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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 study aims to investigate the relationship between academic motivations and the risk of Not in Employment, Education or Training (NEET) among university undergraduates and Vocational Education and Training (VET) undergraduates.

The sample included 402 Vietnamese university undergraduates and 250 VET undergraduates in the southern region of Vietnam. Students took part in a survey, with all participants being informed about the study’s purpose and assured that their involvement was entirely voluntary. In addition to descriptive statistics, the study employed linear regression in SPSS to examine hypotheses.

The findings indicate that, for university students, intrinsic motivation and mastery approach motivation are associated with reduced NEET risk, while performance avoidance motivation is positively linked to this tendency. In contrast, for VET students, extrinsic motivation and performance approach motivation are negatively associated with NEET risk, but mastery approach motivation may exacerbate the risk.

Grounded in the principles of Self-Determination Theory (SDT) and Achievement Goal Theory (AGT), the study proposes that university students may prioritize competence improvement, knowledge acquisition and the satisfaction of their learning interests, which they believe will help them acquire valuable knowledge beneficial for their future careers. Conversely, VET students emphasize performance and external achievement, which may enhance their outcome and reduce NEET risk. These findings offer significant theoretical and practical insights into the adoption of SDT and AGT and also provide educators or policymakers with more detailed information regarding university and VET students’ learning and development.

With the advancement of digital transformation, it is important for e-retailers to use artificial intelligence (AI) for customer engagement (CE), as CE enables e-retail brands to succeed. Essentially, AI e-marketing (AIeMktg) is the use of AI technological approaches in e-marketing by blending customer data, and Retail 4.0 is the digitisation of the physical shopping experience. Therefore, in the era of Retail 4.0, this study investigates the factors influencing the use of AIeMktg for transforming CE.

The primary data were collected from 305 e-retailer customers, and the analysis was performed using a quantitative methodology.

The results reveal that AIeMktg has tremendous applications in Retail 4.0 for CE. First, it enables marketers to swiftly and responsibly use data to anticipate and predict customer demands and to provide relevant personalised messages and offers with location-based e-marketing. Second, through a continuous feedback loop, AIeMktg improves offerings by analysing and incorporating insights from a 360-degree view of CE.

The main contribution of this study is to provide theoretical underpinnings of CE, AIeMktg, factors influencing the use of AIeMktg, and customer commitment in the era of Retail 4.0. Subsequently, it builds and validates structural relationships among such theoretical underpinning variables in transforming CE with AIeMktg, which is important for customers to expect a different type of shopping experience across digital channels.

Given the contradictory findings of standardization/adaptation of marketing strategy in explaining export performance in the extant research, this study aims to examine the contingent effects of managerial ties and born global orientation in the standardized advertising-export performance conceptualization.

The study used two-respondent method in the survey research by a sample of 155 exporting firms operating in the industrial marketing based in Australia and New Zealand and applied hierarchical regression analysis to test the hypotheses.

The findings demonstrate that standardized advertising has a significant effect on export performance and this relationship is positively moderated by business ties. Such effect is particularly enhanced for born global firms (than nonborn global firms). However, political ties negatively influence the impact of standardized advertising on performance and such effect is stronger for born global firms.

A broader perspective of contingent variables should be included to examine the underlying relationship between standardized advertising and export performance in capturing the dynamism in international marketing contexts, such as institutional frameworks or sociocultural environments in host countries.

Standardized advertising is critical for born global firms’ export performance as it can increase efficiency and speed up internationalization processes. Such positive impact of standardized advertising on export performance is further enhanced if born global firms allocate resources to develop strong business ties with host country partners instead of building political ties with host country governments, because smooth business networking can facilitate standardized advertising on industrial marketing, yet justifiable political relations require intricate negotiations that often prolong internationalization progress.

This study incorporates managerial ties and born global orientation as contingent factors in fixing the theoretic interlock between standardization advertising strategy and export firm performance.

Inspired by the high-friction performance of the soft toe pads of tree frogs, this study aims to investigate the effect of elastic deformation on the lubrication properties of squeezing films inside soft tribocontacts with microstructured surface under wet conditions.

A one-dimensional hydrodynamic extrusion model was used to study the film lubrication characteristics of conformal contact. The lubrication characteristics of the extruded film, including load-carrying capacity, liquid flow and surface elastic deformation, were obtained through the simultaneously iterative solution of the fluid-governing and deformation equations.

The results show that the hydrodynamic pressure is approximating parabolically and symmetrically distributed in the contact area, and the peak value appears in the center of the extrusion surface. Elastic deformation increases the thickness of the liquid film, weakens the bearing capacity and homogenizes the liquid flow rate of inside soft friction contact. The magnitude of this effect greatly increases as the initial liquid film thickness decreases. Moreover, the elastic deformation directly affects the average film thickness of the extrusion contact. Narrow and shallow microchannels are found to result in a more prominent elastic deformation on the microstructured soft surface.

These results present a design for soft tribocontacts suitable for submerged or wet environments involving high friction, such as wiper blades, in situ flexible electrons and underwater robots.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-02-2024-0049/

In response to the issue of traditional algorithms often falling into local minima or failing to find feasible solutions in manipulator path planning. The purpose of this paper is to propose a 3D artificial moment method (3D-AMM) for obstacle avoidance for the robotic arm's end-effector.

A new method for constructing temporary attractive points in 3D has been introduced using the vector triple product approach, which generates the attractive moments that attract the end-effector to move toward it. Second, distance weight factorization and spatial projection methods are introduced to improve the solution of repulsive moments in multiobstacle scenarios. Third, a novel motion vector-solving mechanism is proposed to provide nonzero velocity for the end-effector to solve the problem of limiting the solution of the motion vector to a fixed coordinate plane due to dimensionality constraints.

A comparative analysis was conducted between the proposed algorithm and the existing methods, the improved artificial potential field method and the rapidly-random tree method under identical simulation conditions. The results indicate that the 3D-AMM method successfully plans paths with smoother trajectories and reduces the path length by 20.03% to 36.9%. Additionally, the experimental comparison outcomes affirm the feasibility and effectiveness of this method for obstacle avoidance in industrial scenarios.

This paper proposes a 3D-AMM algorithm for manipulator path planning in Cartesian space with multiple obstacles. This method effectively solves the problem of the artificial potential field method easily falling into local minimum points and the low path planning success rate of the rapidly-exploring random tree method.

Surakarta needs to evaluate its flat management as the most populous city in Central Java, Indonesia. This study examines the implementation of flat management in Surakarta City and evaluates and recommends the appropriate flat retribution rate.

This study used the analytical descriptive method to explain the problems in managing flats in Surakarta City. The data were obtained through a review of existing regulations and in-depth interviews with the flat residents and managers. The proposed retribution tariff for flats is calculated with a market approach through a comparison with neighboring cities and/or districts adjusted to the regional minimum wage to accommodate the residents’ ability to pay.

Based on the results of interviews and a review of regulations, problems were identified in the management of flats in Surakarta City. A comparison of levy rates with surrounding districts led to a proposal for new levy rates, which were divided into two tariff groups based on the characteristics of flats in Surakarta City.

The result of this study is a suggested retribution tariff derived from an economic framework that has yet to be validated by assessing the flat residents' willingness to pay.

This paper complements previous studies on the management and financing of flats by more in-depth evaluating the current tariffs and providing recommendations for appropriate tariffs.

The diagnosis and prediction methods used for estimating the health conditions of the bearing are of great significance in modern petrochemical industries. This paper aims to discuss the accuracy and stability of improved empirical mode decomposition (EMD) algorithm in bearing fault diagnosis.

This paper adopts the improved adaptive complementary ensemble empirical mode decomposition (ICEEMD) to process the nonlinear and nonstationary signals. Two data sets including a multistage centrifugal fan data set from the laboratory and a motor bearing data set from the Case Western Reserve University are used to perform experiments. Furthermore, the proposed fault diagnosis method, combined with intelligent methods, is evaluated by using two data sets. The proposed method achieved accuracies of 99.62% and 99.17%. Through the experiment of two data, it can be seen that the proposed algorithm has excellent performance in the accuracy and stability of diagnosis.

According to the review papers, as one of the effective decomposition methods to deal with nonlinear nonstationary signals, the method based on EMD has been widely used in bearing fault diagnosis. However, EMD is often used to figure out the nonlinear nonstationarity of fault data, but the traditional EMD is prone to modal confusion, and the white noise in signal reconstruction is difficult to eliminate.

In this paper only the top three optimal intrinsic mode functions (IMFs) are selected, but IMFs with less correlation cannot completely deny their value. Considering the actual working conditions of petrochemical units, the feasibility of this method in compound fault diagnosis needs to be studied.

Different from traditional methods, ICEEMD not only does not need human intervention and setting but also improves the extraction efficiency of feature information. Then, it is combined with a data-driven approach to complete the data preprocessing, and further carries out the fault identification and classification with the optimized convolutional neural network.

This paper investigates the impact of strategic alliance network centrality and structural holes on firm innovation efficiency. In addition, the paper aims to explore the moderating effects of government R&D subsidies and firm technology standardization.

Based upon the literature on strategic alliance networks, this paper proposes a conceptual model with several hypotheses. The empirical analysis is based on a sample of 736 observations from 92 mechanical manufacturing firms in China from 2010 to 2017. We measured firm innovation efficiency via the DEA model and performed quantitative analysis with GMM estimation.

The results indicate that strategic alliance network centrality is positively related to firm innovation efficiency, and structural holes have a U-shaped relationship with firm innovation efficiency. Government R&D subsidies positively moderate the relationship between centrality and firm innovation efficiency. Firm technology standardization positively moderates the relationship between centrality and firm innovation efficiency and the U-shaped relationship between structural holes and firm innovation efficiency.

Firms should focus on improving innovation efficiency and maximizing innovation output under limited resources. Furthermore, managers ought to strengthen cooperation between firms and external alliances while promoting the utilization of strategic alliance network position resources to benefit innovation efficiency.

This paper considers that innovation efficiency, including input and output processes, is more representative than innovation performance, and few studies have focused on the relationship between strategic alliance networks and innovation efficiency. To fill this research gap, this paper explores the impact of strategic alliance network position embedding on innovation efficiency.

Most of the existing time-cost-quality-environmental impact trade-off (TCQET) analysis models have focused on solving a simple project representation without taking typical activity and project characteristics into account. This study aims to present a novel approach called the “hybrid opposition learning-based Aquila Optimizer” (HOLAO) for optimizing TCQET decisions in generalized construction projects.

In this paper, a HOLAO algorithm is designed, incorporating the quasi-opposition-based learning (QOBL) and quasi-reflection-based learning (QRBL) strategies in the initial population and generation jumping phases, respectively. The crowded distance rank (CDR) mechanism is utilized to rank the optimal Pareto-front solutions to assist decision-makers (DMs) in achieving a single compromise solution.

The efficacy of the proposed methodology is evaluated by examining TCQET problems, involving 69 and 290 activities, respectively. Results indicate that the HOLAO provides competitive solutions for TCQET problems in construction projects. It is observed that the algorithm surpasses multiple objective social group optimization (MOSGO), plain Aquila Optimization (AO), QRBL and QOBL algorithms in terms of both number of function evaluations (NFE) and hypervolume (HV) indicator.

This paper introduces a novel concept called hybrid opposition-based learning (HOL), which incorporates two opposition strategies: QOBL as an explorative opposition and QRBL as an exploitative opposition. Achieving an effective balance between exploration and exploitation is crucial for the success of any algorithm. To this end, QOBL and QRBL are developed to ensure a proper equilibrium between the exploration and exploitation phases of the basic AO algorithm. The third contribution is to provide TCQET resource utilizations (construction plans) to evaluate the impact of these resources on the construction project performance.