4.1.1.1. The definition of monitoring indicators

It has become common practice to include monitoring indicators in the definition of an HR policy. This is in order to measure its application. These monitoring indicators thus include the main dimensions of the policy and define a reporting or dashboard dedicated to monitoring their implementation. For example, if a company has defined that employees with disabilities should benefit from workstation accommodation, a monitoring indicator may be percentage of employees with disabilities who have benefited from workstation accommodation. Similarly, if a company has defined that employees should be able to benefit from an interview with the HR department on request about their career development, several monitoring indicators can be defined:

• – number of employees who have had a career development interview with the HR department;
• – number of employees who have requested an interview;
• – ratio between the number of employees who received an interview and the number of employees who requested one.

The combination of these indicators ensures that the company’s policy has been communicated to employees (and that they are therefore aware of this new right), that it meets a need and that the right is granted as provided for in the policy.

It should also be noted that monitoring indicators can be of several types: fully numerical (e.g. number of employees interviewed) or binary coded in yes/no format (establishment of a committee, commission, etc.). In addition, they can concern the different actors involved in the policy: managers, HR, employees, etc. They can also vary widely, in the sense that the same action can result in a wide variety of monitoring indicators. On the other hand, it is important to be able to establish a form of concordance between the aims pursued by the policy and the monitoring indicators defined (Box 4.1).

Measuring the implementation of HR policies can be an important issue as there may be a significant gap between the definition of a policy and its implementation by stakeholders (Box 4.2).

The definition of monitoring indicators therefore indicates the possible existence of a gap between the planned policy and the measures implemented. However, these indicators are not sufficient to fully reflect the reality on the ground.

4.1.1.2. Monitoring indicators versus appropriation by local actors

Indeed, the monitoring indicators themselves are tools that local actors can appropriate, and can in part divert. Appropriation of management tools theories thus recommend distinguishing three dimensions of management tools (De Vaujany 2005, 2006):

• – a so-called rational dimension, which corresponds to the purposes attributed to the tool by its designers;
• – a so-called psycho-cognitive dimension, which focuses on the learning necessary for the actors to appropriate a tool;
• – a so-called sociopolitical dimension, which looks at the relationships between actors, how they are modified by the tool, and how they affect its appropriation.

If we consider monitoring indicators as management tools (Chiapello and Gilbert 2013), we can therefore apply this framework to illustrate how a set of monitoring indicators is limited when it comes to reflecting the reality on the ground. First of all, it must be stressed that the HR policy itself is a management mechanism or tool that can lead to selective appropriation by local actors. Secondly, monitoring indicators are also management mechanisms or tools, which are also characterized by selective appropriation (Figure 4.1). Finally, HR policies are translated into monitoring indicators based on the policy objectives. At the same time, they are translated into practices that reflect selective appropriation.

The diagram then highlights that monitoring indicators can be doubly ineffective in reporting on the reality of HR policy implementation. First, they provide a distorted view of the practices implemented; second, they can themselves be subject to a selective appropriation (Box 4.3).

Finally, monitoring indicators, however sophisticated they may be, have limitations that make them incomplete measures of HR policy implementation. The three dimensions highlighted by the researches about the appropriation of management tools reflect the different factors of incompleteness (Table 4.1).

Thus, situations where local managers find that a measure is not appropriate, or where they have poor relations with other actors with whom they are supposed to coordinate to implement a measure (e.g. recruitment officers for recruitment related measures), may result in minimalist and partial implementation of the measures (Coron and Pigeyre 2019).

However, the quantified indicators related to the implementation of this measure may not differentiate between a minimalist and a more sophisticated implementation. In addition, these quantified indicators can lead to complex feedback, either because of the complexity when constructing the indicator (e.g. an indicator that would be composed of complex subindicators to be measured, such as the pay gap, as we have seen), or because of the complexity of the feedback chain (in the case of indicators that require feedback from several actors, for example).

Finally, quantification is regularly used to measure the implementation of HR policies. Measuring this implementation is indeed essential, given that there may be a significant gap between the definition and implementation of these policies. However, quantified indicators are often found to be limited to reflect the reality of this implementation and may themselves give rise to partial appropriations.

4.1.2.1. The definition of quantified commitments

More and more, HR policies defined by companies are accompanied by quantified commitments. For example, gender equality policies have targets for increasing the rate of feminization by professional field or by responsibility or for reducing pay gaps; disability policies have targets for the employment of people with disabilities and for workplace accommodation; policies for setting targets for increasing the rate of commitment measured in annual social climate surveys (Box 4.4), etc.

Defining quantified commitments makes it possible to demonstrate the company’s commitment, and gives precise indicators to monitor the company’s progress in the areas concerned. This is a first step toward measuring the effects of the policy. The instructions given by the managerial literature on the definition of quantified objectives refer to recommendations on the definition of objectives for employees: measurable, achievable, time-bound objectives in particular. However, some cases may lead to the definition of unattainable quantified commitments, particularly when stakeholders do not agree on projections showing that they are unattainable (Box 4.5).

Defining quantified commitments and targets is therefore a first step in facilitating the evaluation of HR policies, as it allows for a comparison between what was planned and what has been achieved. However, it is still risky to interpret the achievement or non-attainment of numerical objectives as an indicator of the impact of HR policies.

4.1.2.2. Is isolating the effects of HR policies an impossible task?

Indeed, all public policy evaluation methods face the same difficulty: how can we isolate the effect of policies from contextual or structural effects themselves (Behaghel 2012)? Thus, how can we ensure that any measured change (in the unemployment rate, for example, or the death rate on the roads) comes from the policy implemented, and how can we ensure that a lack of change reflects a lack of policy effects? This question is just as central in HR. Indeed, the majority of HR phenomena are multifactorial, i.e. they react to multiple factors. Thus, a company’s absenteeism rate depends not only on the company’s policy on absenteeism, but also on individual factors (gender, age, number of children, etc.) and external variables such as epidemiology. As a result, if a company defines a policy to reduce absenteeism, and the following year the annual influenza epidemic is particularly fierce, absenteeism indicators may remain stable, giving the impression of there being no effect, while the policy may still have contributed to reducing absenteeism.

An HRM situation may therefore evolve according to structural and contextual effects. Structural effects refer, for example, to the demographic structure of the company. Thus, in terms of workforce, a population with a high average age or a high percentage of employees close to retirement age will structurally experience retirements in the coming years. Contextual effects refer to temporary circumstances at a given time t. For example, in terms of employee commitment, a company experiencing an economic crisis due to an unexpected drop in subsidies may see its employees’ commitment decline because they are aware of the precariousness of their situation.

Isolating the effect of HR policies then requires the ability to compare the situation knowing that the policy has been implemented with an often hypothetical situation of no policy, which makes it possible to control the structural and contextual effects. Several methodological strategies can be used to solve this difficulty (Behaghel 2012): instrumental variables, controlled experiments, such as cohort or panel monitoring. For structural effects only, a somewhat inaccurate but simpler strategy is to mobilize situation projections (Box 4.6).

Despite these methodological strategies, it remains complex to isolate the effects of HR policies. Finally, the evaluation of HR policies, whether to measure their implementation or their effects, remains a very difficult and sometimes impossible operation. However, quantification makes it possible to provide elements for reflection and exchange, particularly with social partners.

4.2.1.1. How can the performance of the HR function be defined?

Many studies suggest that different types of HR function performance should be distinguished (Boudreau and Ramstad 2004):

• – the impact, which refers to the effect of HR policies on the company’s strategic activity. For example, if a company improves its recruitment policy, the question of impact would require considering the effect of a potential better selection on the strategic activity;
• – the effectiveness, which refers to the effect of HR policies on employees. For example, if a company implements a policy to improve working conditions, effectiveness could correspond to a potential increase in commitment;
• – the efficiency, which refers to a cost–benefit calculation, and corresponds to a kind of return on investment for the HR activity. For example, if a company implements an ambitious and costly training program, the notion of efficiency raises questions about the gains it derives from it, and the relationship between these gains and the costs involved (Box 4.7).

These three types of performance can be arguments for the importance of the HR function to the company’s management, but also to the financial and operational management.

However, these indicators have several limitations, and first and foremost they are particularly difficult to translate into calculation rules and therefore to measure. For example, how can we measure the financial efficiency of HR operations, or the benefits associated with HR programs?

Other authors have focused on proposing more precise indicators for measuring HR performance by a major process (Cossette et al. 2014). These indicators also have three dimensions: effectiveness, efficiency and impact (Box 4.8).

Similarly, some of these indicators may be difficult to translate into specific calculation rules (e.g. on learning related to training).

In addition, the same phenomenon is observed as that highlighted in the previous section, namely that indicators are always unable to reflect the totality of a reality. As a result, companies may be tempted to define a very large number of indicators, hoping to better understand the performance of the HR function. However, there is a risk of getting lost in the process and ultimately not really mobilizing this mass of information.

4.2.1.2. Choices of indicators according to the organizational context

In addition, the indicator suggestions provided by these various studies are confronted with an important limitation: the choice of indicators is strongly influenced by the organizational context, and in particular by the HR strategy and strategy of the company concerned. Thus, a company that is dealing with a cost reduction strategy may not define the same indicators of efficiency and effectiveness of its HR function as a company in a development and innovation strategy.

In fact, Cossette et al. (2014) advise the following questions be asked before defining indicators:

• – What are the issues facing the organization?
• – What are the HR issues arising from these organizational issues?
• – Which HR activities are concerned by these HR issues?

Only then can the two questions that will define the indicators of effectiveness and efficiency come up:

• – effectiveness: how can we determine if HR activities are achieving their objectives?
• – efficiency: what are the costs associated with these activities?

The importance and scope of these issues underline the impossibility of adopting a standardized approach to define quantified indicators to measure the performance of the HR function. However, measuring this performance is an important issue for the positioning and legitimacy of the HR function in the company. This is all the more important as most other functions (finance, marketing, etc.) are more easily able to demonstrate their added value in terms of organizational performance.

4.2.2.1. From HR function performance to employee behavior

The transition from the first to the second step is based on the argument that HR function activities are supposed to have an effect, on the one hand, on employee behavior at work (such as cooperation or compliance with procedures) and, on the other hand, on employee attitudes toward the company (commitment, loyalty, for example). This rhetoric is therefore intended to highlight the key role of the HR function for employees. It is based on intuitive reasoning, but also on the perhaps illusory idea that employees react to the incentives put in place by the HR function, by changing their behavior and attitudes.

Thus, with regard to behavior, intuitively, it seems that deploying training on compliance with safety rules on a mass scale leads to better compliance by employees and therefore a reduction in workplace accidents. However, many studies have highlighted the gap between this rational conception of organization and the reality of organizational daily life. Indeed, many factors can limit the effect of HR activities on employees: misunderstanding of objectives, power games, inadequacy of activities with real work, etc. For example, training on safety rules may not be sufficient to change practices, if not complying with the rules saves employees’ time, if they have long established work routines that are based on non-compliance with these rules, or if they feel that these rules limit their individual freedom of action (Greasley et al. 2005).

Concerning attitudes toward the employer, the rhetoric is based on the idea that the relationship between employees and the employer depends on the HR policies put in place by the employer. In fact, several theoretical currents are based on this link or seek to demonstrate it. For example, work on the employer brand concept seeks to link the perception of the benefits of working for a given employer to variables such as business attachment, faithfulness or loyalty (Ambler and Barrow 1996; Charbonnier-Voirin et al. 2014). Similarly, work in the field of organizational justice highlights the influence of perceived justice on the intention to remain in the company, commitment, motivation and loyalty (Cropanzano and Ambrose 2001; Bourguignon and Chiapello 2005; Jepsen and Rodwell 2012; Hulin et al. 2017). Finally, some studies consider that one of the main missions of the HR function is to develop employee commitment as much as possible by using different levers: working conditions, working interest and development opportunities (Cleveland et al. 2015).

The success of this rhetoric in managerial discourses, but also in academic work on the HR function is undoubtedly explained by the need for the HR function to legitimize itself with the management, as well as employees and operational management. It is a question of justifying the importance of its activity and therefore ultimately its operating costs, knowing that it is regularly perceived as a cost center and not a profit center for the company.

However, many methodological obstacles limit the scope of this rhetoric. First of all, quantitative studies often give contradictory results on the extent to which HR activities have an impact on employee behavior and attitudes. Second, and this explains part of the previous point, measuring these different elements (HR activities and employee attitudes) requires the construction of variables that reflect them.

Yet, there may be a wide variety of measures of commitment, faithfulness, loyalty, but also HR activities, as underlined in Chapter 1. For example, measuring commitment can take very different forms, illustrating the methodological difficulty in understanding this construct.

Recently, companies have been offering solutions for measuring the social climate, at a very frequent rate (weekly, for example) and on very specific points. Thus, if a company decides to move its premises, this type of pulse survey can make it possible to quickly evaluate the effect of the moving announcement, and then of each project stage, on the social climate.

These solutions are therefore part of the myth of objectivity described in Chapter 2: the frequency of measurements gives the impression of reporting changes in real time (which is highlighted in the communication of these companies), and the selling points of this type of solution are based on the idea of reporting a reality that would be inaccessible without these data. However, this type of solution must stand out and prove its added value compared to other measures of the social climate, such as annual surveys. Finally, as seen above, it is difficult to isolate the effect of HR activity on employee behavior and attitudes, as the latter are strongly influenced by other contextual and structural factors.

For their part, qualitative studies highlight the gap between the objectives of HR policy makers and what is happening in the field at the local level. Thus, it was recalled that it is not at all certain that a training policy on compliance with safety rules will lead to better compliance with these rules (De Vaujany 2005; Greasley et al. 2005).

4.2.2.2. From employee behaviors to organizational performance

The transition from the second to the third step implies, for its part, demonstrating a link between employee behaviors, attitudes and their performance (and therefore ultimately organizational performance). This is based on several rhetorical arguments. The first argument conveys the idea that more satisfied, more faithful, more loyal employees will perform better. This argument comes from several theoretical currents that have their source in the school of human relations. Indeed, this school and Elton Mayo’s experiences in the Hawthorne factory suggest that employees’ individual productivity depends not only on financial incentives or the way work is planned, like Taylorism, for example, but also on valuing and listening to employees, and paying attention to the work environment. Subsequently, other trends have followed this path, emphasizing the variety of sources of individual motivation and the fact that extrinsic motivations such as remuneration or control are not enough. The second argument suggests that overall organizational performance is closely linked to the individual performance of employees. This argument therefore adopts a rational vision of the organization, where organizational performance is made up of the sum of individual performances. However, this has been challenged by various trends underlining the importance of group formation or the impossibility of detaching individual and collective performance (Marchal 2015).

As in the previous cases, these two arguments are relatively difficult to demonstrate. In addition, they also raise ethical questions. Thus, they tend to subject the imperative of well-being (or autonomy, good working conditions, job satisfaction) of employees to a performance imperative. In Chapter 1, the example of the gender equality business case was given. This business case is criticized in particular from this angle (Sénac 2015): is it legitimate, ethical, to subject the imperative of equality to a performance imperative? What will companies do if one day it is demonstrated that equality does not bring about more performance?

Finally, this business case approach seems to represent an important issue for the HR function, which is seeking to legitimize itself. The many methodological and ideological limitations do not diminish the interest of the HR function in this rhetorical use of quantification. For their part, some academic trends continue to use the staircase model and measure links between HR activities, employee attitudes and behaviors, and organizational performance (Box 4.9).

Finally, the HR function has a strong interest in mobilizing quantification to demonstrate its performance and its effects on, on the one hand, the company’s strategic success (with senior management in particular) and, on the other hand, the social climate and employee commitment (with operational management in particular). Once again, by taking up Sainsaulieu’s (2014) assumptions on collective and individual identity at work, quantification can then contribute to the construction of the professional identity of the HR function, by providing arguments that enable it to strengthen its positioning and legitimacy with other functions and the top management.

4.3.1.1. Automation risk factors

The jobs with the highest automation risks are those that combine information processing tasks with a low relational level (Deming 2017). Indeed, advances in algorithms and artificial intelligence now allow machines to be more efficient than human beings in processing information. This efficiency results in particular in greater speed, and therefore the possibility of processing the exhaustiveness of information, instead of having to carry out a first sorting or a first synthesis as a human being, who does not have the same capacities, particularly in terms of memory, should do. This is one of the purposes of algorithms: sorting, selecting, prioritizing masses of information (Cardon 2015). This information may or may not be structured, and may, for example, consist of words, figures, images, etc. Thus, an algorithm will be much easier (and faster) than a human being to perform, among other things, the following tasks:

• – locating and counting specific words in a text;
• – measuring word co-occurrences;
• – performing calculations based on a set of figures;
• – quickly tagging a set of images.

However, it should be noted that the algorithm does not analyze the meaning of this information as a human being could: this is the difference between the substantial approach and the procedural approach highlighted by Cardon (2018). In other words, the reconciliations that the algorithm will be able to make between two keywords (computer and laptop, for example) will come from calculations that will measure a regularity in the proximity of these two words, and not from an understanding of the meaning of these words. This explains why Google Translation can nowadays offer translations from very rare languages into other very rare languages (in cases where, for example, there is no bilingual dictionary between these two languages): the system is not based on an understanding in parallel with both languages, but on word matching calculations based on very large volumes of written content, and can use a very common intermediate language (such as English) as a bridge between two rarer languages (Mayer-Schönberger and Cukier 2014). However, some discourses explain that the distinction between the substantive and procedural approaches is changing due to the ability of algorithms to find or reconstruct concepts of some kind. Thus, from a large number of cat photos, an algorithm can nowadays reconstruct more or less the cat concept in order to be able to identify cats in other photos: this is the principle of unsupervised learning¹ (CNIL 2017).

On the other hand, algorithms remain relatively less efficient than human beings in terms of relational skills (Villani 2018): for example, they do not currently experience the empathy or emotions that human beings may experience. Similarly, the creative field is still relatively protected from any automation. As a result, the most easily automated tasks are those that combine information processing activities with poor interpersonal skills or creativity.

Physical tasks can also be automated, but here the focus is on the HR function, which is not very concerned with this type of task. I only wish to point out that tasks that seem easy for a human being are sometimes less so for a machine, which has different constraints (fewer physical constraints, but more constraints related to understanding the environment, for example).

Taking these automation risk factors into account has made it possible to develop models that predict the automation risk of each profession. The best-known study on the subject was conducted by Frey and Osborne (2017) and covers data for the United Kingdom. In particular, it was publicized by the BBC, which transformed the study into a search engine, giving each business a probability of automation. It shows that administrative occupations – for example in financial or legal services – present a high risk of automation, unlike occupations that have a strong relational component (e.g. psychologists and social workers²).

4.3.1.2. The HR professions concerned

The HR function is no exception. The most likely occupations for automation are therefore those that combine information processing tasks with few relational components. This sometimes involves distinguishing between different activities within the same profession.

Thus, depending on the company, a recruitment manager can ensure the recruitment process from start to finish, from the sorting of CVs to the integration of the selected candidate into the company. In other companies, this set of activities can be split between several professions: selection officer who is only responsible for pre-selecting CVs, recruitment officer who is responsible for interviewing candidates and liaising with the line-manager concerned, and integration officer who is in charge of the candidate once they have been selected. However, these different tasks do not present the same risk of automation. While CV sorting seems easily automated, since it is an information processing task, which does not involve a relational relationship with candidates, the other two activities seem to present lower automation risks. For example, many companies have embarked on the development of CV pre-selection algorithms (Box 4.10).

Thus, the activity or occupation of CV pre-selection seems to present a significant risk of automation. Similarly, administrative activities and professions are also threatened by the development of algorithms on the subject. More recently, the rise of artificial intelligence has led to considerable progress in the field of conversational robots (chatbots), suggesting many possibilities for automating the handling of administrative issues (Box 4.11).

A certain number of HR activities are thus carried out by machines and no longer by human beings.

4.3.2.1. Professions that will disappear….

The HR professions that will disappear are mainly found in structures where the work of the HR function is highly segmented and compartmentalized. Thus, as we have seen, CV selection officer positions in companies that segment recruitment activity between CV pre-selection and interviewee qualification may be more affected by this threat of automation than recruitment officer positions that handle the recruitment process from start to finish. Similarly, operational HR dedicated solely to handling employees’ administrative issues may see their positions disappear, unlike those dealing with a more diverse range of activities.

Therefore, several options are available to the companies concerned. A first option is to reposition the people concerned in other professions. This option requires a reflection on the skills that can be transferred from one profession to another, but also the collection of the wishes and desires of the employees concerned. Thus, contrary to what seems most intuitive, it is not obvious that a selection officer can easily and willingly convert to the qualification of candidates through interviews. Indeed, this is a position with a relatively similar purpose, but with very different working methods (in terms of human contact, organization of working time, for example).

A second option is to create roles related to machine monitoring, in a movement similar to that of robotization that has prompted plants to create positions related to robot management. An operational HR dedicated to handling routine administrative questions from employees could thus take care of managing the chatbot (which automates part of its tasks) while remaining present to deal with more complex questions that are not handled by the robot.

A third option is to rethink HR occupations to encompass a wider variety of tasks. Thus, companies that, in the interest of profitability, have highly segmented HR activities could revisit this segmentation and think about broader HR activities. The segmentation between CV selection officer and recruitment officer could thus be abolished, as well as the segmentation between HR operational staff responsible for answering administrative HR questions and administrative HR experts.

These three options allow companies to take advantage of the disappearance of certain professions to reduce the HR workforce. Thus, option 1 implies assigning the people concerned to other tasks, possibly outside HR. Option 2 would in any case involve a reduction in the number of jobs, since a machine manager could manage machines replacing several employees. Finally, option 3 would consist of taking advantage of the productivity gains allowed by automation to enrich each profession. Admittedly, it is possible that some companies may initially remain resistant to this automation under pressure from the social partners, for example. But the strong and permanent incentives to reduce the operating costs of the HR function will undoubtedly lead them to revise their positioning.

4.3.2.2. … or jobs that are likely to evolve?

Other professions may evolve. To take the example of recruitment managers who follow the recruitment process from start to finish, this profession would have to evolve, in particular toward a reduction in automated tasks (e.g. CV pre-selection stage) but also toward collaboration with machines or algorithms. For example, a recruitment officer may have to use the results provided by an algorithm to decide on the final list of candidates to be interviewed. This evolution implies two major changes.

First of all, it requires the HR function to be trained in the use of results from algorithms in order to interpret them without fetishizing them. Thus, understanding how results are produced, from which data, on which rules, makes it possible to question them, to reconsider them and finally not to consider them as absolute truths. This freedom of criticism and questioning seems necessary so that HR actors retain responsibility for decision-making and do not delegate it to machines whose operation they do not understand, i.e. ultimately to the designers of these machines.

Second, it would probably be preferable for these HR actors to participate themselves in algorithm design. Indeed, they cannot only provide expertise in the HR field, but also guarantee a form of algorithm ethics. For example, it is the recruitment officers who are able to explain the criteria used to recruit individuals and to stress the importance of the fight against discrimination. However, this change requires collaboration with statisticians, computer scientists and data experts, which will not be self-evident given the distance between the vocabularies, expertise, skills and positioning of these two functions. In addition, this collaboration may be part of a very unbalanced relationship, as the knowledge and skills of data experts and computer scientists may seem more esoteric, and more difficult to explain to outsiders than those involved in the HR function.

In fact, these two changes require specific training to enable the HR function to acquire skills in data analysis, statistics and IT.

This chapter focused on the positioning of the HR function in relation to quantification. Quantification – a tool for evaluating and even legitimizing the HR function – has recently also emerged as a threat for some HR professions. As a result, the relationship between the HR function and quantification may be ambivalent. Thus, it can be characterized by a certain neutrality, when the HR function uses quantification to measure its action and effects, while paying attention to methodological rigor criteria and the limits of these measures. It can also be characterized by a form of instrumentalization when quantification is used for rhetorical purposes; for example, to highlight the contribution of the HR function to organizational performance. Finally, it may be characterized by a kind of fear, due to the most recent advances in the use of data to automate certain tasks previously performed by human beings. Whatever the prevailing feeling, the actors of the HR function cannot avoid a reflection on their individual and collective positioning with regard to quantification, and the new tools that are emerging and becoming more and more important.