08
With employees being frequently touted as the most valuable asset of a business, it makes sense that keeping those employees engaged, happy and committed to the company is a critical activity for any organization. Indeed, a 2015 Deloitte report revealed that 87 per cent of business leaders are very concerned about employee engagement and retention.1 Data and analytics, and particularly AI-based technology like machine learning, are beginning to have a significant impact on every aspect of maintaining and improving employee engagement. In intelligent or data-driven employee engagement, HR teams are looking to connect with employees more seamlessly, measure and improve their experience of working for the company and, in turn, drive employee satisfaction – as well as drive productivity and improve the company’s employer brand (see Chapter 7). In this chapter I explore why employee engagement is ripe for change and look at three key strands of data-driven employee engagement:
· driving employee satisfaction (or how happy your people are);
· measuring and improving employee loyalty and retention;
· improving compensation and benefits with data.
Why employee engagement is ripe for change
Employees who are engaged tend to go ‘above and beyond’ for the company they work for, meaning companies with the most engaged employees outperform companies with disengaged workforces. We know this, and yet companies are not necessarily giving employee engagement the attention it deserves. In fact, one global study found that only 40 per cent of employees feel engaged.2 Why is this percentage so low when the effects of disengaged employees are clearly known? In the United States alone, disengaged employees cost the economy US $500 billion every year in lost productivity. In the United Kingdom, one report claims disengaged employees could be costing the economy as much as £340 billion.3
FIGURE 8.1 Data-driven employee engagement
It is clear that employee engagement is in need of a shake-up. In this age of social media, transparency and connectivity, where people freely share their experiences of the world (including work) online, I believe employee engagement will become a far more critical issue for employers. With advances in data and analytics, companies now can begin to clearly understand their level of employee engagement (including underlying reasons for disengagement) and put these critical insights to use. Most businesses are already using big data in one form or another to assess how satisfied their customers are and drive customer engagement and retention. It is time the same applied routinely to employees, who, let us not forget, are a core customer of the business.
Determining employee satisfaction – how happy are your people?
Measuring employee satisfaction is something companies have been doing for a long time, largely using employee surveys and benchmarking information. More than 80 per cent of companies conduct employee surveys;4 however, I believe this approach is now completely out of date. An annual employee survey is not agile or granular enough to give most businesses the timely, detailed insights they need to monitor and improve employee engagement. After all, employee opinions shift constantly, and change can happen very rapidly in a lot of organizations. There is also the issue that most people hate filling out these lengthy surveys or, worse, they are worried their answers could be traced back to them so they simply say what they think the company wants to hear. It is therefore questionable how useful the feedback even is.
Knowing what people are really thinking and feeling
Data and analytics, particularly AI-related technology, are promising to help organizations really understand what their people are thinking and feeling. These tools are not perfect yet, but the field is developing extremely fast. For me, the real promise lies in measuring employee satisfaction and happiness in far more accurate and agile ways that are less onerous for employees. As well as working out how satisfied (or not) your employees are, data and analytics also can help you to understand exactly what it is that employees do and do not care about and, therefore, what will boost their engagement. There are already many systems on the market that make engaging with employees easier and more successful, and more are being developed all the time. Beyond 360’s system, for example, automates the collection of employee feedback, helping employers take the temperature of employee sentiment and satisfaction quickly and easily. Another service, this time from Veriato, uses artificial intelligence (AI) technology to analyse employees’ e-mails and other messages to work out whether they are happy in their jobs. The system analyses the words and phrases workers use and applies a score for sentiment: positive or negative. By measuring sentiment over time, the system can even create a daily score for each individual employee. In addition, alerts can be sent if a change in tone is detected in a particular team or group. Programmes like these allow HR teams and managers to more accurately assess how the workforce is feeling right down to a team or individual level, meaning they can quickly spot when satisfaction is taking a turn in certain areas of the business and take steps to address this issue.
Getting continuous feedback
In Chapter 7 I briefly mentioned how short ‘pulse’ surveys can take the temperature of employee satisfaction with a quick question delivered at regular intervals, maybe even daily. Tools like these allow companies to get instant, granular feedback on topics as diverse as how much people like the food in the canteen or how well staff understand the company’s strategy.
Feedback in real time
HighGround has developed a platform that mines real-time data directly from employees, providing HR teams and business leaders with better, continuous feedback from their people. The idea behind the platform is to create a continuous dialogue with staff, for example, by asking employees how they feel at work that day. The system works as a simple app downloaded onto employees’ phones; this ease of use promotes employee buy-in, making staff more likely to engage with the system and provide accurate, regular feedback. Another such company, Glint, has produced an app that works alongside HR systems and can be used to ask employees for feedback in real time (which is particularly useful when certain events occur, like a change in leadership). Working with clients like United Airlines and Sky, Glint uses natural language processing and sentiment analysis (more on this in the next section) to analyse employees’ open-ended responses to questions, and can then turn those responses into a visual map of key topics and issues. Glint and HighGround are just two examples of the many continuous feedback systems available today that aim to develop more of a dialogue between an organization and its people. While these systems often use short, simple questions, one company has taken the idea to a new level of simplicity: using smileys.
Employee satisfaction in the emoji age
You may already have encountered HappyOrNot’s terminals in somewhere like an airport or bank, encouraging you to give quick, easy and anonymous feedback on your experience that day. HappyOrNot kiosks are used by approximately 2,000 retail and service providers around the world. Now, the company that made its name in gathering customer feedback is turning its eye to staff feedback. Using HappyOrNot terminals, organizations can get daily feedback on their employees’ experiences. When placed in high-traffic areas like meeting rooms or the canteen, the kiosks ask an employee a simple question and the employee responds by choosing between one of four smiley faces that best demonstrates how much they agree with the question. It is that simple: just one press of a button. HR then can use the data gathered (which are all anonymous) to get a clear picture of staff reactions to things like new initiatives, company policies, overall strategy and direction, facilities … pretty much anything. The beauty of simple, continuous feedback systems like these is not only the quick access to insights, but also the ability to monitor changes and initiatives made in response to the feedback to see how well those changes improve employee satisfaction. What exactly you measure will depend on your company and its goals but some of the key metrics to keep an eye on include, among other things:
· the extent to which employees approve of leadership;
· how happy they are with their working environment;
· how likely they are to recommend the organization as a good place to work;
· the extent to which they understand and approve of new initiatives, policies and strategies.
Measuring employee sentiment
Conducting regular surveys is all well and good but, if you ask your employees open-ended questions (as opposed to hitting a smiley-face button), you could end up with a mountain of unstructured text data that can prove tricky to analyse. Text analytics provides the answer to this problem. Text (specifically, sentiment) analytics and AI capabilities are now beginning to play a big role in measuring employee sentiment. Forward-thinking organizations are already using sentiment analysis to mine social media and other messages for positive and negative phrases that show what customers really think about their company. Applying the same thinking to employees is perhaps the logical next step. Sentiment analysis potentially can be used on any kind of written text, including survey answers, e-mails, intranet posts, internal messaging systems, social media etc. For example, sentiment analysis could show that members of a division with a new manager have shown a dramatic increase in the number of negative wordings used over the past month, which may alert HR to a manager that is struggling to settle in.
Weighing up the pros and cons of sentiment analysis
One of the real advantages of sentiment analysis is that it does not take up any of the employee’s time, as opposed to filling in extensive annual surveys that may be out of date by the time they are analysed. There are obvious privacy concerns surrounding analysing employee communications (see Chapter 6), so it is important you view sentiment analysis as a means to gain a broader picture of employee engagement, and not to police what individuals say or single out employees for punishment. Therefore, implementing any kind of sentiment analysis programme will require careful thought and communication with employees so as not to have a detrimental effect on morale, which is the very opposite of what you want. Making employees aware of the benefits of such analysis is the best way to promote buy-in; for example, you could make it clear that, if the majority of employees disagree with a new policy, it is important for leaders to know this so they can act accordingly to fix issues. In this way, sentiment analysis gives companies the opportunity to gather broad pictures without asking individuals to ‘speak out’ on their own, which may make them uncomfortable.
Real-life examples of sentiment analysis in action
Although sentiment analysis is in its early days in an HR capacity, large organizations like Intel, Twitter and IBM are already using it to better understand their employees.5 Twitter has used Kanjova software to analyse employees’ answers to monthly surveys (with open-ended questions) about their experience of the workplace. Using sentiment analysis, Kanjova ploughs through the narrative answers, identifying patterns and useful insights. At IBM, sentiment analysis is applied to employee posts on its internal social-networking platform. In one example, when IBM was overhauling its performance review system, the company turned to the internal network to ask employees for feedback on ideas for a new review system.5 The company received tens of thousands of responses. Using its Social Pulse text analysis software, IBM surfaced one prevalent concern: employees did not like their performances being graded on a curve. The company was then able to discount this method from performance reviews. Notably, IBM does not mine e-mails, chats or private group messages for insights, preferring to focus on the internal network posts shared with the whole company.5
It is not just about written text
Of course, sentiment analysis does not necessarily apply just to text. We show how we are feeling through our facial expressions, body language, tone of voice and many little ways. With this in mind, two computer scientists at Sathyabama University in India have proposed using facial scans to assess employee attitudes.6 They have developed a system that snaps images of employees’ faces as they enter the premises, and uses these to work out whether those employees are happy, angry, sad etc.
Happy employees are productive employees
I look at performance management in more detail in Chapter 11 but, for now, it is important to highlight again the link between happy, engaged employees and increased productivity. One study by the University of Warwick found that happiness led to a 12 per cent productivity bump.7 Yet, despite evidence that happy employees perform better, studies have shown that up to 71 per cent of employees describe themselves as uninspired or unengaged in a work context.8 This lack of engagement can have a significant effect on productivity; one study found a 30 per cent difference in absenteeism between companies with high versus low employee engagement.9 The message is clear: happy employees are more engaged and perform better, and companies that can harness this knowledge to ensure their employees stay happy will, in turn, perform better.
How organizations can best make (and keep) their employees happy is still up for debate, and there is no clear one-size-fits-all approach that works for all businesses across all industries and geographies. What is clear, however, is that technology is likely to play a greater role in boosting employee happiness in the future. One US-based start-up company, Happybot.ai, has developed an AI-powered robot that serves as an automated Chief Happiness Officer. The bot communicates with employees to help ease pressure and boost their happiness and productivity. Founder Aaron Cohn, who spent years as a ‘people and change’ consultant at PricewaterhouseCoopers, based the bot on his observation that employees continually feel overwhelmed by everyday pressures at work. By communicating with employees and empathizing about those pressures, the Happybot.ai aims to help lift their spirits.10
Measuring and improving employee retention
Employee satisfaction and retention are intrinsically linked. Glint data from over 500,000 employees show that the attrition rate of employees with low engagement or satisfaction scores is 12 times higher than those with positive engagement scores.11 We also know that losing employees is costly (not to mention disruptive and time-consuming). US businesses lose an estimated US $11 billion a year due to employee turnover.12 Therefore, if a company is able to identify who is likely to leave and why, it is then able to take action and address those issues with a view to retaining those critical staff members who are a flight risk. With data and analytics, it is now possible to predict overall retention rates, including when certain individuals are likely to leave the company based on factors like how long they have been in the job.
Workday’s AI-based retention risk tool, for example, uses algorithms based on 25 years’ worth of data from 100,000 individuals. Based on around 60 factors (such as job title, salary, time off and how long it has been since they were promoted), the tool can calculate a risk score for each individual employee.13 But intelligent employee engagement is not just about getting at critical insights like this, it is also about using that knowledge to make improvements. Many retention risk tools also suggest what actions to take to retain valuable workers. Workday’s program, for instance, can suggest next steps in an individual’s career, based on the data showing what others in comparable circumstances have done.
Inspiring loyalty
Businesses talk a lot about customer loyalty and how to encourage customers to remain loyal to a particular brand, product or service. Yet employee loyalty is just as important to a business’s success; in fact, there is a proven link between employee loyalty and retention and customer retention.14 It is therefore vital that companies apply the same level of attention to keeping their employees satisfied as they do to keeping their customers satisfied. Unhappy employees lead to high turnover (which, in turn, can affect customer retention, among other things). I mentioned HighGround’s employee engagement tool earlier in the chapter, which gathers regular feedback from employees about their experience of working for a company. HighGround says that its service actively reduces turnover, citing an example of cutting turnover by 5 per cent at a company called Echo Global Logistics.15 Some HighGround clients have installed the software at every retail store across a national chain to gather daily information on how happy employees are around the country. When stores that have previously shown stable moods begin to show a drop in happiness, management can investigate the cause of that and take necessary action.
Improving your levels of employee loyalty starts right at the time of hiring. In Chapter 7 we looked at how data are revolutionizing the recruitment process. By using some of the machine learning methods outlined, companies can not only find candidates with the best-fitting skills and attributes, but also those who are most likely to commit to the company on a long-term basis (based on their employment history and patterns from other employees who have demonstrated high levels of loyalty). Data and analytics can help you to identify the employees that are more likely to enter into a long-lasting relationship with the company and demonstrate loyalty and commitment. Of course, it is then up to you to ensure they stay that way. Luckily, data can help with this too. As the Xerox example from Chapter 4 shows, data can drastically reduce employee attrition – in Xerox’s case, by an impressive 20 per cent.16
Many people make the mistake of thinking employee retention is all about compensation, but it is not. There are many factors besides salary that inspire and maintain employee loyalty. So, once you have found the people that are the best fit with the organization, you then need to implement all the good practices that keep those employees happy, engaged and loyal. These include communicating and rewarding successes, offering plenty of training and development, providing career progression opportunities etc.
Predicting employee churn
For many companies, being able to accurately predict when someone may be about to jump ship is the holy grail of employee retention, and there are many tools on the market now that claim to be able to do just that. The idea is not a new one. Google has been using algorithms for many years to predict who among its employees are most likely to leave the company. Competition for talent in Silicon Valley is incredibly intense, and while Google remains one of the most popular employers in the United States, it still needs to put the work in when it comes to retaining its talent. Google first developed its algorithm back in 2009, which worked from data including employee surveys and peer reviews.17 The algorithm was a quick success, identifying that ‘feeling underused’ was one of the biggest reasons people left the company. Speaking at the time, Google’s Laszlo Bock said the algorithm could ‘get inside people’s heads even before they know they might leave’.17
Of the tools on the market, you will need to assess which is the best option for your needs. Unless you are a very large organization, it is a good idea to opt for a tool that is based on external data as well as your own internal data (such as employee performance data, employment history, performance review data, survey responses, compensation data, and possibly employee e-mails and communications). Some sort of capability for taking regular pulse surveys and conducting sentiment analysis also, in my opinion, is a must, as the results will feed into your employee churn analytics. The best tools will work right down to an individual level, delivering an alert when a certain employee shows signs of disengaging, or when they become a flight risk. Of course, once you understand your employee churn patterns and can pinpoint those who might be at risk of leaving the company, you can then take steps to turn the situation around. Again, a salary bump is not necessarily going to keep an employee in their job. As we saw in Chapter 7, for many people, career progression opportunities are far more important.
Data-driven compensation and benefits
It is fair to say that, in terms of data and analytics, compensation and benefits comprise the less-developed area of employee engagement. But it is developing fast and more tools and services are coming onto the market to help companies take a more intelligent, data-driven approach to their compensation and benefits structures. Offering a fair compensation and benefits package remains an important part of successful employee engagement. Having found the right people, it is up to you to create a package that engages employees and makes them more likely to remain committed to the company, and therefore more likely to stay with the company. If you bargain down candidates to pay them the lowest possible salary you can get away with, it does not exactly inspire loyalty. And, as we have already seen in this book, different factors will be more important to some people than others. Millennials, for instance, typically want great career progression opportunities.18 The ability to tailor your compensation and benefits packages to individuals, based on what the data tell you about certain demographics, will inspire greater long-term employee engagement.
Focusing on fair market value
Salaries and market value typically have been very secretive, both from the employer and from the employee side. Some companies may not want employees to know their true market value so that they can get away with paying them less. And employees may not want to share their salary data for fear of repercussions from management or co-workers. But such secrecy is not good for anybody. Employees are at a disadvantage when negotiating because they do not know what salary to ask for. And while certain employers might think they are getting a good deal by paying less than market value, it is actually more likely to limit their talent pool and increase staff turnover. Things are changing, however, and it is now far easier for people and companies to discover the fair market value for particular jobs. We are all used to searching price comparison sites for good flight or insurance prices, now the same mentality applies to salary. Glassdoor may have started out by posting employee-generated reviews of companies so that jobseekers could get a feel for what working for a particular company was really like, but now it also provides a tool aimed at making salaries and market value more transparent. Employees and employers can type in a job title and location and clearly see what the average salaries are for that area and position. The site also offers personalized salary reports for individuals based on more detailed information about their skills and experience. Glassdoor claims to have the most complete market salary information because it is based on thousands of users reporting their real salaries anonymously. This means that it is increasingly likely that candidates will go into salary negotiations armed with a very good idea of their fair market value. This allows them to negotiate from a position of power, with the facts on their side. Employers therefore need to raise their own game to ensure they are armed with the same level of knowledge and are prepared to offer fair market value to get the right talent for the company. HR managers should therefore stay up to date with the market value of their employees. Not only does this data-based approach help you to budget appropriately for new hires, it also ensures salaries remain competitive, thereby improving employee morale and loyalty and reducing turnover.
Determining market value
Unless you have an in-house compensation expert – and many small and medium-sized companies do not – it can be difficult to accurately calculate employee compensation. But, thanks to now widely available information on salary and benefits trends, it is becoming easier for any organization to calculate employee compensation in line with market value. Tools like salary.com or payscale.com, for instance, help employers to understand whether they are paying competitively or not. This data-based approach, where you calculate the value of employees and new hires based on external research (as opposed to internal historical data or gut feeling), is the most sensible approach for ensuring your compensation package is fair. But remember to get a complete picture by drilling down into detailed demographics (such as your specific location) rather than just grabbing a general figure for a certain job title. Having determined the fair market value for new hires, you then need to ensure you stay competitive. Be sure to reassess compensation levels on a regular basis. I would say to do this at least every two years, but do it annually if you can or if you are in a field with a lot of fierce competition for talent.
Combining data to create your compensation and benefits packages
Clearly, the realm of compensation and benefits ties in with a number of other areas of HR and the wider business, including performance management, learning and development, and payroll. It is therefore important to create a full picture by combining multiple data sets for the greatest strategic effect, and advances in data and analytics technology make this easier than ever.
Real-life examples
The University of Lincoln, for example, has implemented a new system that integrates HR and payroll in one place to create a comprehensive reporting tool. Speaking to HR Magazine, Ian Hodson, reward and benefits manager at the university, said: ‘the data becomes more powerful when it is overlaid with other information. Our new system has fed data triggers in to populate other systems, and we have a much more cross-function approach to data collation and production than ever before’.19 Using this system, Hodson and his team have been able to draw a correlation between pay and consistently strong performance.
When two giant publishing companies – Penguin and Random House – merged in 2013 to form Penguin Random House, the new company faced significant challenges in redesigning its compensation and benefits offering. Also speaking to HR Magazine, Neil Morrison, group HR director at the publisher, explained how the company used a vast amount of data to determine how benefits and compensation should be structured. In one example, the company used broad data on the take-up levels of benefits, but also drilled down into how take-up varied among different demographic groups, and whether salary played a role in this. According to Morrison, this involved: ‘everything from understanding people with different backgrounds but the same job titles, to the take-up of benefits and the specific value of certain ones. Whether they have the value you think they do and whether that has links with turnover and retention, and whether therefore the investment is adding value’.20 In a specific example of how these data helped the publisher to redesign its benefits offering, Morrison explained how the data showed that young people did not take up private medical care. Based on this knowledge, the company was able to offer ‘the flexibility of a lower-value product’.20 Penguin Random House is working with reward consultancy firm Innecto to look at its salary and incentives schemes, using both internal and external data. According to Morrison: ‘Being able to take external data and analyse and compare it with our internal data and make decisions on pay structures will hopefully take us forward for the next five years rather than just working on individual pay structures’.20
Gaining feedback on schemes
Once a scheme or benefit has been introduced, data and analytics allow you to accurately assess how successful that scheme is and whether certain benefits are influencing engagement. Exactly what kind of compensation and benefits platform you go for will depend on your company’s needs, but key functionality to look out for includes the ability to look at skills and experience within the organization and compare these both internally as well as against national levels. It is also important to be able to assess correlations between salary and specific benefits and employee satisfaction levels. In addition, survey tools are increasingly being built into benefits platforms to gain employee feedback on various rewards, which gives the employer valuable data on how well benefits are being received, and further engages staff by giving them a chance to voice their opinions. Where possible, it is a good idea to build this functionality into your platform at the outset.
The role of AI in compensation and benefits
AI is changing every aspect of business, and compensations and benefits are no different. Just as AI can be used to easily find the most suitable candidates for a particular role and the best fit for the company culture (Chapter 7), it can also help to enhance and automate various aspects of compensation and benefits. One area of benefits that gets a lot of attention is flexible working and the ability to work remotely. While this may not appeal to everyone, there are certain demographics, such as parents and millennials, who prize flexible working above many other benefits. To give you an idea of how highly prized flexibility is, 59 per cent of millennials say flexibility improves their productivity, and 49 per cent say it enhances their happiness.21
It is very likely that, as our workplaces become more flexible, so too will our compensation and benefits structures. Traditional schemes are highly likely to be replaced by flexible, variable compensation and benefits programmes that are tailored more to individuals’ needs. Those companies that are able to adapt and offer flexible compensation and benefits schemes may well find themselves at the forefront of the industry. AI, and analytics in general, will help to make this flexible approach a reality. Think about it: having to manually analyse and tailor compensation and benefits packages to each individual employee would be completely unfeasible without data and analytics, taking up far too much time and resources to be workable. AI-based platforms, on the other hand, make it possible to understand and accurately predict trends, understand the relevance and take-up of various benefits among different demographics and easily create tailored solutions that work on a personal level. What this means for HR professionals who focus on compensation and benefits is that their role is likely to change dramatically. They will probably need to upskill from an in-depth support role to a more strategic role, looking at how to apply AI-based analytics to both internal and external data sets to gain valuable insights on compensation and benefits. In fact, this is an important point that applies to the whole area of employee engagement, not just compensation and benefits. As the technology develops rapidly, it becomes HR’s role to provide strategic direction for the company on how best to apply all of these tools and systems to employee engagement in order to get maximum value.
Key takeaways
In this chapter we have looked at three core strands of data-driven employee engagement – employee satisfaction, employee retention, and compensation and benefits – and explored the following key points:
· Disengaged employees cost the global economy billions in lost productivity. Happy employees, on the other hand, are productive employees.
· Most businesses are already using data to assess how satisfied their customers are and drive customer engagement and retention. It is time we applied the same level of care to our employees.
· Annual employee surveys are nowhere near agile or granular enough to deliver the timely, detailed insights needed to monitor and improve employee engagement.
· Data and analytics technology allow us to measure employee satisfaction and happiness in far more accurate and agile ways, such as by using very brief but regular pulse surveys.
· Sentiment analysis makes it possible to analyse open-ended responses, or any written or spoken text, to determine what your employees are really thinking and feeling.
· High employee turnover is costly for any business. Data-driven employee retention means identifying insights on employee churn, identifying who might be about to leave the company and making evidence-based changes to inspire employee loyalty.
· Data and analytics also help you to determine a fair market value for employees, assess how successful your compensation and benefits programmes are in influencing employee satisfaction and create programmes that chime with what is really important to your employees.
Keeping your people engaged, satisfied, loyal and well compensated is one thing, but you also need to look after their safety and wellbeing if you want them to stay happy, engaged and productive. In the next chapter I explore the fascinating world of data-driven employee safety and wellness, and see how data-related technology is transforming how we look after our employees.
Endnotes
1 Deloitte [accessed 23 October 2017] 2017 Deloitte Global Human Capital Trends [Online] http://www2.deloitte.com/us/en/pages/human-capital/articles/introduction-human-capital-trends.html
2 Zarkadakis, G (2015) [accessed 23 October 2017] Next Generation Employee Engagement [Online] https://www.towerswatson.com/en-GB/Insights/Newsletters/Europe/HR-matters/2015/12/next-generation-employee-engagement
3 Hay Group [accessed 23 October 2017] Employee Disengagement Costs UK £340bn Every Year, press release [Online] http://www.haygroup.com/uk/press/details.aspx?id=7184
4 Galagan, P (2015) [accessed 23 October 2017] Employee Engagement: an Epic Failure? [Online] https://www.td.org/Publications/Magazines/TD/TD-Archive/2015/03/Employee-Engagement-An-Epic-Failure
5 Waddell, K (2016) [accessed 23 October 2017] The Algorithms That Tell Bosses How Employees Are Feeling [Online] https://www.theatlantic.com/technology/archive/2016/09/the-algorithms-that-tell-bosses-how-employees-feel/502064
6 Subhashini, R and Niveditha, P R (2015) [accessed 23 October 2017] Analyzing and Detecting Employee’s Emotion for Amelioration of Organizations [Online] http://www.sciencedirect.com/science/article/pii/S1877050915006407
7 University of Warwick (2014) [accessed 23 October 2017] New StudyShows We Work Harder When We Are Happy, press release [Online] http://www2.warwick.ac.uk/newsandevents/pressreleases/new_study_shows
8 Pepperdine University [accessed 23 October 2017] 7 Ways Managers Can Keep Employees Engaged [Online] http://mbaonline.pepperdine.edu/resources/news-articles/7-ways-managers-can-keep-employees-engaged/?utm_campaign=elearningindustry.com&utm_source=%2Femployee-engagement-and-artificial-intelligence-elearning&utm_medium=link
9 Flink, C [accessed 23 October 2017] Engaged Employees: the Key to a Thriving Brand [Online] http://www.marketforce.com/blog/engaged-employees-key-thriving-brand?utm_campaign=elearningindustry.com&utm_source=%2Femployee-engagement-and-artificial-intelligence-elearning&utm_medium=link
10 Happybot [accessed 23 October 2017] A Bot That Surprises & Delights You. At Work [Online] http://happybot.ai
11 Glint (2016) [accessed 23 October 2017] Glint Raises $27 Million to Boost Employee Engagement with Help from Artificial Intelligence [Online] http://www.marketwired.com/press-release/glint-raises-27-million-boost-employee-engagement-with-help-from-artificial-intelligence-2154186.htm
12 Lipman, V (2013) [accessed 23 October 2017] Why Are So Many Employees Disengaged? [Online] https://www.forbes.com/sites/victorlipman/2013/01/18/why-are-so-many-employees-disengaged/#3a29b5081e22
13 Greenwald, T (2017) [accessed 23 October 2017] How AI Is Transforming the Workplace [Online] https://www.wsj.com/articles/how-ai-is-transforming-the-workplace-1489371060
14 Carter, B (2017) [accessed 23 October 2017] Employee Engagement = Customer Engagement [Online] http://blog.accessdevelopment.com/index.php/2014/03/employee-engagement-customer-engagement
15 White, S K (2016) [accessed 23 October 2017] How Big Data Can Drive Employee Engagement [Online] http://www.cio.com/article/3023311/careers-staffing/how-big-data-can-drive-employee-engagement.html
16 Walker, J (2012) [accessed 23 October 2017] Meet the New Boss: Big Data [Online] https://www.wsj.com/news/articles/SB10000872396390443890304578006252019616768
17 Morrison, S (2009) [accessed 23 October 2017] Google Searches for Staffing Answers [Online] https://www.wsj.com/articles/SB124269038041932531
18 Adkins, A and Rigoni, B (2016) [accessed 23 October 2017] Millennials Want Jobs to Be Development Opportunities [Online] http://news.gallup.com/businessjournal/193274/millennials-jobs-development-opportunities.aspx
19 Giles, H (2015) [accessed 23 October 2017] Where’s the Evidence for Performance-related Pay? [Online] http://www.hrmagazine.co.uk/hro/features/1150736/helen-giles-wheres-the-evidence-for-performance-related-pay
20 Beagrie, S (2015) [accessed 23 October 2017] The Growing Role of Big Data in Reward Strategies [Online] http://www.hrmagazine.co.uk/article-details/the-role-of-big-data-in-reward-strategies
21 Staples [accessed 23 October 2017] Staples 2017 Workplace Survey [Online] https://www.staplesadvantage.com/sites/workplace-index