Remove Banking Remove Ethics Remove Metrics Remove Technology
article thumbnail

How should managers role-model inclusion in their workplaces?

Chartered Management Institute

There are a lot of metrics and evidence out there that says inclusive businesses will outperform those that are not in terms of profitability,” says Catherine, “but it goes much deeper than that. I am a proud man of colour who happens to be gay,” he says. “I Companies with a diversity of thought and opinion avoid groupthink.

article thumbnail

Loyalty vs. Tenure | N2Growth Blog

N2Growth Blog

These companies have placed themselves far behind the technology curve because tenured managers hire employees with obsolete skill sets and together they create mediocre solutions. Reward talent, innovation, loyalty, attitude, creativity, work ethic, contribution, and leadership ability…not tenure. Tenure is too qualitative.

Loyalty 419
Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

The Most (and Least) Empathetic Companies

Harvard Business Review

Businesses are more profitable and productive when they act ethically, treat their staff well, and communicate better with their customers, according to the latest Lady Geek Global Empathy Index. Ironically, the most empathic FTSE companies are a bank and a tobacco firm. Technology. Technology. Technology.

article thumbnail

There Are Two Types of Performance — but Most Organizations Only Focus on One

Harvard Business Review

Every step of the process was measured, and real-time metrics were easily accessible. military says, volatility, uncertainty, complexity, and ambiguity , where technology and strategy changes rapidly. In an experiment, we approached the call center of a bank’s consumer loans business. (We’ve

article thumbnail

When Is It Important for an Algorithm to Explain Itself?

Harvard Business Review

Advocates for rapid technology adoption often argue that humans are no better at explaining decisions than machines, and so we should table the question to accelerate innovation. As with any technology, when you start a machine learning project you have to decide whether to build or buy. (It’s