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Docking technology plays a crucial role in enabling long-duration operations of autonomous underwater vehicles (AUVs). Visual positioning solutions alone are susceptible to abnormal drift values due to the challenging underwater optical imaging environment. When an AUV approaches the docking station, the absolute positioning method fails if the AUV captures an insufficient number of tracers. This study aims to to provide a more stable absolute position visual positioning method for underwater terminal visual docking.

This paper presents a six-degree-of-freedom positioning method for AUV terminal visual docking, which uses lights and triangle codes. The authors use an extended Kalman filter to fuse the visual calculation results with inertial measurement unit data. Moreover, this paper proposes a triangle code recognition and positioning algorithm.

The authors conducted a simulation experiment to compare the underwater positioning performance of triangle codes, AprilTag and Aruco. The results demonstrate that the implemented triangular code reduces the running time by over 70% compared to the other two codes, and also exhibits a longer recognition distance in turbid environments. Subsequent experiments were carried out in Qingjiang Lake, Hubei Province, China, which further confirmed the effectiveness of the proposed positioning algorithm.

This fusion approach effectively mitigates abnormal drift errors stemming from visual positioning and cumulative errors resulting from inertial navigation. The authors also propose a triangle code recognition and positioning algorithm as a supplementary approach to overcome the limitations of tracer light positioning beacons.

This study examines the mediating roles of the three dimensions of business intelligence (sensing capability, transforming capability and driving capability) in the relationship between the three dimensions of big data analytics capability (big data analytics management, technology and talent capabilities), and radical innovation among Chinese manufacturing enterprises.

A theoretical framework was developed using the resource-based view. The hypothesis was tested using empirical survey data from 326 Chinese manufacturing enterprises.

Empirical results show that, in the Chinese manufacturing context, business intelligence sensing capability, business intelligence transforming capability and business intelligence driving capability positively mediate the impact of big data analytics capability on radical innovation.

The results offer managerial guidance for leaders to properly use big data analytics capability, business intelligence and radical innovation as well as offering theoretical insight for future research in the manufacturing industry’s radical innovation.

This is among the first studies to examine three dimensions of big data analytics capability on the manufacturing industry’s radical innovation by considering the mediating role of three dimensions of business intelligence.

Because of the slow market growth of and intense competition among coffee shops, increasing brand preference and patronage intention is crucial in the coffee shop industry. Although place attachment theory (PAT) and social identity theory (SIT) stipulate that place attachment and social identity are key constructs of revisit intention, no studies have yet integrated the dimensions of SIT into PAT to predict place preference (PP) and repatronage intention (RI). In this study, the authors aimed to develop a theoretical model grounded in PAT and SIT to predict PP and RI.

A total of 648 coffee shop customers participated in an online survey, and their data were analyzed through structural equation modeling.

The results indicated that cognitive and affective place identity (PI) directly affected place dependence (PD) but did not directly affect PP. Cognitive PI also indirectly affected PD through affective PI. PD exerted a positive and significant effect on PP and thus affected RI.

These findings provide insights into the importance of cognitive and affective PI in shaping PD, PP and RI. From a place attachment perspective, the theoretical model enables coffee shop managers to cultivate strong PP to increase customer RI.

This study aims to examine the influence of regulatory framework (RF), market dynamism (MDY), competitive intensity (CIN) and environmental public concern (EPC) on responsible innovation (RI) in the manufacturing SMEs of a developing country. Furthermore, the study examines the influence of RI on sustainable competitive advantage (SCA). Moreover, big data adoption (BDA) is used as a moderator between RI and SCA.

The study’s hypotheses are evaluated using the structural equation modeling methodology. The study collected data from a sample of 487 owners/managers of manufacturing SMEs using simple random sampling technique.

The results explain that RF, MDY, CIN and EPC significantly determine RI. Moreover, RI significantly determines SCA. Besides, BDA significantly moderates between RI and SCA.

For manufacturing SMEs to obtain SCA, RI is essential, and BDA is crucial to meet changing consumer demands for environmentally friendly products. With more environment-friendly initiatives, the natural environment will become more sustainable. Moreover, this research offers a comprehensive perspective to the public regarding the extent to which manufacturing enterprises are adopting RI and BDA.

The majority of earlier research on RI and BDA has been carried out in the context of large businesses. Institutional theory was used to look into the drivers of RI. Furthermore, BDA is used as moderating variable between RI and SCA.

This study examines the impact of university science parks’ (USPs) capabilities on revenue generation and introduces regional innovation as a moderating variable. This study aims to provide insights into enhancing revenue generation and fully leveraging the role of USPs in promoting revenue generation.

This study employs system generalized method of moments (GMM) estimation for 116 universities in China from 2008 to 2020, using hierarchical regression analysis to examine the relationships between variables.

The findings suggest that USPs play a beneficial role in fostering revenue generation. Specifically, the provision of incubation funding demonstrates a positive correlation, while USPs size exhibits an inverted U-shaped pattern, with a threshold at 3.037 and a mean value of 3.712, highlighting the prevalent issue of suboptimal personnel allocation in the majority of USPs. Moreover, the analysis underscores the critical moderating influence of regional innovation, affecting the intricate interplay between USPs size, incubation funding and revenue generation.

The single country (China) analysis relied solely on the use of secondary data. Future studies could expand the scope to include other countries and employ primary data collection. For instance, future research can further examine how regional development and USPs strategic plan impact revenue generation.

The study recommends that USPs managers and policymakers recognize the importance of incubation funding and determine the optimal quantity of USPs size to effectively foster revenue generation in USPs. Policymakers can use regional innovation as a moderating variable to reinforce the relationship between USPs size and incubation funding on revenue generation.

The study’s findings can contribute to the strategic industry growth and economic development of nations by promoting revenue generation. Leveraging the role of USPs and implementing the study’s recommendations can strengthen innovation and technology capabilities, driving strategic industry growth and economic development. This can enhance global competitiveness and promote sustainable economic growth.

This study introduces regional innovation as a moderating variable and provides empirical evidence of its influence on the relationship between USPs size and incubation funding on revenue generation. This adds value to research to the existing literature on USPs and revenue generation by showcasing the importance of examining the regional impact in research and innovation.

This study aims to examine the influence of organizational flexibility (OF) and shared vision (SV) on sustainable competitive advantage (SCA) with the mediation role of responsible innovation (RI) in the manufacturing industry of a developing country. Furthermore, big data analytics capability (BDAC) serves as a moderator between RI and SCA.

The study's hypotheses are investigated using the structural equation modeling (SEM) method. Through simple random sampling, information was gathered from 247 owners/managers of manufacturing SMEs.

The results elucidate that OF and SV significantly determine RI and SCA. Moreover, RI significantly mediates between SV, OF and SCA. Besides, RI significantly determines SCA. BDAC significantly leads to SCA. Finally, BDAC significantly moderates between RI and SCA.

RI is crucial for manufacturing small and medium-sized enterprises (SMEs) to gain SCA and BDAC is important to address the changing demands of consumers for environment-friendly products. This study gives the public an overview of the different degrees to which SMEs are embracing RI and BDAC; with more environment-friendly initiatives, the natural environment will become more sustainable. Environmental sustainability will benefit each individual living in society.

This study adds value to the existing literature by focusing on predictors that affect SCA. Using dynamic capability theory, this initial study examines the influence of SV and OF on SCA and RI as mediators. Furthermore, BDAC is used as a moderating variable between RI and SCA. Managers, students and researchers can benefit from this study.

Prior literature has long argued that knowledge networks contain great opportunities for innovation, and researchers can identify these opportunities using the properties of knowledge networks (PKNs). However, previous studies have examined only the relationship between structural PKNs (s-PKNs) and innovation, ignoring the effect of qualitative PKNs (q-PKNs), which refer to the quality of the relationship between two elements. This study aims to further investigate the effects of q-PKNs on innovation.

Using a panel data set of 2,255 patents from the Chinese wind energy industry, the authors construct knowledge networks to identify more PKNs and examine these hypotheses.

The results show that q-PKNs significantly influence recombinant innovation (RI), reflecting the importance of q-PKNs analysed in this study. Moreover, the results suggest that the combinational potential of an element with others may be huge at different levels of q-PKNs.

This study advances the understanding of PKNs and RI by exploring how q-PKNs impact RI. At different levels of PKNs, the potential of the elements to combine with others and form innovation are different. Researchers can more accurately identify the opportunities for RI using two kinds of PKNs. The findings also provide important implications on how government should provide support for R&D firms.

Heating, ventilating, air-conditioning and cooling (HVAC) systems are crucial in daily health-care facility services. Design-related defects can lead to maintenance issues, causing service disruptions and cost overruns. These defects can be avoided if a link between the early design stages and maintenance feedback is established. This study aims to use experts’ experience in HVAC maintenance in health-care facilities to list and evaluate the risk of each maintenance issue caused by a design defect, supported by the literature.

Following semistructured interviews with experts, 41 maintenance issues were identified as the most encountered issues. Subsequently, a survey was conducted in which 44 participants evaluated the probability and impact of each design-caused issue.

Chillers were identified as the HVAC components most prone to design defects and cost impact. However, air distribution ducts and air handling units are the most critical HVAC components for maintaining healthy conditions inside health-care facilities.

The unavailability of comprehensive data on the cost impacts of all design-related defects from multiple health-care facilities limits the ability of HVAC designers to furnish case studies and quantitative approaches.

This study helps HVAC designers acquire prior knowledge of decisions that may have led to unnecessary and avoidable maintenance. These design-related maintenance issues may cause unfavorable health and cost consequences.

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 authors examine the impact of audit quality (AQ) on classification shifting (CS) among non-financial firms operating in the UK and Germany.

This paper used various audit committee variables (size, meetings, gender diversity and financial expertise) to measure AQ and its impact on CS. The authors used a total of 2,110 firm-year observations from 2010 to 2019.

The authors found that the presence of female members on the audit committee and audit committee financial expertise deter the UK and German managers from shifting core expenses and revenue items into special items to inflate core earnings. However, audit committee size is positively related to CS among German firms but has no impact on UK firms. The authors also document evidence that audit committee meetings restrain UK managers from engaging in CS. However, the authors found no impact on CS among German firms. The study results hold even after employing several tests.

Overall, the study findings provide broad support in an international setting for the board to improve its auditing practices and offer essential information to investors to assess how AQ affects the financial reporting process.

Most CS studies used market-oriented economies such as the USA and UK and ignored bank-based economies such as Germany, France and Japan. The authors provide a comparison among bank and market-oriented economies on whether the AQ has a similar impact on CS or not among them.