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Business unit performance and employee engagement

Abstract

The complexity of multilevel relationships –more or less ignored until recently although

present in everyday life- challenges the way of approaching and analysing the causality

between variables that occur at different levels. Within this perspective, the topic of

employee engagement, traditionally seen at individual level, emerges as an explanatory

variable for higher level facts such as performance and turnover, presented by the theory

as potentially affected by the level of engagement. This research embraces the

framework of Coleman‘s diagram to explain the relationship between the micro factor

(engagement) and the macro outputs (business unit performance and turnover) with the

novelty of a micro to macro perspective, one of the less explored in social sciences. Our

findings show an effect of engagement upon performance and turnover which is an

indirect link, mediated by individual level characteristics such as intention to leave –in

the case of turnover- and job level –for performance.

Keywords: multilevel analysis, micro-macro linkages, employee engagement,

performance.

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Introduction

Extant literature about routines and organisational capabilities reinforce the role of

collective level phenomena as opposed to individual level facts (Johnson 2003; Vromen

2008) nevertheless, understanding and improving performance, to attain a sustainable

competitive advantage, ―requires more than good incentive design. It involves creating

internal organisational systems that support the creation of organisational identification

and loyalty.‖ (Teece, 2009)

Johnson and Huff (Johnson; Langley et al. 2007) affirm that efficient responses to

competition include decentralisation, meaning that some strategic decisions are now

made at line-managers level given their proximity to customers and their skills and

knowledge derived from day-to-day praxis. Therefore, despite the macro-level nature of

resources such as knowledge, reputation, creativity, culture and innovation capability

(Carter et al, 2008) it is in the micro-level where individuals direct their actions

―towards achieving organisational objectives‖ (Teece, 2009).

This research will follow Coleman‘s framework to study the interactions that occur

between an individual psychological category –employee engagement- and collective

level outcomes such as department performance and turnover. The unit of study is a

multinational pharmaceutical company –with the fictional name of ARC Company- and

within it 507 employees from 26 departments in four countries. To carry out this

investigation, a quantitative methodology will be used in the form of statistical analysis

of the results derived from an internal engagement survey and Human Resources data.

Theoretically, we will build upon the most recent studies on multilevel research and

dynamic capabilities –understanding employee engagement as one of those capabilities-

and will attempt to address two gaps left by current theories, which will form the core

contribution of this research:

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1. The above mentioned levels of research (individual and collective) have been

separated – the business unit level studied by researchers in marketing and

strategy, the individual level studied by work psychologists. They have not been

linked together in multilevel research designs, which give some novel and

important research questions: What is the link between business unit turnover

and individual employee engagement? Does a high level of employee

engagement lead to greater business unit performance?

2. The ecological or atomistic fallacies that may and actually hinder some of the

recent research by assuming either that a relationship observed between

variables at an aggregated level also occurs at a micro level or that phenomena

described at micro-level can be generalised to collective level.

Linking business unit goals, objectives and strategies to the individual capabilities and

outcomes should provide guidance for management strategies that can lead to attain

sustained competitive advantage and furthermore the developing of concepts and

theories that stand for how organisations can manage themselves in order to compete

effectively within an external environment.

To sum up, this research aims to build on the new agenda in strategic management

research of the micro-foundations of strategy (Felin & Foss, 2006; Teece, 2007). It

brings together macro- and micro-level literatures which are both extensive but have

been largely separated up to now. Both literatures refer to the other, but they make

assumptions about linkages rather than test them empirically. As a result, we do not

know how these linkages work, and we do not know how much the findings from the

separate literatures depend on unproven linkages.

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MULTILEVEL ANALYSIS

The search for the ultimate source of competitive advantage in management studies can

be compared to that of the Holy Grail. Everyone looks for the formula that is going to

take the organisation to the highest degree of profitability at minimum costs, but the

exact location of such power remains elusive. One of those theories that try to explain

how to develop and achieve greater competitiveness is the resource based view (RBV)

which refers to the irreplaceable array of competences that determine success or failure

of one organisation. In simple words, what the organisation is able to do and have,

departing from ―the sum of all intangible and tangible assets‖ (Carter, Clegg et al.

2008).

Following the arguments of the RBV, the scarcity of unique skills, abilities and assets

in today‘s open market as well as the relatively easy access to technology and

information emphasize the need for strategies based on quick adaptation and

exploitation of the enterprise-specific competences (Teece, 2009). ―In these fluid

resource markets, sustainable advantage must lie in micro-assets that are hard to discern

and awkward to trade‖ (Johnson et al, 2003).

Current literature focuses on collective level when referring to strategic management,

routines and organisational capabilities (Yammarino and Dansereau 2003; Teece 2007;

Vromen, 2008). However, the role of individual agents‘ actions and interactions in

originating, maintaining, revising and revising those capabilities is crucial. Felin and

Foss (2006) sustain that ―more attention should be paid to exactly how individual agents

and their actions and interactions are involved in the emergence and functioning of

routines‖, that is micro-foundations.

―Mechanism approaches to sociological explanation require the possibility of breaking

down human and social behaviour into discrete components, or so- called micro-

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foundations, of action, such as Hedström‘s conception that human action is rooted in

individuals‘ desires, beliefs, and opportunities.‖ (Vesely, 2008). Traditionally, research

involved only one level of action –either individual or collective- but more recently, the

complexity of organisational dynamics and business environment have required more

sophisticated analyses that involve phenomena occurring at both levels with highly

complex interactions (Klein and Kozlowski, 2000).

In hierarchically ordered systems, multilevel analysis allows to describe and understand

―the interrelationships among variables measured at different levels of observation‖.

This type of investigation reduces the likelihood to incur into the ecological or atomistic

fallacy. The ecological fallacy can occur ―when researchers generalize findings from the

aggregated to the individual level‖ and the atomistic fallacy can occur ―when they

attempt to generalize from the individual to the aggregate level‖ (Croon and van

Veldhoven, 2007).

―Just because the relation holds at the lower level does not mean it will also hold at

higher levels. Relationships that hold at one level of analysis may be stronger or weaker

at a different level of analysis, or may even reverse direction‖ (Ostroff, 1993). This

multilevel context is particularly useful to address ―psychological effectiveness‖,

meaning for example, ―how individual psychological variables exert an influence on the

performance of higher level units‖ (Croon and van Veldhoven, 2007) as in the present

case, the effect of employee engagement in business unit outcomes (turnover and

performance). The independent variable employee engagement is studied at a micro

level whereas turnover and performance are collected at an aggregate level for the

relevant business units.

Within the multilevel approach to social sciences, the field of micro-to-macro linkages

is one of the least explored. Research usually focuses on the impact of a social context

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into individual behaviours. ―There is much less attention to how properties of

individuals aggregate and shape the social institutions within which stratification

processes take place‖ (Vesely, 2008).

This doesn‘t mean however that pre-eminence should be given to either level. Neither

the macro-level nor the micro-level have superiority upon the other, on the contrary

both need to be thought of and managed effectively for the organisation to succeed in

achieving competitive advantage.

The firm-level goals, objectives and strategies are to be implemented in the workplace

but also should be conceived and planned keeping in mind the individual capabilities

and outcomes. ―The explanation of collective phenomena must ultimately be grounded

in explanatory mechanisms that involve action and interaction that is methodological

individualism‖ (Felin, 2006).

Coleman’s diagram

Figure 1 illustrates one the most popular frameworks to understand and analyse

multilevel relationships in social interactions. Developed by the sociologist James

Coleman (1990), the model represents four different types of possible relationships

between macro (collective) level and micro (individual) level. There is a clear

differentiation between levels of ―action and interaction‖ as well as a proposition of the

way in which both relate together defined by the directions of the arrows.

(Figure 1 about here)

Arrow 1-Macro to micro relationship (expression)

Nelson and Winter‘s (1982) adaptation of the Evolutionary theory to organisational

studies addressed the way in which firm level routines and capabilities are a decisive

factor for success. It was clear then that collective level characteristics were not only the

depository for firm resources but the modifiers of individual-level practices. This first

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arrow accounts for the impact that social or collective phenomena have in the lower

level, a process that has been labelled as expression (Sprigg and Jackson, 2006). Those

ways in which macro-level characteristics influence factors at the micro-level can be

exemplified by the effect that organisational culture exerts upon the identification of

employees with the organisation. Taking the example further, we could add that such

influence transcends the internal dynamics and interacts with the outside environment of

the firm, for instance in attracting certain types of individuals while discouraging others.

Arrow 2- Micro to micro relationship

The micro to micro linkage has been approached by organisational psychologists mainly

in the causality between attitudes or beliefs and behaviours. At the individual level, for

example, the employee‘s psychological capital –consisting of hope, resilience, optimism

and efficacy- correlates positively to performance and it ―mediates the relationship

between supportive climate and performance‖ (Luthans, Norman et al. 2008). Much

debate is still ongoing around the nature of such links. Some authors have noticed that

―one possible reason that the satisfaction-performance relationship has not been

substantiated is that researchers have considered the relationship solely at the individual

level of analysis‖ (Ostroff, 1992 cited by Judge, Bono et al., 2001)

Arrow 3- Micro to macro relationship (emergence)

The process of emergence stresses the ways in which structured order emerges at the

system level as a result of the behaviour of actors at a lower level, for instance,

individual performance transformed into business outcomes. In spite of the obvious

relevance of understanding this process to fully exploit individual capabilities and

transforming them into organisational advantage, little has been done on this regard

within strategic management investigations (Felin and Foss, 2006).

Arrow 4- Macro to macro relationship

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Despite being the most explored relationship in social research (Harter, Schmidt et al.,

2002) interactions that happen only at a macro level remain subject of heated

controversy nowadays.

Felin and Foss (2006) argue that it is virtually impossible to find this type of relation

given that ―there are no conceivable mechanisms on the social domain that operate

solely on the collective level. There simply are no mysterious macro-level entities

directly producing macro-level outcomes.‖ Vromen (2008) differs with the point that

explanations at a macro-level are incomplete because ―they miss out on crucial links in

the causal chain connecting macro phenomena with each other‖. He argues that by

―squaring‖ Coleman‘s diagram ―one can see why and how macro-explanations need not

miss out any link in the causal chains that connect macro phenomena. Micro-analyses

are still needed, but not to specify causal links that macro-explanations miss out on.‖

According to this author individuals are ―constitutive components parts in macro

phenomena‖ (Vromen, 2008). On the contrary, Coleman posits that ―...there is no

tangible macro level...the macro level, the system behaviour, is an abstraction‖

(Coleman, 1990). For him individuals transform the structure of positions under

influence of their changing goals, creating a new context for themselves and thereby

contributing to the transition in the organisation of society (Vromen, 2008).

Arrow 5: macro to micro

The addition of Felin and Foss to Coleman‘s framework is defined by arrow 5 which

they conceived as a direct connection with the individual behaviour coming from

collective entities. Same as the latter explanation, but in reverse sense, this research

intends to focus on the foundations of unit-level facts in the attitudes held at a micro

level and how they interact with a higher instance. Like with arrow 5, our approach –

denoted as arrow 6 in Figure 2- can be described as a ―short-cut‖ between individual

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behaviours and collective outcomes, without going deep into the micro-micro

relationship between individual beliefs and attitudes (arrow 2). This is a novel way to

explore management studies from the perspective of micro-foundations and it follows

on the work of Felin and Foss (2006) as an attempt to address their question ―how do

aggregate structures, institutions, etc. emerge from individual action and interaction?‖

(Figure 2 about here)

Employee Engagement: an individual level category.

Still within Coleman‘s diagram framework, a closer look to the individual-level variable

is required to settle the theoretical linkage with business unit-level outcomes. Already in

1930 researchers began to question the potential link between employee‘s attitudes and

business unit performance. Harter et al. (2002) affirm that employee satisfaction and

commitment directly correlates to business outcomes in terms of ―customer satisfaction,

productivity, profit, employee turnover and workplace accidents‖. Several authors have

re-conceptualized the nature of such relationship turning it into ―relationship between

emotions and performance‖ (Isen and Baron, 1991; Staw and Pelled, 1994; George and

Brief, 1996) with empirical evidence that indicates a relationship between positive

affect or emotions and individual job performance (George and Bettenhausen, 1990;

Cropanzano and Konovsky, 1993).

Judge et al (2001) found that the correlation between satisfaction and performance ―was

stronger in high-complexity jobs‖; moreover, they affirm that several personality traits

(personal standards, moral obligation) mediate that relationship as well as external

circumstances such as the norms valid in the work environment, for example ―where the

norms indicate high performance standards, then dissatisfaction is less likely to result in

reduced levels of performance because to respond in such manner would violate the

norms.‖

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Therefore, we can assume that the variable employee performance links to well-being,

motivation and job satisfaction which bring us to the concept of engagement,

understood by the Institute for Employment Studies as ―a positive attitude held by the

employee towards the organisation and its values. An engaged employee is aware of

business context, and works with colleagues to improve performance within the job for

the benefit of the organisation. The organisation must work to nurture, maintain and

grow engagement, which requires a two-way relationship between employer and

employee‖. (Robinson, Perryman et al., 2004)

Employee engagement, as a complex phenomenon, has diverse drivers that work

differently according to the setting in which it is analysed. ―The results clearly show

that people are directly engaged or disengaged by what they see and experience within

their own company. Put a highly capable individual, a self-starter with clear career

goals, into a disengaging environment and they are likely to become demotivated and

frustrated. Conversely, put the same individual into an engaging environment, and they

will flourish and go beyond what is expected of them‖ (Towers Perrin, 2008).

According to Mercer (2006) ―Employee engagement goes beyond an employee‘s intent

to leave. It includes an employee‘s commitment to the organisation and motivation to

contribute to the organisation‘s success.‖ (Mercer, 2006). The Institute of Employment

Studies report that the effective or ineffective way of managing organisational

behaviour can affect the ―feeling valued and involved‖, driving force of engagement

(Robinson, Perryman et al., 2004) thus, employee performance. Employee engagement

has emerged as an intangible asset that may help release people‘s potential to perform

better at work. ―When employees are engaged, it might be expected that during social

interaction at work they will influence their co-workers to behave and feel in a similar

way‖ (Salanova, Agut et al., 2005). On the contrary ―disengaged employees feel their

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contributions are being overlooked, they concentrate on tasks rather than on outcomes,

and they want to be told what to do. They do not have productive relationships with

their managers or with their co-workers.‖ (Fernández, 2007)

Business unit level outcomes

Mercer‘s (2006) research demonstrates that ―employees‘ view of their work

environment influences not only the quality of their work but also absenteeism and

turnover, operating efficiency, customer satisfaction and retention, sales performance

and shareholder return‖.

There is a complex relationship between employee engagement and turnover, mediated

not only by the emotions related to the workplace. Tower Perrin‘s report shows that at

least one third of the engaged employees would consider another job offer whereas

among the disengaged ―50% are not planning to leave their current job‖ (Towers Perrin,

2008). Meyer (1989) established that ―some employees may find themselves in the

position in which they have little desire to remain with the company but simply cannot

afford to do otherwise.‖ (Meyer 1989). In any case ―turnover is expensive, slows

productivity while new employees ‗learn the job‘ and costs you organisational memory‖

(Fernández, 2007).

From the items mentioned before in relation to engagement, turnover and performance

are usually analysed in a collective level. The reason to do so is the relevance of this

aggregated data for businesses, in practice it makes little sense to do otherwise given

that hierarchical structures as business organisations are configured in minor functional

and structural units –with assumed shared characteristics- where these measures are

simply the average of the individual-level data. This procedure facilitates reporting to

higher instances and allows focusing efforts in the conflict areas.

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However, the aggregation posits some problems for the analysis. Let‘s take for instance

performance; the way to measure it differs from one job function to other, so

standardization of the data across units is not a simple sum of all the individual scores.

This brings about an obstacle for doing comparisons between areas while sampling

becomes a matter of concern (Harter et al., 2002).

The fact that business units‘ members share characteristics or beliefs shouldn‘t be taken

for granted. Although in objective terms it is probably the case, the psychological

features of the group may be heterogeneous, therefore a plain combination of the data

can end up not representing the status of consensus inside the group.

Models to study multilevel

―(...) explaining societal change (e.g. changes in social structure, prevailing norms, or

typical behaviours of individuals in different social settings) requires the identification

and testing of specific mechanisms at the level of individual actors and their interaction,

with the assumption that different configurations of actors (i.e. different values,

properties, etc) would constitute different outcomes at the societal level.‖ (Vesely,

2008) Adapting the above quotation to the present research, we assume that

departmental change, referred specifically to ARZ Company, is mediated by micro-

processes that take place at the level of individuals; therefore, a different setting of

characteristics at such level would produce different results for the group.

Collins (1981) states that the division between micro and macro is of degree, so he

proposes a scale of 5 different levels, namely: one person, small group (business unit),

crowd, organisation, community and territorial society (Vesely, 2008). For the present

research two of such levels are considered, the individual (one person) and the business

unit. Snijders and Bosker (1999) distinguish as well between macro-micro and micro-

macro situations. ―In macro-micro situations, a dependent variable Y measured at the

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lower (individual) level is assumed to be influenced by explanatory variable X, also

measured at the lower level, and by explanatory variable Z, measured at the higher

(group) level. In micro-macro situations, however, a dependent variable Y that is

measured at the higher (group) level is influenced by explanatory variables that are

measured either at the lower (individual) or at the higher (group) level.‖ (Croon and van

Veldhoven, 2007)

Theoretically, in the cases where outcome and explanatory variables are measured at

different levels, two ways have been used to statistically analyse the data. One is

disaggregation which consists on assigning scores to individuals ―on a group-level

variable. All variables are then finally transformed into variables defined at the lower

individual level‖ (Croon and van Veldhoven, 2007). Secondly, the aggregation that

involves assigning average group scores to individual variables. The latter procedure –

aggregation- is used for this study.

―Analyses of data measured at different levels should be based on models that explicitly

acknowledge the existence of these different levels and that attempt to formulate the

interaction between the levels in the production of the outcome variable (Schnake and

Dumler, 2003).‖

Bearing in mind the complexity of multilevel analysis, three models attempt to explain

the interlink between variables measured at different levels. The cross-level direct effect

model, cross-level moderator model and the cross-level frog-pond model (Klein and

Kozlowski, 2000). The cross-level direct effect model, which is the best fit for the

present case, describes the impact of independent variable(s) at one level into dependent

variable(s) at another level, it recognizes the differences between units but not those

within them. However, unlike Klein and Kozlowski (2000) proposition, we believe this

relationship can be observed not only when the dependent variable is in a lower level.

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Our view is that the relationship is valid in two ways and the model shouldn‘t be

circumscribed solely to macro to micro relationships but could also be useful in micro

to micro linkages.

Croon and van Veldhoven (2007) propose another multilevel model to analyse ―an

outcome variable measured at the group level when some (or all) of its explanatory

variables are measured at the individual level‖ following the aggregation strategy but it

is not yet available in the statistical software packages.

Harter, Schmidt and Hayes published a research in 2002 stating the impact of

engagement in profit and productivity (Harter, Schmidt et al. 2002), however, it showed

that ―engagement predicts just one per cent of a company‘s total profit‖ (Krajewski

2008). Much has been speculated about the tangible outcomes that can come from an

engaged workforce and some of the studies described before affirm that lower turnover

rates and better performance can be expected in an organisation where engagement is

high.

It was mentioned earlier that the main objective of this research is to explore the

correlation and linkages between employee engagement, performance and turnover with

the specific query: does a high level of employee engagement lead to greater business

unit work performance and lower business unit turnover?

Method

Research Design

We focused out attention on the Research and Development functions within the three

largest country sites of the company (North America, UK and Sweden), and identified a

total of 26 business units where data were available at both the individual and the

business unit level. The business units varied in size from 8 to 42, in average the groups

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have 20 members. This study was carried out in a sample of 507 individuals from 26

different Research and Development (R&D) departments within ARC Company from

North America (USA and Canada), Sweden and UK.

Since our interest is the relationship between business unit performance and turnover

and individual-level employee engagement we use two sources of information: a

worldwide employee opinion survey to measure individual employee engagement and

Human Resources (HR) which recorded business unit performance and turnover.

Procedure

The opinion survey was distributed across all the areas of the organisation in the

countries where ARC Company has employees, the items in the questionnaire were

developed by the company and the external consultant firm. Questionnaires were

administered online- via a portal, so employees had to go onto a website to respond- but

there were also print copies for those employees who didn‘t have access to computers as

part of their job or those who were in long term leave –like maternity leave. It was open

for about a 3 week period -to try to maximise the number of answers-and its importance

was stressed by means of emails, sent regularly around a month before the actual date of

the beginning of the survey, and face-to-face communication from the line manager and

senior leaders of the organisation. A worldwide participation rate of 86% was achieved.

Measures – business unit level

Work-unit level measures for the twelve month period following the employee opinion

survey were obtained from HR data files.

Performance. The performance of individual employees was assessed by their line

manager and recorded as Unacceptable, Partially Met, Met, Exceeded and

Distinguished, scored on a five-point scale from 1 to 5. These scores were then averaged

to give an aggregate performance score for each business unit.

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Turnover The proportion of business unit employees who left during the twelve month

period following the opinion survey was also recorded, and we distinguished between

two categories of turnover. Voluntary turnover rate records the proportion of voluntarily

leaving employees; while the involuntary turnover rate records the proportion of

employees who were dismissed.

Measures - individual-level

Employee engagement

Think (rational understanding of the organization), Feel (emotional attachment towards

the organisation) and Act (the willingness to go beyond expectations at work), three

spheres that confluence in employee engagement that were covered by the 69 questions

in the survey referring to 13 different items. Answers were recorded on a five-point

scale from disagree, tend to disagree, cannot decide or the answer does not apply, tend

to agree and agree, scored from 1 to 5.

Intention to leave was recorded on a 3-point scale.

A number of demographic variables were also included in our analysis: organisational

tenure (years), gender, and job level. Job level was recorded as: top leaders and

managers of different hierarchical levels, considered as ―Managers‖ and employees

without managerial functions. Over two thirds (70%) of the sample have been in the

company for 10 years or more, and just over half were female (56%). Two thirds of the

sample (65%) were non-managerial employees, while the remainder had some

managerial responsibility.

Analytic procedure

There are two approaches to the analysis of relationships between micro-level predictors

and macro-level dependent variables. The first approach involves aggregation of the

micro-level variables to give a single score for each macro-level work unit. This has the

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disadvantage of discarding variation in scores within work-units, as well as reducing the

sample size to 26 (the number of work-units). We adopted the second approach which

retains the individual-level scores for the predictors and allocates the aggregated

business unit score to every individual within a business unit. This gives a sample size

of 507.

Results and discussion

Overview of the independent variables.

Table 1 gives a general description of the most relevant variables independently. It can

be seen that for the sample, the level of individual-level employee engagement is high

(4.1 out of 5) whereas business unit turnover in general is low but involuntary turnover

is higher than the voluntary. Business unit performance has been stable in both years at

a medium level of Met (3).

(Table 1 about here)

As part of the first exploration of the data, a Pearson correlation (Table 2) of the entire

variables set was performed in order to find the relevant correspondences. We can say,

as a summary, that individual-level employee engagement strongly correlates with the

rest of the behavioural items analysed by the survey –i.e. commitment, innovation, pay

satisfaction, immediate management, work-life balance, integrity, etc.- in a positive

sense.

(Table 2 about here)

Such result can be expected considering previous studies and theoretical constructs and

it means that the higher the level of engagement, the better the rating of the other

categories and vice versa as opposed to leave intention, which shows that people

strongly engaged with the company do not wish to leave.

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On the one hand, business unit involuntary turnover has a robust negative correlation

with business unit performance both in 2007 and 2008, which evidences the

organisational strategy to decrease the number of underperformers, while business unit

voluntary turnover, on the other hand, correlates negatively with involuntary turnover.

That fact could be explained considering that in those departments where more people

are being dismissed, the rest of the employees decide to stay maybe waiting for the

effect of those changes upon themselves, because they feel reassured about the

―security‖ of their own post or more likely because those who are fired are the ones not

performing at an adequate standard and those who remain are the ones with better

performance, therefore, they feel in a superior position than those who are sacked.

Not surprisingly, business unit voluntary turnover is positively related to individual

leave intention, therefore, there is a high probability that people who state in the survey

they are considering to leave actually do it (attitudes leading to behaviour). Something

else that came to light is that business unit voluntary turnover has a positive correlation

with business unit performance in both years which suggests that some of the people

leaving the company voluntarily are high performers who are marketable –therefore

valuable employees in another company-, so they can probably find another job more

easily that low performers. This supports the findings of Towers Perrin (2008b), Mercer

(2006) and other authors (Meyer 1989; Krajewski 2008) who posit that some people

stay because they do not have a better alternative.

Regarding business unit performance, in both years it correlates with the individual job

level, the highest the position within the organisational chart the better performance

rating is obtained. There is a high correlation between business unit performance ratings

in both years which implies that scores per groups tend to remain the same. In 2007,

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there is another minor correlation between gender and performance in the sense that

women tended to have better scores than men.

Multilevel linkages

Having looked at the individual variables, we will focus on the core of this research

which is the link between those variables measured at different levels. Should we be

required to answer the two research questions -what is the link between business unit

turnover and individual employee engagement and does a high level of individual

employee engagement lead to greater business unit work performance- the results of

this research suggest that there isn‘t a direct relation between employee engagement and

turnover; plus it seems that work performance is not mediated by the level of employee

engagement.

However, the complexity of multilevel situations requires more in depth analysis that

will be presented in the framework of Coleman diagram.

Macro to macro relationship.

Both of the macro level variables show a direct relationship with each other. At an

aggregated level, performance and turnover hold a connection as illustrated in Figure 3

where poorly performing business units might lead to an increase in involuntary

business unit turnover and that in turn reduces the voluntary business unit turnover. But

there is also the direct connection between a good business unit performance and

voluntary turnover, not mediated by other circumstances.

(Figure 3 about here)

Coinciding with the views of Coleman (1990), Veselý (2008) and Felin and Foss (2006)

we don‘t think that system behaviour can exist apart from the individual inputs from

actors at a lower level and this finding seems to support that claim in the sense that

although the relationship is visible at a business unit level, performance and turnover

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occur at a lower level, so this relationship is mediated -and we could add is brought to

life- by the input of each employee. In sum, contrary to Vromen‘s (2008) theory the

explanation of this macro link lies in the individual level where the actors‘ behaviour

establishes the causality of the influence.

It has to be noticed however that the explanation of the connection between business

unit performance and turnover does receive the influence of a higher level in the form of

organisational policies which determine that underperformers have to be separated from

the company. But, again, rather than an abstract construct the macro system is

materialised in this case by Human Resources members and senior leaders.

Micro to micro relationship

Most of the variables involved in the individual employee engagement survey showed a

strong relationship with each other, nevertheless the outcome of such process can be

evaluated in this case because business-unit level categories are measured and included

in the analysis. Otherwise, the survey could provide with the attitudinal component of

the members of the groups but the actual impact of them into posterior behaviours could

be hard to appreciate.

The most relevant links in this sense are those between employee engagement and

intention to leave (manifested at individual level), employee engagement and job level

(which is not a psychological dimension but an individual characteristic) and employee

engagement and work-life balance (an individual perception).

Going back to Judge et al. (2001) and their debate on the nature of the relationship in

micro linkages –in terms of causality- in this case it seems adequate to say that beliefs

indeed lead to behaviours when looking at the correlation between individual leave

intention (expressed in 2006) and business unit voluntary turnover (measured in 2009).

Macro to micro relationship.

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Despite not being the object of the present research, it is worth noticing that no

significant relations were found in this sense among the variables of study. Neither

business unit turnover nor performance seem to have a relevant impact in employee

engagement or any other individual-level construct.

Micro to macro relationship.

With our 6th

arrow in Coleman‘s diagram we intended to look at the foundations of two

business unit-level outcomes based on individual-level attitudes and characteristics.

Such direct link in the present case could not be found.

Contrary to the arguments of many consultants (Towers Perrin, 2006; Mercer, 2006),

academics (Gotsi and Wilson, 2001; Smidts, Pruyn et al., 2001) and practitioners

(Robinson, Perryman et al., 2004; Wright, 2006) in the present case the impact of

employee engagement in business unit level outcomes seems to be non-existent.

In our view different explanations can be drawn from this fact. It might happen that

employee engagement is nothing but a minor factor for business unit performance and

turnover within ARC Company and other reasons such as enterprise-level influences or

country-level reasons are more important. This is certainly an argument in favour of all

those studies that have found no relationship among the afore-mentioned three variables

(Judge, Bono et al., 2001; Krajewski, 2008).

It can be, however, that the relationship is less obvious and not direct but still there even

if spurious or maybe the absence of findings in this sense may be explained by internal

characteristics of the company or external environmental factors both of them not

related to engagement.

In the first case we can theorize that despite employee engagement-related factors

appear trivial towards explaining business unit performance and turnover other

interesting relationship were indeed found. A regression analysis (Table 3) with our two

22

dependent variables and those variables both in the lower or same level that seemed to

have a correlation –even though in many cases it was not really strong- taken as

predictors, showed the following connections:

(Table 3 about here)

1. Voluntary Turnover: considering as predictors the tenure, gender, leave

intention, engagement (which was included in all the models despite its

noticeable lack of significance), performance 2007-2008 and involuntary

turnover the regression analysis illustrates that only leave intention –to a lesser

extent-, performance and involuntary turnover appear to explain the changes in

the dependent variable. The relationship between higher-level variables was

detailed above but what is interesting is the link between both levels of analysis.

With the same hypothesis that there might be a spurious relation between

turnover and engagement, we found that a process as described in Figure 4 could

provide a potential link between both.

(Figure 4 about here)

The relationship in Figure 4 derives from the statistical analysis of the factors

that affect voluntary turnover, it was mentioned earlier that intention to leave

does actually relates to voluntarily leaving the organisation, the new facet in here

is that employee engagement also correlates with intention to leave –the more

engaged the less people want to go- so intention to leave emerges as a possible

mediating factor between individual feelings towards the organisation

(engagement) and the business unit-level turnover.

2. Performance: we used as predictors the tenure, gender, level of engagement,

intention to leave, job level and for performance 2008 we included the

performance rating from 2007. Also to explore performance 2007 instead of

23

intention to leave we included work life balance given that the correlations

showed that the first factor was more likely to influence performance in that year

than the latter. We found that for performance 2007 the predictive capacity of all

the independent variables was very small, the only minor influences we saw

were coming from gender and job level. One year later, the predictors of

performance scores demonstrate once more a slight influence of job level and a

great impact of the performance ratings in 2007. Then again, having in common

the individual level factor job level we can think of the possible relation shown

in Figure 5.

The graph represents the direct connection between job level and engagement –

the latter increases with the position within the organisation- and performance

ratings also improve in higher positions.

(Figure 5 about here)

Joining together the correlated variables at the two levels of analysis there is a plausible

relationship from (and within) individual level factors (job level, leave intention and

engagement) to business unit-level facts (voluntary-involuntary turnover and

performance).

(Figure 6 about here)

Although in a multilevel context, this diagram resembles the relationship between job

performance and job satisfaction as described in Judge et al. (2001) meta-analysis of the

different models to explain the nature of such link. Following his theory, the figure

above corresponds to the Models 4 and 5 where a third influence is between the two

categories as the possible linkage.

24

It was mentioned before that a second explanation for the lack of direct relation between

employee engagement and business unit-level facts could be found in the influence of

internal organisational characteristics and environmental factors. If we analyse again the

correlation of leave intention and voluntary turnover, it could be argued that personal

circumstances, such as having a family, change the balance of priorities for employees,

and if added to external circumstances such as feeling insecure about the job –because

of the risks of closure of the unit or any other change than may come as a result of the

internal re-organisation that the company is going through at the moment- then the

intention to leave increases and comes to reality in the voluntary turnover.

Clearly, this consideration leaves apart the engagement-related reasons and regards a

multilevel causality –lifestyle choices, career goals and leave intention in the individual

level and market opportunities related to the specialization of people plus country

conditions and unit-related facts at the macro level- to explain the correlations found.

The macro level factors, both external opportunities and organisational strategies,

probably lead to positive correlations between intentions and turnover –high uncertainty

about the job and outside options. But the individual level factors can work in different

ways, following the example, if one employee has or plans to have a family they may

become more aware of the importance of a stable job and their interest in the

organisational policies towards work-life balance increases considerably therefore, their

situation may lead them to be the ones who care more about the job but also the ones

who leave when there is uncertainty

(Figure 7 about here).

Still within the second explanatory choices for the lack of correlation between employee

engagement, business unit turnover and performance it could be worth to ask if people

have a fair view of their own level of engagement and performance.

25

Since the survey and up to some extent the performance evaluation is an exercise of

self-calibration, in a manner of speaking, the criteria that people give about themselves

or to themselves may be high, but the appreciation of the managers –who finally decide

the performance score for each employee- may well differ.

Going further it could be interesting to appraise if the performance evaluation is done

with enough frequency and sufficient feedback so it doesn‘t come as a revelation for

those employees who think about themselves positively in terms of performance and

engagement and are in the end receiving a different evaluation.

This becomes obvious when we look at the strong correlations between 2007 and 2008

ratings, apparently the do not change much, so high performing business units tend to

remain as such but poorly performing business units as well, which suggests that some

improvements could be done to the management of underperformers. The heavy

dependence on performance evaluation when it comes to dismiss people gives little

chance to take full advantage of the potential of each employee which could be better

exploited with a good coaching strategy or providing regular feedback on performance.

Following this reasoning, the expectations from the managers in respect of the

employees could also be influencing the no correlation between performance and

engagement. If the employee is giving this self-rating with no clear knowledge of what

the manager considers as good, bad or average in comparison with the rest of the team

then of course such relation cannot be found.

On the other hand, if the performance rating is done based purely on quantifiable facts

and does not include any of the engagement-related variables it shouldn‘t be a surprise

that they do not correlate.

Conclusions

26

Throughout this research we have expounded divergent points of view regarding the

relationship between employee engagement, business unit performance and turnover as

well as the controversial views about the nature of multilevel nexuses and the ways to

study it. There isn‘t a definitive answer to the initial questions of this study; despite it

seems to be clear that a straightforward linkage between individual employee

engagement and business unit-level performance and turnover cannot be established,

there are however evidences that point towards a relationship mediated by other

individual-level facts.

Therefore, our proposed 6th

arrow to Coleman (1990) diagram appears not to be

effective in the context of ARZ Company, and what is more, it is confirmed the

relationship described by arrows 2 and 3 as the more plausible explanation for the effect

exerted by individual-level variables into business unit-level outcomes.

Since the correlation between leave intention and engagement is significant –in the

sense that the most engaged employees do not wish to leave- and leave intention

correlates with voluntary turnover it seems there is some kind of linkage between

engagement and turnover, not straightforward but mediated by that individual-level

factor.

Likewise, the positive correlation between performance and job level and job level and

employee engagement suggest another mediated relationship between business unit

performance and employee engagement.

The implications of the explanation we suggest to describe the last two links could be

relevant for the organisation of work and individual-level strategies within ARC

Company. On the one hand, the intention to leave put forward in the survey can be

assumed as an early notice of termination. If assumed in that way –and considering that

people who leave voluntarily are more likely to work in high performing business units-

27

the results of the survey might become into a prompt alert to go deeper in those areas

and find out why valuable people wants to leave.

On the other hand, the close dependence on performance evaluations to determine

personnel reduction can miss out other important factors about the employee which may

increase the losses of potentially important employees.

The apparent distance between the views and feelings of managers and employees

require careful attention, there seems to be a detachment in the perceptions of both

groups that could become an obstacle for the flourishing of the company. Although it is

positive to have managers engaged and performing well, the same should be expected

and obtained from the employees.

We could also suggest going towards changes in the process of coaching, the fact that

poorly performing business units remain the same over 12 months may indicate the

necessity to implement actions –both at individual and group levels- to find out the

cause and put into practice early solutions.

Limitations of the research

Departing from the restrictions of the data in the present research, some important

limitations need to be taken into account when interpreting these results. The first –

closely related to the characteristics of the information obtained- is the nature of the

scores for performance. Despite following scientific methods to treat data measured in

one level and comparing it against data at another level, rigorously speaking, the

performance ratings used in here for the departments are not necessarily the same as

business unit performance.

Notice that the ones we use here come from the individual scores of all the members of

the business units, which were averaged to obtain a single measurement for each

28

department but such evaluation may not be the performance of the business unit that at

an aggregated stratum can function at different level than the simple sum of its parts.

Likewise, the high complexity of the HR reporting system in ARC and the differences

of it with the engagement survey coding hindered the process of sampling reducing

considerably the possibilities to use the large amount of data we had access to for this

research.

Further research

Further research could focus on the indirect effect of engagement on turnover via leave

intention, versus the direct effect on turnover in order to fully analyse the link between

them and provide conclusive evidence of the real impact one has on the other. Likewise,

the correlation of job level with performance and employee engagement deserves a

closer scrutiny.

Finally, the appreciation of all the relationships in a setting where performance is

measured at a group level –as opposed to this case where it was obtained from

individual level data- could shed light to new and interesting multilevel relationships

that could confirm or not the propositions described in here.

29

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Table 1. Descriptive statistics.

Individual

Level

Engagement

Business

unit

Involuntary

turnover

Business unit

Voluntary

Turnover

Business unit

Performance

2008

Business unit

Performance

2007

Valid 507 507 507 507 507

Mean 4.1 .06 .03 3.05 3.09

Mode 5.00 0 0 3 3.10

Std. Dev. .863 .129 .030 .181 0.19

Table 2. Correlation matrix for study variables (n = 507)

Business

unit

involuntary

turnover

Business unit

voluntary

turnover

Business unit

performance

- 2008

Business unit

performance

- 2007

tenure gender job level Individual

leave

intention

Individual

employee

engagement

Individual

work-life

balance

Business unit

involuntary

turnover

--

Business unit

voluntary turnover

-.272** --

Business unit

performance -2008

-.607** .155** --

Business unit

performance -2007

-.639** .121** .657** --

Tenure -.018 .057 .034 .051 --

Gender .030 -.060 .051 .117** -,061 --

Job level .024 .016 -.121** -.117** -.163** -.103* --

Individual leave

intention

-.057 .107* .072 .024 -.050 -.020 -.030 --

Individual

employee

engagement

-.022 .031 .032 -.028 -.050 .013 -.177** .180** --

Individual work-

life balance

.053 .054 -.004 -.054 -.093* -.009 .067 .099* .523** --

33

Table 3 Summary results of multiple regression analyses, standardised regression weights

Performance-

2007

Performance-

2008

Turnover

Voluntary

Turnover

Involuntary

Tenure .043 -.007 .061 .03

Gender .112 * -.031 -.031 .10

Engagement -.042 .036 .00 -.012

Work-life balance -.028

Leave intention .052 .07 -.035

Job level -.111 * -.038

Performance-2007 .661 ** -.11 -.447**

Performance-2008 .03 -.317**

Involuntary Turnover -.32**

Multiple R .182 ** .667 ** .306 ** .697 **

* p < .05; ** p < .01.

35

36

Figure 3. Macro level linkages

37

Figure 4 Mediating factor between turnover and engagement

38

Figure 5 Mediating factor between performance and engagement

39

Figure 6 Macro outcomes and micro characteristics linked to engagement.

40

Figure 7 Internal and external factors mediating the intention to leave (individual level) and group

turnover.