridge may exceed the allowable standard when an earthquake causes vibrations for HSR bridges, which may threaten the safety of running trains. Indeed, few studies have evaluated the exceeding probability of rail displacement exceeding the allowable standard. The purposes of this article are to provide a method for investigating the exceeding probability of the rail displacement of HSRs under seismic excitation and to calculate the exceeding probability.

In order to investigate the exceeding probability of the rail displacement under different seismic excitations, the workflow of analyzing the smoothness of the rail based on incremental dynamic analysis (IDA) is proposed, and the intensity measure and limit state for the exceeding probability analysis of HSRs are defined. Then a finite element model (FEM) of an assumed HSR track-bridge system is constructed, which comprises a five-span simply-supported girder bridge supporting a finite length CRTS II ballastless track. Under different seismic excitations, the seismic displacement response of the rail is calculated; the character of the rail displacement is analyzed; and the exceeding probability of the rail vertical displacement exceeding the allowable standard (2mm) is investigated.

The results show that: (1) The bridge-abutment joint position may form a step-like under seismic excitation, threatening the running safety of high-speed trains under seismic excitations, and the rail displacements at mid-span positions are bigger than that at other positions on the bridge. (2) The exceeding probability of rail displacement is up to about 44% when PGA = 0.01g, which is the level-five risk probability and can be described as 'very likely to happen'. (3) The exceeding probability of the rail at the mid-span positions is bigger than that above other positions of the bridge, and the mid-span positions of the track-bridge system above the bridge may be the most hazardous area for the running safety of trains under seismic excitation when high-speed trains run on bridges.

The work extends the seismic hazardous analysis of HSRs and would lead to a better understanding of the exceeding probability for the rail of HSRs under seismic excitations and better references for the alert of the HSR operation.

This paper aims to analyze unexplored connections between economic, environmental and social dimensions of corporate social responsibility (CSR) and knowledge management (KM) strategies (exploration, exploitation), also considering environmental dynamism as an influencing variable on these connections. The predicted CSR-KM interplay suggests, from stakeholder and knowledge-based views of the firm, the existence of ideal configurations between CSR and KM strategies that generate differentiated impacts on companies’ innovation capabilities, especially in dynamic environments.

Structural equation modeling by means of the partial least squares technique was used to test the study’s hypotheses after collecting survey data from Spanish companies of the renewable energy sector.

The study findings show that in highly dynamic environments, companies will tend to commit prominently in CSR, although their orientation (economic, environmental, social) and effects on innovation capabilities will depend mainly on the selected KM strategies. Social and environmental CSR are found to be highly related to KM exploration, whereas economic CSR is highly related to KM exploitation. Nevertheless, while a significant indirect effect of economic CSR by means of the KM exploitation strategy on innovation capabilities is found, the proposed indirect effect of both environmental and social CSR through the KM exploration strategy on innovation capabilities is not significant.

The results suggest that company managers should be aware of the advantages of following specific paths of investment in KM and CSR initiatives in highly dynamic environments, as there is a potential payoff in terms of innovation capability improvement. The results also suggest that “good” relationships with stakeholders, built from specific CSR investments, make firms able to get valuable knowledge that it is useful to develop KM strategies for innovation capability development.

Previous studies do not consider the interplay between KM strategies and CSR as a catalyzer for developing a firm’s innovation capabilities. This paper contributes to the KM and innovation literatures by introducing CSR into the conversation about how to improve innovation capabilities in dynamic and sustainable industries by using configurations of KM strategies and specific CSR investments in economic, social and environmental areas.

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.

In recent years, the role of environmental, social and governance (ESG) disclosure has become crucial. The aim of this paper is to study how corporate governance affects one part of ESG disclosure: anti-corruption disclosure.

This study examined 140 corporate social responsibility (CSR) reports from companies listed on the Italian stock markets and 50 CSR reports from other companies, then this study analysed the adoption of the Global Reporting Initiative (GRI) standard no. 205.

The results show a low level of disclosure, and that corporate governance issues matter. In particular, the analysis found a positive relationship between the presence of female and outside members, the number of board members and the level of anti-corruption disclosure.

This study acknowledges some limitations. Firstly, the research is based on a one-year sample. Secondly, the research hypotheses are confirmed only when considered in relation to a single section of the GRI standards. Thirdly, this study has a bias towards relatively large enterprises.

It could be worthwhile introducing a soft regulation regarding the composition of the board of directors that requires a certain quantitative and qualitative composition.

To the best of the authors’ knowledge, this is one of the few studies, the first in Italy, that sheds light on anti-corruption disclosure and its determinants.