The world where you can be trusted on a global scale

This is the story that any job interview is a black box with secrets and unexpected things for all its participants. You can make the right guess and find a good co-worker or you can open Pandora’s box. And the most painful thing is that the job candidates suffer themselves for this reason alone. They don’t have enough confidence because of that. Those who have the best personal presentation skills win their job interviews more often.

Where it all began

For more than 8 years already I have been into seeking and recruiting job candidates. I have conducted many job interviews for this period. I wish I could say that I have always been selecting the best candidates/. But it was not so. Some employees didn’t survive the trial period, some employees were burnt out and some of them just left the job. It was quite normal. Or more precisely it seemed normal to me. Well, it seems to be a normal thing that people don’t work the way you expected them to work. Still I have always been trying to improve my selection process for hiring. You know, people are the main key success factor of business.

We have tried different types of job interview: case interviews, various tests, deep-dive interview questions, game mechanics, etc. Yes, I started to understand people, but the whole situation did not change radically.

Any job seeker is a clean sheet of paper. Actually you do not know anything about a person in question. A person you don’t know can tell the truth or just lie. And a candidate’s resume can be really genuine or just fake. I have come across many fake resumes.

A reference from a former employer seems to be the only way to prove skills, competencies and qualifications of a job candidate. But not every recommendation can prove to be useful.

There can be 3 problems with recommendations:

1. Recommendations are too general.
2. Recommendations are subjective
3. It takes a lot of time to check recommendations.

Let's discuss all of them separately.

1. Recommendations are too general.
   They are often written just to observe formalities. In most cases, such general phrases as «a good employee» are used. An employee could have performed poorly, but he/she would still have a positive recommendation. This is one of the possible options for recommendations to make an employee quit.
   General speaking, the definition of «a good candidate» is not very clear. How good is he/she? What was the assessment scale? How well did he/she perform to fulfill the plan? What was the overall performance? What is he/she best at? In most cases, recommendations do not provide this information.

2. Recommendations are subjective. The reverse side of «general» recommendations is the subjective character. These are the cases when a candidate is given a very flattering or praiseful feedback. For example, a candidate with a negative recommendation comes to a company. Does this mean that he/she is an unskilled specialist? It obviously, doesn’t. Perhaps, there was a conflict at the previous work place and, in fact, it was all an employer’s fault, not employee’s. Another case is when a candidate just failed to blend into the team in the previous company, but he/she could be a good fit for your company. Practice proves that an employee without a very flattering recommendation performs quite well in a new team anyway.

3. It takes a lot of time to check recommendations. It really takes a long time to check recommendations. You have to ring a candidate up and talk to him or her. A recommendation may not be available. Then you have to make a note that the second call is necessary. All that procedure takes time that could be used to do something more useful.

I have been constantly contemplating the ways to improve the process of hiring people. I have been constantly contemplating the ways to improve the process of hiring people. In any business the people make all the difference. It is a good and strong team that helps a company to grow and succeed in business. People are fundamentally important for developing any business.

Personally, I fell in love with Blockchain technology when I learnt about its possibilities. What makes it possible to create a reliable and transparent reputation building is the decentralized power of Blockchain. This is the way Aworker project came into existence.

It can be a win-win for everyone because Blockchain is the unique way to create a new world where:

• Businesses can have a reliable reputation system to select the best job seekers
• People can use this system to prove skills, competencies and qualifications

What is Aworker?

Aworker is a platform for professionals to build a better, decentralized world. Employees will be able to obtain a reward for their proper performance, professional and communication skills. With the help of AWE Tokens and Ethereum blockchain technology, we are creating an independent rating of professionals all over the world.

As of today, there exist 2 main difficulties in recruitment and job hunting processes:

1. There is no confidence in candidates who come to a company for a job interview. Their level of professionalism can be confirmed only by the recommendations from previous employers. And they may not be considered as reliable as they should be.
2. Recommendations are too general or very subjective. They do not convey the real professional competencies of a candidate. They are often written just to observe formalities and do not provide with objective information.

Aworker is created to help people boost their careers, show their experience and obtain additional rewards. Blockchain technology is used to secure the reliability of this process. Each person will be able to demonstrate the expertise and create a reputation of a reliable employee.

Aworker has 3 modules: Rating, Skills and Connections.

In order to get into Aworker independent rating, you need to enter data on plan fulfillment for the previous month. Aworker rating is updated every month after manager’s verification and taking into account this information. The best employees get a reward. With the help of the blockchain technology we create an independent and decentralized rating that shows the effectiveness of employees and creates a professional reputation for everyone. Any employee can become the best in the field in a city or country.

An employee specifies skills he/she possesses. If people give decentralized verification of these skills via a smart contract in the blockchain, then this employee gets a reward. Aworker shares the advertising budget of companies interested in these skills.

An employee can also be rewarded for connections. A company will pay a reward for the recommendation if the employee’s friends join this company.

AWE token and ecosystem creation.

Our goal is to create a new ecosystem for Work 2.0 to help specialists boost their professional skills and make names for themselves in the field. The next step is to develop the ecosystem providing employees with special offers on partners’ products. All calculations will be done in AWE Tokens. The higher the Ranking and Reputation of an employee, the more favorable conditions will be granted to him/her. The third step will consist of the customized recommendations and personal assistant development based on the artificial intelligence for each Aworker user.

We are offering the companies an application for recruitment process automation with access to a database, which is without peer. HR managers will be able to see a candidate rating, performance at the last job and confirmed skills. The blockchain will provide the most unbiased and reliable information to fill in this gap on the recruitment market to meet companies needs today.

We want to create the world where people are rewarded for their skills and achievements at work. And companies receive a reliable rating of specialists to facilitate the candidates selection process.

Decentralization provides the best opportunities for creating the brand new ecosystem. Each person will be able to show professionalism and develop a reputation of a reliable employee with just a couple of mouse clicks.