We examine the role of teamwork within the top executive teams in generating management forecasts. Using social connections within the executive team to capture the team’s interaction, cooperation, and teamwork, we find that social connections among team members are associated with higher management forecast accuracy, consistent with economic theories that information is dispersed within a firm and with sociology insights that social connections facilitate information sharing. Further analyses show that the association between social connections and forecast accuracy is stronger when the teams are just beginning to work together, when their firms face more uncertainty or adversity, and when the CEOs are less powerful. Our results hold for a subsample of executive teams that experience pseudo exogenous shocks to their social connectedness. Taken together, our results underscore the importance of teamwork among executives in the forecast generation process. This paper was accepted by Suraj Srinivasan, accounting.

Urban road networks significantly influence traffic noise. However, existing studies have neglected the causal chain between road characteristics and traffic noise; thus, clarity on their influencing mechanisms is lacking. In this study, structural equation models were developed to explore the mediated effect of road characteristics on traffic noise through traffic flow using data from field measurement in Dalian City, China; paired comparisons of scenarios though microscopic and macroscopic traffic simulations were performed for further analysis. The results show that lane number influences traffic noise mainly in terms of the number of vehicles in a group (NVG). More lanes indicate increased traffic demand due to connected urban land, which increases the NVG and, in turn, increases noise intensity but decreases noise amplitude. The influence of road segment length (RSL) on traffic noise mainly depends on the suppression effect. A longer RSL allows for higher vehicle speeds, leading to increased noise intensity and reduced noise amplitude. This also indicates that traffic flows disperse more easily, decreasing the NVG and, in turn, reducing noise intensity and increasing noise amplitude. Road junctions (RJ), which are classified according to the presence or absence of traffic lights, have significant direct effects on both noise intensity and noise amplitude, which are both likely to increase as drivers accelerate or decelerate in the middle of the road segment. These findings provide a reference for local governments and urban planners when working to improve quality of life in urban areas.

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Blockchain-like ledger databases emerge in recent years as a more efficient alternative to permissioned blockchains. Conventional ledger databases mostly rely on authenticated structures such as the Merkle tree and transparency logs for supporting auditability, and hence they suffer from the performance problem. As opposed to conventional ledger DBMSes, we design VeDB - a high-performance verifiable software (Ve-S) and hardware (Ve-H) enabled DBMS with rigorous auditability for better user options and broad applications. In Ve-S, we devise a novel verifiable Shrubs array (VSA) with two-layer ordinals (serial numbers) which outperforms conventional Merkle tree-based models due to lower CPU and I/O cost. It enables rigorous auditability through its efficient credible timestamp range authentication method, and fine-grained data verification at the client side, which are lacking in state-of-the-art relational ledger databases. In Ve-H, we devise a non-intrusive trusted affiliation by TEE leveraging digest signing, monotonic counters, and trusted timestamps in VeDB, which supports both data notarization and lineage applications. The experimental results show that VeDB-VSA outperforms Merkle tree-based authenticated data structures (ADS) up to 70× and 3.7× for insertion and verification; and VeDB Ve-H data lineage verification is 8.5× faster than Ve-S.