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This investigation seeks to elaborate on how proactive market orientation (MO) and responsive MO motivate firms to conduct business model innovation (BMI) through the breadth of market knowledge search (BMKS) and the depth of market knowledge search (DMKS).

Based on the survey data of 259 high-tech firms in the industrial parks of the Yangtze River Delta, this study uses multiple hierarchical regressions to examine the hypotheses and conducts Sobel and bootstrapping methods to further test the mediating effects.

The findings indicate that the positive effects of proactive and responsive MO on BMI are mediated by BMKS and DMKS. It also shows that proactive MO has a greater impact on BMKS than responsive MO, while responsive MO has a stronger impact on DMKS than proactive MO.

Firms with different MOs can choose different types of market knowledge search to promote BMI, which reminds managers to give attention to the importance of bridging MOs with knowledge search strategies in BMI.

This study introduces a constructive theoretical framework by examining the roles of MO and market knowledge search on BMI. The findings reveal that MO as a key initiating factor and market knowledge search as an important conduit play vital roles in the experimental process of BMI and identify the differential effects of proactive and responsive MO on two types of market knowledge search.

Accurate mapping is crucial for the positioning and navigation of mobile robots. Recent advancements in algorithms and the accuracy of LiDAR sensors have led to a gradual improvement in map quality. However, challenges such as lag in closing loops and vignetting at map boundaries persist due to the discrete and sparse nature of raster map data. The purpose of this study is to reduce the error of map construction and improve the timeliness of closed loop.

In this letter, the authors introduce a method for dynamically adjusting point cloud distance constraints to optimize data association (ODA-d), effectively addressing these issues. The authors propose a dynamic threshold optimization method for matching point clouds to submaps during scan matching.

Large deviations in LiDAR sensor point cloud data, when incorporated into the submap, can result in irreparable errors in correlation matching and loop closure optimization. By implementing a data association framework with double constraints and dynamically adjusting the matching threshold, the authors significantly enhance submap quality. In addition, the authors introduce a dynamic fusion method that accounts for both submap size and the distance between submaps during the mapping process. ODA-d reduces errors between submaps and facilitates timely loop closure optimization.

The authors validate the localization accuracy of ODA-d by examining translation and rotation errors across three open data sets. Moreover, the authors compare the quality of map construction in a real-world environment, demonstrating the effectiveness of ODA-d.