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The purpose of this paper is to illustrate a unique opportunity to analyze the implications of two different approaches to develop and deploy a national public electronic procurement system.

The authors used multiple data collection methods. The data were collected from 2009 to 2013 from primary sources, studies, consulting work, seminars, official documents and the written press. The structure includes a teaching case, a teaching note and a discussion on the potential of case research as a powerful method to elicit valuable insights and knowledge creation.

The research shows that literature on E-Procurement and E-Government is oblivious to the disruptive character of this technology in public administration. This case allows the discussion on the consequences of this omission for the success of E-Procurement implementations. Lessons drawn are extendable to other E-Government ventures.

Despite multiple stakeholder views and a long and difficult debate among different technical platforms, the underlying problem remains unnoticed. The authors show that decisions on E-Procurement implementations should also consider organizational design and adoption of innovation strategies. By re-framing the problem, much of the complexity of the decision disappears.

Many nations around the world are developing or revamping their National Public E-Procurement Systems, this parsimonious account of a complex decision allows for the exploration and discussion of the various complexities surrounding technological innovations in public management and brings light to a critical and mostly ignore success factor associated with the choice on the implementation and operational model.

This paper aims to explore netizen’s opinions on cryptocurrency under the lens of emotion theory and lexicon sentiments analysis via machine learning.

An automated Web-scrapping via RStudio is performed to collect the data of 15,000 tweets on cryptocurrency. Sentiment lexicon analysis is done via machine learning to evaluate the emotion score of the sample. The types of emotion tested are anger, anticipation, disgust, fear, joy, sadness, surprise, trust and the two primary sentiments, i.e. negative and positive.

The supervised machine learning discovers a total score of 53,077 sentiments from the sampled 15,000 tweets. This score is from the artificial intelligence evaluation of eight emotions, i.e. anger (2%), anticipation (18%), disgust (1%), fear (3%), joy (15%), sadness (3%), surprise (7%), trust (15%) and the two sentiments, i.e. negative (4%) and positive (33%). The result indicates that the sample primarily contains positive sentiments. This finding is theoretically significant to measure the emotion theory on the sampled tweets that can best explain the social implications of the cryptocurrency phenomenon.

This work is limited to evaluate the sampled tweets’ sentiment scores to explain the social implication of cryptocurrency.

The finding is necessary to explain the recent phenomenon of cryptocurrency. The positive sentiment may describe the increase in investment in the decentralised finance market. Meanwhile, the anticipation emotion may illustrate the public’s reaction to the bubble prices of cryptocurrencies.

Previous studies find that the social signals, e.g. word-of-mouth, netizens’ opinions, among others, affect the cryptocurrencies’ movement prices. This paper helps explain the social implications of such dynamic of pricing via sentiment analysis.

This study contributes to theoretically explain the implications of the cryptocurrency phenomenon under the emotion theory. Specifically, this study shows how supervised machine learning can measure the emotion theory from data tweets to explain the implications of cryptocurrencies.