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Multi-robot laser-based simultaneous localization and mapping (SLAM) in large-scale environments is an essential but challenging issue in mobile robotics, especially in situations wherein no prior knowledge is available between robots. Moreover, the cumulative errors of every individual robot exert a serious negative effect on loop detection and map fusion. To address these problems, this paper aims to propose an efficient approach that combines laser and vision measurements.

A multi-robot visual laser-SLAM is developed to realize robust and efficient SLAM in large-scale environments; both vision and laser loop detections are integrated to detect robust loops. A method based on oriented brief (ORB) feature detection and bag of words (BoW) is developed, to ensure the robustness and computational effectiveness of the multi-robot SLAM system. A robust and efficient graph fusion algorithm is proposed to merge pose graphs from different robots.

The proposed method can detect loops more quickly and accurately than the laser-only SLAM, and it can fuse the submaps of each single robot to promote the efficiency, accuracy and robustness of the system.

Compared with the state of art of multi-robot SLAM approaches, the paper proposed a novel and more sophisticated approach. The vision-based and laser-based loops are integrated to realize a robust loop detection. The ORB features and BoW technologies are further utilized to gain real-time performance. Finally, random sample consensus and least-square methodologies are used to remove the outlier loops among robots.

While job insecurity generally impedes performance, there may be circumstances under which it can prompt performance. The purpose of this paper is to examine a specific situation (reorganization) in which job insecurity may prompt task and contextual performance. The authors propose that performance can represent a job preservation strategy, to which employees may only resort when supervisor-issued ratings of performance are instrumental toward securing one’s job. The authors hypothesize that because of this instrumentality, job insecurity will motivate employees’ performance only when they have low intrinsic motivation, and only when they perceive high distributive justice.

In a survey study among 103 permanent employees of a company in reorganization, the authors assessed perceived job insecurity, intrinsic motivation and perceived distributive justice. Supervisors rated employees’ overall performance (task performance and organizational citizenship behaviors).

Multilevel analyses showed that job insecurity was only positively related to supervisor-rated overall performance among employees with low intrinsic motivation and, unexpectedly, among employees who experienced low distributive justice. Results were cross-validated using employees’ self-rated performance, replicating the findings on distributive justice but not the findings on intrinsic motivation.

The results can inform future research on the specific situations in which job insecurity may prompt job preservation efforts, and call for research to uncover the mechanisms underlying employees’ negative and positive responses to job insecurity. The results and associated implications of this study are largely based on conceptual evidence. In addition, the cross-sectional design warrants precaution about drawing causal inferences from the data.

By combining insights from coping responses and threat foci, this study advances the understanding of when and why job insecurity may prompt performance.

Driven by the dual-carbon target and the widespread digital transformation, leveraging digital technology (DT) to facilitate sustainable, green and high-quality development in heavy-polluting industries has emerged as a pivotal and timely research focus. However, existing studies diverge in their perspectives on whether DT’s impact on green innovation is synergistic or leads to a crowding-out effect. In pursuit of optimizing the synergy between DT and green innovation, this paper aims to investigate the mechanisms that can be harnessed to render DT a more constructive force in advancing green innovation.

Drawing from the theoretical framework of resource orchestration, the authors offer a comprehensive elucidation of how DT intricately influences the green innovation efficiency of enterprises. Given the intricate interplay within the synergistic relationship between DT and green innovation, the authors use the fuzzy-set qualitative comparative analysis method to explore diverse configurations of antecedent conditions leading to optimal solutions. This approach transcends conventional linear thinking to provide a more nuanced understanding of the complex dynamics involved.

The findings reveal that antecedent configurations fostering high green innovation efficiency actually differ across various stages. First, there are three distinct configuration patterns that can enhance the green technology research and development (R&D) efficiency of enterprises, namely, digitally driven resource integration (RI), digitally driven resource synergy (RSy) and high resource orchestration capability. Then, the authors also identify three configuration patterns that can bolster the high green achievement transfer efficiency of enterprises, including a digitally optimized resource portfolio, digitally driven RSy and efficient RI. The findings not only contribute to advancing the resource orchestration theory in the digital ecosystem but also provide empirical evidence and practical insights to support the sustainable development of green innovation.

The findings can offer valuable insights for enterprise managers, providing decision-making guidance on effectively harnessing the innovation-driven value of internal and external resources through resource restructuring, bundling and leveraging, whether with or without the support of DT.

The research findings contribute to heavy-polluting enterprises addressing the paradoxical tensions between digital transformation and resource constraints under environmental regulatory pressures. It aims to facilitate the simultaneous achievement of environmental and commercial success by enhancing their green innovation capabilities, ultimately leading to sustainability across profit and the environment.

Compared with previous literature, this research introduces a distinctive theoretical perspective, the resource orchestration view, to shed light on the paradoxical relationship on resource-occupancy between DT application and green innovation. It unveils the “black box” of how digitalization impacts green innovation efficiency from a more dynamic resource-based perspective. While most studies regard green innovation activities as a whole, this study delves into the impact of digitalization on green innovation within the distinct realms of green technology R&D and green achievement transfer, taking into account a two-stage value chain perspective. Finally, in contrast to previous literature that predominantly analyzes influence mechanisms through linear impact, the authors use configuration analysis to intricately unravel the complex influences arising from various combinatorial relationships of digitalization and resource orchestration behaviors on green innovation efficiency.

The past 40 years of reform and opening up have seen the role of core competence receive unprecedented attention because of the impact of the new normal economy as well as the pressure of transformation and upgrading. Few Chinese enterprises have effectively constructed core competence, and there is also a lack of strategic perspective and contextual embedding of its construction process in theory. This study aims to analyse the unique contextual characteristics of China in the transitional period and the impact on the core competence construction of Chinese enterprises and proposes its construction mechanism for Chinese enterprises.

Through theoretical deduction and speculation, this paper analyses the process of concept formation, clarifies the concept, analyses its connotations and structure and emphasises its relativity. Based on a review of the theoretical research on the core competence construction process and an analysis of dual contextual impact, this paper puts forward a mechanism for core competence construction.

The results reveal that four specific characteristics of entrepreneurs in balancing and coping with the dual nature of external context constitute the key driving force for a Chinese enterprises’ core competence construction; under the influence of this driving force, the core competence construction mechanism includes process mode, knowledge source, management mode and key success factors.

Approaching it from a Chinese context, this study deepens the concept of core competence; enriches and develops the research related to the core strategic research proposition of its construction; and provides positive significance for Chinese enterprises to effectively build, develop and strengthen core competence and enhance their international competitiveness.

Based on the socio-emotional wealth and agency theories, this study empirically investigates the impact of family ownership and management on green innovation (GI) in family businesses, as well as the moderating effects of institutional environmental support factors, namely, the technological achievement marketisation index and the market-rule-of law index.

This study empirically tests the hypotheses based on a sample of listed Chinese family companies with A-shares in 14 heavily polluting industries from 2009 to 2019.

There is a U-shaped relationship between the percentage of family ownership and GI, and an inverted U-shaped relationship between the degree of family management and GI. Additionally, different institutional environmental support factors affect these relationships in different ways. As the technological achievement marketisation index increases, the U-shaped relationship between the percentage of family ownership and GI becomes steeper, while the inverted U-shaped relationship between the degree of family management and GI becomes smoother. The market rule-of-law index weakens the U-shaped relationship between family ownership and GI.

First, the authors enrich the research on the driving factors of GI from the perspective of the most essential heterogeneity of family businesses. This study shows nonlinear and opposite effects of family ownership and management on GI in family firms. Second, this study contributes to the literature on family firm innovation. GI, not considered by researchers, is regarded as an important deficiency in research on innovation in family businesses. Therefore, this study fills that gap. Third, the study expands research on moderating effects in the literature on GI from the perspective of institutional environmental support factors.

This study aims to propose a combined effect framework to explore the relationship between research and development (R&D) team networks, knowledge diversity and breakthrough technological innovation. In contrast to conventional linear net effects, the article explores three possible types of team configuration within enterprises and their breakthrough innovation-driving mechanisms based on machine learning methods.

Based on the patent application data of 2,337 Chinese companies in the biopharmaceutical manufacturing industry to construct the R&D team network, the study uses the K-Means method to explore the configuration types of R&D teams with the principle of greatest intergroup differences. Further, a decision tree model (DT) is utilized to excavate the conditional combined relationships between diverse team network configuration factors, knowledge diversity and breakthrough innovation. The network driving mechanism of corporate breakthrough innovation is analyzed from the perspective of team configurations.

It has been discerned that in the biopharmaceutical manufacturing industry, there exist three main types of enterprise R&D team configurations: tight collaboration, knowledge expansion and scale orientation, which reflect the three resource investment preferences of enterprises in technological innovation, network relationships, knowledge resources and human capital. The results highlight both the crowding-out effects and complementary effects between knowledge diversity and team network characteristics in tight collaborative teams. Low knowledge diversity and high team structure holes (SHs) are found to be the optimal team configuration conditions for breakthrough innovation in knowledge-expanding and scale-oriented teams.

Previous studies have mainly focused on the relationship between the external collaboration network and corporate innovation. Moreover, traditional regression methods mainly describe the linear net effects between variables, neglecting that technological breakthroughs are a comprehensive concept that requires the combined action of multiple factors. To address the gap, this article proposes a combination effect framework between R&D teams and enterprise breakthrough innovation, further improving social network theory and expanding the applicability of data mining methods in the field of innovation management.

Given the regional heterogeneity of economic development, electricity consumption in various regions exhibits a discrepant growth pattern. The purpose of this study is to construct a grey system forecasting model with intelligent parameters for predicting provincial electricity consumption in China.

First, parameter optimization and structural expansion are simultaneously integrated into a unified grey system prediction framework, enhancing its adaptive capabilities. Second, by setting the minimum simulation percentage error as the optimization goal, the authors apply the particle swarm optimization (PSO) algorithm to search for the optimal grey generation order and background value coefficient. Third, to assess the performance across diverse power consumption systems, the authors use two electricity consumption cases and select eight other benchmark models to analyze the simulation and prediction errors. Further, the authors conduct simulations and trend predictions using data from all 31 provinces in China, analyzing and predicting the development trends in electricity consumption for each province from 2021 to 2026.

The study identifies significant heterogeneity in the development trends of electricity consumption systems among diverse provinces in China. The grey prediction model, optimized with multiple intelligent parameters, demonstrates superior adaptability and dynamic adjustment capabilities compared to traditional fixed-parameter models. Outperforming benchmark models across various evaluation indicators such as root mean square error (RMSE), average percentage error and Theil’s index, the new model establishes its robustness in predicting electricity system behavior.

Acknowledging the limitations of traditional grey prediction models in capturing diverse growth patterns under fixed-generation orders, single structures and unadjustable background values, this study proposes a fractional grey intelligent prediction model with multiple parameter optimization. By incorporating multiple parameter optimizations and structure expansion, it substantiates the model’s superiority in forecasting provincial electricity consumption.

The study aims to provide an inventory of the existing English and Chinese research on service quality in China’s hospitality and tourism industry. The study aims to identify the reasons for the lack of research on service quality in China’s hospitality and tourism industry and the ignorance of the Chinese language literature in the field.

A synthesis review is conducted of 31 articles published in 11 leading Chinese and English academic journals from 1998 to 2013.

Popular research themes are service quality management and evaluation in the hotel sector. The majority of the reviewed articles are empirical studies that adopt quantitative methods, and none of the English articles use qualitative methods. Recent studies tend to use sophisticated statistical techniques such as confirmatory factor analysis and structural equation modeling. Changes in disciplines, publication trends and statistical techniques are observed. The theoretical and practical contributions of the Chinese and English publications are compared, and recommendations are made for future research.

This study is one of the first to review and compare the existing publications in the leading Chinese- and English-language journals. It provides a platform for scholars, especially non-Chinese literate researchers, to understand the research on service quality in the China hospitality and tourism industry.

Aluminum alloy is considered an ideal material in aerospace, automobile and other fields because of its lightweight, high specific strength and easy processing. However, low hardness and strength of the surface of aluminum alloys are the main factors that limit their applications. The purpose of this study is to obtain a composite coating with high hardness and lubricating properties by applying GO–PVA over MAO coating.

A pulsed bipolar power supply was used as power supply to prepare the micro-arc oxidation (MAO) coating on 6061 aluminum sample. Then a graphene oxide-polyvinyl alcohol (GO–PVA) composite coating was prepared on MAO coating for subsequent experiments. Samples were characterized by Fourier infrared spectroscopy, X-ray diffraction, Raman spectroscopy and thermogravimetric analysis. The friction test is carried out by the relative movement of the copper ball and the aluminum disk on the friction tester.

Results showed that the friction coefficient of MAO samples was reduced by 80% after treated with GO–PVA composite film.

This research has made a certain contribution to the surface hardness and tribological issues involved in the lightweight design of aluminum alloys.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-12-2023-0427/

This study aims to investigate the impact of the loyalty program members’ satisfaction toward the hotel loyalty program benefits and the customer management relationship initiatives of individual hotels on the brand relationship quality (BRQ), i.e. their relationship quality with the hotel brand, and its outcomes. The moderating impact of membership level on the hypothesized relationships was also investigated.

A usable sample of 920 active members of a hotel loyalty program was obtained. Structural equation modeling with partial least squares method was used to estimate the structural relationships and to investigate the moderating effect of membership level.

Employee’s customer orientation, membership communication and hotel stay-related benefits are determinants of the loyalty program members’ BRQ. BRQ is also confirmed as a higher-order construct of three latent variables which include trust, satisfaction and commitment. Moderating effects of the membership were partially supported in this study. The strongest effect of BRQ is on members’ word of mouth followed by shares of purchase. BRQ is found to have negative relationship with members’ willingness to serve as marketing resource, but the impact was small.

This is a cross-sectional study with a population of active loyalty program members of only one luxury hotel group. The sample size of the top-tier members is also smaller in comparison to the other two groups.

This study contributes to the understanding of the antecedents and consequences of BRQ and the body of knowledge about loyalty program for hotel industry.

This is one of the few studies investigating the effectiveness of hotel loyalty programs from the active members’ perspective and the moderating effect of membership level on the relationships among BRQ, its antecedents and its outcomes.