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.

Purpose: Atlas.ti™ provides useful analytical resources in academic research, particularly for social science disciplines. This technical report presents a case study utilizing a CAQDAS package, as example of research, to show how to utilize Atlas.ti™ version 7. This will serve as a practical guide for qualitative researchers who are beginning to use CAQDAS Atlas.ti™.Design/methodology/approach: Atlas.ti™ software is required to disclose an academic study data introduction, analysis and retrieval outcomes about how commitment, development and innovation contribute to resources mobilization and stimulate capacity of research, development and innovation (R&D&I) team in a traditional business.Findings: Better outcomes have demonstrated Atlas.ti™ usage as a software that organize, retrieve, analyze data in a continuous and cyclical ways in procedures that require constantly comparison.Practical implications: Atlas.ti™ components, such as co-occurrence code, query tool and network view are applied to support data analysis and make easier code validation and emerged categories to understand how to handle qualitative data to organize scientific qualitative approach.Originality/value: Practical presentation of Atlas.ti™ and discussion about benefits of such technology adoption.

This article aims to describe the current state of research on plastic waste management as a circular economy practice at the end of the supply chain. The methodological strategy chosen was a systematic literature review. The articles selected from the Web of Science and Scopus databases were screened, and the research corpus consisted of 201 articles published in journals between 2014 and 2021. The results present 13 study categories, showing topics such as pyrolysis, business models, Industry 4.0, and energy generation. The insertion of Industry 4.0 technologies is still in its initial stages, comprising mainly the generation of inputs to reuse waste for 3D printers’ materials. Regarding energy generation, the insertion of processes such as pyrolysis for fuel generation stands out. The proposed discussion in this article suggests a circular ecosystem in which wastes follow a reuse flow according to their properties; incineration can be an option depending on the stage and benefits generated from the removal of plastic waste from the ecosystem. The highlighted issue is the scalability of the developed processes in the research, which is only possible if the state, universities, and civil society integrate efforts in the construction of a circular ecosystem infrastructure for waste management.

The purpose of this paper is to analyse how the systematisation of environmental training can contribute to the development of individual competences aligned to the environmental dimension of sustainability. A qualitative research approach was used in a multiple case study with three chemical companies: Braskem, Solvay and Beta (fictitious name). Data analysis allowed descriptive characterisation of the phases of environmental training and the identification of individual competencies aligned to the environmental dimension of sustainability in the companies surveyed. The results showed that the systematised environmental training, its phases structured to meet the organisational strategic goals of sustainability, helps to develop individual competences of environmental sustainability in the companies analysed. The contribution of the study to the academy is presented in the deepening of the studies taking into account the gaps presented in the theoretical bases on the subject environmental education. This is the first study that seeks to investigate the stages of environmental training in the development of individual competences in the context of three Brazilian chemical industries. It was possible to describe the process of environmental training in a strategic dimension, used by the companies studied that can contribute to the market, assisting professionals in the areas of human resources and environmental management in the development of training actions. The value of the paper is given by the reflection on the development of individual competencies for sustainability through environmental training, which can guide future public policies of environmental development for companies in the chemical sector.

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.

Collaborative innovation become one of the most strategy decision across firms and a well-defined phenomenon that became popular among practitioners and researchers (A. S. Cui O’Connor, 2012; Liu et al., 2017). Many theories were considered to explain collaboration phenomena such as resources-based view, organization theory, strategy, information processing theory, the economic theory of complementarities among others (Barney, 1991; Cassiman Veugelers, 2006; Daft Lengel, 1986; Milgrom Roberts, 1995; Tushman Nadler, 1978). However, technology advances provide new variations in collaboration to innovativeness. For example, collaborative activities with suppliers and customers (Karhade Dong, 2021), community source projects (Liu et al., 2017) or collaboration with distant partners (T. Cui et al., 2020), corporate engagement with startups (Shankar Shepherd, 2019), innovation networks (Aarikka-Stenroos et al., 2017), and innovation ecosystems (Granstrand Holgersson, 2020).Collaborative innovation takes over the existence of an inter-organizational activities executed by people that together perform with high level of interdependence something innovative (T. Cui et al., 2020; Davis Eisenhardt, 2011). Some authors (Adner Kapoor, 2010; T. Cui et al., 2020; Rico et al., 2008) highlight that this interdependence is characterized along two dimensions: technological and behavioral. Technological interdependence is linked to knowledge and the exchange of resources for research and development, and behavioral interdependence is associated with the field of communication, social interaction between collaborative actors and the coordination of these relationships to innovate.Other perspectives in the literature explain and theorize about collaborative innovation as knowledge-sharing trajectories (Majchrzak Malhotra, 2016; Trkman Desouza, 2012), or multi-actor collaboration (Torfing, 2019), or building collaborative capabilities (Swink, 2006) among other approaches. In this editorial, we bring some thoughts and idea about collaborative innovation under a technological perspective to incentive researchers to go beyond in innovative technologies research embedded in collaboration.Collaboration efforts also became a common way of firms to enhance innovations and its technological development with clear determinants about their beneficial effects, and therefore, the literature is well stablished in this subject (Pereira et al., 2018). However, collaboration only succeeds when technological resources and capabilities are combined, and parties define jointly how to enhance and use them accordingly (Snow, 2015).Collaborative innovation as a new technological paradigm refers to a network innovation model supported by interactions of multiple parties such as enterprises, universities and research institutions as core elements and government, financial institutions, nonprofit organizations, intermediaries as auxiliary elements (W. Zhang et al., 2021). Notwithstanding, collaboration networks operating in different organizational levels are present in various patterns and characteristics of evolution, they require different actors and capabilities in the network composition to become a remarkable asset in developing technologies to be patented afterwards in some cases (Gomes et al., 2017).In facing of risks of failures during innovative trajectories, firms invest in collaborative initiatives as an attempt to mitigate cost impacts, share responsibilities and greater technical performance in the process of technology lifecycle development. Thus, technological alliances are useful means to attend these goals (Kim Song, 2007). Technological alliances are critical to enable digital transformation and innovation. Briefly, Zhang et al. (2021) highlight technological alliance as a voluntary interfirm cooperation involving codeveloping technologies through sharing and exchanging of these technologies to meet business needs (W. Zhang et al., 2021).The collaborations in various technological domains help to bring heterogenous knowledge, complementary resources, and capabilities for a better innovation performance (Swink, 2006; W. Zhang et al., 2021). Under the perspective that innovation is essentially knowledge creation (Nonaka, 1994), collaborative innovation through a technological perspective may be configured by different activities, processes, or routines of generation, sharing, integration, and utilization of knowledge produced during the innovation process lifecycle (Nonaka, 1994; W. Zhang et al., 2021). Further, this configuration of activities, processes, or routines support the development of evolutionary technological capabilities (Sampson, 2007).In the field of technological innovations, the evolution now is more collaborative in nature (J. Zhang et al., 2019). Collaboration is a trend for technological prosperity. Analyzing collaborative innovation in the literature is a great challenge even if the focus on technologies is defined because various aspects and applications of collaboration to innovate invade the academic literature in many forms. For instance, Zhou and Ren (2021) analyzed low-carbon technology collaborative innovation in industrial cluster; Shen et al. (2021) studied collaborative innovation in supply chain systems; Wan et al. (2022) highlight that blockchain application intensify collaborative innovation through distributed computing, cryptography and game theory; Li and Zhou (2022) researched on the mechanism of Government–Industry–University–Institute collaborative innovation in green technology; and Fan et al. (2022) pointed out that collaborative innovation also may act as a driver to mobilize and coordinate scientific and technological resources within a city, further promoting innovative development among cities.On the other hand, technological collaborative innovations has its own dark side for firms: it has been costly, it demands money, efforts, and time...