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The trend among the financial investors to integrate cryptocurrencies, the very first completely digital assets, in their investment portfolio, has increased during the last decade. Even though cryptocurrencies share certain common characteristics with other investment products, they have their own distinct characteristic features, and the behavior of this asset class is currently being studied by the research scholars interested in this domain.

Using the text mining approach, this article examines research trends in the field of cryptocurrencies to identify prospective research needs. To narrow down to ten topics, the abstracts and the indexed keywords of 1,387 research publications on cryptocurrency, blockchain and Bitcoins published between 2013 and 2022 were analyzed using the topic modeling technique and Latent Dirichlet allocation (LDA).

The findings show a wide range of study trends on various aspects of cryptocurrencies. In the recent years, there have been lots of research and publications on the topics such as cryptocurrency markets, cryptocurrency transactions and use of blockchain in transactions and security of Bitcoin. In comparison, topics such as use of blockchain in fintech, cryptocurrency regulations, blockchain smart contract protocols and legal issues in cryptocurrency have remained relatively underexplored. After using the LDA, this paper further analyzes the significance of each topic, future directions of individual topics and its popularity among researchers in the discussion section.

While similar studies exist, no other work has used topic modeling to comprehensively analyze the cryptocurrencies literature by considering diverse fields and domains.

The purpose of this paper is to study the relationship between intellectual capital (IC) and performance of pharmaceutical firms in India. The secondary objective is to propose and test modified models of Value Added Intellectual Coefficient (VAIC™) method.

Data on 22 large pharmaceutical firms collected for empirical investigation. Return on assets and return on sales are performance variables. IC and its components – human capital, structural capital and relational capital (RC), are predictor variables. Three extended and modified VAIC™ models (e-VAIC™) are proposed. Multiple regression technique is applied on pooled data to draw inferences.

Results show instances of positive relationship between IC and performance variables. RC, the new variable, does not demonstrate statistically significant relationship with performance variables.

Due to inadequate reporting of IC and its components, availability of data on various proxies is difficult. The new models proposed in this paper can be a template for future research and model development.

VAIC™ model, the proposed models (e-VAIC™) and the result analysis can be useful for evaluation and value creation purposes.

Previous researchers use original VAIC™ model. This paper modifies and extends the model in accordance with contemporary description and typology of IC. Inclusion of RC as a variable in VAIC™ model and use of new proxies for components of IC are the novelties of this paper.