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Collaborative robots (cobots) are widely used in various manipulation tasks within complex industrial environments. However, the manipulation capabilities of cobot manipulation planning are reduced by task, environment and joint physical constraints, especially in terms of force performance. Existing motion planning methods need to be more effective in addressing these issues. To overcome these challenges, the authors propose a novel method named force manipulability-oriented manipulation planning (FMMP) for cobots.

This method integrates force manipulability into a bidirectional sampling algorithm, thus planning a series of paths with high force manipulability while satisfying constraints. In this paper, the authors use the geometric properties of the force manipulability ellipsoid (FME) to determine appropriate manipulation configurations. First, the authors match the principal axes of FME with the task constraints at the robot’s end effector to determine manipulation poses, ensuring enhanced force generation in the desired direction. Next, the authors use the volume of FME as the cost function for the sampling algorithm, increasing force manipulability and avoiding kinematic singularities.

Through experimental comparisons with existing algorithms, the authors validate the effectiveness and superiority of the proposed method. The results demonstrate that the FMMP significantly improves the force performance of cobots under task, environmental and joint physical constraints.

To improve the force performance of manipulation planning, the FMMP introduces the FME into sampling-based path planning and comprehensively considers task, environment and joint physical constraints. The proposed method performs satisfactorily in experiments, including assembly and in situ measurement.

This study aims to investigate the effect of the reference quality on the optimal production strategy of a component manufacturer (CM), which mainly concerns whether to sell its high quality self-branded products and whether to supply critical components to a competitive original equipment manufacturer (OEM) who produces low quality products.

The study considers a supply chain comprising an OEM, a CM and a third-party component manufacturer (TCM), who produces components with uncertain quality. The OEM selects a supplier between the CM and the TCM to produce products. Anticipating the OEM’s supplier selection, the CM chooses among three alternative production strategies. For each alternative strategy of the CM, the authors derive the equilibrium solutions between the OEM and the CM with or without the reference quality effect. Then, the authors obtain the effect of the reference quality on the CM by comparing the CM’s optimal strategy between the two situations.

First, the reference quality has opposite effects on the CM’s production strategy depending on the competition results. A high reference quality effect motivates the CM to solely sell the self-branded products if the OEM can always enter the final product market when purchasing from the TCM, and to sell both self-branded products and components if the OEM cannot enter the market when using the TCM’s low quality components. Second, the reference quality effect motivates the OEM to accept a higher wholesale price from the CM. Third, the reference quality effect can make the CM benefit from a more stable TCM in competition.

This paper first considers the impact of the reference quality effect on the CM’s production strategy. By considering consumers’ behavior in a co-opetitive supply chain, this paper contributes to both literature and practice.

This study aims to extend the cognitive appraisal theory by developing and validating a conceptual framework to illustrate how travelers' behavioral intention is generated via a multi-stage evaluation of health-related variables.

SEM and moderator analysis were conducted to examine the theoretical framework (post-intervention event travel intention) and to investigate how the appraisal process differs across travelers with various attitudes toward vaccination.

This study found that cruise travel intention was positively influenced by the perceived hedonic value and perceived trustworthiness and negatively influenced by perceived infection risk. Furthermore, whereas perceived hedonic value, perceived trustworthiness and perceived risk of infection were all predicted by crisis management, the dimensions of crisis management operated differently. In addition, vaccination attitudes amplified the unfavorable effect of perceived risk on intention.

Drawing on the CAT, this study developed and validated a conceptual framework to integrate crisis management with customers' behavioral intentions. This study extends existing cruise travel intention theory by demonstrating how post-pandemic travelers' behavioral intention is generated via a multi-stage appraisal-reappraisal process based on the evaluations of infection risks and cruise line crisis management.

The incorporation of phosphide additives is regarded as a highly effective strategy for enhancing the lubricative qualities of base oils. This study aims to assess the lubrication behavior and efficacy of various phosphide additives in polyethylsiloxane (PES) through the employment of the Schwingum Reibung Verschleiss test methodology, across a temperature range from ambient to 300°C.

PES demonstrated commendable lubrication capabilities within the Si3N4/M50 system, primarily attributable to the Si-O frictional reaction film at the interface. This film undergoes disintegration as the temperature escalates, leading to heightened wear. Moreover, the phosphide additives were found to ameliorate the issues encountered by PES in the Si3N4/M50 system, characterized by numerous boundary lubrication failure instances. A chemical film comprising P-Fe-O was observed to form at the interface; however, at elevated temperatures, disintegration of some phosphide films precipitated lubrication failures, as evidenced by a precipitous rise in the coefficient of friction.

The results show that a phosphide reactive film can be formed and a reduction in wear rate is achieved, which is reduced by 64.7% from 2.98 (for pure PES at 300°C) to 1.05 × 10^–9 μm³/N m (for triphenyl phosphite at 300°C).

The data derived from this investigation offer critical insights for the selection and deployment of phosphide additives within high-temperature lubrication environments pertinent to PES.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-04-2024-0139/

The literature on the drivers affecting retail investor preferences towards socially responsible investments (SRIs) has increased significantly over recent years, revealing several influencing factors. Given the wide variety and ambiguity of the available evidence, the purpose of this paper is to analyse the existing literature on this topic and develop a new unified approach to study this phenomenon.

We conducted a systematic literature review, followed by a research profile analysis and a thematic analysis, which uncovered four major emerging foci: the research outcome type, the external environment, sociodemographic characteristics and the internal dimension of retail investors.

Our analysis revealed that studies investigating investor preferences often neglected to consider the concurring influence of multiple perspectives. In fact, we observed how the literature has not yet adequately addressed the mediating and moderating effects of the various factors that determine retail investor decisions regarding SRI.

In response to these shortfalls, we propose a new integrated conceptual framework that may inspire scholars to conduct further studies to refine our understanding of investor preferences towards SRI.

This framework offers some suggestions on how to expand future research and underline some managerial and policy interventions aimed at developing the retail demand for these products.

To our knowledge, this is the first study to perform a systematic review on the drivers of SRIs, elaborating a new conceptual framework to understand the dynamics of retail investor sustainable preferences.

The construction industry has been investigating “where Henry Ford is in the industry system.” Given that listed construction enterprises are the backbone of the promotion of the high-quality development of the industry, their research and innovation are of considerable importance. This study aims to comprehensively assess the research and development (R&D) status quo and trends within various types of construction enterprises in order to identify effective strategies to enhance R&D efficiency in the construction industry.

Based on the data won from annual reports and the CSMAR database for the period 2016–2020, this study examines 104 listed construction enterprises in China. By applying both the data envelopment analysis (DEA) method and the Malmquist productivity index, this research compares and analyzes the static and dynamic differences in R&D efficiency across different types of construction enterprises.

Results suggest that the magnitude of change in the Malmquist decomposition index of 104 listed construction enterprises gradually narrowed, but the comprehensive technological level remained relatively low. Although state-owned enterprises had an advantage in scale efficiency, meaning they could maximize output with given inputs, their technological progress efficiency, also known as the degree of technological innovation, was significantly lower than that of private enterprises. As one finding, state-owned enterprises in comparison with private enterprises experience significant R&D inefficiency. It represents the main cause of their low degree of technological innovation and efficiency.

This study assesses the R&D efficiency of listed construction enterprises in China from the perspective of different market segments, state-owned and private enterprises and suggests approaches to improve strategies for various corporate types. Thus, the study’s new findings contribute to addressing the challenge of low R&D levels in the construction industry in the fields of engineering, construction and architectural management.

This study aims to investigate the performance analysis, science mapping and future direction of artificial intelligence (AI) technology, applications, tools and software used to preserve, curate and predict the historical value of cultural heritage.

This study uses the bibliometric research method and utilizes the Scopus database to gather data. The keywords used are “artificial intelligence” and “cultural heritage,” resulting in 718 data sets spanning from 2001 to 2023. The data is restricted to the years 2001−2023, is in English language and encompasses all types of documents, including conference papers, articles, book chapters, lecture notes, reviews and editorials.

The performance analysis of research on the use of AI to aid in the preservation of cultural heritage has been ongoing since 2001, and research in this area continues to grow. The countries contributing to this research include Italy, China, Greece, Spain and the UK, with Italy being the most prolific in terms of authored works. The research primarily falls under the disciplines of computer science, mathematics, engineering, social sciences and arts and humanities, respectively. Document types mainly consist of articles and proceedings. In the science mapping process, five clusters have been identified. These clusters are labeled according to the contributions of AI tools, software, apps and technology to cultural heritage preservation. The clusters include “conservation assessment,” “exhibition and visualization,” “software solutions,” “virtual exhibition” and “metadata and database.” The future direction of research lies in extended reality, which integrates virtual reality (VR), augmented reality (AR) and mixed reality (MR); virtual restoration and preservation; 3D printing; as well as the utilization of robotics, drones and the Internet of Things (IoT) for mapping, conserving and monitoring historical sites and cultural heritage sites.

The cultural heritage institution can use this result as a source to develop AI-based strategic planning for curating, preservation, preventing and presenting cultural heritages. Researchers and academicians will get insight and deeper understanding on the research trend and use the interdisciplinary of AI and cultural heritage for expanding collaboration.

This study will help to reveal the trend and evolution of AI and cultural heritage. The finding also will fill the knowledge gap on the research on AI and cultural heritage.

Some similar bibliometric studies have been conducted; however, there are still limited studies on contribution of AI to preserve cultural heritage in wider view. The value of this study is the cluster in which AI is used to preserve, curate, present and assess cultural heritages.