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Dynamically tracking the target by unmanned ground vehicles (UGVs) plays a critical role in mobile drone recovery. This study aims to solve this challenge under diverse random disturbances, proposing a dynamic target tracking framework for UGVs based on target state estimation, trajectory prediction, and UGV control.

To mitigate the adverse effects of noise contamination in target detection, the authors use the extended Kalman filter (EKF) to improve the accuracy of locating unmanned aerial vehicles (UAVs). Furthermore, a robust motion prediction algorithm based on polynomial fitting is developed to reduce the impact of trajectory jitter caused by crosswinds, enhancing the stability of drone trajectory prediction. Regarding UGV control, a dynamic vehicle model featuring independent front and rear wheel steering is derived. Additionally, a linear time-varying model predictive control algorithm is proposed to minimize tracking errors for the UGV.

To validate the feasibility of the framework, the algorithms were deployed on the designed UGV. Experimental results demonstrate the effectiveness of the proposed dynamic tracking algorithm of UGV under random disturbances.

This paper proposes a tracking framework of UGV based on target state estimation, trajectory prediction and UGV predictive control, enabling the system to achieve dynamic tracking to the UAV under multiple disturbance conditions.

Based on Kansei Engineering, this study obtained consumers' emotional preferences aiming to enhance the emotional connection between consumers and clothing to extend the service life of clothing and realize sustainable clothing design.

Six Kansei word pairs that are the most important to consumers were identified through literature reviews, magazines, websites, card sorting of consumers and cluster analysis. Finally, the consumers scored the 32 product specimens through a 5-level rating semantic differential scale questionnaire of six Kansei word pairs. The researchers verified the consumers' emotional preferences through principal component analysis and established the relationship between Kansei words and design elements of color through partial least squares.

The study found consumers' emotional preferences: elegant, minimalist, formal, casual, mature, practical and distinctive style. Besides white, black, gray, blue, consumers will also like red and yellow-red in the future. The crucial findings of this study are to get recommended guidelines that consumers' emotional preferences match the corresponding design elements.

The study's findings can be used to style the design of men's plain-color shirts and guide online marketers and designers to design apparel that meets consumers' emotional needs to develop consumers' sustainability reliance on clothing. This study also explains the overall process and methodology for integrating consumer preferences and product design elements.

This paper aims to propose an instrument for measuring social commerce among entrepreneurs from the USA, Mexico and Colombia.

The methodological design was quantitative, explanatory, observational and transversal, where a sample of 1,644 entrepreneurs from the USA (n = 525), Mexico (n = 608) and Colombia (n = 510) was obtained. For the validation and reliability of the instruments, a structural equation model (SEM) was developed for the three studies carried out. Regarding the goodness and adjustment indices of the SEM in the three countries, they turned out to be acceptable.

Through the results obtained in the three studies carried out, it has been verified that the instrument of social commerce in its two factors: Marketing and Market Participation, has sufficient validity and statistical reliability. Likewise, it has been verified that both the manifest variables and the latent variables of the construct show a significant relationship between the three studies carried out in different populations.

The findings obtained in the presented study contribute to entrepreneurs, leaders and managers of the business sector to improve the entrepreneurial project through marketing and market participation of the product and service, as well as the business unit that seeks to position itself in the market. Likewise, it helps entrepreneurs to understand analytically and systematically the constructs that social commerce is made up of, which will help entrepreneurial leaders reduce or control their risk when considering social commerce in their entrepreneurship, achieving the success of the entrepreneurial project through its positioning in the market considering marketing and market participation as main factors of social commerce.

The findings are relevant and of great value to the literature because at present there is not enough research that is focused on the variables analysed related to social commerce in the contexts of the USA, Mexico and Colombia. The relevance of the present scale in comparison to others proposed by the literature is that the proposed scale is focused on entrepreneurs who seek to be more successful through the positioning of their business unit, product or service in the market through the market participation. It is achieved through marketing strategies. Another contribution provided by the present study lies in the methodological robustness of the scale and its analysis, comparing it with studies in three leading entrepreneurship countries in Latin America, comparing the validity and reliability as well as the goodness of fit indices of the proposed model in each of the studies. They were significant and very similar, so the proposed scale is of great value and usefulness in the literature.

This study aims to enhance the effectiveness of knowledge markets and overall knowledge management (KM) practices within organisations. By addressing the challenge of internal knowledge stickiness, it seeks to demonstrate how machine learning and AI approaches, specifically a text-based AI method for personality assessment and regression trees for behavioural analysis, can automate and personalise knowledge market incentivisation mechanisms.

The research employs a novel approach by integrating machine learning methodologies to overcome the limitations of traditional statistical methods. A natural language processing (NLP)-based AI tool is used to assess employees’ personalities, and regression tree analysis is applied to predict and categorise behavioural patterns in knowledge-sharing contexts. This approach is designed to capture the complex interplay between individual personality traits and environmental factors, which traditional methods often fail to adequately address.

Cognitive style was confirmed as a key predictor of knowledge-sharing, with extrinsic motivators outweighing intrinsic ones in market-based platforms. These findings underscore the significance of diverse combinations of environmental and individual factors in promoting knowledge sharing, offering key insights that can inform the automatic design of personalised interventions for community managers of such platforms.

This research stands out as it is the first to empirically explore the interaction between the individual and the environment in shaping actual knowledge-sharing behaviours, using advanced methodologies. The increased automation in the process extends the practical contribution of this study, enabling a more efficient, automated assessment process, and thus making critical theoretical and practical advancements in understanding and enhancing knowledge-sharing behaviours.

The purpose of this study is to explore the motivational role of feedback information (positive and negative) provided by the firm in the face of participant heterogeneity, in terms of past success experience, under the research setting of crowdsourcing contests.

Taking insights from feedback studies and the dynamics of self-regulation theory, four theoretical hypotheses are proposed. An integrated dataset of 4,880 contest-participant pairs, which is obtained from an online contest platform and a survey, is empirically analyzed.

Empirical analysis shows that both positive feedback and negative feedback are able to stimulate the inner needs of participants. Notably, negative (positive) feedback becomes more (less) effective in intrinsically motivating crowds as they gain more successful experience during contest participation.

This study brings some new knowledge for the intrinsic motivation of crowds by exploring its antecedents, which have been undervalued in extant literature. The motivational role of feedback information is particularly explored.

This paper aims to explore how psychological perceptions and family situations drive women into necessity- or opportunity-based female entrepreneurship (NBFE or OBFE) and the moderating role of gender equality.

This study adopts multilevel logistic regression analysis to examine relationships based on a sample of 6,843 women across eight developing countries drawn from the Global Entrepreneurship Monitor (GEM).

The findings suggest that capability and opportunity perceptions positively affect NBFE and OBFE. Family responsibility burden positively affects NBFE and has a U-shaped relationship with OBFE. Household income negatively affects NBFE but positively affects OBFE. Gender equality weakens the U-shaped relationship between family responsibility burden and OBFE but strengthens the positive relationship between capability perception and NBFE and between opportunity perception and NBFE.

The study highlights the need for targeted policies and support that consider the distinct antecedents and mechanisms of NBFE and OBFE, as well as the importance of promoting gender equality and entrepreneurial education to empower women in their entrepreneurial endeavors. A limitation of this study is the reliance on older data from the GEM, which may not fully capture the current dynamics of developing societies. While the study provides valuable insights, future research should incorporate more recent data to enhance the applicability of the results.

This study deepens the understanding of antecedents of NBFE and OBFE, breaking through the existing literature that neglects the heterogeneity of female entrepreneurship (FE).

Lead users are essential participants in crowdsourcing innovation events; their continuance intention significantly affects the success of the crowdsourcing innovation community (CIC). Although researchers have acknowledged the influences of network externalities on users' sustained participation in general information systems, limited work has been conducted to probe these relationships in the CIC context; particularly, the predictors of lead users' continued usage intention in such context are still unclear. Hence, this paper aims to explore the precursors of lead users' continuance intention from a network externalities perspective in CIC.

This work ranked users' leading-edge status to recognize lead users in the CIC. And then, the authors proposed a research model based on the network externalities theory, which was examined utilizing the partial least squares (PLS) technique. The research data were collected from an online survey of lead users (n = 229) of a CIC hosted by a China handset manufacturer.

Results revealed that the number of peers, perceived complementarity and perceived compatibility significantly influence lead users' continuance intention through identification and perceived usefulness.

This work contributes to the crowdsourcing innovation research and provides views regarding how lead users' sustained participation can be developed in the CICs. This work also offers an alternative theoretical framework for further research on users' continued intention in open innovation activities.

Although big data analytics managerial skills (BDAMS) offer opportunities for firms to foster organizational agility, existing studies present inconclusive indications of this impact, with an overlooking of the intermediate pathways involved. This study explored how BDAMS affect organizational agility by investigating the mediation effect of data-driven organizational learning (DDOL) and the moderating roles of technological and market turbulence.

This study employed mediation and moderated mediation analyses to test the hypotheses using data collected from listed Chinese firms. Furthermore, we performed a fuzzy set qualitative comparative analysis (fsQCA) as a supplementary approach to identify the configurations that lead to organizational agility.

This study shows that DDOL partially mediates the relationship between BDAMS and organizational agility. Besides, technological and market turbulence positively moderate the effect of DDOL on organizational agility and the mediation effect of DDOL. Our additional analyses also reveal several patterns of conditions that facilitate agility.

This study offers a comprehensive exploration of the relationship between BDAMS and organizational agility by verifying the mediating effect of DDOL and moderating effects of technological and market turbulence. In addition, the fsQCA results highlighted the combinatorial effects of key factors in this study, reinforcing and refining the moderated mediation results.

The research aims to explore the influence mechanism of peer feedback and users' knowledge contribution behavior. This study draws on the social identity theory and considers social identity as a mediating factor into the research framework.

This paper collected users' activity data of 142,191 ideas submitted by 76,647 users from the MIUI community between October 2010 and May 2018 via Python software, and data were processed using Stata 16.0.

The results indicate that knowledge feedback and social feedback positively influence users' knowledge contribution (quantity and quality), respectively. User's cognitive identity positively mediates the relationship between peer feedback and knowledge contribution behavior, affective identity positively mediates the relationship between peer feedback and knowledge contribution behavior, while evaluative identity positively mediates the relationship between peer feedback and knowledge contribution quality, but there is no mediating effect between peer feedback and knowledge contribution quantity.

This study advances knowledge management by highlighting peer feedback on online innovation communities. By demonstrating the significant mediating effect of social identity, this study empirically clarifies the relationships of peer feedback (knowledge feedback and social feedback) to specific dimensions of knowledge contribution, thereby providing managerial guidance to the online innovation community on incentivizing and managing user interaction to foster the innovation development of firms.

This study aims to streamline and enhance the assembly defect inspection process in diesel engine production. Traditional manual inspection methods are labor-intensive and time-consuming because of the complex structures of the engines and the noisy workshop environment. This study’s robotic system aims to alleviate these challenges by automating the inspection process and enabling easy remote inspection, thereby freeing workers from heavy fieldwork.

This study’s system uses a robotic arm to traverse and capture images of key components of the engine. This study uses anomaly detection algorithms to automatically identify defects in the captured images. Additionally, this system is enhanced by digital twin technology, which provides inspectors with various tools to designate components of interest in the engine and assist in defect checking and annotation. This integration facilitates smooth transitions from manual to automatic inspection within a short period.

Through evaluations and user studies conducted over a relatively long period, the authors found that the system accelerates and improves the accuracy of engine inspections. The results indicate that the system significantly enhances the efficiency of production processes for manufacturers.

The system represents a novel approach to engine inspection, leveraging robotic technology and digital twin enhancements to address the limitations of traditional manual inspection methods. By automating and enhancing the inspection process, the system offers manufacturers the opportunity to improve production efficiency and ensure the quality of diesel engines.