A Brief History of Assessments and Interviews

Assessments in China have a long past. The use of interviews for recruitment purposes stretches back millennia. The civil service examination system (科举, kējǔ) in Imperial China can be taken as a form of interviewing and may be the first documented record of using selection tests. Imperial exams had been around as early as the Han dynasty (206 BC–220 AD), though it wasn’t until the Song dynasty (960–1279) that the exams were institutionalized as a means of recruitment for government office. (1)

 

 

During the Song dynasty the widespread use of one technological advancement was fundamental to the expansion of education and, by extension, the imperial examination system: the printed book. Although its development dates to the first century BC, it was China’s Song society the first to use printed books. Despite efforts to control all printing at first, by the 1020s the opening of schools was encouraged by the government, awarding land endowments to schools enrolling students.(2) The civil service examination system prevailed until 1905 when it was discontinued as a result of pressures from reformers looking to develop a national school system and other modernization measures. In 1915 western psychological testing was introduced in China, but it was not until 1980 when they became popular following on 1978 China’s open policies, favoring the country’s active participation in the world economy rather than a development model based on self-sufficiency.

 

 

The “Modern” Job Interview

 

The invention of the first productive steam engine (1712) was the harbinger that brought the Industrial Revolution to Europe and the US. The steam engine brought the factories and the railway. And with those came a radical change in the structuring of labor. A new labor market developed, away from the traditional (often hereditary) master-apprentice structure that had been at the core of production systems up until the 18th century. Initially, a job “interview” was as simple as showing up at the gate of a factory and hoping to get picked up for a job.

 

A brief history of assessments. Edison Questions Stir Up Storm | ChinaHRnews.com

 

Interviews got more demanding as the pace of technological development increased, requiring more capable, better-educated workers. “Edison Questions Stir Up a Storm”: That went a headline from a The New York Times piece on May 11, 1921. It referred to a questionnaire designed by Thomas A. Edison to select college graduates applying for an executive position at the first commercial central power plant ever built: the Pearl Street Station (named after, you guessed right, the street the plant was sitting at 255-257 Pearl Street in Manhattan).

 

The interview consisted of a list of 141 questions that, according to opinions from interviewees – featured as “victims” by the newspaper – “Only “a Walking Encyclopedia” Could Answer Questionnaire.” Some of the questions were, based on the recollections of the interviewees “What country makes the best optical lenses and what city?”, “Who invented the cotton gin?”, “What is the weight of air in a room 20 x 30 x10?”. The article’s main criticism was that the questions were only a test of someone’s memory, and not a measure of someone’s knowledge, intelligence and reasoning abilities. 

 

Rather than an interview, those 141 questions were more like a personality test, with Mr. Edison present in the room going about his business and waiting for candidates to finish the test. Those succeeding the test would then sit down for an interview with Mr. Edison. Today’s equivalent would be nothing short of having Jack Ma or Elon Musk in the room with you, waiting for you to finish the test. Nothing short of stressful for novice candidates, we would assume.

 

Many scholars take the Edison interview as the origin of the modern employment interview. After Edison’s, other industrial leaders followed suit and began developing their interview processes. This is how it started what would eventually become the employment interview methods that we use today. They evolved into a whole industry. According to the most recent data, the 2018 World Employment Federation Economic Report, the employment industry generated globally (in sales revenue) USD 471 billion in 2016, with five countries making up for the majority of the revenue (US, Japan, UK, Germany, and China).

 

Although almost 100 years have passed by since Edison’s employment interview, and despite the further advancement and sophistication of job application processes and techniques available, the goal in recruitment remains the same: reduce the uncertainty around the future performance of a pool of prospective hires and chose the best ones. With best we mean those who will achieve results consistently, regardless of the environment or circumstances, in a way that is sustainable for the organization.

 

As data scientist Cathy O’Neil writes “How a candidate would actually perform at the company (…) is in the future, and therefore unknown”. (3) So the recruitment process needs to settle for proxies when trying to predict the future. From 1921 until today a whole body of research has emerged to develop and measure the effectiveness of those proxies (See Figure 1).

 

The Role of Interviews in the Selection Process

 

Interviews are the most often used tool for selection in organizations. They play a very prominent role in the overall selection process: final hiring decisions are often based entirely only on the interviews.

 

 

A brief history of assessments. Figure 1 | ChinaHRnews.com

Most of the times the interviews are unstructured. They resemble more a conversation; their content is discretionary and have a loose framework. There are no predefined standards to evaluate candidates’ performance, and it is pretty much up to the recruiter which questions to pose.

 

As research has shown (Figure 1), unstructured interviews are not particularly effective predictors of job performance and, due to its non-standard nature, they leave more room for biases on the recruiter’s side and do not allow for equitable assessments on the performance of several candidates (there is no consistency in the ratings across interviewers).

 

In contrast to unstructured interviews, a more reliable predictor of job performance is structured interviews. These can take the form of:

 

Situational interviews: based on questions presenting the interviewees with hypothetical situations, similar to those they might encounter on the job, and aim to observe hypothetical behaviors. These interviews are designed to measure analytical and problem-solving skills on the spot.

Competency-based interviews (CBI): sometimes referred to as performance-based interviewing. Questions here are designed to assess whether the candidate possesses the required competencies to perform on the job. The interviews are designed to gather evidence that the interviewees had used those competencies in the past.

 

There are other assessment methods, besides interviews: GMA tests, assessment centers, or job knowledge tests to name only a few. They act more as gate-keepers: their role is not as much to find the perfect candidate as to weed out candidates that are not the right fit for one or the other reason (i.e., not the right organizational fit, not motivated enough, or lack of the required body of knowledge).

 

“Looking to target as many candidates as possible to increase the likelihood of finding that needle in a haystack is no longer a sustainable strategy. Quality of hire is now, more than ever, the name of the game.”

 

Most of these assessment methods are the legacy of the Industrial Society, where the supply of talent was higher than the demand, an era of talent surpluses. In our present time that is no longer the case. In the Information and Knowledge Society talent is a scarce resource, and recruiting is no longer a game of large numbers. Looking to target as many candidates as possible to increase the likelihood of finding that needle in a haystack is no longer a sustainable strategy. Quality of hire is now, more than ever, the name of the game.

 

These remarks are not to diminish the value of these tools. After all, interviews are a legacy of the Industrial Society as well. No, the point here is to emphasize the need for these tools to adjust regularly to the circumstances and times they are deployed. If we assume Asimov’s simple paradox that change is the only constant and that critical thinking, communication, collaboration, and creativity (the four C’s) trump technical skills then this provides us a compass on how we should be applying these tools.

 

Just Like That? No Background Check?

James McGill, a disbarred lawyer, is looking for a new job. He is interviewing for a sales manager role at NEFF copiers – a family-owned business distributor of office photocopiers. James has no experience, but he knows his way around copiers from the time he spent in the mail room at the law firm where his brother was a partner. With his usual charms, James (Jimmy) delivers his sales pitch: “The copier is the beating heart of any business (…) you plug one of your new machines into the system (…) that is a healthy business. That is a successful business”. The owner, Mr. Neff, and his assistant are so impressed with his performance that they hire him on the spot. Then Jimmy goes “Just like that? I just came in off the street. No due diligence? No background check?” And he walks away.

The story is a scene from the fourth season of “Better Call Saul“, the popular “Breaking Bad” spin-off, set around the life of the picturesque lawyer James McGill/Saul Goodman. Although a fiction, some characteristics of the scene should string a real cord to any recruiter and HR professional: 1) the halo effect, where hiring managers let themselves be charmed by the candidate; 2) making a hiring decision immediately, without allowing room for further thought or compare with other candidates to make a better assessment; 3) the lack of a background check. All these are real shortcomings that take place more often than they should when recruiting.

In this article, we want to focus on the third one: background checks. We are not interested though in the ins and outs of background checks in Albuquerque, but on those of China.

As competition for jobs in China gets tougher along with slower growth in the economy, there is an incentive for job applicants to beautify or magnify some of their past credentials and professional achievements in their resumes. It is necessary then to confirm a candidate’s credentials and other relevant information. You can do that using background checks. Let’s review the elements you should be checking for while remaining compliant with China’s regulations on personal data.

 

In the Light of What We Know. What to Check?

It is advisable to perform background screening before making an actual employment offer. You should put every effort to verify the candidates’ basic information: level of education, past work experiences, and for certain type of roles, criminal record.

Let’s start with the education credentials. You should request the certificates and diplomas to verify the qualifications stated by the candidate in his/her resume. Since there is no central public database system in China to check for the authenticity of those documents you or a third party acting on your behalf will have to connect with the specific academic institutions. Andrew Houlbrook, a former intelligence analyst at London Metropolitan Police and now business intelligence and investigations at PSU China Consulting based in Beijing, tells chinahrnews: “A common issue is the manufacture of fake degree certificates. Vendors providing these services in Taobao or dedicated WeChat groups are just one-click away. It is all a game of confidence trickery, hoping the HR professionals won’t check”. And even when checking this might not be enough. Mr. Houlbrook also warns against diploma mills: “You may find some Chinese candidates who use diploma mills, especially overseas institutions, to look more impressive on paper, usually at MBA or master level. When you look closer, those allegedly educational institutions do not provide any education whatsoever, but issue diplomas and certificates in exchange for a fee.”

Checks on previous professional endeavors are relatively straightforward. With the candidate’s provided references and his/her consent, you can contact former employers. In addition to that, it is useful to look a little further: “Past terms of employment and employers are fine but also look at what is not in the CV, like undeclared company involvements”, says Mr. Houlbrook. “Candidates may have business ties with companies that weren’t specified in their CV. Sometimes these other business involvements can be easily explained since there is no conflict of interest with the employer’s business operations. Other times, clients are interested in knowing about why the candidate left a previous role: they see an early exit from a past employer and want to know what happened”.

 

“When outsourcing background checks in any capacity you must really understand your third parties and who you are working with. Understand their business model. Criminal law will come into effect if you are sourcing information using incorrect channels”

 

The most complex screening involves checking for criminal records. Certain jobs in China require to have a clean criminal record. These jobs are referred to as “CNNC jobs”, with the acronym standing for certificate of non-criminal conviction. To check for a Chinese national criminal record, an employer can ask the candidate to present it. The law is not clear on whether a CNNC can be provided if the candidate is not applying for a CNNC job (the applicants must submit, among other application documents, an introduction letter from the employer). The CNNC is issued by local security bureaus, at the request of the candidate. An employer cannot request the CNNC directly by itself.

In theory, it is possible for the employer to walk in together with the candidate into a local security bureau or police station to request the CNNC. However, there are many instances in which this might not work: the staff is not familiar with the certificate, or it is but refuses to release it, the file is not stored properly and cannot be retrieved, etc. As Mr. Houlbrook points out: “There is not enough consistency or clarity around these procedures”.

Some agencies provide CNNC’s on request. They claim to have access to criminal record databases of the Ministry of Public Security. However, how exactly they obtain this data is often not transparent. Mr. Houlbrook recommends “When outsourcing background checks in any capacity you must really understand your third parties and who you are working with. Understand their business model. Criminal law will come into effect if you are sourcing information using incorrect channels”.

The only way to get an effective glimpse of any kind of risk or record of any involvement in legal matters from a candidate is via desktop-based online research. This is also the best way to remain compliant with local regulations. “Finding information on criminal records is hard, but not impossible. It takes skills, some time and some patience. Knowing which sites to search, understanding the peculiarities of these sites, and how to research for keywords is something you learn. For example, we source information directly from open court records since this information is publicly available in China. Additionally, it is not only about the individual and search terms around that individual. If he/she was holding an executive position, you might want to look also into some financial issues with the company while that individual was employed there. You can also look at local court records and try to spot issues that came to the surface when that individual was in charge”, adds Mr. Houlbrook.

 

“(Companies) need an assessment in a matter of days, but there is enough information publicly available to be able to provide a risk indicator to support their decision-making process”

 

Background screenings based on online research have their limitations, but the methodology provides quality results in a timely manner. Mr. Houlbrook acknowledges: “You cannot conduct a thorough investigation to produce hard evidence. Clients need an assessment in a matter of days, but there is enough information publicly available to be able to provide a risk indicator to support their decision-making process”.

 

Staying on the Right Side of the Fence. Legal Provisions in China on Data Privacy

In China no explicit legal provisions are preventing an employer, or a third party acting on behalf of an employer, to conduct background checks on candidates.

As of November 2018, China lacks a comprehensive data protection law. However, this does not mean there are no significant legal considerations to take into account before engaging in background checks involving sensitive personal information.

China’s regulatory framework for privacy protection spans through laws and regulations across civil, administrative and criminal areas. In this article, we provide just a short review of the current framework, but this should not be taken as legal advice. To seek legal counsel reach out to specialized bureaus on the subject.

The Tort Liability Law (since July 1st, 2010) includes provisions that relate to the protection of personal data. Article 2 details a list of eighteen civil rights that are protected under the Law, one of them being the right to privacy. If an individual considers his/her right to privacy has been undermined, he/she may sue against the injuring party to seek compensation.

Under China’s Criminal Law (Art. 253), individuals selling or illegally providing personal information obtained from his or her employment are committing a criminal offense. It also applies to individuals acquiring such information (by stealing or by any other means). If those individuals act on behalf of a company or any other type of organization, the entity is subject to a fine and the individuals responsible or directly involved could face imprisonment of up to three years, criminal detention and fines.

The Cybersecurity Law of the PRC (CSL) is perhaps the most relevant piece of regulation for this article, and also the most recent. The Law is valid from June 1st, 2017. The CSL provides a series of data protection provisions in the form of national-level legislation. It is considered a milestone towards the implementation of a national protection law. It defines personal data as information that identifies a natural person either by itself or in combination with other information. That includes a person’s name, address, telephone number, date of birth, identity (ID) card number, medical records, genetic and biometric information, bank account information and transaction records, e-commerce sites account information and transaction records, and personal consumption habits.

A detailed national, non-binding, standard known as the Personal Information Security Specification – making for the unfortunate acronym PISS – came into effect on May 1st, 2018. The PISS separates between general personal data and sensitive personal data. The latter may include, among others, personal ID card numbers, health and biometric data, bank account numbers, personal communications, and credit records. To collect sensitive personal data from an individual requires his/her express consent (in writing or via other affirmative action) only after the individual has been informed of the purpose and intent of collecting such data; for general or non-sensitive personal data, tacit consent is enough (i.e., the individual did not oppose to it).

 

You Do Not Have to Be a Superhero to Have X-Ray Powers

LinkedIn launched its Chinese simplified version site in February 2014. Before that, despite being present in mainland China for over ten years, the platform had just over 4 million registered users in China. Following the rebranding (领英, ling-ying ) and localization of its services, LinkedIn finally took over in 2014. As of 2018, it has 44 million registered profiles in China. That makes China the 3rd largest country in LinkedIn users (the US tops the ranking, with 150 million users; India is second with 52 million).

Now the company is facing headwinds in China: in June 2017 it saw the departure of Derek Chen, LinkedIn’s President for China and key to the company’s growth in its recent past; in December last year the platform blocked advertising jobs posted by individuals to comply with local regulations; and other players such as MaiMai and Zhaopin seem to be growing at a faster pace.

Whichever way things turn out in China for LinkedIn, there is no question its current 44 million users in the mainland are a great asset for sourcing purposes.

 

Get Access to Candidates Beyond LinkedIn 100 Profiles Limit

LinkedIn allows for a maximum of 100 profiles when conducting a search. To tap into those 44 million profiles for recruitment purposes, the best option then is to get a LinkedIn recruiter premium account. But if you do not hire that often, or simply consider that your resources are better invested elsewhere, we want to share with you a newold trick to research candidates in LinkedIn without the need to invest anything. You will still need a LinkedIn account nevertheless, but this you can have it for free.

We say old & new trick because this technique – commonly referred as X-ray search – is as old as the search engines; and since both LinkedIn and search engines evolve constantly, they render today’s latest trick obsolete tomorrow.

What is an X-Ray search? Basically, it is searching online in a “smarter” way than simply typing keywords in your search engine. X-ray searches involve the use of search operators. For this article, we will focus fundamentally on the search operator site:, which returns web pages belonging to the specified site, and touch slightly upon others.

Our site of reference to conduct our x-ray search is going to be cn.linkedin.com. As for the search engine, since we are in China, we will use Microsoft’s Bing (note though that most of the search operators work across other search engines as well).

Let’s imagine that you have the following assignment: to recruit a seasoned quality manager in the chemical industry for an opening position in Chongqing.

In the Bing search box we could then input something like this:

 

site:cn.linkedin.com/in intitle:”quality manager” + “Quality Manager” near:3 present  +Chongqing +Chemical -assistant -(dir|title|groups|company)

 

Now, let’s explain:

  1. site:cn.linkedin.com/in: the operator site: tells the search is to run only at cn.linkedin.com/in. We indicate the subdomain cn.linkedin.com rather than the domain linkedin.com because we want to focus on profiles in China. LinkedIn user profiles are still found in two sites: /in and /pub. However, searches under /in are likely to produce more results than searches under /pub. But you could try with both /in and /pub.
  2. intitle: “quality manager”: the intitle: search operator will find pages that include the specific keyword in the indexed title tag. Because our keyword is a string of two words (quality + manager) we use “ ” to indicate that we want to search only for the exact match “quality manager” (and not, for example, “quality product manager”).
  3. + “Quality Manager” near:3 present: here we are indicating to look for the following string of text: “Quality Manager” unknown-word1 unknown-word2 unknown-word3 present. In LinkedIn profiles we often have the word present placed after the current position of the candidate. Since we prefer to seek for candidates that are working as quality managers today, rather than at some time in the past, we are indicating to look for the exact match “Quality Manager” in the vicinity of the word current. The operator near:3 indicates there will be 3 words between current and “Quality Manager”. You could also try with one-word separation (near:1) or two-words separation (near:2).
  4. + Chongqing + Chemical: these are two additional keywords we want Bing to search in the pages contained within the subdomain cn.linkedin.com/in. The plus symbol (+) indicates the AND operator (you can write either AND or +).
  5. – assistant: we mentioned that we are looking for a seasoned quality manager. Therefore, an assistant quality manager or a quality manager assistant is not a candidate we are interested in. The minus symbol (–) plays the same role as the NOT operator. There are other words that you could try here, like junior, jr. , or assisstant (note that the last one is a misspelling of assistant). Think about how often you find misspellings and abbreviations in resumes: it is useful to keep these in mind also when crafting your searches. An additional tweak would be to use the near:x operator to exclude from the results those profiles where assistant is close to the job title.
  6. – (dir|title|groups|company): these are a list of words known to be part of LinkedIn URLs that point not to an individual’s public LinkedIn profile but to a directory (dir), a promotional landing page (title), LinkedIn group pages (groups), or a LinkedIn company site (company). With the minus symbol (–) we indicate we don’t our search to contain the specified keyword after it. The pipe symbol (|) is equivalent to the OR operator. In Google-based searches we can use the string: inurl:(dir|title|groups|company) which aims directly to the URL. Unfortunately, in Bing (or Yahoo) the inurl: operator does not work, so we use the best next thing which is to look for those words all over the indexed content of the pages.

 

“X-ray searches are only a good-enough free alternative to bypass some of the limitations imposed by LinkedIn”

 

Since we are looking for candidates in China we want to produce also a similar search in Chinese, since most likely most of the profiles won’t be in English. Therefore, our Bing query could look like:

 

site:cn.linkedin.com/in intitle: “质量经理” +”质量经理” near:3 present +重庆市 +化工 –助理 -(dir|groups|title|company)

 

Note that in the Chinese query the word present is used in English. That is because in our LinkedIn version – although we are using the simplified Chinese one – we still get this in English:

 

You Do Not Have to Be a Superhero to Have X-Ray Powers Present Role in Chinese | ChinaHRnews.com

In your version, if you have it in Chinese then adapt the query accordingly.

Additionally for Chinese searches, we should take into account we might use different characters to refer to similar meaning: an assistant is both 助理 (Zhùlǐ) or 助手 (Zhùshǒu); Chongqing is 重庆市, but it can be referred also as 渝 (Yu), its official abbreviation; and for chemical you could go for either 化工 (Huàgōng) or 化学 (Huàxué).

The above Bing query in Chinese produces only one hit (with a search in Hong Kong – search engines will provide different results based also on your location). Trying several iterations, we saw that the main constraint was to have in the search 化工. Removing it from the search, that is, not considering uniquely profiles with chemical in it did give us 12 hits:

 

site:cn.linkedin.com/in intitle: “质量经理” +”质量经理” near:3 present +重庆市 助理 -(dir|groups|title|company)

 

 

What Do Recruitment Algorithms Measure?

“We become what we behold. We shape our tools and then our tools shape us”

John Culkin, professor of communication, Fordham University

 

Early in October, it was reported that Amazon had discontinued its artificial intelligence (A.I.) recruitment tool because it was gender biased for technical roles. According to members of the team working on the algorithm, the system taught itself that male candidates were preferable.

Despite developing software to review job applicants since 2014, Amazon’s algorithm was still in experimental stage as late as 2018. The algorithm assigned job candidates a score ranging from one to five stars – like the system the online retailer offers to shoppers to review products in its platform – but, because it was informed by data coming from applications submitted over a period of 10 years, it reproduced the same gender biases that predominated when humans did the screening.

It is to the credit of Amazon though that this A.I. tool is no longer in use. On the other hand, does this mean they are back to their old ways? If so, its recruitment process might still remain as biased as it was.

Algorithms are ubiquitous, permeating many spheres of our daily lives. They define our credit-worthiness, whom should we date, what news is in the menu for us, what movies to watch, what books to read, where to invest our savings, which college to attend, or the premium on our house/car/health insurance. And, as in the case of Amazon and many others, they also play a role in whether we get a job.

The widespread use of algorithms isn’t inherently good or bad. We might get even tangled on a more philosophical discussion about what does good or bad mean (a subject beyond the scope of this article). However, we could agree that to input data to an algorithm from a system that is flawed will only cause to perpetuate, through the efficiency and scale only algorithms are capable of, the underlying flaws of that system. 

 

Algorithm Dissection

In a nutshell, an algorithm is a step-by-step process that indicates how to combine certain inputs (i.e., a baby crying, a lullaby, a cradle, a pacifier, and a mushy pillow) to get to a certain output (i.e., stop the baby from crying). In this example, the algorithm would look for a combination of the available inputs that optimize for the desired output.

Even a baby would understand the above example (no pun intended). However, algorithms become far more intricate, often hand in hand with the complexity of issues they try to solve: how to optimize a company’s supply chain, how to prevent cancerous cells from spreading, or how to estimate box office numbers for the next Star Wars installment. Algorithms take the form of abstract mathematical models that look to make up for all the nuances and complex relationships underneath the input variables. 

Basically, algorithms crawl datasets (that is, they look at the past) to identify patterns to help achieve a certain goal in the future: a measure of success, or solving a problem.

To illustrate that, let’s use an A.I. recruitment tool from a hypothetical organization as an example. The team working on the machine-learning algorithm to help on recruitment might define success as a function of whether past hires stayed around for a certain amount of time, had been positively reviewed by their managers and peers in regular assessments, and then look at those who eventually got a promotion. I am no data scientist, no software engineer. Nor I need to be to assume these seem to be reasonable metrics to define the success for our recruitment tool. What could possibly go wrong?

However, these metrics – although straightforward in appearance – conceal intricate nuances. What if the corporate culture of this organization has been historically male-oriented? Or what if employees had been traditionally sourced from 985 universities? Additionally, what if the people that made it to the top were die-hard fans of chocolate ice cream? 

On the other hand, what proportion of the final scores in evaluation appraisals that took place in the past could have been biased towards factors that had nothing to do with on-the-job performance? Let’s consider the halo effect: because my co-worker doesn’t like chocolate ice cream, and I absolutely love it, I cannot stand him/her. And, because it is only human, I project this emotion to dislike everything he/she does. On the other hand, the “similar to me” bias might grant higher scores in reviews to co-workers that objectively did not fare very well. We say to ourselves “Yes, I know, he/she needs to improve on that” because consciously or unconsciously we know that he/she is one of us and deserves a second chance.

 

“What does gender, university, or preference for chocolate ice cream tell us about a particular individual’s ability to perform well in a job? Barely anything at all. Correlation does not mean causality”

 

How to discount the biases coming from those evaluations? This is a very complex task. It involves diving into the inner workings of people’s minds, and still, they are extremely difficult to account for and code into software. Similarly, how to discount the preference for candidates from one gender over another. How to level the playing field for candidates that did not study at a specific pool of universities or simply don’t happen to like chocolate ice cream? One possible way could be to give feedback to the algorithm on how candidates that left the company or did not make it passed the screening fared in other professional endeavors. That might reveal flaws in the internal recruitment process if a significant portion of those that were turned away proved to be high-performers elsewhere. However, that data is hard to come by. But unfortunately, this is the sort of feedback that would take to improve the model.

Because of the algorithm measures success leaving out complex but relevant information what we are left is with a model the simplifies reality. Think about it: what does gender, university, or preference for chocolate ice cream tell us about a particular individual’s ability to perform well in a job? Barely anything at all. Correlation does not mean causality. Birds don’t fly because they have feathers. Feathers help to keep the bodies warm during the flight: from an evolutionary perspective, it helps a lot not to freeze to death while flying. But flying itself comes from the difference in air pressure over on top and at the bottom of the wings, that creates a force on the wings that lift birds into the air.

You might argue that, if an organization chooses that the sort of candidate that is the best fit for its culture is a male, who went to a specific set of universities and happens to love chocolate ice cream, then so be it. Leaving aside that some of these criteria fall flat out of the boundaries of the law, let’s assume if only for a moment that we could accept this argument. The problem is this is not what the algorithm was set for. Those were not meant to be the vectors that would translate into success for the initial model. Remember, the algorithm was configured to look for prospective job applicants with a higher likelihood to remain longer at the company, to get a promotion, and to pass assessments from managers with flying colors.

The great irony is that there is no one to blame: no software engineer sat down to explicitly code for chocolate ice cream, no secret management meeting took place in an obscure room to devise a convoluted model with a hidden agenda. As former Google advertising strategist James Williams very elegantly puts it “at “fault” are more often the emergent dynamics of complex multiagent systems rather than the internal decision-making of a single individual” (1).

Wanted: A Headquarter Listening

Imagine the following conversation in a workshop:

Question: “Who knows your regional market better: you as regional Organization or your global Headquarter (HQ)?”
Regional Team: “We!!”
Question: “What does the global HQ think: who knows your regional market better?”
Regional Team: “The global HQ!”

Sounds funny, but it is not.

An article from Harvard Business Review in April 2015, based on research by CEB & Russell Reynolds Associates, supports the implications of the previous conversation. Research stated that only 5% of surveyed CEOs in China felt their voice was being heard by HQ and 75% of China-based CEOs and executives indicated their HQ did not consult them to define the regional strategy, which basically means HQs believe they know better the Chinese market than their regional leadership teams.

You might think that was a 2015 thing. Well, not at all. In our regular Organizational Development Programs (ODP) with international companies in China – the latest as recent as January 2018 – we put together a mixture of Chinese and non-Chinese executives. There we experience first hand how often Chinese managers respond in a similar vein, in varying proportions that go from 50% to 100%. Non-Chinese executives are always surprised about this, and only when they get to hear their local peers they realize how well reasoned their arguments are. This serves as signal and inspiration to think about the global mindset (or lack there of) in their respective organizations, with discussions in the workshops evolving around the potential causes.


What could be the root cause?

Here are some of the aspects that usually come out:

  • Despite a global presence many organizations still have a HQ-centered way of looking at the world. Why is this happening? For starters putting HQ results first may be due to public listings, tax optimization or others; it is also due to a traditional way of thinking in which HQ sees itself as the “we-are-the-origin” and so it truly believes it knows better; finally, there is a certain fear from HQ that other regions, China in particular, will outgrow them as a result of a faster pace of development and challenge the status quo (i.e. taking over jobs). 
  • Intercultural unawareness or lack of prioritization for intercultural aspects. There is not a clear understanding about the impact in collaboration that things like cultural differences, communication styles, work styles and educational background may have.
  • Other times regional specifications might not be accounted for as circumstances that can actually influence outcomes (i.e. different business environments, technical capabilities,…).
  • And finally, the leadership and collaboration culture as a whole does not reflect the global mindset that is required to run daily international operations.

Of course, the above is not an exhaustive list. The aspects mentioned might be the most common ones but, sure, there are others. If an organization could develop the ability to start questioning itself, to reflect on what needs to be adjusted, or to look at things from a different perspective (i.e. culturally), that would be already a great source of inspiration and a good starting point.

Now, this will pose a big internal challenge for it will disrupt long standing conceptions and imply changes that might move the HQ team out of its comfort zone. As the adage goes “change yourself before you will be changed”, so your organization can be in the driver’s seat.

By stimulating a shift towards change and more agile attitudes at the core of the organization, leadership can turn this approach into a competitive advantage, both in terms of market share and in the labor market.

 

Are you acting and thinking globally?

An easy way to check if your organization has a global and open mindset is to answer the following question: “When was the last time you adapted globally an idea or thought coming from a subsidiary?”

If you can answer this question with some important – small or big, but important – changes in your HQ processes, or across the whole organization, then you might be on the right track. Just be honest with yourself.

In another approach to address this topic you can also use tools like the Global Mindset Inventory (GMI), developed by the renowned Thunderbird School of Management. This tool is especially designed for this purpose, although there are other assessments in the market that serve the same purpose.

 

How does it impact your Organization?

Consequences can be a lack of stickiness or problems with the retention of good talent in your subsidiaries. People who feel respected and recognized are much more likely to stay with the organization. Making the effort of identifying and recognizing their contribution is less costly than having to replace talent, and it benefits the organization as a whole for these contributions can be applied globally. In a nutshell, by listening more carefully, acknowledging contributions and deploying them across the board both the business as well as the commitment of talent improves – making it less likely for talent to take off or for competitors to poach from your organization.

Other possible consequence is the delay in taking important decisions in a fast developing and changing market environment. If the issue is not well understood, and perceived only as local matter, the necessary decisions to address it risk to be postponed forever. You might be inadvertently creating an advantage for your competitors.

Resistance to truly collaborate and share knowledge transparently to find a common solution. This is often the case with China. But what is the alternative? If you want to participate in a market today you have to be a real participant in all meanings. Lack of engagement and poor identification with the company very often have consequences.

To avoid all of that it is important to support and demand a collaborative attitude from all the team in your HQ or home-based factories, across several layers of the company. Many of the issues happen in the day-to-day work between front-line managers or engineers. They need role models with clearly set expectations.

Whoever wants to work in an international company has to have an international mindset and proper language skills. A clear, simple and important message to communicate to staff and new hires.