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Business models are crucial for explaining firm performance. In practice, managers apply business models to increase firm’s advantage. Particularly, innovative business models solve many economic and social problems in emerging economies. To show the knowledge development in the field, we explored the knowledge network and identified the role of business models in emerging economies.

To examine the primary topics in business model studies, we reviewed the academic literature and applied main path analysis (MPA) to explain the development in this field since 2000. The valid sample of this study comprised 665 papers, and citation information was used in the MPA. We further investigated the role of business models in emerging economies through keyword analysis and case analysis.

The results for the main path indicate that the development of business model studies proceeded in three stages. In general, the literature in business model studies has transitioned from discussing internal and external value to framing business model structures. Specifically, in the latest stage, sustainability in a business model was conceptualized, and studies focused on how a business model can function as a structured model for explaining how various businesses operate. Moreover, studies on emerging economies have noted the importance of sustainable business models and model adaptations for firms in developing countries.

Evolutionary studies have been of prime importance to life scientists since ancient times. The advancements in technology has made it possible to make available the massive amounts of genomic data. The abundance of genomic data poses new challenges for biologists, computer scientists and mathematicians to develop approaches for discovery of new relationships in data and evolutionary networks. In this work, nucleotide sequences are converted into binary sequences to explore the network among different species. A new approach based on binary sequences has been proposed to reconstruct the accurate phylogenetic network. The algorithm developed is validated by comparing the results with those obtained by already existing method of network construction. A program is also coded in C language on the Intel Core i3 Dell inspiron machine to obtain the evolutionary network. The new approach developed also provides the fast solutions as there is no need of aligning the sequences.

There are a number of standard models for the evolutionary process of mutation and selection as a mathematical dynamical system on a fitness space. We apply basic topology and dynamical systems results to prove that every such evolutionary dynamical system with a finite spatial domain is asymptotic to a recurrent orbit; to an observer the system will appear to repeat a known state infinitely often. In a mathematical evolutionary dynamical system driven by increasing fitness, the system will reach a point after which there is not observable increase in fitness.