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Although product development is recognized as knowledge‐intensive work, we have limited understanding of its impact on product development performance. The mechanisms by which knowledge sharing contributes to strategic imperatives such as time to market and value to customers are also not well understood. Despite increased interest in knowledge sharing in cross‐functional teams, there have been few large‐scale empirical studies of its efficacy. This paper develops a model that explains how shared knowledge, defined in three types – shared knowledge of customers, suppliers, and internal capabilities – enhances process performance, as well as downstream strategic imperatives of time to market and value to customers. The model is tested using 205 responses on product development projects by US automotive engineers. The results show that shared knowledge of customers, suppliers, and internal capabilities positively affect product development performance, as well as indirectly affect downstream strategic imperatives via enhanced process performance.

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Accurate estimation of the spatiotemporal variations of solar radiation is crucial for assessing and utilizing solar energy, one of the fastest‐growing and most important clean and renewable resources. Based on observations from 2,379 meteorological stations along with scare solar radiation observations, the random forest (RF) model is employed to construct a high‐density network of daily global solar radiation (DGSR) and its spatiotemporal variations in China. The RF‐estimated DGSR is in good agreement with site observations across China, with an overall correlation coefficient (R) of 0.95, root‐mean‐square error of 2.34 MJ/m2, and mean bias of −0.04 MJ/m2. The geographical distributions of R values, root‐mean‐square error, and mean bias values indicate that the RF model has high predictive performance in estimating DGSR under different climatic and geographic conditions across China. The RF model further reveals that daily sunshine duration, daily maximum land surface temperature, and day of year play dominant roles in determining DGSR across China. In addition, compared with other models, the RF model exhibits a more accurate estimation performance for DGSR. Using the RF model framework at the national scale allows the establishment of a high‐resolution DGSR network, which can not only be used to effectively evaluate the long‐term change in solar radiation but also serve as a potential resource to rationally and continually utilize solar energy.

Summary The aim of the research was to study the volatile composition of mango wine fermented with two Williopsis yeast strains: Williopsis saturnus var. mrakii NCYC500 and W. staurnus var. suaveolens NCYC2586. Thirty terpenoids, twenty‐six esters, ten alcohols, nine acids, seven aldehydes and ketones, two ethers, two phenols and one sulphur compound were identified in the mango wine fermented with strain NCYC500, while twenty‐seven terpenoids, thirty esters, eleven alcohols, eight acids, eight aldehydes and ketones, three ethers, two phenols, one sulphur compound and one furan were detected in the mango wine fermented with the strain NCYC2586. The kinetic changes, final concentrations and odour activity values of major volatiles were compared between the two Williopsis yeast strains and also with other yeast reported in the literature. The results showed that Williopsis yeast strains NCYC500 and NCYC2586 were high producers of acetic acid and acetate esters, but low producers of medium‐ to long‐chain fatty acids and their corresponding ethyl esters. Unlike mango wine fermented with Saccharomyces cerevisiae, most terpenoids derived from mango juice were retained in the resultant mango wine fermented with the two Williopsis yeast strains, suggesting the mango wine could retain the aromatic hints of fresh mango.