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The production planning problem with fine-grained information has hardly been considered in practice. The purpose of this study is to investigate the data-driven production planning problem when a manufacturer can observe historical demand data with high-dimensional mixed-frequency features, which provides fine-grained information.

In this study, a two-step data-driven optimization model is proposed to examine production planning with the exploitation of mixed-frequency demand data is proposed. First, an Unrestricted MIxed DAta Sampling approach is proposed, which imposes Group LASSO Penalty (GP-U-MIDAS). The use of high frequency of massive demand information is analytically justified to significantly improve the predictive ability without sacrificing goodness-of-fit. Then, integrated with the GP-U-MIDAS approach, the authors develop a multiperiod production planning model with a rolling cycle. The performance is evaluated by forecasting outcomes, production planning decisions, service levels and total cost.

Numerical results show that the key variables influencing market demand can be completely recognized through the GP-U-MIDAS approach; in particular, the selected accuracy of crucial features exceeds 92%. Furthermore, the proposed approach performs well regarding both in-sample fitting and out-of-sample forecasting throughout most of the horizons. Taking the total cost and service level obtained under the actual demand as the benchmark, the mean values of both the service level and total cost differences are reduced. The mean deviations of the service level and total cost are reduced to less than 2.4%. This indicates that when faced with fluctuating demand, the manufacturer can adopt the proposed model to effectively manage total costs and experience an enhanced service level.

Compared with previous studies, the authors develop a two-step data-driven optimization model by directly incorporating a potentially large number of features; the model can help manufacturers effectively identify the key features of market demand, improve the accuracy of demand estimations and make informed production decisions. Moreover, demand forecasting and optimal production decisions behave robustly with shifting demand and different cost structures, which can provide manufacturers an excellent method for solving production planning problems under demand uncertainty.

The emergence of live streaming commerce has injected promising impetus into rural development and attracted many rural streamers. This study aims to explore the influencing factors of rural streamers’ engagement intentions to help promote the sustainable development of rural live streaming commerce.

Grounded in the extended valence framework, this research employs a mixed-methods approach encompassing both qualitative and quantitative methodologies. In the qualitative phase, the authors conduct in-depth interviews with 15 rural streamers, employing data coding techniques to uncover underlying factors. Subsequently, in the quantitative phase, the authors analyze survey data from 282 rural streamers, subjecting hypotheses to validation through structural equation modeling.

The findings derived from the analysis of both interviews and questionnaires reveal that several platform qualities, including platform rural-aiding support, perceived effectiveness of dispute resolution, perceived interactivity and platform reputation, have a positive effect on trust in the platform and validate the extended valence framework in understanding rural streamers’ live streaming intention. In addition, ties with customers have a moderating effect. Specifically, the stronger the ties with customers, the stronger the positive effect of perceived benefits and the weaker the positive effect of trust in the platform on live streaming intention will be.

This study contributes to the rural live streaming commerce literature and trust research from the sellers’ perspective and provides practical implications for policymakers and live streaming platform managers on enhancing rural streamers’ participation.

This study adopts the elaboration likelihood model and configuration perspectives to explore the internal mechanisms underlying the influence of live streaming on consumer trust building and purchase intention.

This study invited 757 experienced live streaming e-commerce users from Chinese platforms such as TikTok and RED, who participated in survey by filling questionnaires collected online. The research employed a mixed-method approach using SEM and fsQCA. SEM was utilized to analyze quantitative data to determine the direct and mediated relationships within product trust, while fsQCA served as a complement to identify the combinations of conditions that enhance product trust.

The findings reveal three important insights. Firstly, in the context of live streaming e-commerce, both product characteristics and streamer characteristics significantly influence consumers' trust in products. The para-social interaction plays a partial mediating role in the relationship between streamer characteristics and product trust. Secondly, four distinct paths are identified that contribute to enhancing product trust in live streaming e-commerce. Thirdly, PSI emerging as a core condition across all four paths, underscores the importance for merchants to foster positive social interactions with consumers beyond the live streaming environment.

This study enhances understanding of the dynamic live streaming e-commerce industry, offering insights into consumer behavior and practical guidance for merchants seeking to build engaged, trustworthy customer relationships.

Scholars and practitioners alike are paying attention to entrepreneurial orientation (EO) as an antecedent of the financial performance of SMEs. Other factors foster and improve SMEs' financial performance. This paper aims to shed the light on other two different strategic orientations that may help enhance SMEs' financial performance in addition to EO, namely; market orientation (MO) and brand orientation (BO).

The three different important strategic orientations are explored through two different studies. The first study was conducted to determine the different effects of the three orientations on SMEs' financial performance. Data were collected using a questionnaire among a convenient sample (131) of business owners/managers, and next PLS-SEM was used for data analysis. The financial performance of firms in the second study is hypothesized to be an outcome of a combination of different strategic orientations; therefore, the fsQCA method is applied to explore the causal recipes of those orientations.

The paper concluded that the three different strategic orientations are collectively, of paramount importance to strategic managers of SMEs.

The brand, market and EOs have been discussed discretely in previous studies and this study attempted to provide managers/owners of SMEs with a holistic view of the three different orientations and the amalgamation among them to be beneficial for better financial performance.

This study analyses the direct and indirect impacts of risks on the service-oriented construction supply chain and its resilience during disruptions.

We utilised the service-dominant logic, contingency and information processing theories to identify service-oriented construction supply chain characteristics and risk behaviours during turbulent times.

Our analysis of 285 construction companies with a strong service orientation revealed that the proposed risk model explains a 33.6% variance in supplier performance, 46.4% operational performance, 47.1% customer satisfaction and 46.5% financial performance. Our findings highlight the importance of effectively monitoring risks in service-oriented construction supply chains and examining complex networks in which risk variables impact construction supply chain performance.

This study examines the influence mechanisms between risks and actors’ performance in construction supply chains, taking a service-oriented perspective.

Previous studies emphasise the risks that construction companies encounter from disruptions, such as maintaining operations and enhancing performance. Nevertheless, the research still needs to establish the transmission mechanism of the simultaneous impact (direct and indirect) of all forms of risk on supply chain performance.

This study aims to investigate the sealing performance of a novel ribbed oil-slinging ring composite seal (ROSRCS) for the axle end of train gearboxes. The ROSRCS design incorporates added ribs and an inclined outer edge to enhance the sealing mechanism and reduce oil leakage.

Computational fluid dynamics simulations were used to analyze the leakage characteristics of ROSRCS under varying angles, outer edge inclinations and rib numbers and heights. The sealing performance was compared to a traditional oil-slinging ring composite seal (OSRCS). Key parameters such as oil leakage rate, turbulence dissipation intensity and jet strength were evaluated.

Results indicate that ROSRCS reduces the oil leakage rate by 5.7% compared to OSRCS. At a slinger ring inclination of 35°, the turbulence dissipation center in the ROSRCS flow field shifts toward the inlet, increasing the maximum turbulence dissipation intensity by 22.56%. A proper outer edge inclination enhances jet intensity, strengthening turbulence dissipation by up to 9.21%. While adding ribs may generate negative pressure zones, strategic rib configurations improve axle-end sealing performance by modifying the number, position and intensity of vortices.

This research presents a refined composite seal design that enhances the sealing efficiency of train gearbox axle ends, demonstrating improved oil retention through innovative geometric modifications. The findings contribute to the development of more efficient sealing technologies in high-speed train applications.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-10-2024-0370/

A transmission tower usually experiences bolt loosening under long-term alternating cyclic load, which may lead to collapse of the tower in extreme operating conditions. The paper aims to propose a data-driven identification method for bolt looseness of complicated tower structures based on reduced-order models and numerical simulations to perceive and evaluate the health state of a tower in operation.

The equivalent stiffnesses of three types of bolt joints under various loosening scenarios are numerically determined by three-dimensional finite element (FE) simulations. The order of the FE model of a tower structure with bolt loosening is reduced by means of the component modal synthesis method, and the dynamic responses of the reducer-order model under calibration loads are simulated and used to create the dataset. An identification model for bolt looseness of the tower structure based on convolutional neural networks driven by the acceleration sensors is constructed.

An identification model for bolt looseness of the tower structure based on convolutional neural networks driven by the acceleration sensors is constructed and the applicability of the model is investigated. It is shown that the proposed method has a high identification accuracy and strong robustness to data noise and data missing. Meanwhile, the method is less dependent on the number and location of sensors and is easier to apply in real transmission lines.

This paper proposes a data-driven identification method for bolt looseness of a complicated tower structure based on reduced-order models and numerical simulations. Non-linear relationships between equivalent stiffness of bolted joints and bolt preload depicting looseness are obtained and reduced-order model of tower structure with bolt looseness is established. Finally, this paper investigates applicability of identification model for bolt looseness.

This paper aims to develop an innovative 3D printer based on material extrusion to expand applied material field and shorten the production cycle. The developed 3D printer can fabricate products directly using various powders, including polymers and fillers. In addition, the influence of extrusion on the orientation of thermal conductive filler is also investigated.

To ensure the plasticizing effect and the mixing ability, the printing head is a conical twin-screw extruder, which have a smaller volume. PA12 and h-BN powders were selected for printing as matrix and filler, respectively. The properties of printing products were characterized.

The results show that the new printer can fabricate products directly using polymer powders because of the mixing ability of the twin-screw. The h-BN filler orient in the PA12 matrix and form thermal conduction paths due to the extrusion process, which make the printed samples have an anisotropic thermal conductivity.

The innovative 3D printer provides a method of printing products directly using powders, which can expand material field and shorten the production cycle. For composites, the extrusion process can make fillers orient in the matrix to fabricate products with anisotropic characteristics.

By incorporating the defect feature information, an ML-based linkage between defects and fatigue life unaffected by the time scale is developed, the primary focus is to quantitatively assess and elucidate the impact of different defect features on fatigue life.

A machine learning (ML) framework is proposed to predict the fatigue life of LPBF-built Hastelloy X utilizing microstructural defects identified through nondestructive detection prior to fatigue testing. The proposed method combines nondestructive micro-computerized tomography (micro-CT) technique to comprehensively analyze the size, location, morphology and distribution of the defects.

In the test set, SVM-based fatigue life prediction exhibits the highest accuracy. Regarding the defect information, the defect size significantly affects fatigue life, and the diameter of the circumscribed sphere of the largest defect has a critical effect on fatigue life.

This comprehensive approach provides valuable insights into the fatigue mechanism of structural materials in defective states, offering a novel perspective for better understanding the influence of defects on fatigue performance.

The purpose of this paper is to examine the potential relationship between green finance and greenwashing to rationalize resource allocation better. Additionally, it explores the interconnections among various subgroups of green finance products (GFPs) and identifies any overlooked or underrepresented subgroups.

This paper uses a mixed-method exploratory sequential design. Initially, the authors collected a sample of 313 relevant documents. Thematic analysis and hierarchical coding were then performed using NVivo software to uncover correlations between various nodes and address our research questions. Additionally, a word cloud analysis was conducted to assess the potential research value of stakeholders as moderating variables. Following this, the role of stakeholders was reevaluated, leading to the selection of 58 samples for separate content analysis.

First, there is a negative relationship between green finance and greenwashing. Second, a negative relationship is observed between GFPs and greenwashing. The authors’ correlation coefficient analysis suggests that environmental, social and governance funds, as a non-mainstream research focus within GFPs, deserve further in-depth investigation.

While a significant portion of the existing literature focuses on the relationship between green bonds and greenwashing, a noticeable gap exists regarding the broader spectrum of GFPs and their potential association with greenwashing. The lack of a direct connection between broader GFPs and greenwashing suggests that this area is underexplored in literature. This paper fills this gap by investigating the role of broader GFPs in either promoting or mitigating greenwashing.