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Int. Journal of Business Science and Applied Management, Volume 7, Issue 1, 2012
Individual perception of different stimuli: Implications for
managers
María Valle Santos- Álvarez
Department of Business and Marketing, University of Valladolid
Avda. Valle de Esgueva, 6, 47011, Valladolid, Spain
Telephone: +34 983 183813
Email: [email protected]
María Teresa Garcia-Merino
Department of Business and Marketing, University of Valladolid
Avda. Valle de Esgueva, 6, 47011, Valladolid, Spain
Telephone: +34 983 183813
Email: [email protected]
Eleuterio Vallelado-Gonzalez
Department of Finance and Accounting, University of Valladolid
Avda. Valle de Esgueva, 6, 47011, Valladolid, Spain
Telephone: +34 983 423387
Email: [email protected]
Abstract
Managerial perception is the process by which managers form an image of the stimuli they receive. According
to research, perception is conditioned by the individual’s cognitive profile. But the different nature of incoming
stimuli suggests that it would be interesting to study whether the cognitive profile’s influence varies in the
presence of different stimuli. This paper analyses the effect of the cognitive profile on perception of differently-
structured stimuli. The results clearly show that the cognitive style, tolerance of ambiguity, and proactivity have
an effect. Specifically, they condition the recognition of stimuli, particularly when the stimuli are relatively
unstructured. The results also show that the cognitive variables have less influence in the interpretation stage.
Keywords: managerial perception, stimulus, structure, strength, cognitive profile
Int. Journal of Business Science and Applied Management / Business-and-Management.org
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1 INTRODUCTION
The cognitive perspective in the management literature recognises managerial perception as the firm’s
“black box” that hides the key parameters determining decision-making from view (Sáez and Jiménez, 2004;
Yanes, 2004). Managers form a mental representation from the stimuli they receive from their competitive
context (external stimuli), and this becomes the foundation on which they adopt their strategic decisions.
Research also shows that managers differ individually in terms of how they perceive, acquire, interpret and use
information (see Walsh, 1995, Livengood and Reger, 2010, Tang et al, 2012, Barreto, 2012). Thus it would be
extremely useful to understand the factors that affect managers’ interpretations of stimuli (Kuvas and Kaufman,
2004), because these interpretations affect their knowledge about internal and contextual factors, and
consequently condition entrepreneurial actions and intentions (Wood, 1991; Miller, 1993; Thomas and Simerly,
1994; Shane, 2003; Baron, 2006; Ubcbasaran et al., 2008; Kaplan, 2008; Blume and Covin, 2011; Plambeck,
2011).
In this respect, research has stressed that managers’ perception is far from being an objective and rational
process. Rather, the process is influenced by aspects as diverse as the context (Santos and García, 2008), the
qualities of the manager, and even the nature of the stimulus (Dutton and Ashford, 1993, Gielnik, et al., 2011;
Plambeck, 2011). The empirical literature offers clear evidence that the organisational context – the type of firm
and its strategic orientation – influences managers’ interpretations of the stimuli coming from the environment
(Thomas and McDaniel, 1990; Ginsberg and Venkatraman, 1992; Thomas et al., 1993, 1994; Calori et al., 1994;
Sutcliffe, 1994; Denison et al., 1996; Chattopadhay et al., 1999; Nadkarni and Barr, 2008; Nicolau and Shane,
2009; Singh, 2010). At the same time, both theoretical and empirical literature recognises that managers’
personal characteristics and beliefs also condition the perception process (Daft and Weick, 1984; Schwenk,
1984; Milliken and Lant, 1991; Kautonen, 2008; Blume and Covin, 2011). The difficulty arises when it comes
to how to operationalise and analyse these individual characteristics that affect perception. Perhaps for this
reason researchers frequently use demographic characteristics (age, education, experience, etc.) to measure
individual characteristics (Schneider and DeMeyer, 1991; Thomas et al., 1993, 1994; Kautonen, 2008). But the
researchers often explain the effect of these demographic variables using arguments involving cognitive aspects
and the different managerial attitudes that affect the process of recognition and interpretation of the competitive
context (Hambrick and Mason, 1984; Gillingwater and Gillingwater, 2009). It would be more appropriate to
recognise that the individual characteristics that most affect the perception process are the cognitive traits.
The cognitive profile refers to the set of individual qualities related to the different forms of thought and
action managers can engage in, in other words their capacity to recognise and interpret information. After a
review of the available literature, we did not find any studies that have analysed the relationship between
managerial perception and cognitive traits.
As proposed in signal detection theory (Swets, 1992), a stimulus is a signal that could foster an idea or
knowledge in the receiver. In management literature it is common to introduce external stimuli as signals that
issue the context but that influence managers’ reactions in such a way that they condition business behaviour
(Miller, 1996; Green et al., 2008). Furthermore, entrepreneurship research emphasizes the key role of stimuli in
opportunity recognition processes (Shane, 2003, Baron, 2006; McMullen and Shepherd, 2006, Ucbasaran, et al.,
2008; Nicolay and Shane, 2009). Additionally, the research stresses the diversity and complexity of the stimuli
that managers receive (Mintzberg et al., 1976; Eggers and Kaplan, 2008; Phan et al 2009, Tang, et al., 2012) and
the varying amount of attention managers pay them. Research has shown that managers pay more attention to
stimuli linked to threats than to those linked to opportunities and linked with previous knowledge (Dutton and
Ashford, 1993, Shepherd and Detienne, 2005; Blume and Covin, 2011). Researchers have also shown that the
strength and clarity of the stimulus moderates the extent to which the receiver’s personality affects the
perception process. Thus the clearer the stimulus is, the less important the individual perception process (Fiske
and Taylor, 1991; Waller et al., 1995; Entrialgo et al., 2001). Other researchers offer features to classify the
stimuli into different categories. Some distinguish between opportunities and threats, while others stress
structure and strength (Haukedal, 1994; Haukedal and Gronhaug, 1994).
Thus the objective of the current paper is to study the effect of the manager’s cognitive traits on the process
of the perception of stimuli. The study also analyses whether the effect of the cognitive profile on perception
depending on the nature of the stimuli. The purpose of this paper is to illuminate the perception of diverse
stimuli. For this purpose, the following section looks at the literature on the perception process and the influence
of the cognitive profile. The stimuli are characterised according to two features (Haukedal, 1994; Haukedal and
Gronhaug, 1994): structure and strength. This leads to the formulation of the model that guides the study.
Section 3 offers the methodology. This work uses experimentation, a technique that is frequently used in
research on cognitive biases and managerial perception (Samuelson and Zeckhauser, 1988; Sterman, 1989;
Schwenk, 1995; Shout and Bolger 2002, Gielnik et al., 2011). The experiment involves business administration
students, who were considered representative of potential managers. These students were chosen for two main
reasons: they had a basic understanding of the workings of the capital market and takeover processes, and they
María Valle Santos- Álvarez, María Teresa Garcia-Merino and Eleuterio Vallelado-Gonzalez
3
had no previous managerial experience, so they would not be influenced by their beliefs (Ucbasaran et al., 2010;
Baron et al., 2012; Aguinis and Lawal 2012). Section 4 offers the main results of the study, and Section 5 the
discussion. The paper ends with some final remarks.
2 INDIVIDUAL PERCEPTION OF STIMULI: A REVIEW
Perception is the process by which the individual forms an image of the surrounding reality. In other words,
it is the cognitive representation resulting from the individual interpretation process (Haukedal, 1994). Two
distinct stages are evident in this process (Starbuck and Milliken, 1988; Santos and García, 2006): (1) the
recognition of the stimuli; and (2) their subsequent interpretation. In the first stage the individuals (managers,
for instance), faced with the stimuli coming from the context, select only those that they consider relevant (i.e.,
classify each stimulus as either a signal or noise). In the second stage the managers attribute meaning to those
stimuli they selected in the first stage. Thus the managers form a more or less reliable image of the original
stimulus. This final image can contain various biases –misperception- that distance managers’ perception from
the original signal. This is the case with omission biases, when relevant stimuli are ignored, or attribution biases,
when the meaning does not correspond to the real strategic importance of the stimulus. What the manager
registers then is not the real stimulus. Something is always added, taken away or changed when the stimulus
passes from the senses to the mind of the manager (Haukedal, 1994).
The effect of the cognitive profile on perception
This perception process is affected by receivers' individual characteristics, especially their cognitive traits.
The current researchers understand the cognitive traits or cognitive profile as the individual framework in which
information is processed and analysed. Specifically, the terms refer to the knowledge structures and mental
models that the individual uses to make assessments, judgements or decisions (Cools and Van den Breek, 2008).
The cognitive profile coincides, in essence, with what is sometimes called “cognitive schema”, defined as a
mental structure that directs information processing, guides attention and memory towards consistent schemata,
and fills gaps in the information (Gioia and Poole, 1984; Ericson, 2001; Baron et al, 2012). Defined like this,
individuals’ cognitive traits undoubtedly govern and condition their perception of stimuli. The central
hypothesis of this research follows:
H1: The individual cognitive profile conditions the perception of stimuli.
Accepting the influence of the cognitive profile on the perception process, the difficulty is in
operationalising these concepts. Researchers use several dimensions in the literature, and the current paper uses
the following three: cognitive style, tolerance of ambiguity, and proactivity.
Cognitive style refers to the individual information processing process1 (Hayes and Allinson, 1994; Sadler-
Smith and Badger, 1998; Cools and Van den Breek, 2008). Cognitive style affects how people look at their
context to gain information, how they analyse their environment, how they organise and interpret that
information, and how these interpretations guide their actions (Hayes and Allinson, 1998). Likewise, the
concept refers to the way individuals perceive stimuli and how they use that information to guide their
behaviour (Hayes and Allinson, 1998). The individual cognitive style can affect preferences for different types
of learning, knowledge, information processing and decision-making (Dubard et al., 2007). Thus the cognitive
style makes it possible for managers to see what they see but at the same time blinds them to other aspects.
Moreover, the cognitive style conditions the stimuli that the individual pays attention to and how that stimulus is
interpreted and understood (Hayes and Allinson, 1998, Dutta and Thornhill, 2008), so it conditions the two
stages of perception.
The literature offers different models of reference for the cognitive style. Authors have identified up to 54
items (Hayes and Allinson, 1994; Sadler-Smith, 1999; Armstrong, 2000; Sadler-Smith et al., 2000; Hodgkinson
and Sadler-Smith, 2003; Kozhervnikov, 2007; Cools and Van den Breek, 2008). Thus some authors have tried
to frame these items in a bipolar scale (Allinson and Hayes, 1996). Of all the scales in the literature, the current
authors use Allinson and Hayes’ (1996) cognitive style index (CSI). The CSI is a bipolar construct that classifies
individuals into two groups: intuitive versus analytical. The reason for this choice is that various studies find a
relation between the CSI and business behaviour (Allinson et al., 2000), making the indicator appropriate for the
current research. The analytical individual studies problems in detail and makes decisions on the basis of mental
reasoning. The intuitive individual makes decisions and deals with problems based on feeling (Allinson and
Hayes, 1996, Dutta and Thornhill, 2008).
1 For a more detailed explanation of the differences between cognitive style and other closely related
concepts – learning style, cognitive strategies, etc. – see Hayes and Allinson (1994).
Int. Journal of Business Science and Applied Management / Business-and-Management.org
4
Tolerance of ambiguity is the way in which the individual deals with an ambiguous situation (Furnham and
Ribchester, 1995; Cools and Van den Breek, 2008). The concept represents the person’s capacity to accept the
lack of information about the range of possible outcomes and their probabilities (Sherman, 1974; McNally et al.,
2009). Tolerance of ambiguity also refers to the individual’s capacity to make decisions in risky or highly
uncertain environments (Westerberg et al., 1997). Individuals with less tolerance of ambiguity perceive
ambiguous situations as threats (Sully de Luque and Sommer, 2000; Ling et al., 2005), while individuals with a
higher level of tolerance perceive such situations as non-threatening (Budner, 1962). Moreover, individuals with
less tolerance perceive a higher level of risk in ambiguous situations than those with a higher level of tolerance
(Conchar et al., 2004). Ambiguous situations are totally new situations, complex situations with a large number
of elements, or contradictory or ill-defined situations (Conchar et al., 2004).
Finally, proactivity refers to individual differences in how people seek to influence and change their
environments (Bateman and Crant, 1993; Cools and Van den Breek, 2008). Proactive individuals look for
opportunities, show initiative, do things, and persevere in their behaviour until they achieve the set goal
(Bateman and Crant, 1993). Proactive behaviours are anticipatory and anticipation depends on imagining future
outcomes. Proactivity helps individuals understand the information they receive (Crant, 2000; Grant and
Ashford, 2008). Proactive individuals also feel less conditioned by the situational forces of their environment
than less proactive individuals (Kickul and Gundry, 2002).
Stimuli: their diversity and perception
Managers are overloaded with information, since they receive a huge amount of diverse stimuli (Mintzberg
et al., 1976; Baron, 1998, 2004). The diversity of stimuli makes it necessary to use some criterion to classify
them into different categories. The literature offers a number of different typologies, for example the one that
stresses the content of the stimuli and distinguishes between opportunities and threats (see Dutton and Jackson,
1987, Kuvaas and Kaufman, 2004). But this classification is somewhat confusing, because depending on the
receiver a stimulus can be classified as either an opportunity or a threat (Ramaprasad and Mitroff, 1984; Kuvaas
and Kaufman, 2004). In this paper, the authors use the classification with the features “strength” and “structure”
(Haukedal, 1994; Haukedal and Gronhaug, 1994) to classify the stimuli. The first feature – strength –
distinguishes between easily recognisable stimuli and stimuli that are more difficult to recognise, discriminating
between stimuli in terms of their visibility. On the other hand, the second feature allows the researcher to
distinguish between well-structured stimuli and stimuli with an undefined, diffuse structure (Simon, 1973). This
feature is difficult to measure, and it is represented by three attributes (Kaufman, 1987): novelty, complexity,
and ambiguity. Novelty refers to the individual’s lack of experience or knowledge that can be directly applied to
the perceived stimulus. Complexity refers to the quantity of information contained in the signal. Finally,
ambiguity refers to situations in which the problem is to decide what the best options are, or which suit the
overall objective best. These are situations with various alternatives and the manager is unsure of which is the
best one. Using these two features: strength and structure, we can distinguish among four different types of
stimulus:
Table 1: Adopted from Haukedal and Gronhaug (1994: 357)
Strength: high Strength: low
Structure: high Type A: Messages Type C: Whispers
Structure: low Type B: Symptoms Type D: Itchers
Type A – “messages” – is surely the least problematic in terms of perception since it refers to stimuli that
are perfectly visible – so they catch the manager’s attention easily – and well-structured – so the managers know
what they mean and how important they are. The stimuli classified as “itchers” (D) are at the other extreme,
since they barely catch the manager’s attention because of their low visibility, and they are also difficult to
interpret. “Whispers” (C) are stimuli that are difficult to recognise but that have a clear, known structure. These
stimuli consequently have a problem of strength. Finally, “symptoms” (B) are strong stimuli but they are
relatively unstructured. Managers faced by these stimuli have the problem of finding the right interpretation and
determining the most appropriate actions.
Thus it can be deduced that type A stimuli are the clearest and type D stimuli the least clear, and the other
two categories are somewhere in between. Moreover, and as mentioned above, different stimuli present different
challenges to managers in their perception process. Thus while some stimuli generate problems of recognition,
others give rise to difficulties in the interpretation process, and the clearest lead to simpler perception processes.
Also, research recognises that the “force of the situation” moderates the extent to which the receiver’s
personality affects the perception process (Waller et al., 1995; Entrialgo et al., 2001). The force of the situation
María Valle Santos- Álvarez, María Teresa Garcia-Merino and Eleuterio Vallelado-Gonzalez
5
refers to the clarity of the stimulus, which is composed of the strength and structure (Fiske and Taylor, 1991).
The clearer the stimulus (high strength and high structure), the less effect the receiver’s individual
characteristics have on the perception process. But in relatively unclear stimuli (low strength and low structure)
the cognitive variables decisively condition the perception process (Sutcliffe and Huber, 1998). The second
hypothesis of this paper follows:
H2: The influence of the cognitive profile on an individual’s perception of stimuli is weaker with clear
stimuli (high strength and high structure).
Comparing two stimuli with the same structure but different strength, the perception process depending on
the ease or difficulty in recognising the stimulus, rather than of interpreting the stimulus or deducing its strategic
implications. Thus the cognitive traits most involved in the process of recognition of the stimulus will be more
relevant than those linked to interpretation. Similarly, comparing two stimuli with the same strength but
different structures, the cognitive traits most involved in the process of interpretation will be more relevant.
On the other hand, the characterisation of the cognitive profile reveals that the three dimensions considered
have different effects in different information contexts. Thus proactive behaviour has more effect in uncertain
situations (Weick, 1979; Grant and Ashford, 2008). Equally, tolerance of ambiguity is also associated with
situations of uncertainty. With regard to the cognitive style, the analytical character seems to be more suited to
structured contexts with information available, while the intuitive character may be more appropriate in
uncertain situations. With all this, the third and final hypothesis of this research is as follows:
H3: Tolerance of ambiguity, proactivity and intuitive character have more effect on the perception of
unclear stimuli (low strength and low structure) than on the perception of clear stimuli.
This completes the analytical model upon which the current research is founded. This research will study
the effect of the cognitive profile on the process of the perception of different stimuli.
3 METHODOLOGY
Experiment design
The empirical analysis aims to analyse whether perception of stimuli is conditioned by the individual’s
cognitive profile, and above all, if the effect of the cognitive profile is stronger with unclear stimuli. For this
purpose, the authors compare the perception of two stimuli with the same strength (high) but different structure
(high vs. low). Of the three attributes determining the level of structure, novelty and complexity are kept
unchanged, and the authors compare the perception of two stimuli that differ only in their level of ambiguity.
They are consequently comparing a type A stimulus (message) and a type B stimulus (symptom). Recall that the
former has high strength and high structure, while the latter has high strength but low structure.
To carry out this study the authors needed to observe the behaviour of individuals in a controlled way, so
they used a laboratory experiment2 (Aguinis and Lawal, 2012). The sessions of the experiment were carried out
in our experimental laboratory. A total of 96 people participated in the experiment, all of them undergraduate
business administration students enrolled at the University of Valladolid (Spain), recruited through a public
announcement. These students were considered representative of potential managers. Incentives were used to
encourage the participants to behave realistically in response to the announcement of a takeover bid (Camerer
and Hogarth, 1999). Participants were remunerated according to the level of coherence of their decisions. The
remuneration was a fixed amount, from which sums were subtracted for each financially incoherent decision.
Specifically, two situations were penalised: (1) responses in which the participant predicted a rise (fall) in the
share price of the firm – bidder or target firm – and then declared their decision3 in the market to be to sell (buy)
those shares; and (2) responses in which the participant predicted a fall in the share price of the target firm.
The experiment involved presenting the participants – in writing – with an event that contained two stimuli.
The event was the announcement of a takeover bid (henceforth, TOB4) in six different scenarios. The event was
the same in each of the scenarios – the announcement of the TOB – but the presentation and source differed.
Thus, for example, some scenarios stated that a financial newspaper reports on the TOB, while others stated that
rumours about a possible bid are heard in the branch of a bank. In each scenario the announcement of the TOB
2 Experimentation is one of the most widely recommended techniques for research into cognitive biases
(Schwenk, 1995). This type of research frequently involves university students (Zalesny and Ford, 1990). 3 For a more detailed explanation, please contact the authors.
4 In general terms, a TOB refers to purchases in which firms acquire securities with voting rights from a
company: shares, convertible bonds, subscription rights, etc. Although bidders generally only want to acquire
part of the firm, TOB operations are considered typical of the market of control (Farinós and Fernández, 1999).
Int. Journal of Business Science and Applied Management / Business-and-Management.org
6
was described, and this gave rise to the two stimuli subsequently analysed: the stimuli referring to the bidder and
the target firm, respectively.
A TOB is a strategic decision that, as Baker et al. (2009) recognise, requires setting a value that is
sufficiently flexible to make the offer attractive to the shareholders and managers of the target firm but that at
the same time avoids giving the shareholders and managers of the bidding firm the feeling they are overpaying.
In these circumstances, the cognitive profile helps manage the complexity of the decision in an environment
with an information overload (Raghubir and Das, 1999). The choice of this event was also based on two other
reasons. First, the event would have barely any affective connotations for the participants, since they had had no
previous experience in the area; they would consequently not be influenced by their beliefs. Second, all the
participants had a basic understanding of how the capital market and takeover processes work, so the event was
familiar to them.
For each scenario, and for both the bidder and the target firm, the participants were asked to say whether
they thought the event stimulus would affect the share price or not. Specifically, they were asked whether, in
light of the TOB announcement, they believed that each firm’s share price would rise, fall or remain unchanged
in the short term (Appendix 1).
The financial literature is unanimous in saying that credible TOB announcements generate powerful
information, create value and produce abnormal profits for the shareholders of the target firm (Farinós and
Fernández, 1999), since they have a positive effect on the firm’s share price (Mitchell et al., 2004). The same
cannot be said for the firm that launches the takeover bid. Firms launch such operations for a wide range of
reasons, and so the effects on the bidder are equally diverse. The bidder’s share price sometimes rises after
announcing a TOB but sometimes falls.
These are consequently the two stimuli that will be used in this study: the TOB announcement for the
bidder firm and for the target firm, respectively. These two stimuli have the same degree of strength since they
appear in the same event. They also have the same degree of novelty (the participants have no previous
experience in TOB operations but understand how they work) and complexity (they receive the same
information). Nevertheless, the ambiguity of the two stimuli differs: the TOB announcement is less ambiguous
for the target firm than for the bidder. Thus the TOB announcement will be a type A stimulus for the target firm
but a type B stimulus for the bidder.
Measures design
The participants were also asked a series of questions to determine the three dimensions of their cognitive
profile: cognitive style (COG), tolerance of ambiguity (TA), and proactivity (PRO). The authors used Allinson
and Hayes’s (1996) cognitive style index (CSI)5 to measure cognitive style. This indicator consists of 38 items
that the participant must score on a three-point scale (“true”, “uncertain”, “false”), and measures how intuitive
or analytical the individual is. Thus the range of scores runs from 0 to 76. Individuals with an analytical style
obtain high scores on the CSI, while intuitive individuals get low scores. The Cronbach alpha for this scale is
0.857, so its internal consistency can be considered satisfactory6.
To measure tolerance of ambiguity, the authors used Acedo and Jones (2007) and included four statements,
which the participants had to score at 1-5 depending on their level of agreement. In this case, the reliability
indicator is acceptable (α = 0.83). For proactivity, the authors included a 10-item scale, each item scored at 1-5
depending on the degree of agreement, as in the previous scale. The Cronbach alpha in this case (0.72)
guarantees the reliability of this scale.
The authors built two individual indicators for the stimuli analysed: an indicator of the proportion of
stimuli classified as relevant (AFF), and another of the proportion of stimuli considered to have a favourable
effect on the firm’s share price (FAV). The first (AFF) reflects the number of times the individual says that the
stimulus received affects the share price in the six scenarios presented in the experiment. The second (FAV) is
calculated as the ratio of the number of times a favourable effect is recognised (the share price rises) to the
number of times the stimulus is considered to have an effect. This indicator varies in a range from 0 to 1. These
indicators were calculated separately for each of the stimuli analysed: AFF-A and AFF-B for recognition of the
stimuli and FAV-A and FAV-B for attribution of meaning to the stimuli recognised as relevant (favourable or
unfavourable) (Appendix 2).
5 The authors used this index with the kind permission and authorisation of its authors, Professors Allinson
and Hayes from the University of Leeds (UK). 6 This result is consistent with results of previous studies, which are in the range 0.78-0.9.
María Valle Santos- Álvarez, María Teresa Garcia-Merino and Eleuterio Vallelado-Gonzalez
7
4 RESULTS
The authors analysed whether the participants’ cognitive profile affects their perception of the two stimuli
analysed. First, they analysed whether recognition of the stimuli depends on the individual’s cognitive profile.
For this purpose, the authors used a regression analysis. Table 2 shows the results of this analysis.
Table 2: Results of regression analysis
AFF-A AFF-B
R2
Adj. R2
F (Sig.)
0.072
0.042
2.39 (0.074)
0.088
0.059
2.97 (0.036)
Constant
COG
TA
PRO
1.14 (0.000)
-0.33 (0.010)
-0.022 (0.9)
-0.063 (0.74)
0.97 (0.00)
0.005 (0.977)
0.398 (0.08)
-0.65 (0.010)
Mean
SD
Wilcoxon signed rank test
Signed test
0.843
0.179
Z: -4.39a (0.00)
Z: -4.18a (0.00)
0.715
0.233
a: (AFF-B)-(AFF-A)
The results show that the level of recognition of stimuli is significantly higher for Stimulus A than for
Stimulus B. As might have been suspected, Stimulus B poses greater problems of visibility than Stimulus A, a
sure consequence of its higher ambiguity. The authors next analysed the explanatory power of each of the three
dimensions included in the cognitive profile. Some interesting points come out of the results. On the one hand,
the regression analyses carried out are statistically significant, thereby confirming that the recognition of the
stimuli analysed depends on the receiver’s cognitive profile. On the other hand, the results show considerable
differences in the explanatory power and in which dimensions of the cognitive profile are influential.
Specifically, the F statistic and the adjusted R2 indicate that the cognitive variables have greater explanatory
power in Stimulus B. In other words, the cognitive profile has an effect on the recognition of stimuli, above all
when the stimuli are relatively unclear.
With regard to the cognitive dimensions that are influential, the recognition of Stimulus A depends on the
cognitive style. Specifically, the results show that the greater the analytical orientation, the lower the level of
recognition of Stimulus A. With Stimulus B, the most significant dimensions are tolerance of ambiguity and
proactivity. Tolerance of ambiguity shows a positive effect on recognition of the stimulus, as would be
expected, while proactivity has a negative effect. This latter result means that proactive individuals may
recognise fewer stimuli as relevant because they anticipate that they are not going to be affected by them. It
should be recalled that proactivity has connotations of anticipation.
Having analysed the stimulus recognition stage, the authors then went on to the interpretation stage, in
other words, the stage in which the participants attribute meaning to the event. The authors analysed the
variables FAV-A and FAV-B for this purpose. The descriptive statistics of these variables show that the mean is
higher for Stimulus A than for Stimulus B (1.8 compared to 1.52). This means that participants recognise a
favourable effect more frequently for Stimulus A than for Stimulus B. The variability of the indicator is clearly
higher in the second case (Sd-A: 0.24; Sd-B: 0.29).
The authors then looked at the dependence relation between the stimulus interpretation variables and the
individual cognitive profile. They decided to make some regroupings. In the case of Stimulus A, they grouped
the sample into two categories: (a) the participants who always say that Stimulus A has a positive effect7; and
(b) the participants who say that the effect of the stimulus varies (sometimes positive, sometimes negative). The
ANOVA analysis shows that none of the cognitive profile dimensions can explain the differences evident in the
interpretation of Stimulus A (Table 3). Consequently, the authors observe that for Stimulus A the attribution of
meaning does not depend on the cognitive profile.
The process was similar for the interpretation process with Stimulus B. In this case the authors grouped the
sample into three categories8: (a) participants recognising a varied influence, but with the negative effect being
in the majority; (b) participants giving an equal number of positive and negative scores; and (c) participants who
7 Recall that Stimulus A refers to the TOB announcement and its effects for the target firm. In this case the
announcement, if credible, leads to a rise in the firm’s share price. 8 Stimulus B refers to the effect of the TOB announcement for the bidding firm, and its effects are more
uncertain.
Int. Journal of Business Science and Applied Management / Business-and-Management.org
8
recognised mainly positive effects. Table 3 shows the results of the ANOVA analysis with the cognitive profile
dimensions. The results show that the cognitive style has a significant effect on the meaning that participants
attribute to Stimulus B. Specifically, intuitive individuals attribute a positive effect to Stimulus B more
frequently than analytical individuals.
Table 3: Results of ANOVA
COG TA PRO
STIMULUS A F: 1.855 (0.176) F: 0.204 (0.653) F: 0.060 (0.808)
STIMULUS B
Levene statistic (Sig.)
F (Sig.)
1.2 (0.29)
3.22 (0.04)
1.13 (0.32)
0.92 (0.40)
1.64 (0.20)
0.29 (0.74)
0: 34 participants
1: 20 participants
2: 42 participants
Total: 96 participants
0: (0.74)
1: (0.74)
2: (0.67)
total: (0.71)
0: (0.61)
1: (0.62)
2: (0.59)
total: (0.60)
0: (0.76)
1: (0.77)
2: (0.75)
total: (0.76)
Finally, the authors analysed how the perception process develops with respect to the cognitive dimensions.
In other words, they examined whether differences exist in the perception process between intuitive and
analytical individuals, and whether the level of proactivity and tolerance of ambiguity have an effect on the
perception process. For this purpose, they split the sample into two groups depending on how subjects score on
COG: low (intuitive) or high (analytical). Then a difference of means analysis (ANOVA) was used to determine
if this grouping was associated with the differences evident in the perception process of the different participants
(AFF-A; AFF-B; FAV-A; FAV-B). The process was identical with the other two dimensions: tolerance of
ambiguity and proactivity. Table 4 shows the results of this analysis.
Table 4: Results of ANOVA
COG:
INTUITIVE/ANALYTICAL
TA:
- TA / + TA
PRO:
- PRO / + PRO
AFF-A 1: 0.89
2: 0.79
TOTAL: 0.84
LS: 6.73 (0.011)
F: 8.71 (0.004)
1: 0.83
2: 0.85
TOTAL: 0.84
LS: 0.26 (0.609)
F: 0.27 (0.602)
1: 0.833
2: 0.85
TOTAL: 0.84
LS: 0.004 (0.952)
F: 0.351 (0.555)
AFF-B 1: 0.70
2: 0.73
TOTAL: 0.71
LS: 0.00 (0.956)
F: 0.39 (0.53)
1: 0.68
2: 0.76
TOTAL: 0.71
LS: 0.42 (0.51)
F: 2.88 (0.093)
1: 0.75
2: 0.674
TOTAL: 0.715
LS: 7.52 (0.007)
F: 2.83 (0.096)
FAV-A 1: 1.77
2: 1.84
TOTAL: 1.80
LS: 0.22 (0.637)
F: 2.12 (0.14)
1: 1.80
2: 1.81
TOTAL: 1.80
LS: 0.103 (0.909)
F: 0.043 (0.83)
1: 0.183
2: 1.78
TOTAL: 1.80
LS: 3.54 (0.063)
F: 0.854 (0.358)
FAV-B 1: 1.59
2: 1.44
TOTAL: 1.52
LS: 1.21 (0.27)
F: 6.14 (0.015)
1: 1.52
2: 1.50
TOTAL: 1.52
LS: 1.21 (0.27)
F: 0.109 (0.74)
1: 1.51
2: 1.52
TOTAL: 1.52
LS: 0.66 (0.417)
F: 0.018 (0.89)
LS: Levene statistic of difference of variances (Sig.).
F: F statistic of difference of means test (Sig.).
The grouping with the variable COG gives rise to 2 groups (intuitive versus analytical individuals). The
results show that significant differences exist between the two groups in the recognition of Stimulus A (AFF-A),
in the difference in the level of recognition of the two stimuli considered (DIFF), and in the interpretation of
Stimulus B (FAV-B). The results show that analytical individuals recognise Stimulus A less than intuitive ones.
Finally, analytical participants recognise the favourable effect of Stimulus B less frequently than intuitive
individuals.
The grouping for tolerance of ambiguity is associated with significant differences only in the behaviour of
the variable that measures the recognition of Stimulus B. Specifically, and consistent with the above arguments,
the results show that participants with a higher TA recognise unclear stimuli as relevant signals more frequently.
María Valle Santos- Álvarez, María Teresa Garcia-Merino and Eleuterio Vallelado-Gonzalez
9
Finally, the analysis shows that the level of proactivity is associated with significant differences in AFF-B.
Proactive participants recognise Stimulus B less frequently than non-proactive ones.
5 DISCUSSION
A number of interesting points can be made from the results of this analysis. First, and most importantly,
the results show that the cognitive profile is a determinant in the process of individual perception. Specifically,
in the experiment carried out here the cognitive dimensions are determinant mainly in the initial stage of the
perception process, in which the individual classifies the stimulus as either a signal (relevant stimulus) or noise
(irrelevant stimulus). These results confirm – at least partially – Hypothesis H1. On the other hand, the cognitive
profile has greater explanatory power in the perception process for Stimulus B than for Stimulus A. In the
recognition stage this difference is clearer when comparing the goodness of fit of the estimated regression
models. In other words, the results show that the cognitive profile is more relevant in explaining the recognition
stage of the stimuli, especially when the stimuli are relatively unstructured, and so unclear. This provides
support for Hypothesis H2, which postulates that the cognitive profile has a stronger effect given relatively
unclear stimuli (low strength and low structure).
Second, the results also indicate that recognition of Stimulus A is associated with the cognitive style. Thus,
intuitive individuals recognise this stimulus more frequently than analytical ones. As could be expected, the
attribution of meaning to Stimulus A is not associated with the participants’ cognitive qualities. It will be
recalled that Stimulus A refers to the effects on the share price of the target firm, and the financial literature
indicates that the likely effects are positive.
And third, recognition of the least clear stimulus (Stimulus B) is related to greater tolerance of ambiguity
and individuals with a low proactivity. Stimulus B’s interpretation stage is related to the participants’ cognitive
style, so that intuitive individuals recognise a favourable effect more frequently than analytical ones. This result
is also consistent with the situation. Stimulus B has some uncertain effects that depend on the bidding firm’s
motives in making its bid. The participants in the experiment have no information about these motives, so any
attribution of meaning can only be based on intuition.
With regard to H3, the results diverge slightly from what was expected. The authors postulated that
cognitive style would have more effect on the perception of Stimulus B, the less well-structured stimulus, than
on that of Stimulus A. The results from the interpretation stage confirmed this hypothesis because cognitive
style helps explain the interpretation of Stimulus B but not that of Stimulus A. But in the recognition stage the
relation is not confirmed because cognitive style explains the recognition of Stimulus A but not that of Stimulus
B. The effect of tolerance of ambiguity, as predicted, contributes to recognising relatively unstructured stimuli
(Stimulus B in the current analysis). Likewise, the previous arguments led the authors to postulate that
proactivity would surely have an active role in the perception of unclear stimuli. The results support this
argument and show that proactive individuals recognise this stimulus as relevant less frequently. Considering
the anticipatory nature of proactivity, this result shows that the most proactive participants anticipate that they
will not be affected by Stimulus B and so ignore it, in other words they classify the stimulus as irrelevant.
In short, the authors show that the cognitive dimensions have a significant effect on the process of the
perception of stimuli, above all in the initial recognition stage and in the presence of relatively unstructured
stimuli. Thus, these results give support to the idea that the perception process is dependent on the manager’s
cognitive traits and that the type of stimulus conditions the relevance of the cognitive dimensions on the
perception process.
6 FINAL REMARKS
The perception of stimuli can be seen as the “black box” that hides the secrets of the managerial decision-
making that marks out the path for firms. The process of managerial perception determines which stimuli
deserve a strategic response and attributes their meaning. Previous research has shown that the individual’s
characteristics condition this perception process. Many authors have studied the effect of the managers’
demographic characteristics, but it is their cognitive traits that are determinant in the process of recognising and
interpreting stimuli. Moreover, the incoming stimuli differ in nature, hence the interest in studying whether the
effect of the cognitive profile varies in the presence of different stimuli. Thus, in the current research the authors
have analysed the effect of the cognitive profile on the process of perception of differently-structured stimuli.
The stimulus was the share price of a firm involved in the announcement of a takeover bid. The cognitive profile
was measured using three dimensions: cognitive style, tolerance of ambiguity, and proactivity.
The results obtained strongly support the idea that the cognitive profile analysed here does have an effect
on the perception of stimuli. Specifically, the cognitive dimensions considered are determinant in the initial
stage of the recognition of stimuli and especially when the stimuli are relatively unstructured. But the results
also show that these cognitive dimensions have a weaker effect in the interpretation stage. With all this, the
results of this research point to the need to study the managerial perception process further in two directions.
Int. Journal of Business Science and Applied Management / Business-and-Management.org
10
Thus researchers need to refine their operationalization of the individual cognitive dimensions to analyse their
influence on the different stages of the perception process in more detail. At the same time, they should examine
the contextual features that would allow them to characterise and differentiate different stimuli to learn more
about the different associated perception processes. Finally, we recognise that interdisciplinary research is likely
to provide novel insight into exploring and conceptualizing organizational behaviour (Gillingwater and
Gillingwater, 2009). All this will mark the direction of future research.
Appendix 1
TYPE-A STIMULUS: TAKEOVER ANNOUNCEMENT – THE TARGET COMPANY
TYPE-B STIMULUS: TAKEOVER ANNOUNCEMENT – THE BID COMPANY
Appendix 2
VARIABLES
Stage I: Recognition of the stimuli
AFF-A= the proportion of stimuli-A classed as relevant:
The number of times the individual says that stimulus-A affects the share price in the six scenarios
presented in the experiment.
AFF-B= the proportion of stimuli-B classed as relevant
The number of times the individual says that stimulus-B affects the share price in the six scenarios
presented in the experiment.
Stage II: Interpretation of the stimuli:
FAV-A = the proportion of stimuli considered to have a favourable effect on the price of the target
company.
The ratio of the number of times a favourable effect is recognised (the share price rises) to the number of
times stimulus-A is considered to have an effect.
FAV-B = the proportion of stimuli considered to have a favourable effect on the price of the bid company.
The ratio of the number of times a favourable effect is recognised (the share price rises) to the number of
times stimulus-B is considered to have an effect.
Cognitive Dimensions:
Cognitive Style: CSI
Tolerance of ambiguity: TA
Proactivity: PRO
Stage I:
Does a takeover
announcement affect
the market price of the
target company?
Stage II:
What is the reaction of the
target company stock
market price?
YES
NO
Favourable
(the share prices rise)
Unfavourable
(the share prices have fallen)
Stage I:
Does a takeover
announcement affect the
market price of the bid
company?
Stage II:
What is the reaction of the
bid company stock market
price?
YES
NO
Favourable
(the share prices rise)
Unfavourable
(the share prices have fallen)
María Valle Santos- Álvarez, María Teresa Garcia-Merino and Eleuterio Vallelado-Gonzalez
11
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Int. Journal of Business Science and Applied Management, Volume 7, Issue 1, 2012
(In)justice contexts and work satisfaction: The mediating role of
justice perceptions
Maria Rita Silva
Business Research Unit of ISCTE – Lisbon University Institute
Av. das Forças Armadas, Edifício ISCTE, 1649-026 Lisboa, Portugal
Telephone: +351 217903001
Email: [email protected]
António Caetano
Business Research Unit of ISCTE – Lisbon University Institute
Av. das Forças Armadas, Edifício ISCTE, 1649-026 Lisboa, Portugal
Telephone: +351 217903001
Email: [email protected]
Qin Zhou
The University of York, The York Management School
University of York Freboys Lane Heslington York YO10 5GD, United Kingdom
Telephone: +1904 325008
Email: [email protected]
Abstract
This study explores the impact of the social context, namely (in)justice climate and target, in workers' justice
perceptions and satisfaction. Individual's justice judgments are expected to mediate the relationship of (in)justice
climate and target with work satisfaction. We found mediation effects of procedural justice in the relationship
between justice climate and satisfaction, and interactional justice in the relationship between injustice target and
satisfaction. Distributive justice does not affect the relationship between the (in)justice context and satisfaction.
Findings demonstrate the relevance of framing organizational justice in a socially contextualized perspective
since they seem to influence individual justice reactions and work attitudes. Using an experimental
methodology, it was possible to explore the role of seldom studied contextual variables.
Keywords: Organizational justice, social context, justice climate, justice target, work satisfaction, mediation
Acknowledgments: Part of this work was supported by the Foundation for Science and Technology, Portugal
[PhD Grant number SFR/BD/61417/2009] awarded to Maria Rita Silva and [PEst-OE/EGE/UI0315/2011] the
strategic project of Instituto Universitário de Lisboa (ISCTE-IUL)
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1 INTRODUCTION
Organizational justice is an important domain in the study of organizations (e.g., Ambrose & Schminke,
2002; Chia, Foo, & Fang, 2006). In organizations, justice is an important feature of outcome distribution (i.e.,
distributive justice), decision making processes (i.e., procedural justice) and daily personal interactions (i.e.,
interactional justice). Justice judgments are related to several aspects of workers’ attitudes that are reflected in
their performance (e.g., Cohen-Charash & Spector, 2001; Colquitt, Conlon, Wesson, Porter, & Ng, 2001; Lam,
Schaubroeck, & Aryee, 2002; Nowakowski & Conlon, 2005). Consequently, it is important to understand the
mechanisms through which justice perceptions develop and are reflected in workers’ behaviours and attitudes.
Many authors (e.g., Inness, Barling & Turner, 2005) have stated the relevance of the social context to
understand workers’ justice-related reactions. Well-established theories, such as the relational (e.g., Lind &
Tyler, 1988) or the group engagement model (e.g., Blader, & Tayler, 2009) have suggested that fairness
appraisals are socially constructed, yet most of the studies that examine the influence of justice perceptions on
workers’ attitudes and behaviour focus on hierarchical decision-making or resource allocation situations
(Fassina, Jones, & Uggerslev, 2008). These studies adopt an “exchange perspective” of justice (Bies, 2005).
Specifically, this approach emphasizes an individualistic view of justice, focused on the transactions between an
agent, the one who acts fairly or unfairly, and a recipient or justice target, the one who makes the fairness
appraisal.
However, a more socially contextualized “encounter perspective” of justice (Bies, 2005) has been
neglected. Justice is conceptualized as a set of socially construed values that guarantee that self-interest and
opportunism do not interfere with the overall social fabric of societies and organizations (Folger & Cropanzano,
1998).
One way to bridge the gap between the conceptualization of justice as a social phenomenon and the highly
individualistic study of justice perspectives is to explore the impact of the immediate social context of justice
events. One aspect of the immediate social context that has been often overlooked in organizational justice
literature is the relationship between co-workers. Namely, how work colleagues’ perceptions and experiences
help shape individuals’ reactions to fairness.
The importance of lateral relationships – relationships with other individuals situated in the same stratum of
an organizational hierarchy and with whom one executes tasks and has routine interactions has increased as
flatter organizational structures and team-based work have become widespread. Indeed, in most jobs, workers
interact more frequently with co-workers than with their supervisors (Chiaburu and Harrison, 2008). Work
colleagues often comment on their experiences and perceptions regarding workplace justice, and so share a
sense-making activity that may shape other workers’ interpretation of organizational events and their subsequent
attitudes and reactions. Therefore, co-workers may be at least as important as other organizational agents, in
shaping individuals’ justice perceptions (e.g., Hung, Chi, & Lu, 2009).
This study intends to explore the way co-workers perceptions and experiences influence the development
of justice perceptions, and their effect on workers’ satisfaction. Specifically, we will examine the influence of
two social context variables: (1) Justice climate, which refers to the degree of justice a group considers it is
receiving; (2) Justice target, which refers to the direction of the (in)justice event, which can target the self or a
co-worker. In addition, it is expected that these justice related social factors will impact employees’ work
satisfaction through their influence on individuals’ justice appraisals. In other words, we investigate justice
perceptions as a potential mechanism linking the social context and employees’ work attitudes.
This study aims to contribute to the literature in two ways. First, this study highlights the relevance of the
social context to the development of individual perceptions of workplace justice and wok satisfaction. It
examines the impact of seldom studied aspects of the (in)justice context, namely injustice target and justice
climate on individual justice judgments, and it links them to worker’s satisfaction. Second, by employing an
experimental methodology, this study contributes to the clarification of the causality directions of these
relationships. Because most management research on organizational justice is cross-sectional there may be
doubts regarding how social context and justice judgments interact to influence work attitudes. Some authors
have considered justice climate as a moderator of the impact of individual justice on workers attitudes (Liao &
Rupp, 2005). We believe that justice climate and target have a direct impact on the development of justice
judgments, and that justice judgments mediate their impact on workers attitudes. Applying an experimental
approach can help to clarify this process as it more accurately illustrates causality.
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2 LITETERATURE REVIEW
Injustice target as an antecedent of justice judgments
People’s perception of the world is built, not only through direct experience but also by social learning,
through the experience of others (e.g., Carroll & Bandura, 1987). A recent meta-analysis by Chiaburu and
Harrison (2008) has demonstrated that co-workers’ actions predict perceptual, attitudinal, and behavioural
outcomes of their colleagues, even when the influence of the direct leaders is accounted for. According to the
authors, co-workers affect how their colleagues perceive and shape their work roles, and how they form, retain,
and access work related attitudes (Chiaburu & Harrison, 2008).
The study of the impact of other person’s justice experiences on one’s own justice perception has focused
primarily on the procedural justice dimension. In two experimental studies, (Van Den Bos & Lind, 2001) show
that the treatment received by others can sometimes be such as potent consideration in procedural justice
judgments as is one’s own treatment. Colquitt (2004) has explored this effect in a field and in an experimental
study in team settings. The author found that individual members’ own procedural justice perceptions interacted
with other members procedural justice evaluations, such that higher levels of role performance occurred when
justice judgments were consistent within the team. (De Cremer & Van Hiel, 2006) based on the results from a
scenario experiment, a cross-sectional survey, and a laboratory experiment, have proposed that how fairly
another is treated influences one’s emotional and behavioural reactions and that the way other colleagues are
traded can be considered important organizational information in shaping one’s own feelings and actions.
Some research on the effects of other people’s justice experiences has been based on the deontonic
perspective, an aspect of fairness theory (e.g., Cropanzano, Goldman & Folger, 2003, Beugré, 2010). This
perspective focuses on the observer’s inherent tendency to notice and react to perceived violations of justice.
Some research has shown that third party observers tend to experience negative emotional responses, and even
to engage in reciprocating behaviours against the perpetrator of the perceived unfairness (Folger, Cropanzano &
Goldman, 2005). These reactions seem to occur regardless of any connection observers might have with the
victim and even in situations where retaliation results in the loss of personal benefits (Turillo, Folger, Lavelle,
Umprheress & Gee, 2002). As such, one might expect that co-workers’ experiences have a strong effect on
employees’ attitudes.
Despite the deontonic perspective assumptions, some research has demonstrated that there appear to be
fundamental differences between the reactions of individuals towards unfair events that target the self as
opposed to events that target other people. Lind, Kray, and Thompson (1998) have demonstrated that justice
perceptions are more negative when the self is deprived of voice than when another person is deprived. Beehr,
Nair, Gudanowski and Such (2004) have found that people tend to consider promotions based on performance
fair, while promotions based on personal characteristics are considered unfair. However, workers tend to
evaluate a promotion based on personal characteristics as more fair if it targets themselves, rather than a
colleague. More recently, Spencer and Rupp (2009) have shown that an individual’s emotional labour increases
as a result of customer unfairness directed towards themselves as well as their co-workers. However, emotional
labour was significantly higher when the injustice was directed at the self.
Accordingly, although we acknowledge the relevance of co-workers’ experiences to an individual’s justice
perceptions, we conservatively expect that faced with an injustice situation, justice perceptions will be more
negative when the injustice is directed at the self. We suppose that the different evaluations regarding one’s own
injustice experiences and those of co-workers may be due to three different reasons: attribution bias, memory
bias and just-world beliefs.
First, individuals may use different criteria for themselves and for others when evaluating the justice of
events. Recent studies on accountability and multifoci justice suggest that injustice events tend to be attributed
to a specific source (Rupp, McCance, Spencer, Sonntag, 2008). Consequent attitudes and behaviours will be
directed back at the perceived source of injustice. It is probable that, when faced with an injustice directed at the
self, individuals tend to be more inclined to consider the injustice as being caused more by another
organizational agent and less by the person being mistreated (i.e. themselves) than when the injustice is directed
at a co-worker.
Second, research concerning self-enhancing bias has shown that people perceive and remember events in a
way that favours their self-image (e.g., Sedikides, Gaertner, & Vevea, 2005). These distortion processes tend to
occur more in regard to one’s own experiences than in the analysis of another’s experiences.
Finally, according to the just-world theory (Lerner, 1980), individuals tend to believe that people are
rewarded or punished as a direct consequence of good or bad deeds, so people supposable get what they
deserve. This theory was been successfully applied to the organizational context (e.g., Bobocel & Hafer, 2007).
The belief in a just world is a “basic illusion”, useful because it leads individuals to perceive the world as a more
predictable and controllable place (Hafer, Bègue, Choma, & Dempsey, 2005). But numerous studies (e.g., Hafer
et. al., 2005) indicate that the belief in a just world contributes to adverse reactions when another person suffers
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an injustice, such as avoidance, depreciation or condemnation of the victim, or the minimizing of the injustice
situation.
H1: Organizational justice perceptions will be more negative when the injustice is directed at the self, than
when it is directed to a co-worker.
Justice climate as an antecedent of justice judgments
The justice climate refers to shared cognition concerning the degree of justice the workers’ normally
receive (Mayer, Nishii, Schneider & Goldstein, 2007). The justice climate can be considered an antecedent of
individuals’ justice perceptions. In contrast to justice perceptions, which are related to the individual cognitions
of each employee, the justice climate, like the organizational climate, is located at the inter-individual level.
Repeated reception of input from co-workers leads to reciprocation and fosters climates that encourage the
individual to display more positive or negative attitudes and interpersonal actions (Chiaburu & Harrison, 2008).
Workers may not spontaneously conclude that a certain event is fair or unfair (Lamertz, 2002). On the
contrary, they may experience a period of ambiguity that motivates them to look for social information to help
make sense of situations through social comparison and interpersonal validation of reality. Additionally, they try
to influence, and are in turn influenced by the people with whom they interact (Lamertz, 2002; Meyer, 1994).
Thus, in order to evaluate the impact of an (un)fair event in workers’ justice perceptions and satisfaction one
should take into account the social context in which it occurs.
Previous studies (e.g., Colquitt, Noe, & Jackson, 2002; Liao & Rupp, 2005; Mayer, et.al., 2007) have
addressed specific facets of the justice climate, such as procedural and interactional dimensions. Those studies
revealed that the justice climate predicted justice perceptions and individual and team outcomes above and
beyond the effects of individual-level justice perceptions.
In the present study, distributive, procedural and interactional justice concerns were operationalized so that
the distinct effects of these dimensions on the different facets of justice perceptions could be evaluated.
Recently, there has been growing interest in holistic justice judgments (Lind, 2001). Ambrose and Schminke
(2009) have suggested that the focus on the effects of specific types of justice may not capture the depth and
richness of individuals’ justice experiences. The authors proposed an overall justice scale comprising two
subscales. The first assesses the individual personal justice experience; the second is more socially focused and
assesses the perception of how fairly employees, in general, feel they are treated by the organization. This
dimension is similar to the justice climates’ construct proposed here.
Workers do not always have access to the causes behind decisions that affect them (Johanson, 2000). As
so, they experience uncertainty concerning their relationship with the organization. In order to avoid exclusion
or social exploitation, workers look for clues that serve as heuristics to evaluate the reliability and degree of trust
that organizational authority figures deserve, as well as for clues to assess their own status as members of the
organization (Lind & Van den Boss, 2002; Thau, Bennett, Mitchell & Marrs 2009). Through social influence
processes, the justice climate may serve as a source of information to help evaluate the organization and
interpret events experienced by workers (Lamertz, 2002).
By acknowledging that the justice climate serves as an information source for the interpretation of events, it
is possible to conceive two alternative relationships between the justice climate and justice event judgments.
The literature on expectancy violation (e.g., Bell, Wiechmann & Ryan, 2006), and some justice theories, such as
the group-value model (e.g., Blader & Tayler, 2009) and the fairness theory (e.g., Folger & Cropanzano, 2001)
emphasize the comparison process individuals engage in when evaluating their justice experiences. According
to these theories, when the justice climate is high, individuals may judge unfair experiences more negatively.
When there is a difference between the unfairness of personal experiences and the justice climate within the
group, it contradicts expectations about what “might” and “should” have been, and may be perceived as a sign
that the individual is not as respected and valued as other group members. On the other hand, the justice climate
may have a direct influence on individual perceptions, leading to an assimilation of the groups’ opinion. In that
case, an injustice event will lead to less negative justice perceptions when the justice climate is high, than when
it is low.
Most studies indicate that shared opinions in the work environment concerning justice influence an
individual’s justice perceptions (Degoey, 2000). For example, Lind, Kray & Thompson (1998) have shown that
communication between workers concerning a supervisor’s personal treatment influenced individuals’ justice
perceptions. Jones and Skarlicki (2005) also observed that a discussion between workers concerning the justice
reputation of an authority figure affected the interpretation of his subsequent behaviour. In both cases
individuals tended to conform to the group’s opinion. In addition, Choi (2008) found that when employees’
perceptions of the organization as a whole are positive, they tend to react less negatively to unfair events. So we
expect that the justice climate will exert an influence on the interpretation of personal experiences and that faced
with an injustice situation, justice perceptions will be less negative when the justice climate is high.
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H2: Organizational justice perceptions will be more positive when the justice climate is high, as opposed to
when it is low.
The (in)justice social context and work satisfaction
The social context in which injustice takes place has an effect on workers’ satisfaction. A work
environment perceived by the work group as fair will have a greater chance of satisfying workers and raising
morale (Forret & Love, 2007) than a climate perceived as unjust. We expect that in high justice climate
situations, participants will experience higher levels of satisfaction. On the other hand, based on attributional
bias, memory bias and the just-world theory, we expected that an injustice directed at the self would result in
lower levels of satisfaction than an injustice directed at a colleague. It is predicted that these social context
variables will have an indirect effect on satisfaction, so when justice judgments are statistically controlled, the
amount of variance in work satisfaction explained by the justice climate or the injustice target will be
significantly reduced.
The relationship between justice perceptions and work satisfaction has been one of the most studied in
organizational justice literature. In fact, the first theories concerning justice perceptions (e.g., Adams, 1965)
stated that the evaluation of a situation as fair would lead to satisfaction, while an unfair situation would lead to
psychological tension and feelings of anger. Since then, many studies have replicated this relationship, finding
that organizational justice perceptions are strong and consistent cognitive predictors of work satisfaction
(Cohen-Charash & Spector, 2001; Colquitt et. al., 2001; Tremblay & Roussel, 2001). These emotional outcomes
have been related to withdrawal behaviours such as employee absenteeism, turnover intentions, work alienation,
and self-medication with alcohol (Howard & Cordes, 2010). Some authors state that among the multiple factors
that affect work satisfaction, the most important organizational characteristic is justice perception (Clay-Warner,
Reynolds & Roman, 2005).
Different approaches have been proposed to understand the relative effects of each dimension of justice on
job satisfaction. Instrumental models argue that distributive justice is the most important predictor of satisfaction
(McFarlin & Sweeney, 1992). These models state that employees value justice because it allows them to predict
and control the outcomes they are likely to receive from organizations (Cropanzano, Bowen & Gilliland, 2007).
On the other hand, some models support a more relational view of justice (e.g., Tyler & Blader, 2000, 2003;
Blader & Tayler, 2009). They state that fair procedures and personal treatment provide identity-relevant
information, which confer respect and status and thus improve the image of the group and increase self-esteem.
This in turn, leads to a positive evaluation of the organization as a whole, and an increase in satisfaction. Thus
far, we have argued that social context influence people’s justice perceptions, which, in turn, contribute to work
satisfaction. Therefore, we propose:
H3: Justice perceptions will mediate the relationship of the (in)justice target and climate with satisfaction.
Figure 1 presents a simplified model of the expected relationship between variables.
Figure 1: Theoretical Model
This model is consistent with socially contextualized justice perspectives such as the group value and the
deontonic models. The social context in which the (in)justice takes place is of great importance.
We expect that when faced with an injustice situation directed at the self, the worker’s justice perceptions
will be more negative than when the injustice is directed at other colleagues. Even if deontonic concerns may
lead people to be sensitive to others’ mistreatments, the joint effect of attribution and memory bias, and just-
world beliefs lead to an emphasis of one’s own experience and a deemphasizing of other people injustice
Justice Perceptions:
Distributive
Procedural
Interactional
Injustice Target
Justice Climate
Work Satisfaction
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experiences. On the other hand, we believe that justice climate will influence the interpretation of justice
experiences. Because individuals tended to conform to the group’s opinion (Jones & Skarlicki, 2005) we
believe that, contrary to group-value model’s (e.g., Blader & Tayler, 2009) and the fairness theory´s
assumptions (e.g., Folger & Cropanzano, 2001), justice perceptions will tend to be less negative when the justice
climate is high.
It is predicted that these social context variables will have an indirect effect on satisfaction, mediated by
justice perceptions. The relative importance of each justice dimension will be investigated, to compare
instrumental models’ (McFarlin & Sweeney, 1992; Cropanzano, Bowen & Gilliland, 2007) and relational
models’ predictions (e.g., Tyler & Blader, 2000, 2003; Blader & Tayler, 2009).
3 METHOD
Experimental design
To analyse the effect that the independent variables, justice climate and injustice target, have on the
mediating variable, organizational justice judgments (i.e., distributive, procedural, interactional), and their joint
effect on work satisfaction as the dependent variable, a 2 x 2 (justice climate: high vs. low x injustice target: self
vs. other) cross-factor design was used.
A scenario methodology (e.g., Lind & Tyler, 1988; Hafer, Bègue, Choma, & Dempsey, 2005) was used to
manipulate the independent variables. According to Lind and Tyler (1988, pp.47) “This method involves the
presentation of descriptions of scenarios, which respondents are asked to imagine happening to themselves or to
others, and the assessment of attitudes and beliefs with respect to the scenarios.” In the present study,
participants are invited to assume the point of view of an employee of a fictitious company, and evaluate what
their attitudes and behaviors would be in that situation.
Participants
A sample of 140 volunteers participated in this study. The participants were approached while waiting in a
government building with several governmental services divisions such as ID and Passport emission, Social
Security Services, Post Office, etc. and asked to complete the survey. Seven participants were excluded from the
sample for having correctly identified the purpose of the study in a control question. This left a valid sample of
133 individuals. Of that valid sample, 57.9% (n=77) were women. Ages varied between 17 and 65, with an
average age of 34 (SD=11.813). The majority of participants, 75% are employed and 44% of those had had
tenure for over 10 years (M=14.65; SD=11.59).
Procedure
The study was presented as an inquiry about “human resources management practices” in national
companies. Participants were given a text to read describing a company, designated company X. They were
asked to imagine they worked for company X and to answer the questions with that in mind.
At the beginning, the text describes the justice climate prevalent in company X. Depending on the
experimental situation, the justice climate is described as either high or low. Then, a specific injustice situation
taking place in this company is portrayed. Depending on the experimental situation, the target of the injustice
situation is presented as the self, as a worker of company X (target self), or as a co-worker (target co-worker).
After reading the text, participants are asked to turn the page and not to consult it again. Afterwards, they are
requested to position themselves on the scales from the point of view of an employee in company X.
Measures
A pre-test was conducted (n=11) to test the effectiveness of the independent variable manipulation, and that
the participants thoroughly understood the scales used. The pre-test was also useful to verify the level of the
scenario’s credibility, since “the key to valid scenario studies is to design the study to deal with situations the
respondents have experienced and understand” (Lind & Tyler, 1988, pp. 47). It was observed that participants in
the pre-test found the story told to be entirely believable, (M=4.36; SD=.667 in a 1 to 5 scale), some participants
even mentioned having had similar experiences in their professional life. Participants did not express any
difficulties in understanding the questions so there was no need to carry out further adjustment to the material.
The text displaying the experimental scenario started by requesting the participants to: “Imagine that you
have been working for some time now in company X”. The justice climate manipulation was adapted from the
procedural justice climate manipulation carried out by Aquino, Trips and Bies (2006). Aspects of distributive
and interactional justice were introduced to allow a manipulation of the different aspects of the justice climate.
“. . . they are known for their good (poor) treatment of employees. Employees feel they are treated with
(little) a lot of respect and consideration. (Often) Rewards are (not) distributed according to the effort each
employee makes. Besides, employees feel they are (never) involved in upper-level decision making. In fact, as
Maria Rita Silva, António Caetano and Qin Zhou
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you and your colleagues have witnessed on numerous occasions, managers have always (rarely) considered
employees’ views before making major decisions, have always applied the rules consistently (frequently applied
the rules inconsistently) across employees, and make sure to base decisions (never ensure that decisions are
based) on accurate information. Even then, if employees disapprove of a decision, managers have shown a
willingness (not been willing) to reconsider.”
Then the manipulation of the target was presented:
“One day, your supervisor tells you and your colleagues that an element of the team, normally in charge of
a particular function is absent. And he says you (your co-worker Joseph) will have to replace him. You (He
tries) try to explain that you have (he has) never performed this task and you don’t (he doesn’t) think you have
(he has) the proper knowledge or skills needed to perform the task. But the supervisor does not reconsider,
saying he is available if you need (your co-worker needs) help.”
The description of the injustice situation was adapted from Kray and Lind (2002).
“(Later, your co-worker Joseph comments to you :) How frustrating! The tasks were difficult, the computer
repeatedly shut down, and to make matters worse, the supervisor didn’t help at all. You (I) sent messages after
each task asking for help because, besides not having sufficient knowledge or skills to perform the task, the
computer was off-line so much that you couldn’t work as hard as you wanted. Each time that you (I) sent a
message, you (I) got a response that made you (me) think your (my) supervisor hadn’t even read your (my)
message. Your (my) doubts and concerns were falling on deaf ears. In fact, on one occasion, the manager even
stated explicitly that your (my) message was unread. You were (I was) told your (my)‘‘excuses’’ weren’t good
enough and that they would not be considered when deciding on your (my) performance appraisal or on pay
bonuses. You think to yourself (I thought to myself): What did I do to deserve this kind of treatment? I try to
work hard...on top of it all, a promotion is going to be decided soon.”
To check the effectiveness of the manipulation of the justice climate a control question was introduced at
the end of the questionnaire: “In general, not taking into consideration the specific situation described in the
text, workers feel justly treated in company X”. In order to check whether participants recognized the situation
described as an injustice situation they were asked if: “The specific situation that happened to the worker in
company X is an injustice situation” and “The specific situation that happened to the worker in company X is
serious”. Finally, an open question, regarding the perceived aim of the questionnaire, was introduced to control
implicit justice theory biases. All items were measured on a 1- Totally disagree to 5- Totally agree Likert type
scale.
Organizational justice perceptions were measured using the justice scales adapted from Niehoff and
Moorman (1993) and Folger and Konovsky (1989). Four items assessed distributive justice (α=. 913) (e.g.,
“Overall, the rewards I receive here are quite fair”), three items assessed procedural justice perceptions (α=.
893) (e.g., “To make job decisions, my supervisor collects accurate and complete information”), seven items
assessed interactional justice (α=. 947) (e.g., “My supervisor treats me with kindness and consideration”).
Work Satisfaction was assessed through an item where participants showed how they would feel working
for company X by choosing from 1 - “Totally unsatisfied” to 5 - “Totally satisfied”. Many researchers (eg,
Hackman & Oldham, 1980, Quinn & Shepard, 1974) have adopted an additive approach in measuring job
satisfaction. Several items are used to separately measure satisfaction with different aspects of organizational
life, them a global satisfaction index is computed (Snipes Oswald, LaTour, & Armenakis, 2005). However,
some authors, such as Scarpello and Campbell (1983) believe that a single item satisfaction measure is more
credible than the sum of scales for each aspect of job satisfaction, since multi items scales may overlook aspects
of work that are important to the employee (eg., Ironson, Smith, Brannick, Gibson, & Paul, 1989; Scarpello &
Campbell, 1983; Wanous, Reichers, & Hudy, 1997). Furthermore, in a meta-analysis by Wanous and colleagues
(1997), the authors compared studies using multi-item scales, and studies using a single item of job satisfaction.
Results showed a high correlation, suggesting that job satisfaction can be operated through a single item. In
addition this study used a single item satisfaction measure because, given that the manipulation was conducted
through the presentation of a scenario, we believed participants would find it easier to assess what their general
levels of satisfaction would be, rather than to rate specific satisfaction dimensions.
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4 RESULTS
Manipulation check and descriptive statistics
Participants agree that the situation described is an injustice situation (M=4.07; SD=.612). Additionally,
72% of the participants consider that the situation described is very serious (M=3.83; SD=.78).
To verify if there is a difference in the justice climate perception between the two situations, a t test for
independent samples was carried out. This revealed a significant difference (t(,414)=6.706; p≤.001) in the
expected direction. Those in the high justice climate situation tended to believe that, in general, the company
dealt with the workers more fairly (M=3.30, S.D= 1.301) than those in the low justice climate situation
(M=1.91, S.D= .91).
Table 1 presents the means, standard deviations and correlations of the variables considered in this study.
As can be observed, all variables are significantly correlated with each other except for: the justice climate and
procedural justice; the justice climate and interactional justice; and the justice climate and injustice target. All
correlations show positive relationships between variables except for the injustice target, which appears to have
a negative relationship with the remaining constructs. These magnitudes of correlations, ranging from relatively
high to medium, have often been found between justice dimensions (e.g., Colquitt et. al., 2001).
Table 1: Descriptive statistics
Measures M SD 1 2 3 4 5
1. Distributive Justice 2.55 1.00
2. Procedural Justice 2.35 1.10 .63**
3. Interactional Justice 2.11 .95 .44** .43**
4. Work Satisfaction 2.48 1.10 .47** .56** .46**
5.Justice Climate .49** .56** .17 .35**
6. Injustice Target -.27** -.10 -.25** -.17 -.022
Notes: **= p≤.001.
Hypotheses testing
Hypothesis 1 predicts that an injustice directed at the self will lead to more negative justice perceptions
than an injustice directed at a co-worker. Hypothesis 2 predicts that when the justice climate is high justice
perceptions will be less negative than when the justice climate is low. Multiple regression analyses were
conducted to assess the effect of these social context variables on justice perceptions. Because the independent
variables are a dichotomy they were recoded as dummy variables. Recoding was conducted in the following
way: justice climate 0= low; 1 =high; injustice target 0 =self; 1 =co-worker. The regression coefficients
represent the difference between the lower and higher values, that is, between low and high justice climate
situations, and between injustice directed at the self or at a co-worker. The results are summarized in table 2.
The model is significant (F(2.123)= 26.214; p≤ .001) in predicting distributive justice perception. The
(In)justice climate and target have an effect on distributive justice perception, which would explain 29% of its
variance. From the low to the high climate situation there is an increase in distributive justice perception.
Distributive justice perception tends to be more positive when the injustice targets a co-worker than when it
targets the self.
The effect of (in)justice climates explains 31% of the variance in procedural justice perceptions (F(2.126)=
29.911; p≤ .001). In the high justice climate situation, perceptions of procedural justice tend to be higher than in
the low climate situation. Although the relationship between the injustice target and procedural justice follows
the expected direction, it is not significant.
The model explains about 8% of the variance in interactional justice (F(2.121)= 6.137; p≤.05). Interactional
justice perception tends to be higher when the target is a co-worker than when it is the self, or when the justice
climate is high as opposed to when it is low. Although the relationship between the justice climate and
interactional justice follows the expected direction, it is not significant.
Maria Rita Silva, António Caetano and Qin Zhou
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Table 2: Organizational Justice regressed on (in)Justice climate and target
Depended Variables Independent Variables βa R2a Δ R2
Distributive Justice Climate .48** .23 .23**
Target -.24* .29 .06*
Procedural Justice Climate .56** .31 .32**
Target -.07 .31 .00
Interactional Justice Climate .17 .08 .00
Target -.25* .08 .09*
Notes: *= p≤.05; **= p≤.001
These results show that distributive and interactional justice perceptions were more negative when the
injustice situation was directed at the self rather than towards a co-worker. So hypotheses 1 was supported for
distributive and interactional justice perceptions. Results also show that distributive and procedural justice
perceptions tend to be less negative in the high justice climate situation. Hypothesis 2 was supported for
distributive and procedural justice judgments.
Although the hypotheses did not predict any interaction between the (in) justice climate and target, we
conducted a multiple variance analysis to explore that possibility. No effects were found.
Hypothesis 3 predicts that organizational justice will mediate the relationship of the justice climate and the
injustice target with satisfaction. The proposed model involves multi mediators that have relatively high to
medium correlation levels. Taking into account that the sample size does not permit structural equation analysis,
we have chosen to follow Preacher and Hayes (2008) recommendations and employ bootstrap analysis. The
authors state this method has four major advantages over classic multiple linear analyses: (1) it tests the “total
indirect effect” of the independent variable on the dependent variable which makes it possible to determine
whether an overall mediation effect exists and to assess its magnitude; (2) it makes it possible to determine to
what extent specific mediator variables mediate the relationship between the independent variable and the
dependent variable, in the presence of other mediators in the model; (3) the likelihood of parameter bias due to
omitted variables is reduced; (4) it makes it possible to compare the relative magnitudes of the specific indirect
effects associated with which mediators within the model. Separate analyses were conducted for each
independent variable including all three justice dimensions as mediators, and satisfaction as a dependent
variable. The results are presented in tables 3 and 4.
Justice perceptions fully mediate the effect the justice climate has on satisfaction. The model explains 37%
of the variance in satisfaction levels (F(4.114)= 17.998; p≤ .001). Only procedural justice was a significant specific
indirect effect, mediating the relationship between the justice climate and work satisfaction. This means that,
within this model and relative to the other mediators, procedural justice explains the overall effect of climate on
satisfaction. Satisfaction tends to be higher when the justice climate is high because it promotes more positive
procedural justice judgments.
Table 3: Bootstrap Analysis: Justice Dimensions as mediators between Justice Climate and Satisfaction
Note— BCa. bias corrected and accelerated; 5.000 bootstrap samples.
Justice perceptions also fully mediate the effect the injustice target has on satisfaction. The model explains
37% of the variance in satisfaction levels (F(4.114)= 18.012; p≤ .001). One can observe that interactional justice is
the only mediator with a significant specific indirect effect. This means that within this model and relative to the
other mediators, interactional justice explains the overall effect of injustice targets on satisfaction. Satisfaction
tends to be lower when injustice is directed at the self, compared to when it is directed at a co-worker, because
injustice directed at the self-promotes more negative interactional justice perceptions.
Ba 95% CI
β P Lower Upper
Direct Effect
Justice Climate .80 .00
Indirect Effects
Justice Climate .16 .41
Total .64 .00 .32 .98
Distributive Justice .10 .34 -.10 .32
Procedural Justice .44 .00 .15 .78
Interactional Justice .10 .07 .01 .26
R2 .37
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Table 4: Bootstrap Analysis: Justice dimensions as mediators between injustice target and satisfaction
Notes: BCa. bias corrected and accelerated; 5.000 bootstrap samples.
The results partially support hypothesis 3. The relationship between the injustice target and satisfaction is
totally mediated by interactional justice. The relationship between the justice climate and satisfaction is totally
mediated by procedural justice. However, no mediating effect of distributive justice was verified.
4 DISCUSSION
This study attempted to understand the influence of the (in) justice social context in the development of
justice judgments, and its consequences. It explored the effect of co-workers’ experiences (i.e., the injustice
target), and co-workers’ shared perceptions (i.e., the justice climate) on employees’ justice perceptions and job
related attitudes, namely job satisfaction.
Our findings indicate that injustice situations experienced by the self-generate more negative distributive
and interactional justice perceptions than injustice situations directed at other people. The injustice target has a
greater effect on interactional justice when compared to distributive justice. Contrary to our hypothesis, as far as
procedural justice judgments are concerned it makes no difference if the unfair event targets the self or a co-
worker. This may be due to the fact that procedural justice judgments are related to structural aspects of
organizational decision making processes. Even if the unfair procedures are experienced by a co-worker, they
may have a more general impact in situations that may affect individuals directly in the future.
Interactional justice judgments mediates the effect injustice target has on satisfaction. Injustices directed at
the self, generate more negative distributive and interactional justice perceptions than those directed at another.
Unlike distributive justice perceptions, which focus on specific outcomes, interactional justice focuses on the
more subjective quality of human interactions most likely to be influenced by attributional and memory biases.
Interactional justice relates directly to the image of supervisors (Colquitt et. al., 2001; Nowakowski & Conlon,
2005; Aryee, Chen, Sun, & Debrah, 2007), who are seen as the key agents of the organization. Thus perceptions
of interactional justice affect the extent to which individuals are satisfied with their employment relationship
(Lamertz, 2002), influencing job satisfaction.
When workers hear about an unfair event directed at a co-worker they consider the responsibility of both
parties involved. Influenced by just-world beliefs (e.g., Bobocel & Hafer, 2007) they attribute some, or most, of
the responsibility to the co-worker, and the event is evaluated as more fair. On the other hand, when the injustice
is directed at the self, workers are influenced by self-enhancing bias (e.g., Sedikides, Gaertner, & Vevea, 2005),
and assign all the responsibility for the transgression to the perpetrator, thus perceiving the interaction as less
fair. This leads to a decline in the supervisor’s image and the employment relationship, resulting in lower levels
of satisfaction. This effect indicates that, despite the deontonic perspective assumption that a perceived injustice
provokes negative emotional and behavioural reactions by third party observers (Cropanzano, Goldman &
Folger, 2003) these reactions are not as extreme as when the self is the target of the injustice.
Regarding the justice climate, findings indicate that justice plays a role as an antecedent of individuals’
justice perceptions. Facing the same injustice situation, individuals’ distributive and procedural justice
perceptions are more positive when the work group considers it is being treated fairly. The results contradict
assumptions based on the literature on expectancy violation (Bell et. al, 2006), and justice theories, such as the
group-value model (Tyler & Lind, 1992) and the fairness theory (Folger & Cropanzano, 1998, 2001). If
individuals experience unfair events in groups that believe they are generally treated fairly, individuals will
likely compare their experience with that of the group. Even so, instead of generating more negative justice
perceptions and dissatisfaction, the dissimilarity between what was experienced and the fairness people are
generally treated within the organization, may lead them to think of the incident as a one-off occurrence and to
give it less importance.
β 95% CI
β p Lower Upper
Direct Effect
Injustice Target -.41 .03
Indirect Effect
Injustice Target -.14 .40
Total -.27 .00 -.55 -.03
Distributive Justice -.06 .38 -.22 .07
Procedural Justice -.09 .24 -.31 .05
Interactional Justice -.12 .05 -.29 -.03
R2 .37
Maria Rita Silva, António Caetano and Qin Zhou
25
The justice climate has a stronger impact on procedural than on distributive justice judgments, and has no
effect on interactional justice perceptions. This may be related to the differences between these justice
dimensions. According to Greenberg (1993a), people are conceptually able to form justice judgments that
evaluate relatively stable aspects of the work environment, as well as events that happen at work on a day-to-day
basis. Procedural justice perceptions relate to structural aspects of organizational decision-making. Interactional
justice on the other hand, pertains to the quality of event-level interactions between employees and direct
supervisors. Therefore, procedural justice may have a more general impact on work groups than more situational
interactional justice. As such, individuals may rely more on socially influenced information processes, like the
justice climate, to make procedural justice evaluations. While interactional justice judgments are more prone to
particular event related attribution processes.
Procedural justice mediates the effect of the justice climate on satisfaction. When the justice climate is
high, procedural and distributive justice perspectives will be more positive. But unlike distributive justice, which
targets particular individual outcomes, the fairness of organizational procedures is more widespread, so an
individual may resort to group-level information for their evaluation. Satisfaction is said to occur not only as a
result of affective reactions but also as a consequence of calculative evaluation about the organization's fair
dealings in the employment relationship (Lamertz, 2002). Procedural justice is a good indicator of the fairness
of structural institutional organizational practices and as such, it contributes to the cognitive facet of satisfaction.
Because of its generalized character, reflected in the coherence norm, workers’ may rely on social information
sources, like the justice climate, to make procedural justice judgments, which are then reflected in satisfaction
levels.
No mediating effects of distributive justice were found. Distributive justice does not appear to play a major
role in the relationship between contextual social factors, like the (in) justice climate and target, and satisfaction.
This finding questions the assumptions of instrumental models of justice (e.g., Thibaut &Walker, 1975;
McFarlin & Sweeney, 1992). Satisfaction does not appear to be primarily influenced by distributive concerns.
On the other hand, results illustrate the importance a justice related social context has on satisfaction, and they
support the prediction of group value models (Tyler & Blader, 2000, 2003) that fair procedures and personal
treatment provide identity-relevant information influencing satisfaction.
Unlike distributive justice, procedural and interactional justice can serve dual roles: as outcomes in their
own right, and as sources of information about events (Barclay, Skarlic & Pugh, 2005) and social identity
(Blader & Tayler 2009). Individuals are sensitive to procedural and interactional justice because they signal the
degree to which members are valued and respected in the group and because transgressions contradict
expectations about the norms that regulate procedures and personal treatment. When individuals’ expectations
for socio-emotional outcomes are violated, they can experience emotional reactions regardless of whether their
economic expectations have been violated (Barclay, Skarlic and Pugh, 2005). Although violations of any type of
justice perspective can trigger attributions, procedural and interactional justice, carry attributional information
which can be used in the appraisal of the situation. As such, procedural and interactional justice judgments are
important to attitudes workers form based on social information.
Like most studies, the present study has its limitations. Some authors (e.g., Fraizer, Barron & Tix, 2004)
recommend that in the case of multiple mediator model analyses of structural equations, analysis should be used
in order to control the combined effect of the variable and the fit of the overall model. In this study that was not
possible due to the sample size. However, we followed Preacher and Hayes’ (2008) recommendations and
conducted bootstrap analysis. Although bootstrap analysis does not indicate the overall fit of the model, or
permit the simultaneous testing of both independent variables, nevertheless, it allows us to compare the effects
of multiple mediators.
Another limitation is the fact that participants of this study were presented with a description of an injustice
situation and not involved in a real life situation. Although we made certain that the described situation was
believable, differences may exist between the way people perceive they would behave and the way they actually
would. In part because “people are especially inaccurate in predicting how they will behave in a new situation”
(Lind & Tyler, 1988, pp. 47) and in part, because this method is particularly likely to be biased by social
desirability, since instead of acting spontaneously, the participants cognitively esteem how they would behave.
Future research could test our model in an organizational field study.
Our findings have some implications for human resources management. On the one hand they suggest that
the social context in which the (in)justice occurs has an effect, both in workers’ justice perceptions, and in their
satisfaction levels. Work environments normally characterized as fair have a buffering effect in the development
of workers’ negative justice perceptions. This suggests that organizations and their supervisor must strive to
maintain a high justice climate work context, if they wish to encourage higher satisfaction levels and more
positive justice perceptions. So, it is not only necessary to treat workers fairly, organizations must strive for
workers to have a shared perception that indeed, fair treatment is part of “the way things work around here”. On
the other hand, our findings indicate that, at least as far as procedural justice judgments are concerned, it makes
no difference if the unfair event targets the self or a co-worker. This indicates that organizations should strive to
Int. Journal of Business Science and Applied Management / Business-and-Management.org
26
be consistent in the way they treat their employees. Furthermore, it seems that procedural and interactional
justice play an important role in the relationship between the context in which the (in)justice occurs and workers
attitudes. Managers should be aware that even if work outputs are fairly distributed, the way policies are
designed, and implemented by supervisors, have an impact in workers’ satisfaction.
Justice is an important predictor of workers’ attitudes and behaviours. These findings are relevant to human
resources management practices, since they imply refocusing justice concerns so that they are not solely on
individuals but extended to groups. If co-workers’ experiences and perceptions have an impact on individual
justice perceptions, we can infer that practices aimed at dealing fairly with individuals will have less effect if
employees, as a whole, do not believe they are treated fairly. Especially because of the increasing relevance of
teams in the structuring of today’s organizations, the notion that injustices directed at co-workers may influence
other individuals’ justice judgments is well worth noting. Further research should be conducted on the effect co-
workers’ injustice experiences have on employees’ justice perceptions. Results indicate that a greater effort
should be made towards a finer integration of conceptual models of justice, which frame the development and
functioning of organizational justice perceptions in a socially contextualized perspective.
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Int. Journal of Business Science and Applied Management, Volume 7, Issue 1, 2012
Transformational leadership and job satisfaction: The mediating
effects of perceptions of politics and market orientation in the
Japanese context
Takuma Kimura
Faculty of Lifelong Learning and Career Studies, Hosei University
2-17-1 Fujimi Chiyodaku Tokyo, Japan
Telephone: +81-3-3264-6602
Email: [email protected]
Abstract
This paper investigates the causal relationship among transformational leadership, perceptions of organizational
politics, market orientation, and work-related outcome. In this study, we assumed that organization-level
perceptions of organizational politics and market orientation mediate the relationship between top
management’s transformational leadership and employees’ work-related outcomes and that perceptions of
organizational politics diminish market orientation. Data were collected from a sample of 200 employees
working in Japanese companies. To test the hypothesized correlations, we used structural equation modelling
using AMOS 16.0. As we hypothesized, both perceptions of politics and market orientation mediated the
relationship between transformational leadership and employees’ job satisfaction. However, contrary to our
expectation, perceptions of organizational politics were not significantly correlated with market orientation. This
study is the first empirical research of organizational politics using a Japanese sample. In future studies, we
should resolve methodological limitations of this study and develop theoretical frameworks that reflect cultural
difference among countries.
Keywords: transformational leadership, organizational politics, market orientation, job satisfaction
Acknowledgments: The author would like to thank the anonymous reviewers for their insightful comments and
suggestions to improve the quality of the paper. And the author wishes to thank Ryuichi Hirano and Miku
Yamagishi at Neo Marketing Inc. for their support in conducting this research.
Int. Journal of Business Science and Applied Management / Business-and-Management.org
30
1 INTRODUCTION
Since the 1960s, organizational politics has been a prominent issue in organizational management studies.
One of the most often cited definitions of organizational politics is Mintzberg’s which views it as “individual or
group behaviour that is informal, ostensibly parochial, typically divisive, and above all in a technical sense,
illegitimate—sanctioned neither by formal authority, accepted ideology, nor certified expertise (although it may
exploit any one of these)” (Mintzberg, 1983, p. 172). Some studies define organizational politics as a broader
and more general set of social behaviours (e.g., Pfeffer, 1981). However, for the purpose of this study, we
limited our focus to narrower and more specific definitions, such as Mintzberg’s which regards politics as
behaviours strategically designed to maximize short-term or long-term self-interest (c.f. Ferris, Russ & Fandt,
1989).
Even more recently, there has been a considerable number of theoretical studies and empirical research in
organizational politics (cf. Atinc, Darrat, Fuller, & Parker, 2010). Although some recent seminal studies used
samples from Chinese organizations (e.g., Wei, Chiang, & Wu, 2012; Wei, Liu, Chen & Wu, 2010), most
studies were carried out in the Western context. To the best of our knowledge there are no studies that
empirically examine organizational politics in Japanese companies.
However, organizational politics is a common issue in Japanese firms. Japanese firms widely exhibit
political behaviours, such as sub-optimization, power struggles, factional disputes, and tactics used by
individuals to wield influences, such as ingratiation. Despite the lack of academic research, a lot of Japanese
business people have pointed out the existence of organizational politics and its harmful effects. For example,
Tsujino (2010), in memoirs of his working life at Sony stated that the recent slump of Sony was partly due to the
spreading of an introvert attitude and a decline in market orientation caused by organizational politics.
Market orientation enhances business performance and leads to other positive work-related outcomes
(Jaworski & Kohli, 1993). Thus, if organizational politics detrimentally affects market orientation, firms would
be well advised to effectively manage politics in their organizations. One of the keys to promoting market
orientation while diminishing political behaviours and their detrimental effects is transformational leadership
(e.g., Avolio & Bass, 1991; Bass & Avolio, 1993). Previous empirical research has shown that transformational
leadership behaviours are negatively related to perceptions of organizational politics (POPs), and positively
related to market orientation (Vigoda-Gadot, 2007; Menguc, Auh, & Shih, 2007; Menguc & Auh, 2008).
However, the relationship between POPs and market orientation has not yet been empirically examined.
In this study, we investigated correlations among transformational leadership, organizational politics,
market orientation, and work-related outcomes. More specifically, we examined the correlation between
transformational leadership and job satisfaction, and the mediating effects of POPs and market orientation on
the relationship. We tested our model by using a sample of employees working in Japanese firms. Cultural
differences among countries may lead to differences in some aspects of organizational politics. However,
because there have been no academic studies of organizational politics in the Japanese context, and some
previous studies implied the applicability of the Western framework to the Japanese context, we relied on
theoretical framework of Western studies.
As described below, previous Western studies have already examined many of our hypotheses. In this
respect, our study is an application of a Western theory in the Japanese context. However, to the best of our
knowledge, our study is the first to integratively investigate correlations among the four focal constructs of
transformational leadership, POPs, market orientation, and job satisfaction. Thus, our study aimed to not only
replicate results of previous Western studies but to also investigate an unexamined research issue.
2 THEORY AND HYPOTHESES
2.1 Transformational Leadership
In leadership literatures, we often distinguish two types of leadership; transactional leadership and
transformational leadership. This distinction was first suggested by Burns (1978) and has been developed by
subsequent studies (e.g., Avolio & Bass, 1991; Bass & Avolio, 1993). Whereas transactional leaders seek to
satisfy the current needs of followers through transactions or exchanges mediated by contingent reward
behaviours, transformational leaders arouse heightened awareness and interests in the group or organization,
increase confidence, and move followers gradually from concerns for existence to concerns for achievement and
growth (Yammarino & Dubinsky, 1994).
Previous empirical research revealed that while transactional leadership behaviours are negatively related
to organizational performance, transformational leadership behaviours lead to higher organizational
performance (Howell & Avolio, 1993; MacKenzie, Podsakoff, & Rich, 2001; Parry, 2003). Although some
studies emphasized a ‘culture-specific’ perspective of leadership effectiveness (for review see Dickson, Den
Hartogo, & Mitchelson 2003), recent empirical research has found the effectiveness of transformational
leadership across cultures, supporting Bass’s (1997) ‘universal’ perspective (Madzar, 2005, Muenjohn &
Takuma Kimura
31
Armstrong, 2007; Wang, Law, Hackett, Wand, & Chen, 2005). Moreover, in a team-level analysis, Ishikawa
(2012) found a positive influence of transformational leadership on R&D team performance in Japanese firms.
Traditionally, researchers define and measure transformational leadership behaviours in various ways.
Reviewing influential studies of transformational leadership, Podsakoff, MacKenzie, Moorman, and Fetter
(1990) identified six key behaviours of transformational leaders: (1) identifying and articulating a vision, (2)
providing an appropriate model, (3) fostering the acceptance of group goals, (4) high performance expectations,
(5) providing individualized support, and (6) intellectual stimulation.
Podsakoff et al. (1990) demonstrated that there is a great deal of consensus among the researchers on some
of these behaviours, but not on others. For example, almost all of the reviewed literatures had established
“identifying and articulating a vision” as an important component of the transformational leadership process.
Similarly, more than half of the studies had identified “fostering the acceptance of group goals” and “providing
an appropriate model” as elements of transformational leadership. In contrast, concerning the other three
behaviours (i.e., high performance expectations, providing individualized support, and intellectual stimulation),
only a few studies suggested their importance as aspects of transformational leadership.
2.2 Perceptions of Organizational Politics (POPs)
The literature on organizational politics can be classified into three approaches: (1) studies on influence
tactics, conflict, and actual political behavior in organizations, (2) studies on POPs, (3) studies on political skills
and capacities of the self within the workplace (Drory & Vigoda-Gadot, 2010). Since the seminal work by Ferris
et al. (1989) proposed a model for the examination of employees’ POPs, researchers have conducted empirical
analyses mainly on the second approach. This line of research relied on Lewin’s (1936) proposition that
individuals respond based on perceptions of reality rather than on objective reality. Thus, in this approach,
organizational politics are conceived of as a state of mind rather than as an objective state (Gandz & Murray,
1980; Harris, Andrews, & Kacmar, 2007).
Theoretical and empirical studies of POPs have focused on the negative side of organizational politics.
Indeed, a lot of empirical research has shown that POPs bring about negative outcomes such as turnover
intentions, job stress, job dissatisfaction, and declines in organizational commitment (cf. Atinc et al., 2010;
Kacmar & Baron, 1999: Miller, Rutherford, & Kolodinsky, 2008). Therefore, reduction of employees’ POPs
should be viewed as an important issue both in the theory and practice of organizational management.
Political behaviours are likely to occur when a work environment is characterized by a high degree of
uncertainty or ambiguity (Ferris et al., 1989) which are generated when objective and specific criteria for
decision making are absent. Such uncertainty and ambiguity give organization members room to engage in
opportunistic behaviours and influence-related tactics. Even though an organization has some formal rules and
regulations, employees’ perceptions of the decision-making process can be political if the evaluation criteria for
decision making are obscure. Such a political environment will be viewed as unjust and unfair (Andrews &
Kacmar, 2001). Indeed, Andrews and Kacmar’s (2001) empirical research revealed a significant negative
relationship between POPs and organizational justice.
Vigoda-Gadot (2007) assumed that transformational leadership reduces POPs on the grounds that a
transformational leader offers a vision, a mission, and an operative plan for goal achievement. This, then,
eliminates ambiguity and professional uncertainty, and validates the feeling that it is possible to deal with
organizational challenges in a decent way based on justice and fairness. In their empirical analysis, Vigoda-
Gadot (2007) found a negative relationship between transformational leadership and POPs. Thus, we
hypothesized the following;
H1: Transformational leadership is negatively correlated with POPs.
As noted above, Ferris et al.’s (1989) POPs model assumes that POPs negatively influence work outcomes.
Indeed, a lot of empirical research has shown various detrimental effects of POPs on employees. Ferris et al.’s
(1989) model posits that organizational politics is a source of stress in the work environment, and thus POPs
induce negative work-related outcomes. Some researchers have argued that if the work environment is political,
employees’ investment into the organization (i.e., expenditure of effort to work in the organization) becomes
more risky (Cropanzano, Howes, Grandey, & Toth, 1997; Randall, Cropanzano, Bormann, & Birjulin, 1999). In
a political environment, rewards tend to be allocated based on informal power structures rather than on
contribution or efforts, and the rules may change from one day to the next. Because of this uncertainty,
individuals are less likely to be confident that their efforts will produce any outcomes beneficial to themselves.
Thus, individuals see their long-term contribution to such organizations as a risky investment, with the result
they are more likely to withdraw (Cropanzano et al., 1997).
Although POPs affect various kinds of work-related outcomes, we limited our focus to their effect on job
satisfaction. This was because of research implementation limitations we encountered. When decision-making is
governed by political considerations, employees usually view their work environments as unfair. In such unfair
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32
environments, employees feel unhappiness and stress, and therefore reduced satisfaction with their job (Ferris et
al., 1989; Ferris, Frink, Galang, Zhou, Kacmar, & Howard, 1996; Poon, 2003). Indeed, a lot of empirical
researches found the negative relationship between POPs and job satisfaction (e.g., Cropanzano et al., 1997;
Ferris & Kacmar, 1992; Ferris et al., 1996; Harris et al., 2007; Poon, 2003; Vigoda-Gadot & Talmud, 2010).
The POPs model was established in the Western context (Ferris et al., 1989), and much empirical research
on the model was conducted in Western countries. However, in recent years some research has replicated the
antecedents and consequences of POPs suggested by Ferris et al.’s (1989, 2002) model in the Eastern context
(e.g., Huang, Chuang, & Lin, 2003; Liu, Liu & Wu, 2010; Poon, 2003; Poon, 2006). Thus, we assume that POPs
have negative effects on work-related outcomes in Japanese organizations as well. However, there has been no
empirical research in the Japanese context. According to Hofstede (2001), a notable feature of Japanese culture
is high uncertainty avoidance in which people tend to feel stressed by uncertain or unknown situations.
Therefore, Japanese employees may be likely to suffer a feeling of dissatisfaction when they perceive their
organization as a political environment, or in other words, work environment with high uncertainty. Thus, we
hypothesized that:
H2: POPs are negatively correlated with job satisfaction.
2.3 Market Orientation
Market orientation is defined as “an organization culture that most effectively and efficiently creates the
necessary behaviours for the creation of superior value for buyers.” (Narver & Slater, 1990, p.21). Narver,
Slater, and Tietje (1998) argued that market orientation must be understood as an organizational culture and not
merely a set of processes and activities separate from the it. According to Narver et al. (1998), the central
principle of market orientation is that every person in the organization understands that each individual and
function can, and must, continuously contribute skills and knowledge to create superior value for customers.
In addition to viewing market orientation as a kind of organizational culture, Narver et al. (1998) suggested
that top management plays a critical leadership role in achieving and maintaining successful changes in
organization’s culture. They argued that appropriate leadership is essential to create market orientation in an
organization. Jaworski and Kohli (1993) echoed these viewpoints. They argued that unless an organization gets
clear signals from top managers about the importance of being responsive to customer needs, the organization is
not likely to be market oriented. Indeed, their empirical analysis showed that the greater top management’s
emphasis on market orientation, the greater the market orientation in the organization.
To reinforce market orientation, top management should articulate their vision and propagate it among
members so as to integrate the interests and values of various individuals and/or departments/groups within the
organization, and enable members to prioritize goals and objectives of the organization. We can regard these
leadership behaviours as essential parts of transformational leadership. Specifically, these leadership behaviours
are equivalent to “identifying and articulating a vision” and “fostering the acceptance of group goals”
(Podsakoff et al., 1990). Empirical studies have supported these arguments. Menguc et al. (2007) and Menguc
and Auh (2008) directly examined the relationship between transformational leadership and market orientation,
and found a significant and positive relationship. Therefore:
H3: Transformational leadership is positively correlated with market orientation.
Political behaviours are unsanctioned attempts at using influence to promote self-interest at the expense of
organizational goals (Ferris & Judge, 1991; Ferris & Kacmar, 1992; Ferris et al., 1996). Thus, in a political
organization, individuals and/or departments/groups tend to prioritize their own interests, and pay less attention
to the interests of other groups and/or that of the overall organization. They are prone to engage in political
behaviours in an attempt to receive favourable treatment and acquire more resources. These behaviours distort
resource allocation leading to detrimental effects on organizational performance.
To be market oriented, an organization requires cross-functional customer-value creation processes and
activities. In other words, each member, function, department, and/or group has to cooperate to create desired
customer value (Narver et al., 1998). However, in a political organization, because of self-serving behaviours of
each group or member, cross-functional customer-value creation processes and activities might be unlikely to
emanate. Thus, a political climate might impede the development of market orientation. Organizational politics
reflects a process of power struggles among conflicting individuals and groups attempting to further their own
self-serving goals mainly in decision-making (cf. Drory, 1993). If such power struggles escalate, employees or
groups are likely to compete with co-workers or other groups within their organization rather than with outside
competitors. This predisposes an organization to being “self-oriented” rather than market oriented, and therefore
less likely to gain a competitive advantage.
Nwanko, Owusu-Frimpong, and Ekwulugo (2004) examined the relationship between organizational
climate and market orientation and concluded that to make a market orientation program more sustainable, an
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organization needs to create a supportive climate. Nye and Witt (1993) argued that employees who perceive
their work environment as political may be less likely to view their organization as supportive because they
cannot believe that the organization is truly interested in their welfare. Previous empirical research has
confirmed their argument, finding a negative relationship between perceptions of organizational support and
POPs (Nye & Witt, 1993; Harris, Harris, & Harvey, 2007).
On the basis of these arguments and findings, we can assume there is a negative association between POPs
and market orientation. Indeed, empirical research has shown that the extent of political behaviour by
organizational members is negatively associated with the degree of market orientation (Harris & Piercy, 1999).
Thus, we hypothesized:
H4: POPs are negatively correlated with market orientation.
Empirical research has shown that market orientation leads to positive work-related outcomes, such as job
satisfaction, trust in management (Ruekert, 1992), and organizational commitment (Jaworski & Kohli, 1993).
Empirical research has also supported a positive relationship between market orientation and organizational
performance, such as ROA, sales growth, new product success, customer retention, market share, subjective
business performance (Greenley, 1995; Jaworski & Kohli, 1993; Kirca, Jayachandran, & Bearden, 2005; Narver
& Slater, 1990; Narver & Slater, 1993; Ruekert, 1992; Slater & Narver, 1994). Although contextual factors
significantly affect the strength of the link between market orientation and performance, the link itself
universally exists across various contexts (Ellis, 2006). Therefore, we can assume the same association between
market orientation and its outcome in the Japanese context as in the Western context. As noted above, because
of the restrictions in research implementation, we limited our focus to job satisfaction. Thus, we suggested the
following hypothesis:
H5: Market orientation is positively correlated with job satisfaction.
Summarizing the above discussion, we suggest the model presented in Figure 1. This model proposes that
POPs and market orientation mediate the association between transformational leadership and job satisfaction,
and that POPs negatively affect market orientation. As noted above, previous Western studies examined our
hypotheses and most of their results support the hypothesized correlations. However, no empirical studies have
tested these correlations in the Japanese context. Thus, this study examined whether the findings in the Western
context could be replicated in the Japanese context. Furthermore, no empirical study ―including those in the
Western context― has examined the integrative mediating model of these four focal constructs. Therefore, our
study aimed to not only replicate results of previous Western studies but to also investigate an unexamined
research issue.
Figure 1: The Research Model
Transformational
Leadership
Perceptions of
Organizational
Politics
Market
Orientation
Job Satisfaction
H1
H3
H4
H5
H2
-
+ +
-
-
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34
3 METHOD
3.1 Sample and Procedure
The sample consisted of 200 full-time employees working in Japanese companies. Participants were
selected from registered members of an online research program named iResearch administered by Neo
Marketing Inc., a private Japanese research company. In this program, the company invites people who wish to
participate in research and registers them as members. The company conducts various kinds of research on
requests from firms, universities, or individual researchers. Registered members who become respondents
include full-time company employees, those who are self-employed, public servants, part-time workers. From
those registered, we selected people who satisfied the requirements of our survey (i.e. full-time employees,
tenure at their organization of ≥2 years, in sales, general affairs, research and development, accounting, finance,
or planning. These requirements will be explained below), and sent them written requests by e-mail. The survey
was conducted by an online form questionnaire accessed via a URL written in the request e-mail.
We sent screening questionnaires to 13,625 randomly selected members, all of whom were company
employees. This screening questionnaire was intended to sort out members who met the research requirements.
It included questions regarding job, organization tenure, and job category. A total of 5,657 responses were
obtained, representing a response rate of 41.5%. Among them 303 respondents met the research requirements
and were then sent the main questionnaires. In this main questionnaire we collected the data regarding our four
focal constructs (i.e., transformational leadership, POPs, market orientation, and job satisfaction). We obtained
200 responses (66.0% of 303 qualified members). To ensure anonymity and confidentiality, response data were
automatically collected and converted into comma separated values. In this collection and conversion process,
participants’ names or identifying information were not recorded.
All of the respondents were Japanese and 60% were male. Ages of the respondents ranged from 25 to 68
years with a mean age of 38 years. Judging from the results of the Japanese Labour Force Survey conducted by
The Statistics Bureau and the Director-General for Policy Planning of Japan, these percentages and
distributions of gender and age nearly corresponded to that of all employees in Japan.
Because of concerns about response burden, tenure at organization was measured categorically with
responses being grouped in four blocks (i.e. 2 years and more to less than 3 years, 3 years and more to less than
4 years, 4 years and more to less than 5 years, 5 years and more). We only included respondents with 2 years
and more at their organization because a certain degree of time is necessary for an employee to understand
subtle features of their work environment, especially organizational politics. Although lacking a scientific
rationale, as a rule of thumb in Japan it is said that it takes about 3 years for new employees to be well informed
about their workplace. However, limiting respondents’ tenure to 3 years and more might have resulted in too-
small a sample. Thus, we limited respondents’ tenure to 2 years and more. We also limited respondents to full-
time employees, omitting part-time workers. In Japanese firms, full-time and part-time employees generally
work under different human resource management systems even if they are in a same workplace. A lot of part-
time workers are housewives and students. Therefore, there are large differences between full-time employees
and part-time workers in social interactions and attitudes at work (Kimura, 2011). In this study, we focus on
full-time employees and limited the scope of our research to them.
The sample included various job categories: sixty-seven (33.5%) in sales, forty-nine (24.5%) in general
affairs, forty-four (22.0%) in research and development, twenty-nine (14.5%) in accounting and finance, and
eleven (5.5%) in planning. Generally, in Japanese work settings, interactions among members are required, with
power struggles and conflicts a common and inevitable outcome in these jobs. Therefore, they are appropriate
for investigating organizational politics. Although this occupation classification is popular in Japan on a
practical level, it does not fully correspond to that of Japanese government surveys. Giving priority to
respondents’ convenience, we adopted this type of popular classification. Thus, we could not rigorously
compare the distribution of job categories in our sample with that of the entire Japanese labour force.
Nonetheless, by comparison with the results of the Japanese Labour Force survey, we would say that the job
distribution of our sample was not very different from that of the national labour force.
3.2 Measures
All items were measured on a five-point Likert scale. The anchors were “1 = strongly disagree” to “5 =
strongly agree”. The items in the scales were simply averaged to create an overall mean for each variable (i.e.,
not weighted by using the loadings of factor analysis from the construct).
Transformational leadership behaviours: Top management’s transformational leadership was measured by
nine items from Podsakoff et al.’s (1990) scale. Of the nine items, five items reflect behaviours of “articulating a
vision”. Sample items include “Our top manager has a clear understanding of where we are going.” and “Our
top manager paints an interesting picture of the future for our group.” The remaining four items reflect
behaviours of “fostering the acceptance of group goals”. Sample items include “Our top manager fosters
Takuma Kimura
35
collaboration among work groups.” and “Our top manager gets the group to work together for the same goal.”
Cronbach’s alpha for scores on this scale was 0.94.
Perceptions of organizational politics: Ferris et al. (1989) noted that political behaviour can occur in
multiple levels, namely, individual, group, and organization. Recently, some theorists have emphasized the
importance of applying a multi-level perspective to empirical analyses of organizational politics (e.g. Darr &
Johns, 2004; Fedor, Maslyn, Farmer, & Bettenhausen, 2008; Hochwarter, Kacmar, Perrewé, & Diane, 2003;
Maslyn & Fedor, 1998). The larger organization can be nonpolitical while an employee experiences high levels
of politics in his or her immediate work group (Maslyn & Fedor, 1998). Indeed, Maslyn and Fedor (1998) and
Hochwarter et al. (2003) found that employees distinguish between political behaviours occurring within their
own work groups versus those in the larger organization.
Since our focus was at the organization-level, in the analysis we focused on organization-level variables,
namely, top management’s leadership behaviours, and organization-wide market orientation. Therefore, as for
organizational politics, we also focused on organization-level POPs. We measured POPs with Maslyn and
Fedor’s (1998) four-item scale of organization-level POPs. Sample items from this scale include “There has
always been an influential department in this organization that no one ever crosses.” and “I have seen changes
made in policies here that only serve the purposes of a few individuals, not the work unit or the organization.”
Cronbach’s alpha for scores on this scale was 0.61.
Market orientation: Market orientation was measured by an eight-item scale developed by Farrell and
Oczkowski (1998). Sample items from this scale include “We monitor our level of commitment and orientation
to serving customers’ needs.” and “Our strategy for competitive advantage is based on our understanding of
customer needs.” Cronbach’s alpha for scores on this scale was 0.89.
Job Satisfaction: Job satisfaction was measured by a two-item scale developed by Hackman and Oldham
(1975). A sample item from this scale is “Generally speaking, I am very satisfied with this job.” Cronbach’s
alpha for scores on this scale was 0.82.
3.3 Factor analysis
We performed exploratory factor analysis (EFA) on these items by the principal factor method with
varimax rotation and extracted predicted factors (i.e., transformational leadership, POPs, market orientation, and
job satisfaction). Since two items of POPs, “Rewards come only to those who work hard in this organization”
(reverse scored) and “Pay and promotion decisions are consistent with existing organizational policies” (reverse
scored) demonstrated low factor loadings (i.e., lower than 0.50), we deleted these items from the analysis below.
Next, we performed four separate confirmatory factor analyses (CFAs) to assess the discriminant validity
of these constructs. CFAs were conducted on the proposed four-factor model and three alternative models.
Then, we compare the fitness of the four-factor model with those of alternative models. We used chi-square
value, Tucker-Levis index (TLI), the comparative fit index (CFI), and the root mean square error of
approximation (RMSEA) as comparative criteria. Table 1 shows the CFAs results.
Table 1: Results of Confirmatory Factor Analyses
chi-square df p TLI CFI RMSEA
4-factor model 20.415 14 .118 .982 .991 .048
3-factor model 1 33.751 17 .009 .961 .976 .070
3-factor model 2 118.735 17 .000 .765 .857 .173
1-factor model 183.937 20 .000 .678 .770 .203
Note: 3-factor model 1: transformational leadership and market orientation are combined
3-factor model 2: transformational leadership and job satisfaction are combined
The CFA results provided support for the four-factor model indicating the distinctiveness of the four
constructs. The chi-square value for the four-factor model (chi-square=20.415, df=14, p=0.12) was lower than
those for the three-factor model 1 (combining transformational leadership and market orientation; chi-
square=33.751, df=17, p<0.01), the three-factor model 2 (combining transformational leadership and job
satisfaction; chi-square=118.735, df=17, p<0.01), and the one-factor model (chi-square=183.937, df=20,
p<0.01).
Int. Journal of Business Science and Applied Management / Business-and-Management.org
36
Furthermore, the fit indices indicated a better fit for the four-factor model (TLI=0.982, CFI=0.991,
RMSEA=0.048) relative to the three-factor model 1 (TLI=0.961, CFI=0.976, RMSEA=0.070), the three-factor
model 2 (TLI=0.765, CFI=0.857, RMSEA=0.173), and the one-factor model (TLI=0.678, CFI=0.770,
RMSEA=0.203). Results of CFAs showed that the four-factor model is more appropriate than alternative
models. Thus, we proceeded to test the four-factor model.
3.4 Statistical analysis
We used structural equation modelling (SEM) methods for data analysis using AMOS 16.0. We used
maximum-likelihood estimation methods. We assessed the goodness-of-fit of our model by absolute and relative
indices (Jöreskog & Sörbom, 1993). The absolute goodness-of-fit indices calculated were (1) the chi-square
goodness-of-fit statistic, (2) the root mean square error of approximation (RMSEA), (3) the goodness-of-fit
index (GFI), and (4) the adjusted goodness-of-fit index (AGFI). The relative goodness-of-fit indices computed
were (1) the normed fit index (NFI), (2) the comparative fit index (CFI), and (3) the incremental fit index (IFI).
4 RESULTS
4.1 Descriptive statistics and correlations
Table 2 shows descriptive statistics and correlations among the study variables. This table demonstrates
that the correlations between the research variables were in the expected directions. Top management’s
transformational leadership was negatively correlated with organization-level POPs (r=-0.205, p<0.01) and
positively correlated with market orientation (r=0.755, p<0.01).
Organization-level POPs was negatively correlated with market orientation (r=-0.172, p<0.05), and job
satisfaction (r=-0.201, p<0.01). Market orientation was positively correlated with job satisfaction (r=0.499,
p<0.01).
Table 2: Means, Standard Deviations, and Correlations
Mean S.D. 1 2 3 4
1. Transformational
leadership 2.790 .917
2. Perceptions of
Organizational Politics 3.118 .992 -.205 (***)
3. Market Orientation 2.866 .773 .755 (***) -.172(**)
4. Job Satisfaction 2.915 1.001 .436 (***) -.201(***) .499(***)
Note: N=200; *p<0.10, **p<0.05, ***p<0.01
4.2 Hypotheses Testing
Figure 2 shows the SEM results of the structural model. Each numerical value shown in Figure 2 is a
standardized coefficient. The resulting fit indices indicated an acceptable fit of the model as all fit indices
showed a good fit. As for the absolute goodness-of-fit indices, chi-square=1.640 (p=0.20), RMSEA=0.057,
GFI=0.996, AGFI=0.959. As for the relative goodness-of-fit indices, NFI=0.993, CFI=0.997, IFI=0.997.
Figure 2 shows that top management’s transformational leadership was negatively correlated with
organization-level POPs (r=-0.205, p<0.01). Organization-level POPs was negatively correlated with job
satisfaction (r=-0.119, p<0.10). Thus, hypothesis 1 and hypothesis 2 were supported..
Top management’s transformational leadership was positively correlated with market orientation (r=0.751,
p<0.01). Market orientation was positively correlated with job satisfaction (r=0.478, p<0.01). Thus, hypothesis 3
and hypothesis 5 were supported. However, organization-level POPs was not correlated with market orientation
(r=0.018, p=0.71). Therefore, hypothesis 4 was not supported.
Finally, to test whether the mediation of POPs and market orientation was ‘partial’ or ‘full’, we set a
comparative ‘partial’ model which assumed partial mediation: the direct effects of transformational leadership
on job satisfaction. In this comparative model’s setting, we added an arrow from transformational leadership to
job satisfaction, and deleted the arrow from POPs to market orientation. The resulting fit indices indicated that
the fitness of the hypothesized model was better than that of the ‘partial’ model. As for the absolute goodness-
of-fit indices of the ‘partial’ model, chi-square=8.370 (p<0.01), RMSEA=0.192, GFI=0.980, AGFI=0.798. As
for the relative goodness-of-fit indices, NFI=0.971, CFI=0.974, IFI=0.974.
Takuma Kimura
37
Our SEM results supported many of the hypotheses suggested in this study. Taken together, these results
demonstrate that organization-level POPs as well as market orientation may mediate the relationship between
top management’s transformational leadership and job satisfaction. To put it another way, top management’s
transformational leadership is positively correlated with job satisfaction through reducing POPs and enhancing
market orientation.
Figure 2: Results of the Proposed Research Model (Standardized Coefficients)
Transformational
Leadership
Perceptions of
Organizational
Politics
Market
Orientation
Job Satisfaction
-.205***
.751***
-.018
.478***
-.119*
Note: N=200; *p<0.10, **p<0.05, ***p<0.01
chi-square=1.640 (p=0.20), RMSEA=0.057, GFI=0.996, AGFI=0.959, NFI=0.993, CFI=0.997, IFI=0.997
5 DISCUSSION
5.1 Findings
Our SEM results support many of our hypotheses. In line with our expectations, top management’s
transformational leadership had a significant influence on job satisfaction through organization-level POPs and
market orientation. As stated by Podsakoff et al. (1990), essential components of transformational leadership
include identifying and articulating a vision, and fostering the acceptance of group goals. These functions serve
to make employees concentrate their attention on their company’s goals and to reduce unproductive conflicts.
Thus, top management’s transformational leadership can serve to enhance organization’s market orientation and
lower the level of organizational politics.
Since organizational politics represents certain unique domains of organizational culture and climate
(Shaker, 1987), it may be that we need to examine POPs in consideration of the national culture. However, since
there has been neither theoretical nor empirical research on POPs in the Japanese setting, we developed a
correlational model based on Western literatures. Our findings are in line with previous empirical research in the
Western context (e.g., Vigoda-Gadot, 2007). We thus assumed a similar theoretical basis and expected similar
correlations to be found for various aspects of POPs in the Japanese context as has been reported for that in the
Western context.
Unexpectedly, organization-level POPs were not significantly correlated with market orientation. Although
a variety of theoretical reasons may account for this result, one possible explanation is that our measure of
organization-level POPs did not reflect the all the aspects of organizational politics in Japanese firms. Based on
the EFA results, we used only two items of Maslyn and Fedor’s (1998) POPs measure. Kacmar and Ferris
(1991) argued that POPs consist of three dimensions―“general political behaviour”, “go along to get ahead”,
and “pay and promotion”―and much recent empirical researches has adopted this view. Thus, the two items
used in this study may not have been enough to measure whole aspects of organizational politics in the
workplace. This may be one of the reasons why POPs was not correlated with market orientation.
The EFA result that two items showed low levels of factor loading might reflect work environment features
in Japanese organizations. These two deleted are matters of distributions and procedures of rewards such as pay
and promotion. In Japanese firms, pay and promotion decisions are sometimes distorted not only by
organizational politics but also by “seniority-based evaluation”. Since the 1990s, many Japanese firms have
discarded the “seniority-based personnel system” and introduced a “performance-based personnel system”
(Keizer 2010). However, attitudes of seniority orientation still exist in many workplaces in Japan. Thus, we can
Int. Journal of Business Science and Applied Management / Business-and-Management.org
38
infer that when Japanese read the sentences of these two items, some of them may have interpreted them as
items concerning seniority-based systems and not as having to do with political climate. That may be the reason
why these two items were not integrated into the factor of POPs.
5.2 Implications for Theory and Future Research
This study makes some important contributions. First, we explored correlations among transformational
leadership, POPs, market orientation, and work-related outcomes (i.e. job satisfaction). While previous
empirical research has examined the relationship between two (some three) of these variables, the present study
is the first to comprehensively explore the relationship among all of these variables.
Second, our study used a Japanese sample. Although a lot of empirical research on POPs has been
conducted over the last several decades, most of it was in the Western context. As far as we know, this is the
first empirical research on POPs using Japanese sample, and confirming, at least in part, the replicability of the
POPs model (Ferris et al., 1989) in the Japanese context. Although many of our hypotheses replicated those
made in the Western context, few studies have examined the mediating effects of POPs and market orientation
as examined in our study. In future studies, we should examine our mediating model in various contexts other
than a Japanese one.
As Ferris et al. (1996) stated, politics are inherent in the very contextual fabric of organizations. This may
be true for Japanese organizations as well because our data indicated that a considerable number of our
respondents showed relatively high POPs scores: on the average score of five-point items of POPs, 31.5% of
respondents had scores ≥3.5, 18.5% had ≥4.0, and 17.0% had ≥4.5. However, it is possible that general features
of organizational politics reflect the traits of national culture. According to Hofstede (2001), Japanese
organizations are more collectivistic than Western organizations. Thus, it may be that political behaviours in
Japanese organizations are more group or clique-centred and less individual-centred than in Western
organizations. Moreover, Japanese people have a tendency to avoid uncertainty compared with Western people.
Therefore, Japanese may be more likely to feel stress when in uncertain environments such as political
workplaces. That is, it is possible that POPs have more detrimental effects on work-related outcomes in Japan.
Although these cultural traits of organizational politics in Japan are only speculative, this conjecture can provide
an avenue of future research in Japanese organizations.
Another important contribution of this study is the multi-level perspective of the analysis. Focusing on
organization-level variables, we were able to explore correlations of top management’s leadership with
organization-level politics and market orientation. Future studies could use this approach at a group level.
5.3 Implications for Practice/Management
Our findings have useful practical implications for Japanese firms. Although organizational politics and
market orientation have received a lot of attention in practical fields, few empirical studies have examined the
relationship between these constructs and leadership in the Japanese context.
The results of our study suggest that top management’s transformational leadership is correlated with and
may lead to the reduction of POPs and the enhancement of market orientation. In the Western context, a lot of
previous empirical research has confirmed the impact of organizational politics and market orientation on a
firm’s performance. Our study suggests the importance of transformational leadership for Japanese firms
suffering from the prevalence of organizational politics and a shortage of market orientation.
5.4 Limitations
This study has several limitations. First, our analysis was based on the same source of cross sectional data
and could thus be affected by common-method variance. Because we only examined correlations, this study did
not look at causal relationships among the variables. To investigate causality, a ‘predictive’ research design in
which independent variables are measured before dependent variables is optimal. However, we relied upon a
‘contemporaneous’ design in which the independent variable, the mediating variables, and the dependent
variable were measured contemporaneously. Future research should use a longitudinal and multi-source
approach to examine questions of causality.
Second, the validity of our POPs scale is questionable. To measure POPs, recent empirical research
generally used Kacmar and Carlson’s (1997) fifteen-item, empirically validated scale (e.g., Miller & Nicols,
2008; Vigoda-Gadot & Talmud, 2010; Zetller & Hilbig 2010). Because of concerns about organization-level
POPs, we used Maslyn and Fedor’s (1998) four-item scale. However, this scale showed a 0.61 of Cronbach’s
alpha which is a little lower than desired level (Henson, 2001). Furthermore, two of them were deleted in the
analysis because of their low levels of factor loading. Thus, it is possible this scale did not sufficiently measure
organization-level POPs.
Third, we did not analyse all aspects of transformational leadership. Namely, our analysis did not include
some aspects of transformational leadership such as individualized consideration, inspirational motivation, and
intellectual stimulation (Avolio, Waldman, & Yammarino, 1991). These factors might contribute to reduce
Takuma Kimura
39
political behaviours and to strengthen market orientation by building a collective identity and mutual trust
among subunits. Finally, as for work-related outcomes, we only examined job satisfaction. Because of some
restrictions in the research implementation process―mainly, a concern for response burden―we did not include
scales of employees’ performance. Future studies should measure employees’ or organizations’ performance
and include them in research models.
6 CONCLUSIONS
Despite several limitations, this study makes some contributions by finding correlations among
transformational leadership, POPs, market orientation, and work-related outcomes. This is the first study
applying POPs to empirical research in the Japanese context, and our findings did not contradict results of
previous Western studies. Therefore, the theoretical framework of POPs generated in the Western context might
be applicable to the Japanese context.
However, our research indicated the possibility that the POPs measure should be adjusted to fit more with
the Japanese context. Furthermore, because of the characteristics of Japanese culture and the climate of Japanese
firms, in Japanese organizations it is possible that POPs have different antecedents and/or outcomes than that of
Western organizations. Differences in the mediators and/or moderators of the effects of POPs may also exist.
Future studies should consider cultural differences in more depth and reflect them in the research framework.
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Int. Journal of Business Science and Applied Management, Volume 7, Issue 1, 2012
Developing a performance measurement system for public
research centres
Deborah Agostino
Dipartimento di Ingegneria Gestionale, Politecnico di Milano
Piazza Leonardo da Vinci, 32 - 20133 Milano, Italy
Telephone: +39 02.2399.4073
Email: [email protected]
Marika Arena
Dipartimento di Ingegneria Gestionale, Politecnico di Milano
Piazza Leonardo da Vinci, 32 - 20133 Milano, Italy
Telephone: +39 02.2399.4070
Email: [email protected]
Giovanni Azzone
Dipartimento di Ingegneria Gestionale, Politecnico di Milano
Piazza Leonardo da Vinci, 32 - 20133 Milano, Italy
Telephone: +39 02.2399.3981
Email: [email protected]
Martina Dal Molin
Dipartimento di Ingegneria Gestionale, Politecnico di Milano
Piazza Leonardo da Vinci, 32 - 20133 Milano, Italy
Telephone: +39 02.2399.4044
Email: [email protected]
Cristina Masella
Dipartimento di Ingegneria Gestionale, Politecnico di Milano
Piazza Leonardo da Vinci, 32 - 20133 Milano, Italy
Telephone: +39 02.2399.4081
Email: [email protected]
Abstract
This study aims at developing a performance measurement system (PMS) for research and development (R&D)
activities carried out by public research centres. Public research institutions are characterized by multiple
stakeholders with different needs, and the management of R&D activities requires balancing the multiple goals
of different stakeholders. This characteristic is a key issue in the process of construction of the PMS. Empirical
evidence is provided by an Italian public research centre, where the researchers carried out a project aimed to
develop a PMS following action research principles. This project gave the possibility to researchers to interact
with different stakeholders and integrate their different information needs in a comprehensive set of key
performance indicators (KPIs). As a result, multidimensional framework for measuring R&D performance in a
public research centre is proposed and a set of Key Performance Indicators is developed, suggesting
implications for academics and practitioners.
Keywords: performance measurement, public research centres, stakeholders, accountability, decision-making
Int. Journal of Business Science and Applied Management / Business-and-Management.org
44
1 INTRODUCTION
In recent years public research systems in several industrialized countries, underwent an intense
transformation process due to the increasing organizational complexity of research & development (R&D)
activities and the hit of the worldwide financial crisis. Leading research institutions claim that many complex
problems of the society demand innovative solutions which combine knowledge from different scientific areas
that can be achieved through interdisciplinary research (National Academies, 2005). To achieve this, very often,
research centres carry out interdisciplinary R&D projects, which require collaborative and sometimes informal
behaviours (Cross, Borgatti and Parker, 2002; Allen, James, & Gamlen, 2007) and new forms of organization
and management (Welter, Jooß, Richert, Jeschke, & Brecher, 2012). At the same time, the financial crisis has
reduced the overall government spending and specifically the budget for research centres (e.g. Arena
&Arnaboldi, 2013). This reduction of funding has increased the competition between research institutions, while
at the same time they are required to demonstrate their value for money. To face these challenges, research
institutions need to consciously manage their core processes, the creation and development of their knowledge
assets (Rowley, 2000), and consistently redesign their support processes and managerial instruments (e.g.
Arena, Arnaboldi, Azzone & Carlucci, 2009; Arena, Arnaboldi & Azzone, 2010a;Welter et al. 2012).
In this context, our paper focuses on one specific managerial instrument - i.e. performance measurement
system (PMS). In the last decades, measuring R&D performance has become a fundamental concern for R&D
managers and executives. Scholars and practitioners have already recognized the relevance of performance
measurement in R&D in relationship to different purposes (e.g. Chiesa, Frattini, Lazzarotti&Manzini, 2009;
Kulatunga, Amaratunga&Haigh, 2011). A PMS can be useful for motivating researchers, evaluating R&D
projects’ profitability, supporting decision-making, communicating the centre’s results to external
constituencies, and stimulating organizational learning.
However, most of the current research about PMS in R&D focuses on private sector organizations (e.g.
Bremser&Barsky, 2004; Chiesa et al., 2009) and these results are not easily applicable to Government-funded
research centres, since they overlook some key characteristics of these organizations. In industrial firms, R&D
activities are primarily financed by the company itself and they represent one of the activities in their value
chain. In this context, the company does not have the need of searching for research funding, given that it is self
financed and research outputs represent the input for further processes of the firm’s value chain. The research
results are incorporated into products and, in the end, brought to the market and sold by the company,
‘hopefully’ resulting in an increase in the revenue and profits of the firm, and contributing amortize the R&D
investment. On the contrary, in Government-funded research centres, such as public research institutions, the
research activity represents the core mission of the organization and the R&D activity needs to be financed by
external parties. This structure has two main implications. On the one hand, the role played by the research
activity is central, because the output of the research represents the end itself. Research institutes contribute to
the early stages of the innovation process of various customers within the national innovation system and serve
thus as an important research infrastructure, (Shenker, 2001; Leitner & Warden, 2004). On the other hand, the
need for searching funding is associated with the pressure of demonstrating the ability of generating research
outputs that provide value for the society. The output of public research centres is expected to have a positive
impact on the wider society; their principal aim is to disseminate the research results and to have a return in
terms of scientific-technological progress, driving the national strength (Senker, 2001; Coccia, 2004). However,
once the funding are received, mainly by the public government (Senker, 2001; Coccia, 2004), the research
centre has to be accountable on how it spend public money. In this sense, Coccia (2005) argued that research
institutions are dependent on the government for carrying out their activities. As consequence of these
specificities, public research centres are subject to different and contrasting stakeholders pressures that are wider
compared to private sector organizations (Dixit, 2002; Coccia, 2004; Arnaboldi, Azzone & Savoldelli, 2004).
Public government, the wider society, the researcher, the administrative manager but also private partners, have
different objectives and their alignment can be extremely complex, especially in the current R&D landscape
characterized by reduced finance and higher competition between institutions.
To our best knowledge, relatively a few contributions have dealt with the issue of R&D performance
measurement in the public sector (e.g. Coccia, 2001a; 2004; Leitner & Warden, 2004; Secundo, Margherita, Elia
& Passiante, 2010) and a comprehensive framework for measuring performance of public research institutes is
still missing, setting the motivation for this work. This paper has the objective to develop a performance
measurement system (PMS) for a public research centre. The centre has to deal with the informative needs of a
diversified range of stakeholders that require accountability in relationship to the centre’s activities and, in
particular, the use of funding, but also aim to encourage collaboration between different research units and
support decision making processes. The PMS has been developed through action research, whereby the
researchers interacted with different stakeholders and integrated their different information needs to a
comprehensive set of key performance indicators (KPIs). Interviews and meetings with scientific and
Deborah Agostino, Marika Arena, Giovanni Azzone, Martina Dal Molin and Cristina Masella
45
administrative directors over a time horizon of ten months were useful for designing a dashboard for public
research institutions that integrates the information needs of a plurality of stakeholders.
The paper is organized as follows. The next section outlines the state of the art on the issue of performance
measurement for R&D activities. The third section details the methodology used for data collection and
analysis, describing the phases of the research project. The fourth section presents the PMS framework and the
set of indicators that have been developed. Finally, the last section clarifies contributions and limitations of this
research.
R&D performance measurement in research centres
Various studies on public research centres show a growing interest in R&D performance measurement
(Luwel, Noyons & Moed, 1999; Senker, 2001;Coccia, 2004; Coccia & Rolfo, 2007). Originally, particular
attention has been given to the use of bibliometric and technometric indicators to evaluate public research centre
performances (e.g. Narin & Hamilton, 1996; Luwel et al., 1999; Noyons, Moed & Luwel, 1999). Bibliometric
involves the quantitative analysis of the scientific and technological literature. It relies on the assumption that
the frequency with which a paper or a patent is cited is a measure of the impact of the paper or patent. The most
common bibliometric indicators are literature indicators – i.e. indicators that measure the scientific performance
based on the number of publications and the count of citations in the scientific literature, and, obviously, the
higher the better. Similar considerations are applicable to patents too. Those patents which are most highly cited
and most science linked are also the patents that tend to be most heavily licensed, and are therefore making the
largest contribution to the economy (Narin & Hamilton, 1996).However, bibliometric indicators provide a
partial and non-systemic picture of the performance of a research centre, because they overlook some relevant
elements such as the impact of the research activity on the society and they ignore the role played by
government funding (Coccia, 2004).
Moving from these considerations, Coccia proposed a weighted index to provide a synthetic measurement
of public research’s activities - Relev Model I and Relev Model II - (Coccia2001b; 2004, 2005). The Relev
Model is based on an input-output model, where inputs consist in public funds, personnel payrolls and cost of
labour, and outputs are represented by self-financing deriving from activities of technology transfer, training
(e.g. number of degree students, PhD students), teaching (measured as the number of courses held by
researchers), international and domestic publications, international and domestic conference proceedings. Based
on a weighted input – output ratio, the Relev model attempts to synthesize the performance of public research
centres in a unique score. This score has the final objective to support external accountability, providing
evidence of the value for money generated by these institutions. Indeed, these studies generally adopt one
specific perspective. They reflect the aim of governments to define metrics that could be used to assess the
research centres performances in order to facilitate the identification of the most and the least productive
laboratories, and to support policy decisions on the level and the direction of the public funding of research
(Coccia, 2004). These kinds of metrics, instead, are less useful to support internal decision making and report a
centre’s performances to a broader range of stakeholders.
To respond to this need, a few studies started to focus on the use of key performance indicators for
assessing R&D results in public sector research centres (e.g. Leitner & Warden, 2004; Chu, Lin, Hsiung & Liu,
2006; Secundo et al., 2010). This stream of research is based on Intellectual Capital reporting models (e.g.
Stewart, 1997; Edvinsson & Malone, 1997). At a general extent, the Intellectual Capital encompasses three
components: human capital, structural capital, and relational capital. The human capital refers to the
characteristics of the entire organization’s staff and management. The structural capital refers to organizational
infrastructure. The relational capital refers to the establishment, development and maintenance of relationships
with external stakeholders. The combination and integration of these forms of capital determines the entity’s
results that include both economic performances and other intangible results, such as research- and society-
oriented results (Leitner & Warden, 2004). These approaches to performance measurement provide a more
complete picture of the resources available to research centres, however they seem to overlook the performances
of the centres in the transformation process of inputs in outputs (e.g. they overlook issues such as efficiency and
productivity).
Finally, further evidence in relationship to R&D performance measurement is provided by the private
sector literature, where the topic has been investigated from two different perspectives. In particular, one stream
of research focuses on the choice of the performance dimensions and the performance indicators that are best
suited to the characteristics of R&D. Some of these works consist in the application of well-known PMS
frameworks, such as Balanced scorecard, Skandia navigator, Intangible asset monitor, to R&D activities (e.g.
Kerssens-Van Drongelen & Bilderbeek, 1999; Bremser & Barsky, 2004). Other works consist in the
development of ad-hoc frameworks and set of indicators to assess the performance of R&D (e.g. Pawar &
Driva, 1999; Kim& Oh, 2002; Marr, Schiuma & Neely, 2004; Mettänen, 2005). These papers can provide a
reference point for the identification of performance indicators for R&D, though they are not tailored to the
specific characteristics of public sector organizations. The second stream of research, instead, focuses on the
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strategic design of the PMS in R&D firms and addresses the relationship between the characteristics of the
PMS, the objective of the PMS and contextual variables, such as type of R&D activity, industry and company’s
size (e.g. Kerssens-van Drongelen & Cook, 1997; Chiesa & Frattini, 2007; Chiesa et al. 2009). These papers
highlight how the PMS can be configured in a different way on the basis of the objectives that it is supposed to
pursue (e.g. diagnostic, motivation and coordination), hence suggesting to pose particular attention to the
different objectives of the stakeholders of a research centre.
The above review has highlighted that there has been widespread dispute among researchers and
practitioners about which measures are more suitable in assessing the performance of research organizations,
and, at present, this debate is still open (Elmquist & Le Masson, 2009; Whelan, Teigland, Donnellan & Golden,
2010; Dumay & Rooney, 2011).
2 RESEARCH METHOD
Choice of the research method
This paper reports the results of a project aimed at developing a PMS for an Italian technological research
centre. To carry out this goal we adopted action research (AR) as research methodology. Action research has
been selected given its aim “to contribute both to the practical concerns of people in an immediate problematic
situation and to the goals of social science by joint collaboration within a mutually acceptable ethical
framework” (Rapoport, 1970: 499). On the one hand, this methodology gives the possibility to focus on a
problematic practical situation, the need to introduce a PMS to deal with the current complex research
environment that represents at the same time a relevant academic concern. On the other hand, the collaborative
nature of the interaction between the researchers and the employees of the research centre allowed capturing the
specific requirements from each stakeholder supporting the definition of the most appropriate set of KPIs.
Specifically, we conducted AR drawing on the seminal work of Susman & Evered (1978). According to the
authors, AR is seen as a cyclical process articulated in five phases: diagnosing, action planning, action taking,
evaluating, and specifying learning (see Figure 1). At the centre of the cycle there is a client system that is the
social system in which the members face problems to be solved by action research. Different techniques were
used for data collection and analysis, including in-depth interviewing, direct observation and documental
analysis (data were drown from the records, memos, and reports that the client system routinely produces). The
interviews were recorded and transcribed, though the empirical material was not codified, but instead analyzed
textually, with each author highlighting emergent themes. Overall 64 interviews were performed and three
official plenary meetings took place; the process was highly participative (Reason & Bradbury, 2001) and
different stakeholders involved continued to provide comments about the researchers’ proposals.
Deborah Agostino, Marika Arena, Giovanni Azzone, Martina Dal Molin and Cristina Masella
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Figure 1: The action research cycle (Susman and Evered, 1978)
ACTION PLANNING
Considering alternative
courses of action for
solving a problem
ACTION TAKING
Select a course of action
EVALUATING
Studying the
consequences of action
SPECIFIYING LEARNING
Identifying general f indings
DIAGNOSING
Identifying or def ining a
problem
Development
of a client
system
inf rastructure
The client system infrastructure
The client system is a public research centre in Italy, active in different fields of technological
development. The centre currently employs more than 1,000 people, with more than 900 researchers. The centre
is governed by an executive committee that defines the centre’s long term strategies and is in charge of its
management. It is characterized by a dual structure: the scientific director is responsible for all the research
activities of the centre; the administrative director, on the other hand, is responsible for the administrative
structure. The distinctive characteristic of the centre is represented by interdisciplinary R&D activities that cover
several fields, ranging from robotics, neuroscience, energy, to smart materials and drug discovery. The
interdisciplinary nature of the research activity is then reflected in the organizational structure that consists of 12
central research units, 11 research centres located at the premises of, and in collaboration with, other research
institutions both in Italy and abroad, and 3 facilities that provide support services to the research units (e.g.
animal facility).
At the beginning of the project, a working group was constituted within the client system, including the
Head of the Management Control Office and a member of its staff, the Head of the ICT Office, and two internal
consultants. The working group interacted with the researchers in all the phases of the AR cycles. In addition,
different employees of the centre were involved as informants and prospect users of the PMS (see Table1).
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Table1: The client system
Participants and informants Organizational unit Role in the project
Scientific Director Central administration Initiator and user
Administrative Director Central administration Initiator and user
Head of the Management Control Office Central administration Head of the client working group
Management and Control Office Staff Central administration Member of the client working group
Head of ICT Office Administrative office Member of the client working group
Consultant 1 External Member of the client working group
Consultant 2 External Member of the client working group
Head of RU 1 Research unit Informant and user
Head of RU 2 Research unit Informant and user
Head of RU 3 Research unit Informant and user
Head of RU 4 Research unit Informant and user
Head of RU 5 Research unit Informant and user
Head of RU 6 Research unit Informant and user
Head of RU 7 Research unit Informant and user
Head of RU 8 Research unit Informant and user
Head of RU 9 Research unit Informant and user
Head of RU 10 Research unit Informant and user
Head of RC 1 Research centre Informant and user
Head of RC 2 Research centre Informant and user
Head of RC 3 Research centre Informant and user
Senior Scientist 1 Research unit Informant and user
Senior Scientist 2 Research unit Informant
Senior Scientist 3 Research unit Informant
Senior Scientist 4 Research facility Informant
Technician 1 Research facility Informant
Technician 2 Research unit Informant
Technician 3 Research unit Informant
Team Leader 1 Research unit Informant
Team Leader 2 Research unit Informant
Head of RF 1 Research facility Informant and user
Head of RF 2 Research facility Informant and user
Head of RF 3 Research facility Informant and user
Head of Research Office Support office Informant and user
Head of Project Office Support office Informant and user
Head of Technology Transfer Office Support office Informant and user
Head of Human Resource Office Administrative office Informant
Head of Engineering office Administrative office Informant
Head of Health and Safety Administrative office Informant
Head of Purchasing Office Administrative office Informant
Head of Legal Office Administrative office Informant
Head of Internal Audit Office Administrative office Informant
Head of Accounting Department Administrative office Informant
Deborah Agostino, Marika Arena, Giovanni Azzone, Martina Dal Molin and Cristina Masella
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The AR Cycles
In this section we outline the phases of the two AR cycles, posing particular attention to specifying the
sources used for data collection in each step of the research.
AR Cycle I
The diagnosing phase started with a literature review concerning performance measurement in research
organizations, a series of project meetings and a brainstorming session on the role of the performance
measurement system in the specific setting. The outcome of this phase was the identification of the process-
oriented model (Pollanen, 2005) as a starting point for the development of the PMS and the acknowledgement
of the existence of different information needs in relationship to the PMS from a plurality of stakeholders.
Action planning consisted in mapping data and information needed to support the development of the PMS. A
list of informants and relevant documentation was prepared by action researchers in collaboration with a
working group of the research centre. In addition a list of critical issues (interview protocol) to be discussed with
the informants was prepared. In the action taking phase, 38 interviews were carried out with the heads of
scientific units and researchers of the centre, and also the administrative personnel. The analysis was
complemented with public documents and confidential reports, entering in detail the scientific activity carried
out by each department. Based on these data, a prototype of the PMS model and a preliminary set of KPIs were
developed. In the evaluating phase the proposed model and the preliminary set of KPIs were presented in two
plenary meetings, one involving the scientific director, the administrative director and a selected number of
other internal officers and one involving the heads of the research units. Feedback, comments and suggestions
for improvements were collected. Finally, specifying learning consisted in summing up the learning outcomes of
the AR cycle. All the comments and feedbacks deriving from the evaluation phase were integrated. The
outcome of this phase was twofold: the validation of the overall PMS model and an ‘explosion’ in the number of
KPIs due the receipt of many proposals from the participants. Overall 42 indicators and several variations to
each of them were suggested, posing the basis for the second AR cycle, aimed to the refinement of the set of
indicators. The following table outlines the techniques used for data collection and analysis in different steps of
the AR cycle, the role of the researchers and the client working group and the output produced (Table 2).
Table 2: AR Cycle I
Techniques Role of the researchers Role of the client
working group
Output
Diagnosing
phase
Literature review,
Project meetings,
Brainstorming meetings
Challenge the
informants to identify
emerging issues
Contribute to the
discussion
Definition of the role of
the PMS in the research
centre
Action
planning
Literature review
Project meetings
Define the agenda and
design data collection
tools
Support the researchers
in identifying potential
informants and provide
feed-backs and
suggestions
Design of the data
collection tools to
support the preparation
of the PMS prototype
Action
taking
Literature review
Interviews
Project meetings
Perform interview and
data analysis
Contribute to the
discussion and
participate in data
analysis
Development of the
PMS overall model and
preliminary set of
indicators
Evaluating Plenary meetings Present the PMS
prototype
Contribute to the
discussion
Presentation of the PMS
prototype in two plenary
meetings
Specifying
learning
Internal meetings Collect and analyse
feed-backs and
suggestions
Support researchers in
interpreting feed-backs
and suggestions
Integration of feed-backs
and suggestions
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AR Cycle II
The diagnosing phase started with a structured analysis of the comments received concerning the set of
indicators and the acknowledgement that the proposals received needed to be organized and selected. Action
planning consisted in the definition of shared selection criteria to reduce the number of the indicators, still
ensuring that the information needs of different stakeholders were met. To this aim, the following selection
criteria were adopted: relevance, measurability, cost and timeliness (Lynch and Cross, 1991; Neely et al., 2003).
In addition, to support the selection of the KPIs, a second round of 26 interviews was planned with selected
informants. Action taking consisted in carrying out the interviews with an application of the selection criteria to
define the new set of KPIs. For each KPI, an information protocol was prepared (see Table 4). It defines in
operational terms how the indicator should be computed, the unit of measure, the level of detail, the relevance of
the indicator, the frequency of data collection and the owner of these data (see also Arena & Azzone, 2010).
Table 3: The information protocol
KPI Number of Patents
Definition Number of patents distinguished by category
Computation procedure Number of patents of the year distinguished by the following categories:
First filing
Extension abroad
Abandoned
Granted
Data source: Patents database
Data calculated over the following time horizon: year
Unit of measure Units
Level of detail Scientific structure
Research unit
Research team
Relevance of the indicator This KPI evaluated the output of the research centre and its related research units in
terms of patents, with respect to the different phases of a licensing process (first filing,
extension abroad, abandoned and granted)
Frequency of data collection Year
Owner of the measure Technology Transfer Office
In the evaluating phase the results were presented in a new plenary meeting involving the scientific and
administrative directors. Feed-backs we received mainly dealt with some specifications in the information
protocols in the indicators, leading to new revisions. Hence, after this second round of evaluation the final set of
KPIs was proposed suggesting practical directions for the research institute, but also general guidelines at the
academic level (see Table 4).
Deborah Agostino, Marika Arena, Giovanni Azzone, Martina Dal Molin and Cristina Masella
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Table 4: AR Cycle II
Techniques Role of the researchers Role of the client
working group
Output
Diagnosing
phase
Project meetings Identify key issues based
on the output of the first
AR cycle
Contribute to the
discussion
Identification of the key
issues
Action
planning
Literature review,
Project meetings
Define the selection
criteria and design data
collection tools
Support the researchers
in identifying potential
informants and provide
feed-backs and
suggestions
Design of the selection
criteria and data
collection tools to
support the PMS
revision
Action
taking
Project meetings,
Interviews
Perform interview and
data analysis
Participate in data
analysis and support the
researchers in the
definition of the
information protocols.
Development of the final
set of indicators based
on the selection criteria
Evaluating Plenary meetings Present the final set of
KPIs
Contribute to the
discussion
Final presentation
Specifying
learning
Project meetings Collect and analyse
feed-backs and
suggestions
Practical directions for
the research institute, but
also general guidelines
at the academic level
3 RESULTS
Results are organized in three sections. First, we introduce the overall PMS model; then, we outline
different performance dimensions; finally, we present the complete list of KPIs.
The PMS model
The starting point for the development of the PMS model is the production system process of a R&D
organization. It can be represented through a process-oriented model(Brown & Svenson, 1998; Pollanen, 2005),
in which research inputs are processed and transformed into output, such as publications or patents. Accordingly
to this general model, performance measures can be defined on the basis of three interrelated elements: input,
output and outcome. Input refers to the amount of resources used in performing a certain activity; output refers
to the result of a transformation process; outcome refers to the long-term impact of the output on the external
environment(Lettieri & Masella, 2009). Based on the three above elements, three performance dimensions can
be identified, namely effectiveness, efficiency and impact (Azzone, 2008). Effectiveness refers to the output
characteristics, both quantitatively and qualitatively; efficiency refers to the ratio between output and input;
impact is a measure of the outcome and it refers to the long-term effects of the output on the external context.
Interaction with research and administrative personnel was useful in recognizing the importance of the
aforementioned dimensions. The quality of the output, the impact of the output on the society and the amount of
resources required for generating the output emerged as crucial aspects for successfully manage the research
centre. However, two further aspects were highlighted as central and they were consequently added to the
traditional model: risk and network. The risk is associated with the uncertainty that characterizes the research
activity. It is widely acknowledged that the context in which research institutes operate is characterized by
continuous changes and high variability (Leitner & Warden, 2004). When discussing with the administrative
and the research director, a question continuously emerged was the following: do we have enough resources, in
terms of quantity and quality, in order to deal with the unpredictability of the events, such as a change in
technology or a budget reduction? The importance of posing the attention to the quantity and quality of
resources to deal with uncertainty, led us to complement the traditional model with the risk dimension. The
network dimension is specifically related to the research activity. The heads of research units underlined several
times the importance of working collaboratively in order to achieve results. Publications as well as projects or
patens are rarely the result of a single researcher; rather, they derive from the joint working of more teams,
belonging to the same department, but also to different departments or different research institutions. This
reason justifies the choice of adding a further dimension to the model, the network, in order to capture the ability
of working collaboratively and output of this collaboration.
The final model we obtained (Figure 2) is characterized by five dimensions, suggesting that the holistic
management of a public research centre requires to consider the quantity and the quality of the output
(effectiveness), the impact of the output on the society (impact), the ability in transforming input into output
(efficiency), the quantity and the quality of the available resources (risk) and the ability and effects of working
collaboratively (network). Following, each dimension will be discussed separately, posing the attention on the
different relative importance given to stakeholders.
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52
Figure 2: The proposed PMS model
RESEARCH
CENTERINPUTS OUTPUTS
Effectiveness
Publications
Projects
Patents & licences
Technology transfer
Human resources
Financial resources
Equipments
COMMUNITYOUTCOME
Impact Efficiency
Risk
Network
The performance dimensions
Effectiveness
The effectiveness dimension allows to evaluate the output of the R&D activities defining the level of
achievement of research objectives (Garcia-Valderrama and Mulero-Valdigorri, 2005). This aspect is related to
a key issue that is what is the output of a research centre and how to measure it (Steiner and Nixon, 1997). In the
case at hand, the output is represented by publications, funded projects, patents and technology transfer
activities. Publications and projects were the ‘immediate answer’ when the informants were asked about the
output of the research centre. This answer is in line with extant studies on research institute that recognized that
publication, books and reports represent the explicit transfer of knowledge of research bodies (e.g. Coccia,
2001b). On the other hand, patents and technology transfer activities emerged in a more ‘fragmented’ way.
Some scientific directors and researchers were fully aware of the relevance of these activities, as emerges from
the following quotation:
“Technology transfer is a key pillar in the mission of our institution, otherwise,
what are we here for?” (Head of RU 8).
Whilst in the other case, patenting was seen somehow as a secondary activity that can be explained
considering that the institute was at that time measuring outputs in terms of number of scientific publications
and funding obtained through competitive projects. The Head of the Technology Transfer Office pointed out:
“Technology Transfer activity is a green field. It needs to be organized and then
evaluated because in the early years of the research centre, the attention has been
attracted by scientific publications only. Yet we are growing and we have now a
portfolio of 68 patents and several commercial projects. They need to be monitored
and managed” (Head of Technology Transfer Office)
Based on these considerations, seven KPIs were defined to measure the research centre effectiveness: four
of them aim to evaluate the quantity of the output and the other three the quality (see Table 5 for the details of
the KPIs).
It is interesting to highlight that there was a polarization in the perceived usefulness of different aspects of
effectiveness depending on the personnel qualification. The administrative personnel and the executive
committee were mainly interested in the quantity of the output generated in terms of ‘how much money’, ‘how
much patents’, ‘how much publications’ (several informants). They considered these KPIs particularly useful to
support external accountability; in fact they have been included in the annual report and have been published on
the website of the research institute. Instead, the scientific personnel considered these measures too ‘generic’
Deborah Agostino, Marika Arena, Giovanni Azzone, Martina Dal Molin and Cristina Masella
53
and posed particular attention to the quality of the output of research activities, highlighting the importance to
account for ‘good publications’, ‘impact on the scientific community’ and ‘participation in breakthrough
innovation projects’ (several informants). From this perspective, the Impact Factor (IF) and Citation Index (CI)
distribution were evaluated particularly useful to support motivation and decision making processes of research
units. Finally, the scientific director underlined the importance of the integration of the two types of indicators,
for a decision making purpose, to have an overall idea of the productivity of each RU. At the same time, he also
recognized the limitations of these figures: the impossibility to compare them between RUs because their
average value varies widely from one research area to another one.
Efficiency
The efficiency dimension evaluates the amount of resources used to generate the output, (i.e. publications,
funded projects, patents, and technology transfer activities). It represents a relevant aspect in publicly funded
services as governments are interested in receiving feedback on how public resources are used (Coccia 2001a;
Moreno & Tadepalli, 2002; Lettieri, Masella & Nocco, 2009).
The diversity of the output of research centres requires to define different measures in order to assess the
value of the resources to generate a unit of the output, either a publication, a patent or a financed project, leading
to the identification of four KPIs (see Table 2).
The relative importance of these measures was different according to the stakeholder and its organizational
position. The administrative director and the scientific director were the stakeholders most interested in
efficiency indicators. They strongly underlined the need to keep under control how many resources the centre
employs for carrying out research activities and the time spent doing this.
“Monitoring costs is a key issue for us. We have to keep out costs under control and
we need to be able to show how much efficient we are. […] From time to time,
someone stands up and claim they we spend too much. We need to be able to prove
that it is not true, we spend money, but we produce more” (Administrative director)
In order to satisfy his requirements, a synthetic efficiency measure to evaluate the overall ability of the
administrative staff in supporting research activities was included: the ratio between the external budget and the
number of FTE (Full Time Equivalent). The fundamental resource of the institute is represented by people, and
this measure gives the possibility to evaluate the cost for managing a unit of personnel. The executive
committee considered efficiency relevant too, though from a different perspective. The committee poses
particular attention on two specific performance indicators, cost of a scientific publication and incidence of the
external budget on the internal budget, both considered useful in supporting the external accountability. These
numbers are already included in the annual report of the institute and they are explicitly depicted with the
purpose to increase the awareness of external stakeholders about the research activity. The heads of the research
units and the researchers, on the other hand, downplayed the importance of efficiency measures, following the
idea that ‘research is what counts’ no matter how much it costs. Some of them even argued that measuring the
‘cost per output’ is not relevant for the management of a department, ‘what they need to know is the budget’.
Impact
The impact dimension measures the outcome, i.e. the effects of the output of the research activity on the
external environment. This aspect has been highlighted several times as crucial for the research centre. It
represents the mission of the institute:
“The Foundation has the scope to promote the technological development of the
country and the technological education, in line with the scientific and technological
national policies with the final aim to support the development of the productive
Italian system” (Foundation Statute, 2012: 3).
The importance of measuring the outcome is closely connected with the need of being externally
accountable, providing information to external stakeholders on how public money is used. The administrative
director was clear on this point since the kick-off meeting of the project:
“The philanthropic mission of our Institute is to generate know how and
employment for our country. We are receiving public money and we have, not only
the duty to account about how we are using public funding, but also we want to
demonstrate that we are using this money to support the technological development
of our country”(Administrative Director).
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However, the selection of the most appropriate impact indicator required to balance the trade-off between
the relevance of measuring the outcome and the cost for collecting these data. In fact, the measurement of this
aspect is far from being straightforward given the intangibility of the output and the existence of external factors
that makes it difficult to isolate the impact of research activities on the territorial environment. The KPIs finally
selected were related to cash flows from patents, licenses and technology transfer (see Table 2). They are a
proxy of the outcome because they give the possibility to account for the first order effect of a research activity,
which are leading indicators of second orders effect about impacts on the environment.
The scientific and the administrative directors of the centre were particularly keen on starting measuring
these aspects, whilst part of the administrative and research staff were somehow worried. The motivation at the
basis of this situation was that at present that the cash flows generated by these activities are still modest and
they feared that numbers could lead to wrong interpretations. Directors of research units did not perceived the
importance of measuring impact, being more attracted by KPIs about the quantity and the quality of the
publications.
“Patents, for my department, represent a cost. They do not generate income; this
information can be useful at the overall institute level. It takes some years for a
patent before gene rating money; hence, it is not relevant to manage my activity.
I’ve already seen the result of my research!” (Head of SF3)
Risk
The risk dimension is associated with the need to deal with the uncertainty and the variability of the
research context, as stated by the scientific director:
“Research trends change every year. On the basis of what happens outside, I need to
reconfigure the research activity inside here. The organizational structure should be
flexible and follow these changes accordingly. I need to be able to modify timely
the structure of scientific units; acquire new competencies, understand what
happens if I close a department and open a new one” (Scientific Director).
In particular, the emphasis given to flexibility and adaptability drove the choice of posing particular
attention to how the centre can monitor its resources in order to ensure its ability to respond to unexpected
events. In highly uncertain contexts, resources and the capability to reconfigure them are conceived as the basic
mean through which an organization can face risks (Arena, Arnaboldi & Azzone, 2010b; Arena, Arnaboldi &
Azzone, 2011; Lettieri, 2009; Lettieri, Masella & Radaelli, 2009). Hence, monitoring the ‘state of resources’ can
provide relevant information about how the organization could face a transformation. Obviously, this also poses
the problem of identifying which are the key resources, i.e. which are the resources critical for the success of
research institute. In the case at hand, four main elements were identified: human resources, financial resources,
scientific equipment and reputation; and seven indicators were defined to monitor these elements (see Table 5).
Once again, a different level of awareness and sensitivity of the stakeholders towards different issues
became visible. In particular, the scientific director was very attentive to read the implications of any variation
in different types of resources, going beyond numbers.
[…]“I want to monitor the age of scientific personnel because it provides very
useful information to me. I want a young research centre with a high turnover and
many PhD: by monitoring the age of the staff I can account for this aspect avoiding
the risk of increasing the average age” […]“The trend of PhD students for me is a
signal. If their number falls down, it is an alarm bell to me. First, they are a
resource, they are our primary row material. Second, if they stop coming here, they
going somewhere else. This means that other institutions are more attractive than
us. Why? I mean, it’s something to keep under control” (Scientific Director).
On the other hand, the administrative director and the executive committee appeared more focused on
financial resources and reputation (in particular in relationship to the public opinion) that was sees as a
considerable source of risk.
“We are on the newspaper almost every day. Public opinion poses particular
attention on the way in which we are employing public money. If we do not justify
how the public funding is used, we are immediately accused of wasting financial
resources that could be used for doing something else” (Administrative Director).
Deborah Agostino, Marika Arena, Giovanni Azzone, Martina Dal Molin and Cristina Masella
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Finally, directors of RUs showed more interest for the scientific reputation as it gives the possibility to
understand how individual researchers, not only the centre as a whole, are known and considered in a specific
field of research.
Network
The network dimension refers to the ability of the research personnel working together with researchers
from internal teams or from other external institutes. This aspect finds a rationale in the organizational changes
that are affecting the research activities. Collaboration is nowadays considered an essential requirement (Saez et
al., 2002) to survive in a research landscape characterized by inter-disciplinary activities (National Academies,
2005). The network dimension accounts for this collaborative behaviour considering the structure of
relationships between actors, the quantity and the quality of the joint output and the cost of collaboration. This
aspect is emerging in the public sector literature as a relevant performance dimension to evaluate collaboration
(e.g. Provan & Milward, 2001), while it has received less attention in the R&D field (e.g. Allen at al., 2007).
Three KPIs were included in the set of indicators, focusing on the quantity and the quality of joint publications
(see Table 2). Even though collaborative activities relate also to patents and research projects, no network KPIs
were associated with these typology of output. With respect to patents, the decision was to not include indicators
because of the limited amount of patents compared with publications. Considering projects, measurement
difficulties were identified, mainly related to the complexity of accounting joint internal projects. The main
concern associated with this aspect was the following:
“The majority of financed projects is carried out in a collaborative way; it is
therefore not useful to know the level of collaborations as it is by definition that we
are collaborating. This is not true for internal research projects, but they are usually
carried out by the single research team”. (Head of RC 3).
Again, the importance of evaluating the network dimension was different depending on the type of
stakeholder. Collaborative activities emerged several times during interviews and meeting with the scientific
personnel and the scientific director.
“We are working several times with people from other research centres. This is
associated with a greater effort in terms of coordination but better results because of
different competences (Senior scientist 1).”
On the contrary, they were never mentioned by the administrative staff and the executive board.
Int. Journal of Business Science and Applied Management / Business-and-Management.org
56
The final set of indicators
Table 5 outlines the final set of indicators related to the five performance dimensions previously introduced
and highlights the priorities assigned to each indicator by different stakeholders.
Table 5: The set of indicators and stakeholders priorities
Dimension KPI
Stakeholders
Admin.
Director
Scientific
Director
Executive
committee
Head of
RU
Effectiveness
Number of scientific publications X X X
Number of patents X X X
Number of technology transfer activities X X X
Stock of competitive projects
X X X
IF distribution
X
X
CI distribution
X
X
Success rate of competitive projects
X X
Outcome Revenue generated by patents, licenses and spin-off X X X
Revenue generated by technology transfer activities X X X
Efficiency
Cost of a scientific publication X
X
Cost to manage and maintain a patent X X
External budget/Internal budget X X X
Internal budget/FTE X
Risk
Scientific reputation X X X X
Press coverage X X X
Capex and Opex X
Employees (category, nationality, age, degree, genre)
X X
Turnover
X X
Organizational climate
X
X
Saturation of equipment
X
X
Network
Number of joint publications
X
X
IF of joint publications
X
X
CI of joint publications
X
X
During the second phase of the action research cycle, the cost and validity of the proposed set of KPIs was
verified, providing practical guidelines to the research centre for the implementation of these measures. Results
of this phase vary according with the performance dimension.
KPIs related to effectiveness are diversified in terms of cost of measures. Most of them can be easily
calculated because they base on data that are already collected for different purposes. The most relevant
exceptions are the Impact factor and Citation index distribution. These indicators, though considered very
important for the assessment of the quality of the research by the scientific director, require ad hoc data
collection and further elaboration in order to ‘clean’ data. Effectiveness indicators are particularly relevant to
have an overall idea of how the centre is performing and to assess the performances of each RU with respect to
its research areas; however, they require some cautions in relationship to data interpretation, since different RUs
cannot be easily compared with each other based on these numbers (e.g. bibliometric indicators change
significantly in different research fields).
KPIs in the outcome dimension aim to provide a proxy of the impact of the research activity on the society.
Data about cash flows are already monitored for accounting purposes. However, they provide only a partial
Deborah Agostino, Marika Arena, Giovanni Azzone, Martina Dal Molin and Cristina Masella
57
information about the impact of the research activity on the society. Hence, it could be interesting to
complement them through qualitative information to have a more comprehensive idea of research outcomes.
KPIs related to the efficiency can be quite easily calculated because the institute already monitors cost
information very precisely. However, the interpretation of these data can be controversial. In particular, this is
the case of the cost per scientific publication that answer to the need of external accountability. Similar to
effectiveness indicators, it varies significantly across different research units depending on the specific research
field (equipment, materials, and other research instruments significantly vary in different research areas,
resulting in different costs per publication).
KPIs concerning the risk dimension are characterized by some differences in term of measurement costs.
Capex, opex, turnover and data about employees can be determined easily, instead this is not the case for
indicators about the reputation, saturation of key equipment and the organizational climate that entail ad hoc
data collection. Reputation and saturation of key equipment capture two aspects that are particularly relevant for
the research centre, but, at present, they cannot be calculated automatically, and the computation ask a great
effort to collect data and to elaborate them; in the next future, these indicators could benefit from the
exploitation of some supporting tools to collect data routinely. The organizational climate, on the other hand, is
assessed through a survey yearly and it suffers from the limitations that are typical of similar approaches (e.g.
risk of bias, limited timeliness).
Finally, KPIs in the network dimension exploit the same data base of those related to effectiveness, and
share their strengths and weaknesses. In addition, they provide an overall picture of the extent to which
researchers of different RUs collaborate, without entering into details of the contribution of different parts to the
research outputs.
4 CONCLUSION
This paper aimed at constructing a performance measurement system (PMS) for a public research centre
characterized by the presence of a diverse set of stakeholders with multiple objectives, ranging from making
decisions, motivating researchers to demonstrating external accountability. The PMS has been developed
through action research with respect to a specific context, an Italian technological research centre, where the
researchers recursively interacted with different stakeholders in the construction of the PMS. Based on the state
of the art literature and data collected from the interviews, a multidimensional framework for measuring R&D
performance was proposed and a set of KPIs was developed. The traditional process-oriented model (Pollanen,
2005) that encompasses the performance dimensions of effectiveness, efficiency and impact, was complemented
with two further dimensions of risk and network. The former aims to monitor the uncertainty that characterizes
the research activity, and the latter allows to capture the ability of working collaboratively. The introduction of
these two dimensions moves from the idea that the PMS should be aligned with the characteristics of the
organizations in which it is used and the trends of development they should deal with (e.g. Chiesa et al.,
2009).The set of KPIs puts together the information needs of different stakeholders that showed a definite
interest in relationship to a certain subset of measures, often disregarding the others. The integration of different
information needs in a comprehensive set of indicators aimed at providing a holistic picture of the centre
performances to all the relevant actors, raising their attention on the existence of potential trade-offs between
different measures.
This study contributes to the extant literature in two different ways. First, models, frameworks and
methodologies for measuring R&D performances have mostly focused at the firm level, with an economic or
strategic focus (Secundo et al., 2010). We deployed them in the context of a public research institute, where
attention of researchers, so far, concentrated on the one specific stakeholder – the government – that is mainly
interested in an overall assessment of research centres’ performances in relationship to funding allocation.
Second, we tried and integrated the concepts for performance measurement and performance management of
interdisciplinary collaborative research, which depicts new challenges for promoters and science managers in
the research environment of the twenty-first century (Agostino & Arnaboldi, 2012; Arena & Azzone, 2005;
Arnaboldi & Azzone, 2010).
Since the research was conducted in a specific organization, the possibility of generalizing the results to
similar institutions is a key issue. Hence, it is important to distinguish between what is general in scope and
what is case-specific. First, the multi-dimensional framework proposed aims to be general in scope. The use of
the process oriented model as a starting point and the integration of the two dimensions of risk and network aim
to make the PMS model more coherent with the pressures that public research organizations are currently
experiencing in different countries. Focus on efficiency and effectiveness, call for interdisciplinary research and
growing uncertainty are key challenges for these institutions. Moreover, risk and network represent two trends
of development in relationship to performance measurement in different types of organizations (e.g. Provan and
Milward, 2001), though, in most of cases these performances are not formally integrated in the PMS (e.g. Arena
et al., 2011). This paper represents a possible way to explicitly integrate these aspects in the PMS, opening the
path to future research to further adapt the proposed model to different contexts. The second result of general
Int. Journal of Business Science and Applied Management / Business-and-Management.org
58
validity is the approach followed to develop the set of KPIs. The choice of the KPIs was guided by the priorities
of different stakeholders. Their requirements and the purpose of use in relationship to different information were
mapped and cross-checked in order to build a set of indicators that would be comprehensive and transversal to
the whole organization. The set of indicators, on the other hand, is case specific and reflects the characteristics
of the research centre under investigation, even though it could provide source of inspiration for similar
organizations.
Finally we address the limitations of this work. The data were collected through a qualitative and
collaborative methodology, action research; hence the results suffer from the limitations that are typical of this
research methodology. In particular, action research is situational, hence, many of the relationships between
people, events, and things are a function of the situation as relevant actors currently define it and they change as
the definition of the situation changes (Susman & Evered, 1978). Accordingly, while the collaboration between
the researcher and the institute staff led to the identification of the KPIs, it may have also resulted in a degree of
bias. However, this methodology gave us the possibility of having lively, detailed and involved discussions with
members of the organizations. Our ideas about performance dimensions and KPIs were critically challenged in
order to satisfy their specific requirements. Moreover, the active participation of users and the integration of
their different informational requirement contributed to reducing resistance to the project and promoting the
development of the PMS. An important field of future research remains the applicability of the proposed model
and methodology to other public research organizations in order to verify its generalizability in different
institutions operating in different countries.
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