University-firm-government interactions on industry dynamics have been widely discussed, accentuating the role of the government and universities. However, a case study of the solar photovoltaic industry in Taiwan reveals that pioneering firms play a leading role in the creation of an industry in a knowledge-importing economy, whereas government and universities stimulate further growth of the industry. This industry dynamic among university-firm-government enriches triple helix studies and is important for firms intending to initiate an industry and policy-makers in the field of science and technology in knowledge-importing economies.

Nowadays, service sectors are facing a data tsunami. Previous studies on service sectors have been conducted using questionnaire surveys and have been subjectively analyzed using statistical analysis techniques. Such techniques make it difficult for non-statisticians to integrally explore the overall nature of questionnaire data in the big data paradigm. To further discover the quantitative and qualitative nature of a data set, granularity computing is used to make up for the weaknesses of statistical techniques and a rough set (RS) based solution approach is proposed. The Multi-Value Rule Generation (MVRG) algorithm is developed to analyze questionnaire data and deal with the roughness problem of multiple-values in outcome features. The rules resulting from the MVRG algorithm exhibit both the relationships between dependent and independent variables and the content of the relationships. Rules, rather than numerical charts, can be understood by non-statisticians. Two cases of tourism and hospitality are restudied and comparisons between the proposed approach and traditional analytical techniques are made to validate the complementary benefits of traditional statistical analysis. This comparison shows that the proposed solution approach provides further hidden knowledge behind the data set. The MVRG algorithm can complement statistical methods in finding hidden knowledge and providing comprehensive rules to non-statisticians.

This study examines how network embeddedness and partner exploration shape technological innovation capability through absorptive capacity. Drawing on more than 200 Taiwanese manufacturing firms and covariance-based SEM, the findings show that absorptive capacity fully mediates the effect of network embeddedness on innovation capability. By contrast, partner exploration has a positive but non-significant effect, suggesting limits in converting heterogeneous external knowledge into internal capability. The study highlights asymmetric pathways of social capital transformation and underscores the need to align external knowledge access with absorptive mechanisms.