Reference Article: https://www.shrm.org/resourcesandtools/hr-topics/technology/pages/the-new-analytics-of-workplace-culture.aspx
This article studies the effects of tools using language-based culture measures can raise ethical questions. I agree that, while algorithms make estimates, it is eventually humans’ responsibility to make informed judgments using them and management must keep metadata anonymous and should verify that algorithmic decision-making is not subject to bias and negative consequences. However, I believe there are still a large number of unexplored areas that natural language processing.
As we can see, there are many applications of Natural Language Processing for People Analytics. However, social information can vary greatly between language and cultures and research using text analysis methods should be expanded to capture those subtle cultural differences that would uncover social relations.
For example, Dr. Keith Chen, a behavioral scientist, had discovered that languages vary in how much of a grammatical distinction they require speakers to make between the present and the future (M. Keith Chen, American Economic Review, 2013). His hypothesis was that languages that grammatically associate the future and the present, foster future-oriented behavior. For example, English requires speakers to talk about the future as different from the present (“it will rain tomorrow”), while German allows speakers to talk about the future as though it is the present (“Morgen regnet es” or “it rains tomorrow”). This example from German language is something that he refers to “futureless language” in an article about the way grammatical distinctions in languages affects economic behavior (The ‘Futureless zone’: Can language affect economic behavior? | DW | 24.06.2013)
So the way our language makes us think about time – affects our propensity to behave in the real world and across time. With English, a “futured language” that means every time you discuss a future event, you’re being made to cleave that from the present and treat it as if it’s something completely different, according to Dr. Chen. Suppose these difference makes you subtly dissociate the future from the present every time you speak. It then, makes the future feel something more distant and more different from the present, that’s going to make it harder to save, for example. If, on the other hand, you speak a futureless language, the present and the future, you speak about them identically. If that subtly nudges you to feel about them identically, that’s going to make it easier to making saving.
In a similar fashion, if a German colleague, whose language is “futureless” commits to a project and she is frustrated with the motivation of a colleague from a “futured” culture that affects interpersonal relationships, leading to conflicts – the kind of ‘fires’ that could be put out at the early stages and lead to smooth intercultural teams’ collaboration. This is the type of issue that could potentially be resolved by the use of Natural Language Processing in bridging a gap in language difference, lowering misunderstanding and “translating” the subtle cultural meanings, without human interference.
There is potential for Natural Language Processing in People Analytics to provide transnational corporations with a tool to improve interpersonal communication across it multicultural teams as well as “soften the blow” of cultural biases and misunderstandings, uncovering what’s otherwise “lost in translation”.
It is also, an interesting topic to explore in workforce motivation strategies across a diverse range of cultures, including the language data for bilingual or multilingual employees. Employees would not have to rely solely on corporate intercultural training and fragmented work experiences alone, and instead, utilize factful ‘off-the-shelf’ tools as both People Analytics and Natural Language Processing becomes more widespread.