Green innovation has become one of the mainstream concepts to address environmental issues. However, research on green innovation, especially at the firm level, is still evolving. Building upon the natural resource-based view (NRBV) and resource-based theory (RBT) lenses, this study proposes a research framework to explore determining factors of green innovation adoption (GIA) in the Brazilian cassava agribusiness sector. The research design from the semi-structured interviews method was employed in firms associated with ABAM (Brazilian Association of Starch and Cassava Producers) and SIMP (Union of Cassava Producers in Paraná). The findings suggest that behavioral factors for GIA, such as collaborative behaviors, productivity, safety behaviors, and green behaviors, are directly associated with other determining factors, such as knowledge, operating costs, stakeholder pressure, economic benefits, information access, customer assessment, organizational compatibility, and business support. Furthermore, GIA can lead to the effective implementation of sustainable practices to enhance economic performance and effective environmental management. The originality of this work stems from including behavioral factors for managers’ decision-making regarding resource allocation for GIA. It also includes a guideline to increase competitiveness in active green markets. It also contributes to a vision of GIA to evolve NRBV and RBT theories.
Purpose The purpose of this study is to analyze the systematic relationships among dynamic capabilities in startups’ survival. Design/methodology/approach This study is based on a systematic literature review on dynamic capabilities related to startups’ survival, following the content analysis approach. Findings This study presents four different perspectives of analysis about dynamic capabilities from resources exchange and business factors that meet needs of startups' survival. It also points out new area for future research in this field. In doing so, this study differentiates itself by its approach not limiting dynamic capabilities research and enriching entrepreneurs' capability theory. Practical implications By indicating an evolution of dynamic capabilities theory among tangible and intangible resources exchange in a more favorable adaptation to startups growth, this study boosters and contributes to the society, economy in general and to the science of business management in various perspectives such as overcoming cognitive barriers, entrepreneur’s commitment, innovation capabilities and knowledge capacity of startups. Originality/value This study amplifies dynamic capabilities vision in startups’ survival as one of the main sources for growth in this type of organizations. It also develops a deeper understanding about new avenues for dynamic capabilities theory among tangible and intangible resources exchange.
Innovativeness is a characteristic of digital technologies (DT), and they have been assuming an important role in economic, social, and environmental dimensions. Therefore, DT are relevant contributors for sustainable development goal (SDG) achievements. This study aims to compare the preference for artificial intelligence-driven digital technologies (AI-Driven DT) to achieve SDGs in Brazil and Portugal. An independent sample t-test analysis and Levene test are performed to identify potential artificial intelligence-driven digital technologies (AI-Driven DT) as favorable facilitators for SDG achievements in Brazil and Portugal. Based on the findings, a broader analysis is provided, to (i) indicate potential favorable SDGs, (ii) discuss differences between the countries in AI-Driven DT preferences in each SDG, and (iii) make recommendations for potential technologies that could receive more attention and investments in both regions to make emergent digital technologies succeed, with a particular emphasis on cleaner production. The analysis is organized into three dimensions: economic, social, and environment. At the end, a closing discussion is provided about the key guidelines and prospects that could be adopted to keep a strong and positive shift of AI-Driven DT developments and applications towards fully supporting the attainment of the SDG of United Nations Organization (ONU) Agenda 2030.
PurposeThe authors examined intellectual capital (IC) components, namely human, structural and relational capital, on firm performance (FP) and innovation performance (IP), while also examining the role of knowledge management (KM) in this context.Design/methodology/approachThe authors employed a meta-analysis using 81 studies from 2006 to 2020 using bivariate analysis, meta-analytic structure equation modeling (MASEM) and fuzzy-set qualitative comparative analysis (fsQCA) to study IC components on FP and IP.FindingsThe MASEM results show that IC affects positively on FP, but not in a significant level and affects positively and significantly on IP. The findings also reveal that the moderation effect of KM affects positively on FP but not on IP. Additionally, the fsQCA analysis shows that KM and its multidimensional role has a positive impact on FP and IP and has a potential to be consistent as a dynamic component for IC.Research limitations/implicationsThe results may be limited by different statistical biases and inverse causality issues or associated with contextualities related to the studies of the sample selected by our criteria.Practical implicationsManagers can identify the appropriate IC elements and act accordingly. The study suggests that mobilizing human, structural, relational and knowledge capital must begin from the firms' birth and continue further during firms' stages of the business.Social implicationsIC is the bridge of evolution for future societies. Knowing how its components impact all levels of corporate environment indirectly influences how societies build up their social bases and policies to fulfill new professional generations.Originality/valueBy using the MASEM and fsQCA, the authors have more detailed insights into the multidimensional context of KM in IC components on firm and innovation performance identifying configurations of intangible resources.
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