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This study aims to examine direct effects of qualified team gatekeepers on absorptive capacity (AC), and the mediating roles of combinative capabilities – knowledge integration capability (KIC) and interteam coordination.

A social networking analysis was used to analyze a unique data set collected from all members of 32 Japanese research and development (R&D) teams to identify key individuals who perform daily gatekeeping functions. This study analyzed the data through partial least squares structural equation modeling with higher-order latent variables. Finally, cross-validation tests were used with holdout samples to test the model’s predictive validity.

Qualified gatekeepers directly contribute to teams’ realized AC but not to their potential AC. Furthermore, qualified gatekeepers can improve their teams’ capability to absorb and exploit external knowledge by facilitating their capability to consolidate knowledge, that is, its KIC and interteam coordination.

Unlike prior research that asks top managers to identify team gatekeepers, this study used social network analysis to identify these vital individuals. This study provides a new framework indicating how qualified gatekeepers impact the AC of R&D teams through the examination of both the direct and indirect paths of gatekeeping abilities, two combinative capabilities as mediators and team AC.

The purpose of this study is to measure the predictive value of future scenarios prepared using horizon scanning. The future scenarios prepared at the initiative of the Japanese Government have had low predictive value. They have frequently failed to contribute to industrial development and caused social loss. Horizon scanning, which is a key methodology applied in foresight activities, has begun to be used in countries as part of their national innovation systems in lieu of conventional forecasting methods based on the assumption of technological innovation. Research was conducted to actually measure the predictive value of future scenarios prepared using horizon scanning.

An online survey in Japan was conducted on ordinary people’s attitudes. The questionnaires presented 20 scenarios regarding future society, which were created with the conventional method or horizon scanning method.

Survey results verified that horizon scanning-based scenarios provided significantly higher predictive value than scenarios prepared using conventional methods.

Implication 1: By eliminating bias in input data and perspectives adopted when considering scenarios, it may be expected that scenarios will be derived that have even higher “predictive value.” Implication 2: By setting the layers of anticipated outputs high and the fields broad, it may be expected that scenarios will be derived that have even greater “change.”

The relatively high rate for the predictive value of the horizon scanning method, more than 40%, validated in this study was significant.