Could Big Data Be the New Gender Equality Tool?

Even in the age of big data, some numbers pertaining to women are glaringly missing: numbers on global maternal mortality rates are incomplete, statistics regarding women and unpaid work are flawed and conflict-related gender-based violence figures are lacking.

To begin tackling this problem from the bottom up, Data2X—a joint project of the Clinton Foundation, the United Nations Foundation, the William and Flora Hewlett Foundation and the Bill & Melinda Gates Foundation that was first introduced in 2012—announced new regional and topical partnerships at a press event in New York City Monday. By partnering with a multitude of organizations, the Data2X platform says it hopes to start a “gender data revolution,” which will allow policymakers to recognize problems more clearly and better create informed policy.

“I have been championing the rights of women and girls around the world and here at home for many years,” former Secretary of State Hillary Clinton said at the event, “and I got tired of seeing…foreign leaders, business executives, even senior officials in our own government…smile and nod when I raised these issues… ‘Oh right, I knew she was going to raise women and girls, I will just sit here and smile, it will pass, and then we’ll talk about really important things.’”

Clinton said this scenario played out countless times over the years and inspired her involvement in the Data2X project. “You can’t understand what the problem is if you don’t have a good grasp of what the facts and figures are,” she said.

The new partnerships will focus on six categories of data:

Civil Registration and Vital Statistics (CRVS)

Civil registration, which is the continuous recording of vital life events like birth, marriage and death, is incomplete around the world. To address missing records, Data2X is teaming up with organizations in Africa and Asia, like the United Nations Economic Commission for Africa and U.N. Economic and Social Commission for Asia and the Pacific.

Though the births of girls and boys are almost equally registered globally, ensuring women an individual legal identity by maintaining a quality CRVS system could provide a better account of early and forced marriages and help women retain their share of assets in the event of a divorce.

Women’s Work and Employment

In partnership with the International Labour Organization, Data2X is redefining what is considered work to include unpaid work, including work for the home. Expanding the work framework will give the global community a better idea of how women contribute to the economy.

For example, Clinton said that data collection and analysis in India showed that women spend an average of six hours per day doing unpaid labor. If these women were to participate in the formal workforce at the same rate as men, she contended, India’s gross domestic product would increase by $1.7 trillion.

Supply Side Data on Financial Services

Women face both financial service access and service gaps globally—they have trouble proving their business case and struggle to get the resources they need. But data pertaining to these experiences with financial services is not kept. “If you can’t measure it, you can’t manage it,” former New York City mayor Michael Bloomberg told the audience.

Now, the Global Banking Alliance for Women and the Inter-American Development Bank are teaming up to incentivize collection of this data as a first step to close the gender gap in financial services. Proposals for how to support this venture will be presented at the Global Data Symposium in September 2015.

Women’s Subjective Well-Being and Poverty

The lack of gender-segregated data has clumped women’s poverty with household poverty, meaning the exact poverty numbers for women and girls is unknown. Data2X is joining with the Government of Mexico’s National Institute of Statistics and Geography (INEGI), which already has a comprehensive approach to discovering gender differences in well-being. One of INEGI’s tactics to understanding women’s subjective well-being is analyzing Twitter feeds to determine who and where posts with positive sentiments are coming from.

Big Data and Gender

Data2X is collaborating with U.N. Global Pulse, U.N. Women and academic researchers to analyze cell phone data usage patterns to infer women’s socioeconomic welfare, mobility patterns and financial activity. The project also plans to use remote sensors to reveal epidemiological trends and provide information on women’s access to services.

Improved Gender Data on U.S. Foreign Assistance Programs

Not only does Data2X want more complete data to be included when setting the global agenda, but the project aims to inform U.S. development policy and investment. In conjunction with the U.S. President’s Emergency Plan for AIDS Relief and Millennium Challenge Corporation, sex and age specific data will be released to ensure those most in need of aid are reached and to assess the impact of current U.S. assistance programs.

Data collection, Clinton said, could “build a case strong enough to convince the skeptics, based on hard data and clear-eyed analysis, that creating opportunities for women and girls across the globe directly supports everyone’s security and prosperity, and therefore should be an enduring part of our diplomacy and development work.

Lauren Walker

Originally posted via “Could Big Data Be the New Gender Equality Tool?”

Source: Could Big Data Be the New Gender Equality Tool?

Fortune 100 CEOs And Their Path To Success

Fortune 100 CEOs And Their Path To Success
Fortune 100 CEOs And Their Path To Success

Ever cared to know what it takes to be fortune 100 CEO? What it took, how their career path progressed? If yes, thanks to N2Growth for an amazing infographics. It spreads some lights on how these CEOs grew up and what differentiates them and put them almost in the similar silos among themselves. Take a peak at the infographics and see what it will require to be a CEO, how you could set your path to such progression. Surely, a good place to start if you are not running already.

Fortune 100 CEOs | Demographics, Education, and Career Path

Explore more infographics like this one on the web’s largest information design community – Visually.

 

Originally Posted at: Fortune 100 CEOs And Their Path To Success by v1shal

Map of US Hospitals and their Patient Experience Ratings

Hospital RatingsHospitals are focusing on improving the patient experience.  The Centers for Medicare & Medicaid Services (CMS) will be using patient feedback about their care as part of their reimbursement plan for Acute Care Hospitals. Under the Hospital Value-Based Purchasing Program (beginning in FY 2013 for discharges occuring on or after October 1, 2012), CMS will make value-based incentive payments to acute care hospitals, based either on how well the hospitals perform on certain quality measures or how much the hospitals’ performance improves on certain quality measures from their performance during a baseline period.  The higher the score/greater the improvement, the higher the hospital’s incentive payment for that fiscal year.

Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS)

Patient feedback is being collected using a survey known as HCAHPS (Hospital Consumer Assessment of Healthcare Providers and Systems). HCAHPS (pronounced “H-caps“) is a national, standardized survey of hospital patients and was developed by a partnership of public and private organizations. I recently wrote about HCAHPS in a prior post. The survey asks a random sample of recently discharged patients about important aspects of their hospital experience. The data set includes patient survey results for over 3800 US hospitals on ten measures of patients’ perspectives of care.

The data.gov site indicates that the data files were updated in 5/30/2012. Based on HCAHPS reporting schedule, it appears the current survey data were collected from Q3 2010 through Q2 2011 and represent the latest publicly available patient survey data.

Map of US Hospitals and their Patient Experience Ratings

As consumers of healthcare, you need to understand how well hospitals are delivering a good patient experience. Using the HCAHPS data, I developed a map to help you easily identify and understand how your hospital ranks in patient experience. In the map below (see Figure 1), the colors are based on the patient advocacy index I created (average of top box scores for two questions: Overall hospital quality rating and Recommend hospital. In my prior analysis, this Patient Advocacy Index (PAI) had a reliability estimate (Cronbach’s alpha) of .95, suggesting that it is a reliable measure.

The colors for each hospital are based on their PAI (red = 0 – 20; purple = 21-40; yellow = 41-60; blue = 61-80; green = 81-100). If you click on one of the buttons, you will see detailed information about the patient experience metrics (if survey data were collected for that hospital) as well as response rates, sample sizes and other notes (if available). NOTE: Some hospitals do not have any ratings (those are typically red).

[iframe_loader src=”https://www.google.com/fusiontables/embedviz?viz=MAP&q=select+col6+from+15Ebp9YRZDttkQTg8-xiBS6m44TIDHmJIOlBhBqo&h=false&lat=36.49197347059371&lng=-93.77016217500001&z=4&t=1&l=col6″]
Figure 1. Map of US Hospitals and their Patient Experience Ratings

Originally Posted at: Map of US Hospitals and their Patient Experience Ratings by bobehayes

Big data offers telcos new revenue streams

Telcos need to find ways to process and analyse large amounts of customer data quickly and in real-time in order to create new revenue streams and engage with customers effectively.

So said Rams Srinivasan, telco industry value engineer at SAP Middle East and North Africa, speaking yesterday at the SAP Forum in Sandton.

According to Srinivasan, telecommunications companies are struggling to break through and engage with their customers because of legacy systems that make it difficult to have a complete view of customers and deliver efficient service across multiple channels.

He explains telcos must adapt in order to attract and retain a new generation of customers who are highly informed, socially connected and very mobile.

Telcos can track conversations on social media to understand what customers are saying about their products and services, and take proactive measures to defend their brand image and reputation in real-time using analytics, said Srinivasan.

Operators are looking for new ways to increase revenues and profits – but few have shown the know-how needed to make the most of new digital channels, said Srinivasan.

According to a Frost & Sullivan report, with an increasingly saturated market, and declining traditional mobile voice and SMS revenues, operators are exploring new business models as part of their growth strategy.

Also, the declining cost of devices is driving the ecosystem to explore new possibilities through connected devices, adds Frost & Sullivan.

By analysing varied and unformatted digital data from digital channels like social media and through mobile phones, telcos can reveal new sources of business economic value and provide fresh insights into customer behaviour, said Srinivasan.

He pointed out operators need a strategy to accurately mine and analyse both structured and unstructured data. This will give them an opportunity to get deeper insights into customer behaviour, their service usage patterns and preferences in real-time.

“To be able to remain relevant and competitive in a world where technology giants like Google, Facebook and Apple are causing disruption, telcos need to engage with the customer in a better way by improving operational efficiency through data analytics.”

Today’s lifestyle demands continuous connectivity and excellence of service, leading to a highly competitive and diverse market, said Srinivasan. There are more digital connected devices than human beings and the numbers are growing.

Another new source of revenue for telcos is selling insights about customers to third parties, said Sherif Hamoudah, industry head, telecom, at SAP Africa, Middle East and Pakistan.

By leveraging the data stream, telecommunications companies can tailor their marketing campaigns to individual customers using location-based and social networking technologies, he said. Also, big data offers event-based marketing campaigns that use geolocation and social media, allowing differentiated response.

Big data offers telecom operators a real opportunity to gain a much more complete picture of their operations and customers, and to further their innovation efforts, he said.

Originally posted via “Big data offers telcos new revenue streams”

Source: Big data offers telcos new revenue streams

Every step you take: Who owns our mobile health data?

Gadgets that track your steps, sleeping and heart rate could help us live longer and cut national healthcare costs by billions – or so we are told.

Microsoft has just launched its first wearable health gadget, the Band, in the US ahead of its global launch.

Similar products from Samsung and Google are already on the market and early next year the much-hyped Watch from Apple will go on sale.

Millions of us are going to be having our most intimate bodily functions monitored by these gadgets, creating more health data than has ever existed before.

Why do these machines help us stay fit and more importantly what happens to all that information we are generating and sharing?

Tim Cook introducing the Apple Watch
Apple will soon follow Microsoft and Google into the mobile health device market

Massive market

Before the giants of the tech world realised that wearable, health-focused gadgets were the new big thing the market was already thriving.

In March the European Commission published its green paper on mobile health, which contained some mind-boggling statistics.

It suggests that 97,000 apps are on sale in the mobile health sector, which includes tracking apps but also apps that help patients make appointments and keep track of medication.

It predicts that by 2017 more than 1.5 billion people around the world will be using these apps, generating total revenues of £14.5bn ($23bn).

In the EU alone it is estimated that these apps and gadgets could reduce health costs by £77.5bn (99bn euros).

Sector pioneers

Most of the growth has come from start-ups that saw the potential early and now face a competitive onslaught from the big technology companies.

Five years ago French firm Withings launched its wireless scales – the device feeds data back to you, by plotting a graph of your weight over time.

“It started with the scales because we thought that was the one dimension that would make sense for people to track,” Julien De Preaumont, chief marketing officer at Withings, says.

“The first rule of data is to make people aware of their health to make them realise how their weight is evolving.

black wireless scales by Withtings
The wireless scales by Withings uses data visualisation to help dieters lose weight

“The curve reveals the impact of life changes, it will show how a divorce, a diet or a new job will affect your weight.”

After the scales took off, Withings launched wearable gadgets that track your movement, heart rate, blood pressure and sleep.

The company maintains that the data it collects belongs to the user only.

But it has published reports revealing the most obese cities in France and the US, as well as another study showing sleep patterns across Europe.

Withings says this does not compromise the privacy of the individual user’s data because it is aggregated and anonymised.

Business games

While Withings has grown to be a global business, US firm Fitbit has also seen its business thrive beyond its borders.

Founded in 2007 Fitbit offers wireless scales, wearable devices that monitor movement, heart rate, sleep and blood pressure, and is evangelical about the motivating power of targets and data on our health.

Fitbit also offers companies its gadgets and software for corporate use.

Its “corporate wellness” scheme started in the US and companies can use the scheme to get a rebate on their taxes.

A screengrab from a Fitbit challenge
Games and challenges can be used to motivate people to compete against each other

Clients so far include blue-chip multinationals such as BP and Time Warner.

Employees can sign up and different divisions can compete against each other over the number of steps taken or stairs climbed.

“The key is to make the product sticky,” says Gareth Jones from Fitbit, and the key to that is gamification.

“Our software incorporates challenges like daily showdowns and weekend warriors which motivate people and keep them coming back.”

But should employees be worried about sharing their every movement, 24 hours a day with a corporate scheme?

“We don’t have data about this, it’s very much a choice of the individual as to whether they sign in for the programme. We see the result of that as purely the people who agree to participate and the people who don’t,” says Mr Jones.

“We might share with the corporate administrator information that 50 people have been invited and 45 have said yes. How the company uses that information is up to the company.”

‘In the hands of the people’

The potential of all the data that is now being collected is huge, both for business and for public health bodies.

Imagine going to the doctor and being able to show them how much exercise you do, how much sleep you get and your blood pressure for the last year.

While the insurance industry is using mobile applications for arranging appointments and giving health information, they are yet to fully embrace the use of wearable devices and the data they collect, though it is a development that could completely change their business as many research papers suggest.

Meanwhile the use of the data for medical research is also a long way off.

Professor John Newton from Public Health England would like to see a more joined-up approach.

“We’ve got the world of apps, a huge investment from the technology companies, but the healthcare sector hasn’t made the link,” he says.

“If you were able to make the link between a hospital service like a diabetic clinic with a patient’s mobile phone data, they could tell immediately whether that person’s diabetes was going out of control.”

His message is clear: “Put the data into the hands of the people who can use it to make a difference.”

Like all the new data that is being recorded and analysed the possibilities are massive but the ethical and privacy issues surrounding our personal information will not go away quickly.

Originally posted via “Every step you take: Who owns our mobile health data?”

Source: Every step you take: Who owns our mobile health data? by anum

Linkage Analysis for Your VoC Program – Free Paper

Linkage Analysis for Your VoC Program
Linkage Analysis for Your VoC Program - click to download

Customer feedback provides useful information about the health of the customer relationship. Relationship and transactional surveys, commonly used to capture customer feedback, are used to assess and improve that health. While customer feedback metrics provide great value in and of themselves, when used with other types of business data, they can address meaningful business questions:

  • Are the customer feedback metrics predictive of future financial performance and business growth?
  • Do customers who report higher loyalty spend more than customers who report lower levels of loyalty?
  • Where do we set operational goals to ensure we maximize customer satisfaction?
  • Does employee training help improve the customer experience?

To answer these questions, companies look to a process called business linkage analysis. Download the free paper here.

Source: Linkage Analysis for Your VoC Program – Free Paper

Why Your Company Should Use Data Science to Make Better Decisions

Data science is one of the most recent buzzwords that is gaining popularity in tech circles. With the number of job advertisements on the rise, one may think that data science and their professionals, data scientists, would become one of the most sought-after professionals in the technology job market over the next decade. Indeed, there are strong reasons to believe so, and surprisingly the cause won’t be the big companies, but the small and medium-sized ones, which are eager to find ways to interpret the data they now collect at an exponential rate.

Every company, big or small, is surrounded by data

Some people only relate data science with social networks like Facebook or Twitter; in general, networks with huge amounts of user information that is primarily used to deliver better advertisements. But that is only one side of the coin —albeit an important one— The democratization of the Internet has had two important consequences for almost any company, even if it is not related to technology:

  • Companies can now reach a greater number of potential customers from all over the world, with many different backgrounds and interests.
  • Mobile devices are always connected and are also extremely portable, which means that the number of transactions and customer interactions per product and service has increased substantially in recent years. One example of this is the mobile impact in e-commerce, and 2015 may be the year of Apple Pay.

How data influences decisions

Arguably one of the most important values that a company can have is its ability to make good decisions. This includes how to respond to the competition, how to plan future products and services, and so on. Data science specialists guide decisions by ensuring that the right questions are asked on data. Like Alice in “Alice in Wonderland”, if you ask the wrong questions on data, it does not matter which decision you take.

A data scientist uses math concepts to extract insights from data, but this is not limited to standard data mining that target users for marketing purposes or fraud detection, like banks have been doing for a long time. It is more like treating each internal or external business problem from a data perspective. For example, a company may discard a long project even before starting it if rigorous data analysis shows that it may not generate enough value. For this purpose, I think that a multidisciplinary team is needed with not only data scientists, but also economists, psychologists, and engineers.

To sum up, data science is here to help businesses in many ways. The first step an organization should take is to come up with a methodological way to extract insightful information from data. Once the organization has the specialists for this task, the next logical step is to close the gap between data and decision-making, by viewing each business case as a data problem.

What do you think? Do you think investing in data science gives companies a competitive advantage? Do you think data science can be applied to decision-making inside an organization? Join the comments below.


Daniel_Marti¦ünDaniel is a software engineer at Fon and PhD student at the Artificial Intelligence and Software Engineering department of Complutense University of Madrid. His research interests are computer vision and machine learning. He is specially interested in the applications of the emerging field of deep learning.

You can reach him at LinkedIn, or Twitter.

 

Originally posted via “Why Your Company Should Use Data Science to Make Better Decisions”

Originally Posted at: Why Your Company Should Use Data Science to Make Better Decisions by anum

The Data Analytics of Halloween

The-Data-Analytics-of-Halloween–2015-Costume-Edition

On All Hallows Eve, little yellow Minions, pint-size Yodas, and pretty pink princesses–not to mention ghosts, goblins, rock stars, witches, superheroes, and cowboys–will roam through the neighborhoods in search of bite-size pieces of sugary goodness.

It’s All About the Force (and the Minions)

The Minions as well as Yoda and his friends from “Star Wars” are among the top costumes that adults, children, and even pets will be donning on Halloween, according to the National Retail Federation’s (NRF) 2015 Halloween Consumer Top Costumes Survey. Prosper Insights & Analytics conducted the poll of 6,754 consumers from September 1-8, 2015.

More than 1.8 million children will trick or treat dressed as “Star Wars” characters, while 1 million will choose to become Minions, those dungaree- and goggle-wearing loyal yellow servants.

“As we’ve seen for several years, Hollywood and pop culture both have a tremendous impact on how adults and their children decide to dress the part each Halloween, and it’s evident some of the biggest newsmakers of the year will be out in full force this fall,” says NRF President and CEO Matthew Shay.

Princesses and Witches Rule, Too

As for the princess costumes–they top the kids’ costume list for the 11th year in row. In fact, about 3.2 million little tikes will be living out their favorite fairytales dressed in stunning silky gowns with sparkling jewels. Another 2 million children will become their favorite Disney “Frozen” characters–Anna, Elsa, and that adorable little snow guy, Olaf.

And for the 11th year in a row the witch costume came in as the top adult costume, with more than 4.3 million adults going with the old standby. “Star Wars” characters are also big with the grown ups, ranking 5th for adults this year after tying for 12th last year.

Han Solo, Princess Leia, Yoda! Oh, my!

According to the NRF, 1.4 million adults will channel Han Solo, Princess Leia, Yoda, or other beloved “Star Wars” characters this Halloween. Animal characters (2.4 million), “Batman” characters (2 million) and zombies (1.9 million) round out the top five adult costumes. And if all that wasn’t scary enough, 774,000 adults are planning to dress as their favorite political characters this year.

About one in 10 of the lovers of all things Halloween (12.9%) will dress their furry friends in costumes this year. Of the 80% of those surveyed who have already chosen their pets’ costumes, 10.6% have decided to turn Kitty and Spot into little round pumpkins. A few pet owners plan to outfit their four-legged friend in “Star Wars” (8th on the list) or Minion costumes (11th).

The NRF estimates that 68 million Americans will dress up this Halloween and another 20 million pet owners will dress up their pets. And total spending for the holiday will hit $6.9 billion, including an average of $27.33 spent on each costume.

“It’s easier than ever for consumers to find creative Halloween costumes given the popularity of Pinterest and Instagram and the immediate access to pop culture trends,” says Pam Goodfellow, an analyst at Prosper Insights & Analytics. “It’s always a nice surprise to see what tops the lists each year and to see just how creative people will get when it comes to their own and even their pets’ costumes.”

Here is a great infographic from the Opensky Merchant Community Blog that shows this data from the National Retail Federation:

halloween infographic The Data Analytics of Halloween–2015 Costume Edition

Infographic sourced from: http://blog.opensky.com/2015/10/05/infographic-boo-halloween-2015-by-the-numbers/
View original post HERE.

Originally Posted at: The Data Analytics of Halloween

Clarifying Employee Engagement: A Review of Four Employee Engagement Measures

The concept of employee engagement is a popular one. I have seen many claims that companies with higher employee engagement have better outcomes (e.g., higher customer loyalty, increased employee performance, business growth) than companies who do not. Consultants even tout their own measure of employee engagement and present research to show its effectiveness. From the stuff I read about the benefits of employee engagement, I figured I should learn more about this area. Turns out, there is a lack of critical thinking when it comes to employee engagement.

I recently stumbled upon an excellent article by Bill Macey and Ben Schneider from Valtera. In their paper, The Meaning of Employee Engagement, the authors reviewed prior research that they felt best represented the conceptual space of employee engagement. They present a conceptual framework by which to understand this loose engagement concept, helping to clarify the different meanings of employee engagement. This useful framework not only helps us speak clearly about this engagement construct, it can help companies understand how this “employee engagement” construct is impacted by the work environment and how it relates to important business outcomes. I will present a brief summary of their work below along with my review of some measures of employee engagement. For those of you who are interested in learning more about the concept of employee engagement, I highly recommend you read the Macey and Schneider article.

The Employee Engagement Construct

Macey and Schneider found a commonality across the various definitions of employee engagement that reflect three things about the concept of engagement:

  1. Employee engagement is a desirable condition
  2. Employee engagement has an organizational purpose
  3. Employee engagement suggests absorption, dedication, passion, enthusiasm, focused effort and energy on the part of the employee.

The authors continue to clarify the notion that employee engagement is different than employee satisfaction. Employee satisfaction is more about satiation; that is, employee satisfaction is about the employees’ evaluation of different parts of their work environment, something external. Either the work environment has certain characteristics, or it does not. On the other hand, engagement connotes activation on the part of the employee, the willingness to expend his or her’s discretionary effort to help the employer. The measurement of employee engagement needs to extend beyond the work environment and focus on something about the employee, something internal.

The Three Faces of Employee Engagement

The authors, to bring employee engagement into the measurable world, conceptualize the area of employee engagement as three distinct things.

  • Disposition or Trait Engagement: This type of engagement reflects peoples’ predisposition  to experience the world from the perspective of enthusiasm and positive affectivity. Some people just have a positive outlook on life.  This type of engagement suggests that certain people will naturally be predisposed to being engaged employees because that is how they approach everything in their lives.
  • State Engagement: This type of engagement is psychological in nature and reflects internal feelings of energy and absorption. State engagement is impacted (directly and indirectly) by trait engagement, and different aspects of the work environment (e.g., job variety, autonomy, senior leadership and other HR practices).
  • Behavioral Engagement: This type of engagement is represented in terms of discretionary effort on behalf of the employee (employees who consistently go above and beyond what is expected of them) to help the employer succeed.
The concept of employee engagement, then, includes three distinct, but related concepts. As you will see below, I will focus on employee engagement measures that assess state employee engagement.

Evaluating Employee Engagement Measures

I was able to find four measures of employee engagement in a short Web search. While these four metrics are not meant to be an exhaustive list of employee engagement measures, understanding the review process can help you evaluate your own employee engagement measures. I will evaluate each employee engagement metric using the four criteria I use when evaluating any metric derived from survey responses (see Four Things You Need To Know About Your Customer Metric): 1) definition of the metric, 2) how metric is calculated (including items and scoring method), 3) measurement properties of the metric (e.g., reliability and validity) and 4) usefulness of the metric (where is the business value?).

PeopleMetrics’ Employee Engagement Index (EEI)

  1. Definition: PeopleMetrics offers no clear definition of this metric.
  2. Calculation: No information is offered on the items or how they are aggregated to calculate the final score.
  3. Measurement Properties: No reliability evidence is provided. To support the validity, they do show the benefits of increased employee engagement; the EEI does predict important business outcomes.
  4. Usefulness: Even though the EEI does predict business outcomes, the use of the term “employee engagement” is confusing. Without knowing the specific questions (or even just a representative sample of them), we do not know what is being measured. While the researchers attribute “employee engagement” as the underlying cause for the differences found using their metric, could those differences be explained through an “employee satisfaction” model. It is difficult to know exactly what is being measured by this index.

Gallup’s Employee Engagement (EE)

  1. Definition: No definition of employee engagement is offered by Gallup.
  2. Calculation: This metric includes 12 questions  (they appear in their brochure and are referred to as 12 Elements of Engagement). They calculate an Engagement Ratio but never specify how this ratio is calculated (e.g., what are the cutoff points on the rating scale that divides respondents to Engaged, Not Engaged and Actively Engaged employees?).
  3. Measurement Properties: There is no evidence of reliability of their metric. They do provide evidence that their EE metric predicts useful business outcomes (e.g., higher profitability, lower turnover); but, upon inspection of the actual survey questions, their employee engagement measure is really a measure of employee satisfaction. The questions focus on the employee’s work environment (e.g., I have the materials I need to do my work right; My supervisor, or someone at work seems to care about me as a person; I have a best friend at work.).
  4. Usefulness: Even though the EE predicts business outcomes, the use of the term engagement to describe what is being measured is not warranted. The EE questions are simply descriptions of the work environment. They can be best described as employee satisfaction measures about different work areas.

Temkin Employee Engagement Index (TEEI)

  1. Definition: The Temkin Group offers no formal definition of this metric.
  2. Calculation: The TEEI is based on three questions: 1) I understand the overall mission of my company; 2) My company asks for my feedback and acts upon my input; 3) My company provides me with the training and the tools that I need to be successful. For each question, employees rate their level of agreement on a 1-7 scale. The overall metric is the sum across all three questions.
  3. Measurement Properties: There is no evidence of the reliability of their metric (does summing these three different questions make statistical sense?). They do offer some evidence of validity in that scores on the TEEI predict some business outcomes (e.g., higher employee loyalty, better customer experience).
  4. Usefulness: Similar to the EE above, the use of the term engagement to describe what this index measures is not warranted.  The TEEI’s three questions do not require the use of a new term, engagement, to describe what it measures. They are simply descriptions of the work environment or HR practices perceived by employees as facilitating their work. These items could be best described as employee satisfaction measures about these three work areas.

Schaufeli, Salanova et al.’s Utrecht Work Engagement Scale (UWES)

First, this scale assesses different components of employee engagement: 1) Vigor, 2) Dedication and 3) Absorption.

  1. Definition: The authors provide a straightforward definition for each of their metrics. The authors state, “Vigor is characterized by high levels of energy and mental resilience while working, the willingness to invest effort in one’s work, and persistence even in the face of difficulties. Dedication refers to being strongly involved in one’s work and experiencing a sense of significance, enthusiasm, inspiration, pride, and challenge. Finally, Absorption is characterized by being fully concentrated and happily engrossed in one’s work, whereby time passes quickly and one has difficulties with detaching oneself from work.”
  2. Calculation: The UWES has 17 questions (9 for the short form – UWES-9). The Vigor Scale has 6 (3) questions; the Dedication Scale has 5 (3) questions; the Absorption Scale has 6 (3) questions. For each questions, the employee is asked to indicate how frequently they felt this way at work on a 0 (Never) to 6 (Always / Every day) scale. A score for each of the three metrics is calculated as the average across their respective questions. An Overall Score for the entire UWES is calculated as the average rating across all 17 (9) questions.
  3. Measurement Properties: There ample evidence provided regarding the reliability and validity of this scale. Each scale has acceptable levels of measurement precision (they can detect small differences). They provide factor analytic results to show that their measure of employee engagement is different than employee burnout. Inspecting the survey questions, we see that the UWES reflects something about the employee’s internal state (state engagement) rather than his or her evaluation about their work (e.g., At work, I feel full of energy; I am enthusiastic about my job; I am immersed in my work).
  4. Usefulness: These author’s show that the UWES does predict service climate which, in turn, predicts employee performance and customer loyalty. Units with higher employee engagement had better outcomes (better service climate, better employee performance and higher customer loyalty) than units with lower employee engagement.

Summary

It appears that the concept of employee engagement suggests an underlying energetic/effort component felt on behalf of the employee that is favorable to the organization. Measures of employee engagement can include such feelings as  absorption, dedication, passion, enthusiasm, focused effort and energy on the part of the employee. Employee engagement can be conceptualized as either a trait, a state and a behavior.

The employee engagement measures reviewed here differ in their quality as true measures of employee engagement. Based on the survey questions of some of these metrics, they are really measures of employee satisfaction with different areas of the organization and not employee engagement. Some measures lack a clear definition of the metric and the authors do not present information needed to critically evaluate their measures (e.g., sample of items, reliability, validity). Of the employee engagement metrics reviewed here, the best measure of state employee engagement is the Ulrecht Work Engagement Scale. This UWES reliably measures three underlying components of employee engagement. Scores on the UWES measure the internal state of the employees, not their satisfaction with the working conditions.

Problems Remain

I have not seen any evidence that the use of employee engagement metrics provides additional value in understanding business growth beyond what we know using employee satisfaction metrics. Even though the UWES has been shown to be predictive of good business outcomes, I know of no evidence to show that it provides additional predictive power beyond what traditional employee surveys measure. To be of value to business, employee engagement measures need to tell us something more about the health of the employee relationship beyond what we already know through our traditional measures of employee satisfaction. Adding employee engagement questions to an already long employee survey could adversely impact response rates while providing little added (no) value.

Does the use of employee engagement metrics help us identify how to better allocate our resources to ensure long term business success? Until somebody shows me some convincing evidence that employee engagement measures provide value beyond what we know using traditional measures (e.g., employee satisfaction and employee loyalty), I will likely not use them in my practice.

Final Thoughts

The term, “employee engagement,” is used loosely and carelessly across the blogosphere. This lazy practice only slows down the progress of our collective knowledge of what is real and what is not. Fortunately, you can challenge what you are told. The next time you read something about employee engagement, insist on a definition of their metric and some sample items. Are these proclaimed employee engagement metrics measuring something entirely different than employee engagement? A cursory examination of the questions would be a good start.

This lack of clarity in thought and writing is not unique to the concept of employee engagement. I see loosey goosey uses of words throughout the field of customer experience management (CEM). Specifically, the term “customer engagement” also suffers from lack of clarity and precision. Some measures of customer engagement include questions that are traditionally labeled as customer loyalty questions. Until there is clarity in our understanding of what we mean when we say “customer engagement,” that term is meaningless to me. If the CEM field  is to advance as a profession, it needs to use more precise terms to describe the variables with which it works.

Take a look at this recent segment on The Colbert Report as he mocks some of the terms we use.

Source: Clarifying Employee Engagement: A Review of Four Employee Engagement Measures