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The synonymic use of sustainable innovation types obstructs the impact of increasingly disperse research on sustainable innovation, environmental innovation, eco-innovation, and green innovation. To identify the meaning and contributions of each innovation type over time, we apply co-word analysis as a bibliometric technique to 1,985 papers, analysing the evolution of motor, emergent and basic themes for each type. For environmental innovation, the focus has shifted from environmental regulations and policies to patents and inventions with an environmental impact, while in sustainable innovations the societal impact of technology adoption has become a driver. Green innovation increasingly concerns environmental technology and its management, whereas eco-innovation studies aspects related to efficiency and decision making. Clear distinctions among sustainable innovation types will increase the impact of this expanding body of research and make it more available to managers and policymakers.

The synonymic use of sustainable innovation types obstructs the impact of increasingly disperse research on sustainable innovation, environmental innovation, eco-innovation, and green innovation. To identify the meaning and contributions of each innovation type over time, we apply co-word analysis as a bibliometric technique to 1,985 papers, analysing the evolution of motor, emergent and basic themes for each type. For environmental innovation, the focus has shifted from environmental regulations and policies to patents and inventions with an environmental impact, while in sustainable innovations the societal impact of technology adoption has become a driver. Green innovation increasingly concerns environmental technology and its management, whereas eco-innovation studies aspects related to efficiency and decision making. Clear distinctions among sustainable innovation types will increase the impact of this expanding body of research and make it more available to managers and policymakers.

Data envelopment analysis (DEA) is the most widely used non-parametric method in healthcare operation management to measure technical, productive, and allocative efficiency. As healthcare is characterized by complex production processes, we need some other subsequent techniques. Thus, integrated with DEA models, additional estimation procedures have been applied to evaluate efficiency. Although the literature on DEA is prevalent, there exists a lack of evidence in the studies using two-stage DEA in healthcare efficiency analysis. This chapter aims to review publications about two-stage DEA, which is a specific variation of conventional DEA, and to explore how two-stage DEA procedures are prevalent in healthcare. Investigating the state of the art of two-stage DEA models can add value for researchers who plan to conduct research using DEA. This chapter offers a rapid review and bibliometric analysis to explore publications regarding the relevant topic. The number of publications reached a peak in 2021. Review articles focused on various healthcare specialties. Seventeen articles were related to hospitals and healthcare centers, and tobit regression remained the primary choice of analysis for the dependent variable in 11 articles. It was widely used across various units, such as health regions, health systems, and patient-level treatment. Some concerns and controversies were addressed to improve validation and prove practical usefulness.