Even with ‘soft’ and ‘hard’ skills – let’s get smart and sharp instead – Charles Fine, Loredana Padurean

Charles Fine, President and Dean, Asia School of Business

Loredana Padurean, Assoc. Dean and Faculty Director for Action Leaning at ASB, and International Faculty Fellow at MIT

When Loredana Padurean, a co-author of this article, shared her concern that she might not have the right skills to co-lead the startup Asia School of Business (ASB), her former boss responded: “The job is easy; the people are not.” While her initial worry was that she didn’t have enough “hard skills,” such as finance or accounting, she realized very early on that her boss was right about the people issues. If Loredana wanted success, she needed to step up her “soft skills,” such as cognitive readiness, strategic and critical thinking, emotional maturity, adaptability, and ethical and cultural sensitivities.

At ASB, established in 2015 in collaboration with the MIT Sloan School of Management, we have expanded on the concepts of soft and hard but eliminated that terminology; we’ve replaced it instead with “smart” and “sharp” skills, with a pedagogical change to reflect the linguistic change.

Dictionaries define the word “soft” with words like smooth, mild, gentle,
quiet, tender, and weak. However, it turns out there is nothing “soft” about managing diverse teams. Navigating competing perspectives and cultures does not come smoothly; pitching and presenting projects is not a tender act; handling and delivering critical feedback often is not mild; and dealing with office politics is certainly not for the weak. So why do we still refer to these skills as soft?

Analogously, “hard” is defined with words like firm, rigid, resistant, free of weakness, unlikely to change, harsh, severe. But should the so-called hard skills required today—such as coding, finance, accounting, statistics, mathematics, machine learning, engineering—be defined by these attributes? Considering the constant changes in technology, and the subsequent need for users to adapt, these characteristics hardly seem fitting.

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How leaders can maximise their impact – Deborah Ancona, Henrik Bresman

MIT Sloan Prof. Deborah Ancona

From Insead Knowledge

Effective leaders need to know whether their ‘people hat’ or ‘P&L hat’ fits most comfortably.

A leading supermarket chain in an eastern European Union country feared an 8 percent drop in sales as discounting giant Lidl was about to enter its market. So, in collaboration with researchers, it decided to run a randomised controlled experiment. The goal was to reduce its costly personnel turnover problem, in a bid to improve quality and operational efficiency. Selected store managers received a letter from top management, encouraging them to do something about the 90 percent yearly staff turnover. It worked: Over the next three quarters, the monthly quit rate fell by 20 to 30 percent. However, surprisingly, this vast improvement led to no discernible effect on the predefined performance metrics (sales and value of perished food). In interviews, the researchers found the explanation. As store managers focused more on HR issues, they spent less time interacting with customers (to increase sales) and dealing with the flow of goods (to reduce food wastage).

Of course, the firm still benefitted through a reduction in hiring and training costs (estimated at a minimum of 400 euros per head). But at the store level, the data showed a remarkable truth: It is rather difficult – perhaps even impossible – for a single manager to wear all hats equally well. This confirms what we have been saying for years: Distributed leadership, which leverages expertise and vision wherever it sits in the organisation, is the way forward. Organisations with distributed leadership are more innovative and more adaptable. They are permeated by a greater sense of purpose and meaning.

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Twitter chat: #MITSloanUchile Data Analytics Certificate – Soledad Onetto, Richard Weber, Lee Ullmann

El certificado del MIT Sloan y la Universidad de Chile en Data Analytics es el primer programa que MIT hace en conjunto con una Universidad Latinoamericana. Tiene como objetivo preparar profesionales de alto nivel del sector privado y público para la toma de decisiones estratégicas basadas en datos (“data-driven decision making”).

Únanse para una conversación entre Lee Ullmann (@MITSloanLatAm), director de la Oficina para América Latina de MIT Sloan; Richard Weber (@Richard_Weber), profesor asociado y director de posgrado en la Universidad de Chile; y Soledad Onetto (@SoledadOnetto), periodista y presentadora de televisión chilena. Platicaremos sobre la colaboración entre MIT Sloan y la Universidad de Chile, sobre el recién creado programa Data Analytics, y sobre la importancia de Data Analytics en el mundo de hoy.

La plática por Twitter tendrá lugar el 11 de abril desde las 13:00 hasta las 14:00.

¿Cómo pueden participar? ¡Es sencillo! Si tienen una pregunta, respuesta o comentario, simplemente incluyan #MITSloanUchile en sus Tweets.

Para más información, lean el comunicado de prensa.

 

Algorithmic bias or fairness: the importance of the economic context – Catherine Tucker

MIT Sloan Distinguished Professor of Management and Professor of Marketing Catherine Tucker

From the Shorenstein Center

As a society, we have shifted from a world where policy fears are focused on the ubiquity of digital data, to one where those concerns now center on the potential harm caused by the automated processing of this data. Given this, I find it useful as an economist to investigate what leads algorithms to reach apparently biased results—and whether there are causes grounded in economics.

Excellent work from the discipline of computer science has already documented apparent bias in the algorithmic delivery of internet advertising [1]. Recent research of mine built on this finding by running a field test on Facebook (and replicated on Google and Twitter), which revealed that an ad promoting careers in science, technology, engineering, and math (STEM) was shown to between 20 and 40 percent more men than women across different age groups [2]. This test accounted for users from 190 different countries, with the ad displayed to at least 5,000 eyeballs in each country. In every case, the ad was specified as gender-neutral in terms of who it should be shown to.

When my team and I investigated why it was shown to far more men than women, we found that it is not because men use these internet sites more than women. Nor is it because women fail to show interest or click on these types of ads—thereby prompting the algorithm to respond to a perceived lack of interest. (In fact, our results showed that if women do see a STEM career ad, they are more likely than men to click on it.) Nor does it seem to echo any cultural bias against women in the workplace. The extent of female equality in each of the countries as measured by the World Bank was found to be empirically irrelevant for predicting this bias.

Instead, we discovered that the reason this variety of ad is shown to more men than women is because other types of advertisers actually seem to value the opportunity to get their ads in front of female (rather than male) eyeballs—and they’ll spend more to do it. Some advertisers’ willingness to pay more to show ads to women means that an ad which doesn’t specify a gender target is shown to fewer women than men. In essence, the algorithm in this case was designed to minimize costs and maximize exposure, so it shows the ad in question to fewer expensive women than what amounts to a greater number of relatively cheaper men.

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Culture wars in the lab – Gad Yair

MIT Sloan Visiting Researcher Gad Yair

From Scientific American

Culture matters when it comes to science. An uncomfortable and regrettable incident at Duke University in Janurary sent shock waves through the scientific community when a professor and program administrator suggested that her Chinese students “commit” to speaking English in professional settings. Although the administrator later apologized, the incident continues to reverberate.

Cultural misunderstandings like this are growing as campuses internationalize. In recent interviews with scientists at Harvard, M.I.T., Boston University and other institutions, I found that respondents embrace diversity in their workplaces but also raise concerns about puzzling behaviors of their international students. They say that cultural diversity in research settings is crucial but point out that some international students are “too obedient” or “hard working yet lacking in originality.” Without training in cultural sensitivity, they are often surprised and occasionally make errors of communication.

We scientists must learn to work together with our differences and appreciate that we are as culturally unique as any other cultural group. Indeed, most scientists are unaware of the intricate ways by which cultural elements enter their lab, for they do so in subliminal ways. Culture affects the way scientists perform and document their work in laboratories, respond to reviews, and talk with their students. Scientists, after all, are bearers of culture—just as their international students are. For an effective meeting of minds, we need to understand how cultures actually form them.

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