1. MEASURING HR AND HUMAN CAPITAL
The research referred to in previous chapters (especially Chapter 11 on Strategic aspects of performance) has a very clear focus on identifying and measuring a range of best practices in terms of workforce organisation and management (such as self-managing teams, high training spend, reduced status differentials) and relating these to impact on productivity and profitability. This is a very specific approach to ‘proving’ that HR practices affect bottom-line performance. In this chapter we later focus on this type of measurement in the context of specific organisations, but we begin by taking a wider perspective and will review a broader and simpler range of measures which are used to demonstrate how the HR function and HR capital contribute to the organisation.
HR measures are sometimes talked about in the context of measuring the contribution of the HR function. An example of such measures might be the staffing costs of the HR function, recruitment speed, training delivery, management satisfaction with HR advice and services, and so on. The Window on practice provides one of many approaches to such measurement.
Such factors are clearly the responsibility of the people in the HR function and are under their control. They are designed to show how the HR function adds value to the organisation and can provide a way of capturing how that value is improved over time. Most HR measures, however, are within the control partly of members of the HR function, and partly of others in the organisation, particularly where HR is devolved to line managers. For example absence and employee turnover are typical measures in many organisations. But to what extent are absence levels, for example, the result of the absence policy (and HR may or may not have designed this alone)? Other factors that may have an effect are: the way the policy is implemented by line managers, the influence of other policies (such as work-life balance), the influence of the way that work is structured and commitment to peers (as for example in self-managing teams)? The list could go on. It could be argued that the HR function has an ultimate responsibility for all of this. In reality, however, this is not a tenable view. There is also a very great emphasis on partnership in HR, requiring many activities to be business driven and owned rather than HR driven and owned. Thus many HR measures represent aspects of human capital in the organisation on which the HR function has some influence. IRS (2002a) in its survey found that respondents commented on the inherent difficulty of identifying the contribution of the HR function in many measures.
We move on now from the specific contribution of the HR function to discuss human capital measurement (HCM), which has attracted considerable attention, with some organisations setting up dedicated roles and units to undertake HCM, either within or attached to the HR function. We identified broadly above the ultimate purpose of measurement in that it identifies the contribution of people to the performance of the organisation and enables the organisation to improve this contribution. This contribution is as we have noted previously very difficult to disentangle from other factors in the situation and is also difficult to measure quantitatively. As we showed in the earlier part of this chapter many HRISs still focus on administration at the expense of analysis and evaluation, thereby possibly limiting what the organisation can achieve in this area. We have discussed previously the nature of human capital and the resource-based view (in Chapter 2) and human capital and knowledge management (in Chapter 12) and it may be useful to read or re-read these sections before moving on.
There is considerable evidence that there exists no single measure or set of measures that represent the value of human capital in the organisation (see, for example, Kingsmill 2003). This is largely because every context is different and, as Elias and Scarborough (2004) discovered, different organisations are driven by different things in their human capital measurement. They found that approaches differed in two significant ways. One major area of difference concerned whether the whole or part of the workforce was covered in human capital reporting; the other concerned whether a strategic, holistic and aggregate (such as balanced scorecard) approach was taken or whether individual aspects were reported. Interestingly they found that most organisations in their sample reported on individual aspects. CIPD (2006b) identifies three different levels of sophistication of human capital data: basic; intermediate and higher, which we explain below.
At a basic level it is suggested that actions include the collection of basic data (see the examples discussed above) and the use of existing data to communicate to managers essential departmental information on such matters as absence, accidents, turnover and so on. In addition trends and patterns in the data are identified and causes investigated. The outcome of this exercise is to provide measures of efficiency and effectiveness so that identified problems can be tackled, for example to reduce absence or accidents or to improve the diversity profile. These basic measures represent the ‘individual’ measures that Elias and Scarborough (2004) found to be most common.
At an intermediate level CIPD (2006b) suggests that actions involve designing data collection for specific human capital needs, such as designing an employee attitude survey to measure satisfaction, and then using this data to inform the development of people policies and procedures. In addition correlations are investigated, say, between levels of satisfaction and potential antecedents such as levels of training, flexible working, line manager coaching, and so on. The final action is to communicate to line managers which processes influence desired outcomes such as satisfaction, and therefore highlight the value of these processes. The outcome at this level is to provide measures of processes in order to aid the design of the most appropriate HR model for the context, and to communicate to managers why the processes are important and what they can achieve, as well as how to implement them.
At the higher level CIPD suggests that actions include the identification of key performance indicators in relation to business strategy and the collection of qualitative and quantitative data specifically to measure these, and to feed these data into a model such as the balanced scorecard. Managers are then provided with a range of indicators on a range of measures which they can use to monitor the progress of their department. The resulting data can be used to inform decisions and communicate human capital measures to a range of audiences. The outcome at this level is to identify the drivers of the business, to provide better informed internal decision making and to report externally on progress in relation to strategy.
We focus on simple basic measures initially and then move to the higher-level ones.
1.1. Frequently used basic measures
IRS (2002a) divides measures into hard and soft measures, with training days, for example, being a hard (objective) measure and employee satisfaction, for example, being a soft measure. In its survey IRS found that employers most frequently calculated absence rates (96 per cent), employee turnover (98 per cent) and expenditure on training (88 per cent). However, these figures are based on a small sample, so actual percentages should be treated with caution. Other popular measures were employee relations indicators (such as number of grievances and tribunal cases), training days, cost to fill vacancies, time to fill vacancies, HR costs as a proportion of profit or total costs and time spent communicating with staff. Only nine per cent of the organisatons surveyed measured productivity.
In terms of soft measures, IRS (IRS 2002a) found that 85 per cent of the sample measured employee satisfaction, 72 per cent measured line manager satisfaction, 68 per cent measured senior manager satisfaction and 60 per cent measured customer satisfaction. Employee satisfaction was considered to be the most effective soft measure.
Such measures are frequently collected in an ad hoc manner, are not integrated or tied in with business strategy and may not result in action being taken. The CIPD Factsheet (2006b) on human capital provides a very useful framework in which to understand the different areas of human capital that may be evaluated. CIPD suggests that the data collected fall under five broad headings: performance data; demographic data; recruitment and retention data; training and development data; and opinion data. In the following section we give some examples of the more popular and simple measures using the categorisation proposed by the CIPD (2006b).
Performance data – specific example: absence analysis and costing
CIPD suggests that performance data may include performance management data, productivity and profitability data, customer satisfaction and loyalty data. Absence and attendance clearly comes into this category. For aggregate analysis the absence rate is the number of days of absence, that is, when attendance would have been expected, of all employees. The absence percentage rate is this figure divided by the total number of actual working days for all employees over the year, multiplied by 100. This simple percentage figure is the one most often used and enables the organisation’s absence level to be compared with national figures, or those of other organisations in the same sector.
The absence frequency rate is the number of spells of absence over the period, usually a year. Comparing this and the absence percentage rate gives critical information about the type of absence problem that the organisation is experiencing.
Absence data, as well as enabling external comparisons, can be analysed by department, work-group, occupation, grade and so on. In this way the analysis will throw up problem areas, and additional analysis can be undertaken to try to identify the causes of differing levels of absence in different parts of the organisation. The data may be supplemented by information from questionnaires or interviews with employees or line managers.
The purpose of producing this information is to understand the causes and extent of absence in order to manage it effectively. So, for example, such analysis may result in a new absence policy, employee communications about the impact of absence, appropriate training for line managers, changes to specific groups of jobs and the introduction of a new type of attendance system such as flexitime. The information provides a base for future monitoring. Absence data can be analysed further to provide benchmarks of ‘high’, ‘medium’ and ‘low’ absence levels in the organisation, and can be used to set improvement targets. This analysis can also be used to trigger specific management actions when an employee reaches different benchmark levels. For example, a trigger may be the number of days or number of spells per year or, as in the Bradford factor (see Figure 15.2 on p. 345 for the formula), a combination of both.
The costing of absence needs to have a wider focus than just the pay of the absent individual. Other costs include:
- line manager costs in finding a temporary replacement or rescheduling work;
- the actual costs of the temporary employee;
- costs of showing a temporary employee what to do;
- costs associated with a slower work rate or more errors from a temporary employee;
- costs of contracts not completed on time.
These costs can be calculated and provide the potential for productivity improvement.
Performance data – specific example: relating the workforce to organisational performance
Various analytical methods relate the contribution of the workforce to organisational performance. This analysis can be used to control headcount, and to measure organisational effectiveness and compare it with that of similar organisations. The information can also be used to communicate to employees what their contribution is to the business. Turnover per employee and profit per employee can be calculated in order to monitor performance and to demonstrate to each employee the importance of cost consciousness. If an employee of an organisation employing 3,000 employees realises that profit per employee is only £900 this means far more to that individual than expressing profit as £2.7 million. Cost consciousness suddenly becomes important as the fragile and marginal nature of profits is demonstrated. A further calculation expresses the cost of employees in relation to the total costs of production. To work this out, turnover less profit (that is, the cost of production) is compared with employee costs (salary plus on-costs). The percentage of production costs accounted for by employees will vary markedly according to the nature of the business. For example, in some pharmaceutical businesses people costs will account for 70 per cent of all production costs (due to a heavy emphasis on research and development) whereas in a less people-intensive business, as found in other parts of the manufacturing sector, people costs may only account for around 15 per cent. Changes in the percentage of people costs over time would need to be investigated. People costs are a good way of communicating to employees just how important they are to the success of the business.
Demographic data – specific example: equal opportunities analysis
CIPD defines these as data on the composition of the workforce, and these may include equal opportunities analysis which aims to provide an organisational profile of, most frequently, ethnic origin, gender, age and disability. The resulting percentages from this can be compared with national and local community figures to give an initial idea of how representative the organisation is. Further analyses break these figures down to compare them by department, job category and grade. It is in this type of analysis that startling differences are likely to be found, for example as shown in Figure 33.1.
The information gleaned can be used to:
- question the extent and spread of disadvantaged groups in the organisation;
- identify specific barriers to a more representative spread;
- formulate appropriate policy and action plans;
- set targets to be achieved and to monitor year on year compared with these base figures.
Other analyses can be carried out to show promotion, internal moves and secondment figures for disadvantaged groups compared with advantaged groups, for example white males. Further mention is made of these and the recruitment system in the following section.
Recruitment and retention data – specific example: turnover analysis and costing
We cover this aspect in Chapter 9 on Staff retention.
Training and development data
Typical training and development data include annual training days per employee, training spend per employee, existence of personal development plans for what percentage of the workforce and for which job roles, and the elapsed time between an employee joining an organisation and being involved in an induction programme. Other measures may cover the number of accredited or trained coaches and mentors, or the competencies/ skills of staff as judged by a variety of measures.
Whilst opinion survey data are not new, considerably more attention has been given to such data of late. For example in the National Heath Service there is an annual employee opinion survey which focuses on employee views of hours worked, appraisal, training, teamwork, injuries, harassment, work-life balance, job satisfaction, work pressure, intention to leave, quality of leadership, and positive feelings within the organisation, as well as other issues. Hospital Trusts are able to compare their ratings against the national picture and can also break down their data by job type.
Basic measures can be benchmarked externally against other similar organisations which would allow a meaningful comparison, such as competitors. They can also be benchmarked internally by comparing departments, different locations and so on. IRS (2002a) found that around one-third of the organisations it surveys regularly carried out external benchmarking and the same percentage regularly carried out internal benchmarking.
Whilst the measures described above can clearly add value, much of this work is done in an ad hoc manner and lacks strategic integration. We turn now to the higher, strategic end of the spectrum of HCM.
1.2. Strategic frameworks and scorecards for HCM
Higher-level approaches are more representative of human capital management, which the Accounting for People Task force defined as ‘an approach to people management that treats it as a high level strategic issue rather than an operational matter “to be left to the HR people” and seeks systematically to analyse, measure and evaluate how people policies and practices contribute to value creation’ (Kingsmill 2003, p. 5). The emphasis is not just on measurement, but on using this measurement approach as a management tool for effecting change and improvement. Such an approach addresses the problem often found in measurement, namely that the wrong things are measured (see, for example, Chartered Management Institute 2006, quoted in IRS 2006; Gratton 2004).
Through the use of a strategic framework or model the organisation can identify what drives the performance of employees in relation to the organisation’s strategy. These drivers can then be expressed as a range of measures with indicators, and targets can be set at about the levels that need to be achieved. These targets are often cascaded down to employee level, and form the base for the ‘hard’ metrics approach to performance management as opposed to the ‘soft’ good management approach to performance management which we discussed in Chapter 13.
There are a variety of models that can be used and Matthewman and Matignon (2005) identify six such frameworks: the human resource benchmarking model; the balanced scorecard; the human capital monitor; the human capital index; the engagement model; and the organisational performance model. We look at the Human Resource Scorecard and Mayo’s Human Capital Monitor in more detail below, and the Window on practice is about the Royal Bank of Scotland Group who use the engagement model.
Considerable attention has been given to the use of scorecards, such as the balanced scorecard (Kaplan and Norton 1992) and, later, the HR scorecard (Becker et al. 2001), in linking people, strategy and performance. These are perhaps the best-known scorecards, but many different scorecards have been developed over the last decade or so. Such scorecards utilise a range of measures of HR which are viewed as critical to the success of the business strategy, and which move the process of measurement on from an ad hoc to a strategic and integrated approach. Kaplan and Norton widened the perspective on the measurement of business performance by measuring more than financial performance. Their premise is that other factors which lead to financial performance need to be measured to give a more rounded view of how well the organisation is performing. This means that measures of business performance are based on measures of strategy implementation in a range of areas. Kaplan and Norton identify three other areas for measurement in addition to financial measures: customer measures, internal business process measures and learning and growth measures. In each of these areas critical elements need to be identified and then measures devised to identify current levels and to measure progress. Some organisations implementing this scorecard have developed the learning and growth area to include a wider range of HR measures.
Becker et al. (2001) argue that it is important to have a ‘measurement system [that] convincingly showcases HR’s impact on business performance’ (p. 4), otherwise, they argue, the HR function cannot show how it adds value and risks being outsourced. The system they suggest focuses on ‘HR architecture’, and by this they mean the ‘sum of the HR function, the broader HR system, and the resulting employee behaviours’ (p. 1). This is therefore a broad view of HR measurement, as we discussed at the beginning of this chapter. Becker and his colleagues have designed a seven-step process to clarify and measure HR’s strategic influence:
Step 1. Clearly define business strategy in a way that involves discussing how the strategy can be implemented and communicated.
Step 2. Develop a business case for HR as a strategic asset explaining how and why HR can facilitate business strategy – Becker and his colleagues suggest how current research relating HR to firm performance can be useful here.
Step 3. Create a strategy map – which should involve managers across the organisation, and needs to address the critical strategic goals, identify the performance drivers for each goal, identify how progress towards goals can be measured, identify barriers to goal achievement, identify required employee behaviour for goal achievement, question
whether the HR function is developing employee competencies and behaviours needed to meet the goals, and if this is not happening, what needs to change.
Step 4. Identify HR deliverables from the strategy map – which may include performance drivers and enablers; for example low turnover, high levels of specific competencies and so on may be needed to reduce product development time.
Step 5. Align HR architecture with the deliverables in step 4. Policies can be developed to result in these deliverables – for example policies encouraging low turnover may be supported by family-friendly and work-life balance policies, diversity policies, career development opportunities and so on.
Step 6. Design a strategic HR measurement system. This requires that valid measures of HR deliverables are developed. For example in specifying low turnover it would be important to identify which particular staff groups this applies to, whether voluntary turnover only is to be calculated, whether internal job moves are included and so on. Step 7. Implement management by measurement – Becker and his colleagues suggest that once the measurement system has been developed this can then become a powerful management tool.
In designing measures Becker and his colleagues suggest that HR efficiency as well as deliverables need to be measured. Efficiency measures tend to be cost measures, for example cost per new hire, or HR cost per employee. They suggest that these are both lagging indicators. Leading indicators can also be measured. These are defined as measures of ‘high-performance work system’ and HR system alignment. The high- performance work system appears to be defined in terms of best-practice-type measures, for example hours of training received each year by each employee, or percentage of the workforce regularly undergoing annual appraisal. HR system alignment indicates the extent to which the high-performance work system is tailored to business strategy via supporting each HR deliverable. Useful lists of HR deliverables and efficiency measures can be found in Ulrich (1997).
An alternative framework for monitoring, measuring and managing human capital is the ‘human capital monitor’ and this has been developed in the UK by Andrew Mayo (2001). The human capital monitor is designed to connect the intrinsic value of the human capital in the organisation with the working environment. It includes processes and systems which impact on employees’ behaviour together with the value that is created by people. As with the previous models discussed this is not specifically designed for the HR function to monitor itself. The model adds together the value of people as assets (box 1) and the motivation and commitment (box 2) to produce the people contribution to added value (box 3). The model is shown in Figure 33.3.
The first box in the model, people as assets, provides a method of balancing people costs with a measure of the value that they contribute. Mayo argues that calculating the value of people is important for four reasons. First, resourcing decisions should be about more than just cost; second, it is important to understand relative values of individuals and teams; third, it helps make informed investment decisions showing the relative benefits of investing in people as opposed to other assets; and finally, it enables the company to monitor whether its talent is increasing or decreasing. To demonstrate the types of measures that Mayo suggests, we use the example of capability, where the following measures are provided: personal behaviour; business and professional know-how; network of contacts; qualifications and experience; attitudes and values. In terms of maximising human capital we will look at potential. Here Mayo suggests that success in acquisition could be measured by total human asset worth, average IAM (individual asset multiplier) of new recruits and the increase in strategically important core capabilities. The drivers for acquisition of potential include employer brand and acceptance rates, among others.
We turn now to the second box concerning motivation and commitment. Here Mayo suggests such measures as absence levels, satisfaction surveys, attrition rates and reasons for leaving, among others. He suggests five influencing factors as listed in the model, and for the work group, for example, he proposes two measures: team assessments of working practices and a team stability index.
In the final box, people contribution to added value, Mayo suggests that the focus should be on wealth creation, which is a much broader concept than profit, as some of the wealth created can be reinvested in the business. To this end he compares a conventional income statement with a value-added financial statement, which goes beyond seeing people as just costs. In assessing current value Mayo suggests that work needs to be analysed into work which creates value and non-value added work (such as re-doing work, duplication, computer downtime, cross-charging and so on), and that the percentage of each type of work needs to be a focus. He argues that building future value is dependent on innovation and that measures of this need to be derived.
Case 33.2 on this book’s companion website, www.pearsoned.co.uk/torrington, focuses on human capital.
2. HUMAN CAPITAL REPORTING
Elias and Scarborough (2004) liken internal and external human capital reporting to management accounting and financial accounting, in that the first is internal and aimed at managing the organisation in an increasingly better way whereas the second is for external consumption. Currently there is minimal external reporting of human capital but a greater extent of internal reporting, which we suggest is a necessary precursor.
The British government’s White Paper, Modernising Company Law (2002), suggested that the largest 1,000 companies should publish an annual operating and financial review (OFR), and experts believed this would need to include a review of the ways that employees are managed (People Management 2002a). However this planned requirement has now been scrapped and a simpler version will now be required which meets the minimum demands of the EU Accounts and Modernisation Directive (EAMD) (Scott 2005). The report now required is entitled the ‘Business Review’, forms part of the Director’s Report, and actually came into force in April 2005. The Business Review requires ‘a balanced and comprehensive analysis of the business’, and that human capital management issues should be included ‘where material’. The scrapping of the OFR raised fears that human capital reporting would have less priority in the business; however the Business Review does apply in some form to all organisations except small firms. Angela Baron suggests that the better the information an organisation has on its human capital the more likely it is that business benefits will accrue (Manocha 2006), and it has been suggested that many companies that have prepared for the OFR will go ahead and produce the more extensive report. The Department for Business, Enterprise and Regulatory Reform (DBERR) has not discounted the possibility of the OFR being resurrected.
Matthewman and Matignon (2005) state that human capital reporting needs to be tailored to the goals, needs and character of each organisation. However the variety of internal and voluntary external reporting appears to be one of the factors that seem to hold back some form of mandatory external reporting. Whilst the Kingsmill Report (2003) declared that there could be no single approach to HCM others have not given up on this. For example Ruth Spellman of the IiP has set up a Human Capital Management Standards Group with the aim of establishing a universal set of metrics which can be used by organisations of any size and in any sector (IRS 2006). A less ambitious project is in the public sector where the Public Sector People Management Association is currently developing a template to allow all local authorities to evaluate the impact of human capital (Stuff 2007).
Other barriers to external reporting have been identified by Elias and Scarborough (2004) as a lack of interest among important external audiences, and Huselid (2003, quoted in Stiles and Kulvisaechana 2003) suggests a major problem is the lack of sophisticated HRISs and a fear that human capital information is too sensitive to reveal to competitors. In addition he suggests that there are concerns from unions and employees that such information might be divulged. The type of information which is most likely to be reported externally, according to Huselid, includes the percentage of employees in stock plans; average pay; revenue per employee; training expenditure and compensation.
Source: Torrington Derek, Hall Laura, Taylor Stephen (2008), Human Resource Management, Ft Pr; 7th edition.