Thứ Tư, 21 tháng 9, 2016

When your co-worker earns more than you

When your co-worker earns more than you

It can come as quite a surprise if you happen to learn that a co-worker whom you thought you held the same rank as is actually earning more than you.

Though a debate is growing around whether companies should make pay information transparent, the status quo is currently to keep individual pay a private matter between the employee and HR. This is why it can come as quite a surprise if you happen to learn that a co-worker whom you thought you held the same rank as is actually earning more than you.

So what are your options besides feeling inadequately compensated? Several HR and pay experts weigh in on how to change your compensation, improve your career path and the steps you should avoid taking.



Don’t turn to your co-workers for information

If your first instinct is to ask your co-worker what qualifies him to earn more, or to ask other co-workers how your pay is determined, stop right there. Deb LaMere, vice president of HR strategy and employee engagement at human capital management services and technology firm Ceridian, says, “Speaking with co-workers about their pay level in relation to your own often results in negative consequences. This type of conversation can lead to resentment and anger, effectively changing relationships for [the] worse between co-workers, project teams and possibly with direct management.”

While transparent pay information would resolve the secrecy issue that can trigger problems at work, it holds true that compensation levels can vary widely for valid reasons. “There are many factors to consider when it comes to evaluating individual pay, especially length and type of experience,” LaMere adds. “Having a salary comparison conversation with a co-worker is not constructive to understanding ones' own pay rate and possibly influencing changes to individual pay and compensation levels.”

Research compensation trends and standards

Instead of turning to your co-workers for information, rely on outside sources and garner as many points of data as possible. “Lots of information is readily available through salary surveys and websites, industry associations, recruiters/headhunters who place candidates in your industry and space and through actively networking with colleagues and developing real meaningful professional relationships… so that delicate topics like salary, bonus and benefits will be discussed openly and shared comfortably,” says Roy Cohen, career coach and author of “The Wall Street Professional's Survival's Guide.” “You also need to be absolutely clear on what the numbers represent. Are they for equivalent positions and for equivalent performance?”

Prove your worth

Once you have a well-researched idea of the pay level you could and should be on, gather evidence for your boss that echoes those numbers. “One option is to volunteer for and take on visible, challenging initiatives and then manage them successfully,” Cohen says. “That is just half the battle and it is often where the process breaks down. While a project is underway and once it is completed, key stakeholders must be made aware of your significant contributions both during and after...The gift that keeps on giving. It is helpful to have a mentor within the company who can advocate for you and enhance your visibility as well as serve as a sounding board for advice on how to approach your boss.”

Whether you have office backup or you’re presenting on behalf of yourself, it’s important to prove to your boss that a pay raise is deserved because of your merits, not that you’ve simply learned of the pay discrepancy.

Take it to your boss

You’ve done the research and ensured that your request will be backed up by proof of your hard work. So how do you begin this conversation with your boss? Katie Donovan, a salary and career negotiation consultant, equal pay advocate and founder of Equal Pay Negotiations LLC, says, “Start the process of discussing a raise or salary adjustment with your direct manager. I recommend asking for help, not demanding a raise. Say something like, ‘I recently discovered that I am paid below the market value for this job. What can we do to rectify it?’ This makes it a collaborate discussion and gives management the opportunity to come up with a solution, which might be better than you anticipated.”

Heading into the meeting, “bring with you the research you did on pay for the job so you can discuss your research,” Donovan says. “Also, be prepared to highlight your contributions to the company as reasons you deserve to be paid on the high end of the pay range for the job. If you can, compare it to the lesser results of co-workers. Very effective reasons are contributions that saved the company money or generated revenue for the company. Do not expect a solution in this first meeting but do ask for a response in a certain time so this does not drag on forever. Something like ‘Can you get back to me by Friday on this?’”

Negotiating pay is a tough part of advancing in your career, but receiving the compensation that you deserve is well worth the time.

(Picture Source: Internet)
HRVietnam - Collected
Rethinking HR in banking

The book 'The Future of human resource Management' edited by three vanguard HR professionals: Mike Losey, Dave Ulrich, and Sue Meisinger sparked me. The book is an eclectic mix of articles written by 64 HR thought leaders. These stellar academics, consultants, and practitioners look at the future of human resources and explore the critical HR issues of today and tomorrow. The book reveals how leading companies hire and retain their talents, explore HR's role in brand development, highlights HR's contribution to business strategy and many more. While reading the book, I was pondering about the current and future HR issues in banking. I would like to share some glimpses of my musings in this article.

In any organisations, HR professionals turn organisational aspirations (mission) into actions. To carry out this, they generally focus organisation's three attributes: talent, cultures and leadership. Talent encompasses competence (skills and abilities), commitment (willingness to engage and work hard), and contribution (ability to find meaning from the work) of the entire human resources of the organisation. Culture entails the right organisational capabilities that enable to shape an identity outside and pattern of behaviour inside the organisation. And, leadership includes the succession planning in such a way that the leaders throughout the organisation are focused on the right things and execute strategy in right ways.

As banking is a service industry, Human Resource Management (HRM) plays an instrumental role for banks. Management of people and management of risk are generally two factors that determine the success in the banking business. But efficient risk management may not be possible without efficient and skilled manpower. So banking has been and will always be a 'People Business'. As skilled manpower is becoming scarce both in quality and quantity, it is quite straightforward that they need to be properly managed for the benefit of society, in general, and of the bank in particular.

Apt manpower planning integrated with the business plan and strategy of the bank is a pivotal part in HRM. It captures the type of people a bank requires, the level at which they are required and clearly defined roles for everyone. This plan may also entail lifecycle approach of an employee from his/her joining to retirement, steady, carefully calibrated recruitment programme, and cultural adjustments and change management among various generations of employees.

In recruitment stage, banks need to revisit their existing recruitment strategy to review whether they target right kind of people. For example, if the competitive advantage of a bank is mass banking with a lot of rural branches, then the bank naturally structures its recruitment strategy to attract the talents who have the right attitude to work in rural areas to serve the mass people. As a result, in addition to problem solving skills, various psychological skills may be incorporated in the recruitment test. In lateral recruitment, banks may think to induct professionals outside the banking with specific skill sets and experience pool.

Though competitive remuneration is a vital reason why people select and stay with a particular bank, other factors such as images, especially in transparent situations with a high level of competitiveness of the bank, training and re-skilling of employees, performance measurement, promotion policy, transfer policy, talent management, communication, etc are also crucial for employee retention. The changing nature of banking business requires massive re-skilling of the existing workforce and continuous skill up-gradation. Online platforms may be used for in-house training facilities. Banks may cut layers of bureaucracy that have created over the years and adopt an effective way of delegation to empower their people.

Exit interviews of the employees may be an effective way to determine why people are leaving the banks. It will not only help to find out intrinsic system failures in the banks, but also gain some effective recommendation to develop them. Banks may leverage the inherent loyalty of their retired people in brand building efforts, financial inclusion initiatives and other non-financial activities.

Mohammad Arifur Rahman | thefinancialexpress-bd.Com

Thứ Sáu, 29 tháng 7, 2016

Lying in the hiring process: What human resources needs to know

Lying in the hiring process: What human resources needs to know


 People lie all the time during the hiring process. It’s up to Human Resources and hiring managers to catch those liars. Where are those fibs being told — and how can you prevent them?
human resoureces learn to catch those liars

 

Resume lies


In this intense job market, it’s no surprise that many applicants exaggerate parts of their resumes to look more enticing to potential employers.
The concept is so widespread, however, that nearly half of all applicants admit to lying on their resumes.
That’s according to a 2009 study from ADP, which found that 46% of all applicants commit some form of resume fraud.
Where are those lies being concentrated? Here are the 10 most common lies on resumes, courtesy of Marquet International:
  1. Stretching work dates
  2. Inflating past accomplishments and skills
  3. Enhancing job titles and responsibilities
  4. Exaggerating educational background
  5. Inventing periods of “self-employment” to cover up unemployment
  6. Omitting past employment
  7. Faking credentials
  8. Falsifying reasons for leaving prior employment
  9. Providing false references, and
  10. Misrepresenting a military record.

Interviewing lies


Your job would be a lot easier if you could easily spot those resume lies and nix those candidates from consideration.

But no matter how clued in you are to what applicants fib about, you’ll still inadvertently bring many of them in for interviews.

That’s when your skills at judging character come in. So who’s the best at screening potential talent? Is it someone who’s skeptical and suspicious about most applicants, or a person who’s trusting?

If you guessed that skeptical managers would do a better job, you’re not alone. You’re also wrong.

That’s according to a recent study from psychologists Nancy Carter and Mark Weber, which was recently highlighted in The Washington Post.

A large majority (85%) of participants said a skeptical interviewer would do a better job spotting dishonesty in job interviews.

But a subsequent study found that people who trust others — or who assume the best in other people — are the best at identifying liars.

How’s this so? On human resources expert explains:

… Lie-detection skills cause people to become more trusting. If you’re good at spotting lies, you need to worry less about being deceived by others, because you can often catch them in the act.

Another possibility: People who trust others become better at reading other people because they get to see a range of emotions during their interactions. That gives them more experiences to draw from to tell when someone is lying and when someone is telling the truth.

Human resources leaves employers with some advice on who they should have in the interviewer role to prevent applicants from duping you into hiring them:

Human resources expert - we need leaders who demonstrate skill in recognizing dishonesty. Instead of delegating these judgments to skeptics, it could be wiser to hand over the hiring interviews to those in your organization who tend to see the best in others. It’s the Samaritans who can smoke out the charlatans.
Of course, faith in others can go too far. It’s important to sprinkle a few ounces of skepticism into each pound of trust. Ultimately, while the best leaders don’t trust all of the people all of the time, the keenest judges of character may be the leaders who trust most of the people most of the time.
Source:http://www.Hrmorning.Com/

Teaching Machines to Think About HR

For all its promise for HR, big data and its “machine-learning” component still only give us facts about, and factual relationships within, our workforces; not conclusions based on the statistical analyses HR has always needed—and always will—to make meaningful predictions.

By Peter Cappelli

Workforce analytics. Big data. Machine learning.

The above terms—or “buzzwords” if you don’t like them—are popping up in many discussions of human resources, mainly involving vendors with solutions that make use of new data aimed at answering traditional workforce questions. Is there anything really new in these approaches, and, if there is, should we be paying attention to it?

The answers are yes and yes.

Let’s start with the term workforce analytics. In some ways, this term is to traditional measures of outcomes as human resources was to personnel. It is about addressing traditional, evergreen questions in different, more sophisticated ways.

Workforce analytics describes an effort to use data and sophisticated analyses to address HR problems. The most topical ones at the moment are: “Which candidates will make the best hires?” and “Which employees are most likely to leave?”

There is nothing new about those questions, and there isn’t much new about how they are being approached. The novelty comes, in part, from the fact that, after the early 1980s, big corporations gave up trying to address these questions in a sophisticated manner, so most people in business aren’t aware that similar approaches were tried a generation or so ago.

But there are some differences. One is a greater interest in analyses pertaining to financial outcomes: e.G., It will save us $5,000 per employee by reducing turnover.

The second difference involves the type of data available. The “Manplan” program in the 1960s required HR staff to read information about an employee from one file, mechanically punch it onto a card, then get different information for that same employee from another file, punch it onto a different card, then do that for every employee they wanted to study. Only when those steps were completed could they start looking to see what factors predicted turnover. It cost a fortune to look at even a small set of employees.

Virtually all HR data now is kept electronically, and, in most companies, the information on every applicant who tried to get a job with them is sitting somewhere in a dataset. It’s much easier and cheaper to look at huge numbers of observations, which makes it much easier to find potentially useful results. Being able to capture every “hit” on your employee-benefits website, for example, can tell us almost instantly what kinds of employees are worried about what types of issues.

But here’s the brake to the Big Data bandwagon: Not all HR data is big. The key piece of information needed to make workforce analytics valuable is a measure of job performance. We can’t say anything about which of our 1 million applicants will do well without being able to identify who among our employees is a good performer. In most companies, that information is no better than it was in the 1950s, and, in many companies, it is actually worse, as we’ve gone from assessment-center scores to a supervisor’s guess about potential. The phrase “garbage in/garbage out” is highly relevant here.

That takes us to the last and most obtuse of the buzzwords: machine learning.

It is a different way to think about data than most of us have previously seen, one that came from people whose expertise was rooted in computers rather than statistics per se. The “learning” idea comes from the fact that computer programs (i.E., The “machine”) can be designed to look at data and find patterns that allow them to make predictions.

How exactly machine learning differs from statistics is a topic of endless fascination to people in those two fields, but for the rest of us, here’s what matters: The traditional, statistical approach to analyzing HR data begins with hypotheses that come from prior research. It includes careful statements about assumptions in the study, or studies, and whether those assumptions are true. Traditional machine learning, in contrast, is theory-free and assumption-free. It just looks for patterns in the data, and it uses different techniques from what had commonly been used in statistics to find the clearest patterns.

A statistical examination of whether a given employment test predicts good hires concludes with either “yes” or “no,” where "no" means we can’t rule out that the relationships were due to chance.

In the case of a machine-learning examination, an employee might instead conclude that, while there is no overall relationship, there is a very powerful relationship for this subset of employees, nothing much for that subset, and, for a third subset, a strong relationship that was different than that of the first group.

The power of machine learning comes from the fact that it might well find important predictors that we never thought of before because prior theory didn’t include them—e.G., The distance an applicant’s home is from the work location predicts turnover—and wasn’t particularly adept at “mining” through lots of seemingly unrelated data to find predictors.

All that sounds really promising for machine learning, but there are a bunch of things about this approach that we’d better consider pretty carefully before diving in.

The first of these is a reminder that machine learning produces facts, rather than conclusions. It tells us “X is related to Y,” but not why they are related. Without hypothesis testing and clear statements of assumptions, we don’t learn much about what a given relationship means or why it exists. Perhaps most important, machine learning can’t tell us much about the likelihood that a relationship observed in this dataset will be useful in another context.

This matters in HR because most of the frameworks that support modern employment—especially legal frameworks—relied on the scientific method and traditional statistical tests for their foundation.

Consider, for example, the legal norm that selection tests should not discriminate and the “Uniform Guidelines of Employee Selection Procedures” put together by psychologists over the past generation to ensure that hiring practices are both valid and don’t discriminate against protected groups. Machine learning, as traditionally practiced, would surely uncover relationships that, if applied to hiring, would violate the law.

Machine learning applied to big data will certainly turn up a lot of interesting facts for workforce analytics to ponder. Transferring those facts to practice, however, is still a big leap. On their own, these “buzzword” approaches won’t get us there.

Peter Cappelli is the George W. Taylor Professor of Management and director of the Center for Human Resources at The Wharton School of the University of Pennsylvania in Philadelphia. His latest book is Why Good People Can't Get Jobs: The Skills Gap and What Companies Can Do About It.

Hreonline.Com/HRE/view/story.Jhtml?id=534358638

Thứ Tư, 27 tháng 7, 2016

Top 10 Ways to Find Your Career Path



If you're not sure which direction your career should go in, you're thinking about making a career change, or you just want to feel more fulfilled in your career, these ten tips might help.

Ideally, everyone would know their true calling early in life and find happiness in their work, but it often doesn't work that way. One survey (of New York professionals) found that they expected to change careers three times in their lifetimes; lifelong careers may not be the norm any more.

That said, we know there are better ways to choose a career than just following your parents' footsteps or choosing randomly. Here are some ideas.

10. Think About What Excites and Energizes You



This one's the first obvious step—we all want to enjoy and actually like our careers. (Perhaps the biggest sign you're on the wrong path is if you dread talking about your job.) While passion isn't the only requirement for being content in your career, many would say it's still essential, if only because passion is what keeps you going even through the tough times. Is there a job you would do job for free?

9. But Also Keep in Mind What You're Good At



Maybe you don't feel that passionate about any specific career—or you love multiple areas and can't decide on just one. Then it's time to think about your personality and focus on the skills you have. "Don't do what you love. Do what you are."

8. Take a Test



Well, you say, what if you don't know what you're good at or even what you're interested in? Career assessment tests in college or even high school help narrow down a field (perhaps with the Myers-Briggs personality index), but if it's been a while since you took those tests, there are other kinds of assessment tests you can take. This one from Rasmussen College matches your self-reported skills and interests with potential jobs. (And they also have a salary and job growth interactive chart.) For potential programmers, Switch recommends a coding career based on your preferences. About.Com's Job Search site has a collection of other career tests.

You can also find a career that fits your motivational focus with this assessment test.

7. Try an Internship



If you have flexibility when it comes to salary, an internship could be a great way to test out an industry or type of career—and eventually get a full-time job (especially if you have no prior experience). Even if it doesn't turn into a job or you find out it's the wrong career for you, an internship can help build your network—from which you can get career and job advice. (Not all internships are just about picking up coffee. For example, Google internships, while hard to come by, put you to real work.)

6. Find a Mentor



A mentor could help you take your career to the next level and give you the insider insight to help you make sure you're on the right path. Here's how to ask someone to be your mentor.

If there's a career you're interested in, you might also check to see if any companies or people in that line of work would let you shadow them for a few days to see what it's really like.

5. Explore Unconventional Careers



We all know the popular careers available to us—doctor, lawyer, teacher, computer engineer, police officer, store owner, etc. If you feel uninspired by the typical choices, know that there are thousands of unusual jobs you might not have heard about, hidden, perhaps, in the Bureau of Labor Statistics' Occupational Handbook. Mashable has a list of six dream jobs that pay well (panda caretaker! Chocolate inspector!), Thought Catalog highlights 10 more (sex toy testers?!), and Chron lists a couple of others (along with related articles like "Unusual careers with animals" and "unusual accounting careers").

4. Ask Other People



Perhaps the best way to discover a new career is to ask other people about theirs—assuming you come into contact with people who don't all work in the same field. Your LinkedIn network (or other social media sites, but especially LinkedIn) might be a good place to start mining for information. Also, don't forget your local library's reference librarian can point you to career resources.

3. Use the G+P+V Formula



The perfect career for you would most likely fit the G+P+V formula, which stands for Gifts + Passions + Values. Consider your strengths and passions, as we've noted above, and your values—what's nonnegotiable about the way you work?

2. Make a Career Plan



As with most things, your career will benefit if you have goals and a plan for it. Maybe you think you want to be a writer, but the next step after that, is editing. (Do you really want to do that?) Or maybe you want to transition from being an editor to a restaurant owner. (How are you going to get there?) Map out where you want to go, with concrete milestones, as if it were a four-phase project.

1. See Your Career as a Set of Stepping Stones, Not a Linear Path



Of course, all these plans and ideas are never set in stone. Your career is a marathon, not a sprint and it can turn out to be a very winding road indeed, knitted together from all of your experiences into, hopefully, a career worth having.

Photos from VoodooDot (Shutterstock), OpenClips (Pixabay), Mopic (Shutterstock), sacks08, auremar (Shutterstock), Little Birth, bobsfever.

Lifehacker.Com
Teaching Machines to Think About HR

For all its promise for HR, big data and its “machine-learning” component still only give us facts about, and factual relationships within, our workforces; not conclusions based on the statistical analyses HR has always needed—and always will—to make meaningful predictions.

By Peter Cappelli

Workforce analytics. Big data. Machine learning.

The above terms—or “buzzwords” if you don’t like them—are popping up in many discussions of human resources, mainly involving vendors with solutions that make use of new data aimed at answering traditional workforce questions. Is there anything really new in these approaches, and, if there is, should we be paying attention to it?

The answers are yes and yes.

Let’s start with the term workforce analytics. In some ways, this term is to traditional measures of outcomes as human resources was to personnel. It is about addressing traditional, evergreen questions in different, more sophisticated ways.

Workforce analytics describes an effort to use data and sophisticated analyses to address HR problems. The most topical ones at the moment are: “Which candidates will make the best hires?” and “Which employees are most likely to leave?”

There is nothing new about those questions, and there isn’t much new about how they are being approached. The novelty comes, in part, from the fact that, after the early 1980s, big corporations gave up trying to address these questions in a sophisticated manner, so most people in business aren’t aware that similar approaches were tried a generation or so ago.

But there are some differences. One is a greater interest in analyses pertaining to financial outcomes: e.G., It will save us $5,000 per employee by reducing turnover.

The second difference involves the type of data available. The “Manplan” program in the 1960s required HR staff to read information about an employee from one file, mechanically punch it onto a card, then get different information for that same employee from another file, punch it onto a different card, then do that for every employee they wanted to study. Only when those steps were completed could they start looking to see what factors predicted turnover. It cost a fortune to look at even a small set of employees.

Virtually all HR data now is kept electronically, and, in most companies, the information on every applicant who tried to get a job with them is sitting somewhere in a dataset. It’s much easier and cheaper to look at huge numbers of observations, which makes it much easier to find potentially useful results. Being able to capture every “hit” on your employee-benefits website, for example, can tell us almost instantly what kinds of employees are worried about what types of issues.

But here’s the brake to the Big Data bandwagon: Not all HR data is big. The key piece of information needed to make workforce analytics valuable is a measure of job performance. We can’t say anything about which of our 1 million applicants will do well without being able to identify who among our employees is a good performer. In most companies, that information is no better than it was in the 1950s, and, in many companies, it is actually worse, as we’ve gone from assessment-center scores to a supervisor’s guess about potential. The phrase “garbage in/garbage out” is highly relevant here.

That takes us to the last and most obtuse of the buzzwords: machine learning.

It is a different way to think about data than most of us have previously seen, one that came from people whose expertise was rooted in computers rather than statistics per se. The “learning” idea comes from the fact that computer programs (i.E., The “machine”) can be designed to look at data and find patterns that allow them to make predictions.

How exactly machine learning differs from statistics is a topic of endless fascination to people in those two fields, but for the rest of us, here’s what matters: The traditional, statistical approach to analyzing HR data begins with hypotheses that come from prior research. It includes careful statements about assumptions in the study, or studies, and whether those assumptions are true. Traditional machine learning, in contrast, is theory-free and assumption-free. It just looks for patterns in the data, and it uses different techniques from what had commonly been used in statistics to find the clearest patterns.

A statistical examination of whether a given employment test predicts good hires concludes with either “yes” or “no,” where "no" means we can’t rule out that the relationships were due to chance.

In the case of a machine-learning examination, an employee might instead conclude that, while there is no overall relationship, there is a very powerful relationship for this subset of employees, nothing much for that subset, and, for a third subset, a strong relationship that was different than that of the first group.

The power of machine learning comes from the fact that it might well find important predictors that we never thought of before because prior theory didn’t include them—e.G., The distance an applicant’s home is from the work location predicts turnover—and wasn’t particularly adept at “mining” through lots of seemingly unrelated data to find predictors.

All that sounds really promising for machine learning, but there are a bunch of things about this approach that we’d better consider pretty carefully before diving in.

The first of these is a reminder that machine learning produces facts, rather than conclusions. It tells us “X is related to Y,” but not why they are related. Without hypothesis testing and clear statements of assumptions, we don’t learn much about what a given relationship means or why it exists. Perhaps most important, machine learning can’t tell us much about the likelihood that a relationship observed in this dataset will be useful in another context.

This matters in HR because most of the frameworks that support modern employment—especially legal frameworks—relied on the scientific method and traditional statistical tests for their foundation.

Consider, for example, the legal norm that selection tests should not discriminate and the “Uniform Guidelines of Employee Selection Procedures” put together by psychologists over the past generation to ensure that hiring practices are both valid and don’t discriminate against protected groups. Machine learning, as traditionally practiced, would surely uncover relationships that, if applied to hiring, would violate the law.

Machine learning applied to big data will certainly turn up a lot of interesting facts for workforce analytics to ponder. Transferring those facts to practice, however, is still a big leap. On their own, these “buzzword” approaches won’t get us there.

Peter Cappelli is the George W. Taylor Professor of Management and director of the Center for Human Resources at The Wharton School of the University of Pennsylvania in Philadelphia. His latest book is Why Good People Can't Get Jobs: The Skills Gap and What Companies Can Do About It.

Hreonline.Com/HRE/view/story.Jhtml?id=534358638