Amelia

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Thanks for your comments. Indeed you are right, as according to one of my favourite movies, I remember the quote “with great power, comes great responsibility”. I think each countries have different considerations and concerns based on the norms and culture of each country, and some countries have perhaps even consulted on similar issues as to public sentiment in their countries.

I think this is a timely topic, as it is useful to reflect on how the virtual year has affected our studies and internships, and the pros and cons of each. Indeed, as you said, if we reflect deeply on the pros and cons and more importantly on why such pros and cons, then we could incorporate best practices in people analytics to continue in the future. For example, Zoom has allowed some interns greater flexibility and confidence in speaking up and raising their hands, compared with regular in person meetings. So, a company could consider having both in-person and Zoom meetings on alternate weeks, once or twice a month, to suit the preferences of both extroverts and introverts. I wonder whether Zoom will be organizing such studies given that it seems to be one of the more popular platforms?

On April 14, 2021, Amelia commented on Extending People Analytics to K-Pop :

Since I enjoy opera singing very much, I was very interested to learn that people analytics can be applied to k pop as well! The method seems to be NLP, in different lyrics. I wonder whether it could work for songs in different languages, e.g. Italian, French, German, the ones predominantly used in opera singing as well? Indeed it is very interesting that the tone, and “energy” of the words is captured as well, and I wonder whether in the future people analytics can be used to capture not only the words, but the tone, and mood of the song, as sometimes the words in the song signify one emotion, while the use of major and minor keys, rhythm and body language of the performer suggests otherwise.

I think it is promising as to how people analytics can help, at a rate of 67%, though I wonder how that number can be increased by machine learning as to power words commonly associated with particular behaviours. Of particular interest to me, is the embedded words, which I think would rely on more technical computer skills. I wonder why the Christmas tree distribution was chosen, as it is very interesting.