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This study aims to investigate how small talk interaction affects knowledge sharing, examining the mediating role of interpersonal trust (affect- and cognition-based trust) and the moderating role of perceived similarity among the mechanisms of small talk and knowledge sharing.

This research conducts complementary studies and collects multi-culture and multi-wave data to test research hypotheses and adopts structural equation modeling to validate the whole conceptual model.

The research findings first reveal two trust mechanisms linking small talk and knowledge sharing. Meanwhile, the perceived similarity between employees, specifically, strengthens the affective pathway of trust rather than the cognitive pathway of trust.

This study combines Interaction Ritual Theory and constructs a dual-facilitating pathway approach that aims to reveal the impact of small talk on knowledge sharing, describing how and when small talk could generate a positive effect on knowledge sharing. This research provides intriguing and dynamic insights into understanding knowledge sharing processes.

Meteorological disasters pose a significant risk to people’s lives and safety, and accurate prediction of weather-related disaster losses is crucial for bolstering disaster prevention and mitigation capabilities and for addressing the challenges posed by climate change. Based on the uncertainty of meteorological disaster sequences, the damping accumulated autoregressive GM(1,1) model (DAARGM(1,1)) is proposed.

Firstly, the autoregressive terms of system characteristics are added to the damping-accumulated GM(1,1) model, and the partial autocorrelation function (PACF) is used to determine the order of the autoregressive terms. In addition, the optimal damping parameters are determined by the optimization algorithm.

The properties of the model were analyzed in terms of the stability of the model solution and the error of the restored value. By fitting and predicting the losses affected by meteorological disasters and comparing them with the results of four other grey models, the validity of the new model in fitting and prediction was verified.

The dynamic damping trend factor is introduced into the grey generation operator so that the grey model can flexibly adjust the accumulative order of the sequence. On the basis of the damping accumulated grey model, the autoregressive term of the system characteristics is introduced to take into account the influence of the previous data, which is more descriptive of the development trend of the time series itself and increases the effectiveness of the model.

With the development of intelligent technology, deep learning has made significant progress and has been widely used in various fields. Deep learning is data-driven, and its training process requires a large amount of data to improve model performance. However, labeled data is expensive and not readily available.

To address the above problem, researchers have integrated semi-supervised and deep learning, using a limited number of labeled data and many unlabeled data to train models. In this paper, Generative Adversarial Networks (GANs) are analyzed as an entry point. Firstly, we discuss the current research on GANs in image super-resolution applications, including supervised, unsupervised, and semi-supervised learning approaches. Secondly, based on semi-supervised learning, different optimization methods are introduced as an example of image classification. Eventually, experimental comparisons and analyses of existing semi-supervised optimization methods based on GANs will be performed.

Following the analysis of the selected studies, we summarize the problems that existed during the research process and propose future research directions.

This paper reviews and analyzes research on generative adversarial networks for image super-resolution and classification from various learning approaches. The comparative analysis of experimental results on current semi-supervised GAN optimizations is performed to provide a reference for further research.

The purpose of this article is to investigate and analyze the interactions between economic and sustainable development elements in the context of remanufacturing in India.

To comprehend the hierarchical and contextual link among factors impacting remanufacturing in India, the study used interpretive structural modeling (ISM) and decision-making trial and evaluation laboratory (DEMATEL) methodologies.

The integrated ISM-DEMATEL approach identifies optimal utilization of the resources as the most crucial factor influencing remanufacturing in India, followed by reducing landfills, conserving energy and low cost. The study also reveals that optimal utilization of resources, reduction of landfills, conservation of energy and incorporated advanced technology impacts most of the factors but get affected by a few factors.

Industry practitioners and policymakers should consider the remanufacturing process to achieve sustainable and economic development. The government and other stakeholders can use the ISM framework and cause-and-effect diagram to classify the impact factors and their impact on the Indian economy and environment.

This study supports the process to save the landfills and curbing pollution, conserve energy and optimize utilization of the resources, generate employment and supporting the development of the economy. Remanufacturing will undoubtedly contribute to the development of an environment and economy in India that benefits both.

ISM and DEMATELs strategy offers a tiered model and a cause-and-effect relationship between the variables affecting remanufacturing in India.

Inland shipping has been extensively recognised as a sustainable, efficient and good alternative to rail and road modes of transportation. In recent years, various authorities and academic researchers have advocated shifting from road to other sustainable modes like inland waterway transport (IWT) or rail transport. Academic work on modernisation and technological innovations to enhance the effectiveness and efficiency of waterborne transportation is becoming apparent as a growing body of literature caused by the need to achieve a sustainable transport system. Thus, it became apparent to explore the research trends on IWT.

A systematic and structured literature review study was employed in this paper to identify the challenges and concepts in modernising inland waterways for freight transportation. The review analysed 94 articles published in 54 journals from six well-known databases between 2010 and 2022.

The key findings of this review are that despite various challenges confronting the sector, there have been successful cases of technological advancement in the industry. The main interest among scholars is improving technical and economic performance, digitalisation, and safety and environmental issues. The review revealed that most of the literature is fragmented despite growing interest from practitioners and academic scholars. Academic research to address the strategic objectives, including strengthening competitiveness (shipbuilding, hydrodynamics, incorporating artificial intelligence into the decision-making process, adopting blockchain technology to ensure transparency and security in the transactions, new technologies for fleets adaptation to climate change, more effective handling, maintenance and rehabilitation technologies), matching growth and changing trade patterns (intermodal solutions and new logistics approaches) are major causes of concerns.

By employing the approach of reviewing previously available literature on IWT review papers, this review complements the existing body of literature in the field of IWT by providing in a single paper a consolidation of recent state-of-the-art research on technological developments and challenges for inland waterways freight transport in the intermodal supply chain that can act as a single resource to keep researchers up to date with the most recent advancements in research in the domain of inland waterway freight transport. Additionally, this review identified gaps in the literature that may inspire new research themes in the field of IWT.

To achieve the Sustainable Development Goals (SDGs) in the era of Logistics 4.0, machine learning (ML) techniques and simulations have emerged as highly optimized tools. This study examines the operational dynamics of a supply chain (SC) in Vietnam as a case study utilizing an ML simulation approach.

A robust fuel consumption estimation model is constructed by leveraging multiple linear regression (MLR) and artificial neural network (ANN). Subsequently, the proposed model is seamlessly integrated into a cutting-edge SC simulation framework.

This paper provides valuable insights and actionable recommendations, empowering SC practitioners to optimize operational efficiencies and fostering an avenue for further scholarly investigations and advancements in this field.

This study introduces a novel approach assessing sustainable SC performance by utilizing both traditional regression and ML models to estimate transportation costs, which are then inputted into the discrete event simulation (DES) model.

With the continuous improvement of public–private partnership (PPP) projects, the participants' value creation goals are not only limited to achieving the basic performance objectives but also to realising value added. However, the effect of traditional contract management on realising the value creation objectives of PPP projects is limited. According to the view of multifunctional contract, joint-contract functions that integrate contract control and flexibility are likely to be effective in enhancing the value creation of PPP projects. This study aims to explore the effects of joint-contract functions on PPP project value creation and relevant influencing mechanism by investigating the mediating effect of in-role behaviour and extra-role behaviour.

After collecting 258 valid questionnaires from PPP professionals in China, this study used structural equation modelling to validate the hypotheses.

Contract control and flexibility can improve PPP project value creation. Specifically, contract control improves the achievement of the basic contract objectives of PPP projects, whereas contract flexibility enhances the achievement of the value-added of PPP projects. Moreover, only in-role behaviour mediates the effect of contract control on value creation. In addition, the mediating effect of extra-role behaviour on the impact of contract flexibility on value creation is stronger than that of in-role behaviour. The mediating effect of in- and extra-role behaviour is mainly reflected in the realisation of basic and value-added performance, respectively.

The findings of this study can help realise value creation in three ways. Firstly, new perspectives for PPP project value creation should be proposed by combining the improvement of contract objectives and the realisation of the participants' implicit demands. Secondly, the effects of different contract functions on value creation should be analysed instead of a single dimension of contractual governance. Thirdly, the mediating effects of different types of cooperation behaviour that may influence the relationship between contractual governance and value creation should be evaluated.

This study verifies the impacts of different contract functions on PPP project value creation. In addition, cooperative behaviour is embedded as a mediating variable, and the mediated transmission path from contract function to cooperative behaviour and further to PPP project value creation is systematically analysed.

This study aims to investigate the impact of digital transformation on supply chain resilience. Additionally, the paper examines the mediating effect of supply chain process integration as well as the moderating effect of environmental uncertainty in the relationship between digital transformation and supply chain resilience.

Drawing on digital empowerment theory, this study proposes a theoretical model. Using survey data collected from 216 enterprises in China, the study employs structural equation modeling to validate the theoretical model.

The results reveal that digital transformation has a significant impact on supply chain resilience. Three dimensions of supply chain process integration, namely, information flow integration, physical flow integration, and financial flow integration mediate the relationship between digital transformation and supply chain resilience. In addition, environmental uncertainty including market uncertainty and technology uncertainty positively moderates the relationship between digital transformation and supply chain resilience.

First, this paper provides empirical evidence on both the direct and indirect effects of digital transformation on supply chain resilience. Second, this paper enriches the understanding of how supply chain integration impacts supply chain resilience in the digital transformation era by adopting a more granular perspective of process integration rather than broad external and internal integrations. Furthermore, this paper extends the knowledge of the role of external environment in digital transformation and supply chain risk management by examining the moderating effects of market uncertainty and technology uncertainty.

Brand information is ubiquitous online and offline; consumers exhibit brand avoidance tendencies towards brand stimuli when there is a discrepancy between a brand image/personality and one’s self-concept. Given the multifaceted culturally constituted self-domains and self-importance, this research investigates how cultural variation affects reactions to self-brand discrepancy, considering two types of narcissist orientations.

Using national culture as proxy for cultural orientation, sample data were collected through surveys administered to 410 participants (210 in China and 200 in the USA). A multi-group structural equation model was adopted to examine the conceptual model and proposed hypotheses. The follow-up qualitative study was conducted to allow further discussion of the quantitative results.

The results show that self-brand discrepancy can only be converted into brand avoidance tendency through the activation of cognitive dissonance for both Americans and Chinese. Specifically, for Chinese consumers only (ideal) social identity self-brand discrepancies can activate avoidance behaviour. In addition, grandiose and vulnerable narcissism orientations co-exist for both Chinese and Americans, these negatively moderate the relationship between social self-brand discrepancies and cognitive dissonance. For US consumers, idealised identity discrepancies mitigate dissonance; only those with a vulnerable narcissistic orientation would act on avoidance when experiencing dissonance.

By incorporating cultural variations in the investigations of self-brand discrepancy, this paper advances existing knowledge on dissonance and coping mechanisms. In addition, by bringing narcissistic orientations to the fore, it allows for a deeper understanding of how these cultural variations operate. In addition, our research provides important guidelines for brand practitioners to better leverage their marketing campaigns in offline and online contexts and to reduce brand avoidance tendencies across the international marketplace.