a typology of calculative · pdf filea typology of calculative culture ... we propose a...
TRANSCRIPT
1
A TYPOLOGY OF CALCULATIVE CULTURE
(Version: July 2016 – to be presented at 10th International Management Control Conference in Antwerp)
Mojca Marc
University of Ljubljana Faculty of Economics
Kardeljeva pl. 17, 1000 Ljubljana, Slovenia
E-mail: [email protected]
Phone: 00386 1 5892 764
Darja Peljhan (corresponding author)
University of Ljubljana, Faculty of Economics
Kardeljeva pl. 17, 1000 Ljubljana, Slovenia
E-mail: [email protected]
Phone: 00386 1 5892 480
Abstract
A calculative culture describes managerial predilections towards alternative logics of calculation
that ultimately affect the design and use of management control systems (MCS) in companies. It
is a mechanism for collective sense-making as it conveys the way in which managers use MCS
information provided in their decision-making, therefore it implicitly influences organizational
efficiency and effectiveness. The aim of our paper is to investigate if this concept has the
potential to become one of contextual variables in management accounting research. We develop
and validate the scale for measuring calculative culture in the context of MCS using a mixed
methods approach. We use interviews and a cross-sectional survey to collect data on calculative
culture, and we run a confirmatory factor analysis (CFA) to empirically test our proposed
measuring instrument. Finally, we propose a typology of calculative cultures and analyse it
empirically. We find that calculative culture indeed acts besides other contextual variables, such
as size, age, or industry, and can be thus used as an additional contextual variable in future
studies of MCS design and use.
Key words: calculative culture, culture typology, management control systems, confirmatory
factor analysis, scale development.
2
INTRODUCTION
A managerial predilection towards alternative logics of calculation affects the way managers
interpret and use accounting information. Power (2007) and Mikes (2009) developed the notion
of calculative culture as a theoretical concept that reflects these predilections. Arguably, it affects
the design and use of management control systems, ultimately affecting performance.
Calculative culture is a relatively new and thus under-studied research construct. So far it was
analysed in field research. We investigate if this concept has the potential to become one of the
contextual variables in survey-based management accounting research. In this paper, we thus
develop a measurement instrument and propose a typology of calculative cultures.
The origins of the concept of calculative culture can be traced to Douglas and Wildavsky’s
(1982) theory of risk and culture. As such, the concept was first developed in the field of risk
management systems. However, risk management systems are recognized as part of management
control systems (Gordon, Loeb, & Tseng, 2009; Soin & Collier, 2013). Reflecting on her case
studies and on Bhimani (2003), Mikes (2009) suggests that a given calculative culture shapes
managerial predilections (or resistance) towards new management control systems (MCS),
serving as an important determinant, and as well as result, of the fit between MCS and
organizational contexts. As such, it has the potential to take part in contingency-based research.
However, we do not know if calculative culture as a concept even exists outside the Mikes’ cases
from the banking industry. If calculative culture indeed exists and has effects on the design and
way a MCS is used, it can indeed affect performance and firm value. Management can then look
for solutions to change the calculative culture for a better fit with the organizational context.
Culture is a very important and often unconscious set of forces that determine individual and
collective behaviour. In our study, we are interested in the calculative culture at the group level,
i.e. at the level of top management. Calculative culture is a “mechanism for collective sense
making” (see: Sackmann, 1992) as it conveys the way in which managers use the MCS’
information provided in their decision-making, therefore it implicitly influences organizational
efficiency and effectiveness.
We contribute to the existing literature first by identifying a potential new contextual variable,
acting beside other contextual variables, such as size, age, or industry. Second, answering calls
for a more mixed method research (e.g. Bhimani, 2003), we use a mix of qualitative and
quantitative methods to investigate calculative culture. Building on the analysis of existing case-
studies, we further substantiate the concept using qualitative methods, such as interviews and a
focus group, which we supplement with a quantitative analysis of survey data. Third, we develop
a measurement instrument for calculative culture’s dimensions and we test it empirically on a
3
sample of 124 companies. Fourth, we propose a framework of calculative cultures typology and
empirically examine the relationships among the proposed cultural types and contextual factors.
The remainder of the paper is structured as follows. First, we present the theoretical importance
and existence of the calculative cultures construct. Next, we explain how we developed the
questionnaire. Then we describe sampling and data collection, followed by statistical analysis,
and statistical evidence of the construct. Finally, we propose and empirically analyse a typology
of calculative cultures. The paper concludes with limitations and further research implications of
our study.
1. THEORETICAL IMPORTANCE AND EXISTENCE OF THE CONSTRUCT
In scale development, it should be clearly stated what the construct is about and what is to be
included in the construct. Netemeyer, Bearden, and Sharma (2003) explain that it is crucial to
assess the boundaries of the construct domain and not to include extraneous factors or domains
of other constructs. Additionally, the domain of the construct should not be set too narrowly and
fail to include important facets of the construct. Besides the definition, it is important to specify
dimensions of the construct (Haynes, Richard, & Kubany, 1995). We achieved this by an in-
depth literature review as suggested in DeVellis (2003), Netemeyer et al. (2003), and Slavec and
Drnovšek (2012). Moreover, we explored the existence of the new construct also in practice
(Slavec & Drnovšek, 2012) by conducting interviews and focus groups with two top-level
managers (one from a bank, one from a non-financial firm) to brainstorm, confront, and upgrade
ideas and views on the target construct. A well-expressed theoretical base that clearly specifies
the content domain of the construct is crucial for all subsequent steps in scale development
(Netemeyer et al., 2003). After specifying the domain of the construct we generated a large pool
of items (step two) that are candidates for eventual inclusion in the scale (DeVellis, 2003; Slavec
& Drnovšek, 2012) and that captured the domain (Churchill, 1999) of the calculative culture
construct. An initial list of items was generated using a literature review (Churchill, 1999) and
expert judges (DeVellis, 2003; Hardesty & Bearden, 2004). We do not examine existing scales
related to the calculative culture construct as the construct was not operationalized previously,
although evidence from the literature and the field suggests its importance. These three steps,
content domain specification, item pool generation, and content validity evaluation are crucial in
scale development since they influence construct validity.
1.1. Content domain specification
Calculative culture is described in terms of managerial predilections towards alternative logics of
calculation in the context of risk management (Power, 2007). Collier et al. (2007) argue that we
know little about how managers consider risks (Bettis & Thomas, 1990). Mikes (2009)
4
distinguishes between two calculative cultures: quantitative enthusiasts and quantitative sceptics.
Quantitative enthusiasts rely on analytical models, quantification of risk, and believe the
numbers. Quantitative sceptics are sceptical about analytical models and risk quantification, and
take the numbers only as a starting point for discussions. Quantitative sceptics do not rely only
on numbers per se, they interpret risk measurements as indicators of dynamics. They link
information on different risk types and interpret the numbers in a broader context. They regard
numbers “…as attention-directing devices, with no intrinsic claims to represent reality” (Mikes,
2009, p. 28). For quantitative sceptics, risk figures are trend indicators, “which they seek to
complement, and often overwrite by senior managerial discretion, experience and judgment”
(Mikes, 2009, p. 22). They emphasise the use of ‘softer’ instrumentation to frame and visualize
non-measurable uncertainties (Mikes, 2011). This also corresponds to Collier et al.’s (2007)
implications suggesting that appropriate and effective risk management tools should be
supplemented by experience, intuition and judgement.
However, risk management has moved away from being an issue of narrow concern in finance
(value at risk, derivatives, etc.) or accounting (financial statement disclosure, etc.) to an issue
about management control (Soin & Collier, 2013). Gordon et al. (2009) describe risk
management system as a subset of an organization’s management control system (MCS). Mikes
(2009) argues that risk management is another facet of organizational control and accountability.
The common area of interest is the roles and organizational significance of calculative practices.
Mikes (2009) argues that the concept of calculative cultures might be applicable in other
contextual analyses of management control system (MCS) adoptions.
Culture determines individual and collective behaviour, and can be studied at different levels
ranging from a group level to an organizational level or a national level (Erez & Early, 1993).
Culture as a property of a group refers, for example, to the level of small teams and workgroups.
Individuals in a given group accept many of the cultural themes that others in the group share
(Schein, 1999) because of the group dynamics (Denison & Spreitzer, 1991). Schein (1999)
argues that the essence of culture is jointly learned values, beliefs, and assumptions that become
shared and taken for granted as the group continues to be successful. To explain individual
behaviour, we have to go beyond personality and look for group memberships and the cultures of
those groups. We are interested in top management’s predilections towards alternative logics of
calculation, and we therefore study calculative culture at the group level. In this sense,
calculative culture refers to the top management members’ shared mental models that hold and
are taken for granted. As a shared mechanism for collective sense-making it alleviates the
communication among top management members when discussing MCS’ information, hence
influencing organizational efficiency and effectiveness. What differentiates collective sense
making from the individual one is that the former is commonly held by a group of people in a
given organization. In the process of enculturation, cognitions become rooted in the group and
5
exist independently of an individual group member (Sackmann, 1992). In the domain of
calculative culture that means that we are looking at the shared values of top management as
regards the prevailing logic of calculation and number interpretation. This is evident in top
management ‘behaviour’ when they are making decisions – e.g. do they take numbers for
granted or do they question them and discuss possible scenarios.1
To avoid possible misunderstandings, it is important to emphasise that calculative culture is not
risk culture. Risk culture is one facet of organizational culture that focuses on the cultural
dimensions of risk-taking and control. Douglas and Wildavsky (1982) argue that risk perception
is a social process because different social principles that guide behaviour affect the judgement
of what is dangerous and what risks are worth taking. Bozeman and Kingsley (1998) define risk
culture as an organization’s propensity to take risks, and they also label it as “the risk orientation
of organizations.” Risk culture is closely linked to an organization’s risk appetite policy
statement as this is the tangible and formal representation of the implicit limits that decision
makers will impose on themselves. Thus, risk appetite and its internal enforcement are regarded
as a visual manifestation of risk culture (Power, Ashby, & Palermo, 2013). From the behavioural
and internal control point of view, risk culture is a means to ensure that employees are doing ‘the
right thing’ and that they ‘understand risk and compliance rules,’ leading them to make
appropriate risk-taking and control decisions (Farrell & Hoon, 2009; Power et al., 2013).
Identifying the associations among calculative cultures, risk culture, and organizational culture is
not the purpose of our paper.
1.2. Item pool generation
We started developing a measurement instrument for calculative culture by extracting
descriptions of calculative culture, quantitative sceptics, and quantitative enthusiasts from Mikes
(2009). Both authors of the paper individually searched the paper and produced a separate list of
descriptions found. Descriptions of quantitative enthusiasts from both lists include, for example,
“manage risk by the numbers, replacing judgemental risk assessments with risk quantification.
… [They] agree that risk measures are capable of reflecting the underlying economic reality well
enough to induce requisite economic behaviours in the light of these. Therefore they put a high
priority on building, maintaining, and improving the ‘robustness’ and accuracy of their
1 A nice illustration of how differences in calculative culture affect communication, reporting, and decisions is the
following well-known joke: An astronomer, a physicist, and a mathematician are on a train in Scotland. The
astronomer looks out of the window, sees a black sheep standing in a field, and remarks, “How odd. All the sheep in
Scotland are black!” “No, no, no!” says the physicist. “Only some Scottish sheep are black.” The mathematician
rolls his eyes at his companions’ muddled thinking and says, “In Scotland, there is at least one sheep, at least one
side of which appears to be black from here some of the time.”
6
analytical models.” Descriptions of quantitative sceptics from both lists include, for example,
“place a much lesser degree of ‘trust in numbers’ . . . They . . . regard risk figures as trend
indicators, which they seek to complement, and often overwrite by senior managerial discretion,
experience and judgment.” (Mikes, 2009, p. 22); “They regard numbers as attention-directing
devices, with no intrinsic claims to represent reality” (Mikes, 2009, p. 28). We discussed and
crosschecked each item with other items on both lists and compiled them into one list. The list
contained 17 items (sentences) describing quantitative sceptics, and 19 items describing
quantitative enthusiasts (see Appendix A1). To form clear and simple items we followed some
basic rules (e.g. Clark & Watson, 1995; DeVellis, 2003; Netemeyer et al., 2003; Nunnally &
Bernstein, 1994; Slavec & Drnovšek, 2012): we paid attention to the reading level ease of the
target population; we avoided multiple negatives, double barrelled sentences, and use of jargon
or trendy expressions; we wrote items in a way to ensure variability in responding. The initial
item pool included a large number of items since at this stage over-inclusiveness is preferred to
under-inclusiveness (Slavec & Drnovšek, 2012).
Next, we discussed the concept of calculative culture in interviews with two top-level managers:
one from a bank, and one from a non-financial firm. The goals of the interviews and subsequent
joint discussion were: to establish whether these two managers ever observed in their respective
organizations characteristics of calculative culture, quantitative scepticism, and enthusiasm; to
identify possible additional characteristics of these concepts; to identify potentially overlapping
items on our list; and to identify whether quantitative scepticism and enthusiasm are two separate
concepts or two opposing poles of calculative culture. We first asked each manager to describe
what type of information he uses in different decision-making situations. Then we asked them to
comment the general attitude towards numerical/quantitative information in their respective
organizations (e.g. what is the structure of reports for board members in terms of
quantitative/qualitative information). Next, we held a joint discussion (focus group) with both
managers when we explained the concept of calculative culture and invited them to describe
if/how they can relate it with their observations and experience from their respective
organizations.
Without us (i.e. authors) explicitly mentioning ‘quantitative scepticism’ or ‘quantitative
enthusiasm,’ both managers described the different attitudes towards numbers of people from
their organizations in similar terms, e.g. some people ‘like numbers’ and some people ‘do not
like numbers.’ We asked them to describe the characteristics of both ‘types’ of people in terms
of: how they tend to interpret numbers; if they request additional, more sophisticated
calculations; do they sponsor the development of analytical models for decision-making, etc.
After that, we focused the discussion even more, by asking them directly whether they think
‘quantitative scepticism’ or ‘quantitative enthusiasm’ exist as separate concepts, or they
represent two opposing poles of calculative culture. Both managers believed that there are some
7
diametrically opposite characteristics of these two concepts, for example, a general trust or
distrust in numbers or quantitative modelling. However, there are also characteristics that
identify just one of these two concepts, for example, a wish for having cutting-edge, quantitative
models can be more or less pronounced in quantitative enthusiasts, but it is not relevant for
quantitative sceptics. Similarly, because they take them as indicators only, quantitative sceptics
interpret numbers with more or less precision; quantitative enthusiasts take numbers at face value
and there is no interpretation (of the number itself) involved. This discussion indicated that
quantitative scepticism and quantitative enthusiasm are probably separate concepts, and that
calculative culture has four ‘building blocks’ (properties):
- General trust in numbers and analytical models: the level of managers’ trust in numbers
being capable of reflecting the underlying economic reality
- General trust in quantitative (analytical) modelling: the level of managers’ trust in analytical
models being capable of reflecting the underlying economic reality
- Importance of accuracy (precision) of numbers: the level of managers’ preference for
accurate quantification of fewer variables as opposed to crude, but comprehensive (including
quantifiable as well as non-quantifiable risks) measurement. This property also refers to the
interpretation of numbers which can be interpreted as accurate assessments or just as
indicators of dynamics.
- Importance of having a cutting-edge calculative methodology: how managers perceive the
quality of analytical methods used, and the priority given to developing the analytical models
Then, we showed the managers our list of sentences describing the concepts. The goal was to
identify the match between what was discussed and what was described in writing as calculative
culture, quantitative scepticism, and quantitative enthusiasm. We also paid particular attention to
the difficulties and ambiguities in understanding written sentences. Based on the interviews and
discussion, we made the necessary wording corrections and reduced the list of items to 23 (11 for
quantitative scepticism and 12 for quantitative enthusiasm, see Appendix A2). We divided the
items into four groups: (1) items describing a general trust in numbers, (2) items describing a
general trust in quantitative modelling, (3) items describing the interpretation of numbers, and
(4) items describing the importance of having cutting-edge analytical models. We revised the
items to a MCS context. Since general trust in numbers and general trust in quantitative
modelling reflect personal values and assumptions and not group sense-making, we discarded the
items from these two groups. Preferences towards the interpretation of numbers and advanced
analytical approaches develop mostly during group interaction, such as management decision
making, discussions, and reporting. Based on this we concluded that there are probably two
dimensions of calculative culture: ‘analytical enthusiasm,’ which refers mainly to the enthusiasm
about advanced analytical methods, and ‘numerical pragmatism,’ which refers mainly to an
interpretation of numbers. We decided not to use the terms quantitative sceptics and quantitative
enthusiasts, as they represent two extremes of calculative culture. Our aim was to depict as many
8
types of calculative culture as possible. When we have two dimensions, this can lead us to four
combinations – types of calculative culture (for more on that see Section 4).
1.3. Content validity evaluation
Content validity refers to the degree to which elements of a measurement instrument are relevant
to, and representative of the targeted construct (Haynes et al., 1995). Elements of a measurement
instrument include items, response formats, and instructions. These elements represent key parts
of a questionnaire that was used for data collecting in our research. All items prepared to be
included in the questionnaire were reviewed by judges (Hardesty & Bearden, 2004), i.e.
knowledgeable people in the content area (DeVellis, 2003) to check for their content validity.
We included two end-users of the measurement instrument that are also subject-matter experts
(academics that would use it in their research), two researchers with experience in scale
development, and representatives of potential respondents (two top-level managers). At this
stage suggestions for modification, addition, and exclusion of items were made, especially to
shorten the number items as the proposed questionnaire was quite exhaustive (c. 30 minutes to
fill in).2 We took notes of all the comments that judges express in regard to representativeness,
clarity and wording of items, clarity of instruction, response formats, and sequence, length, and
appearance of the assessment instrument (Slavec & Drnovšek, 2012). Moreover, the calculative
culture constructs were presented and discussed at the academic conferences,3 where experts and
academics from the relevant field contributed their comments and suggestions, and corroborated
the relevance of calculative culture in a broader context of MCS, not just RM systems. We
concluded the qualitative part of our study by revising and finalizing the measurement
instrument (see Section 2 for the final version).
2. QUESTIONNAIRE DEVELOPMENT AND PILOT STUDY
When designing the questionnaire we followed the following guidelines (Dillman, Smyth, &
Christian, 2009; Slavec & Drnovšek, 2012): ensure confidentiality, provide information about
the survey, ask for help, say thank you, make it convenient to respond, and send reminders. Our
survey was pre-tested with people who have specialized knowledge on some aspects of
questionnaire quality (Dillman et al., 2009). For our study, we decided to use an on-line survey.
We pre-tested the whole questionnaire with two academics from the subject field. The aim of this
pre-test was to check content validity and to refine the list and wording of the measurement
instruments (see also section 1). We explained the meaning of calculative culture, quantitative
2 We would like to point out that measuring of calculative culture was part of an extensive study on ‟Risk
Management as Part of Management Control” with more than 30 total questions. 3 ERMAC 2014 conference (Vienna, Austria) and 6th European Risk Conference ‟Multiple Perspectives on Risk
Management” (Naples, Italy).
9
scepticism, and quantitative enthusiasm in more detail, and asked for opinion on their relevance
for risk management and MCS. Both respondents agreed that calculative culture indeed might
affect the choice of management practices (techniques, models), and thus organizational MCS
design. We originally developed the questionnaire in English, then translated the final version
into Slovenian and Croatian4, and finally back-translated into English to evaluate the equivalency
of the original version.
With a pilot study on the target population, we tested the proposed questionnaire to identify
potential problems with the questionnaire (Dillman et al., 2009; Slavec & Drnovšek, 2012). The
respondents were five postgraduate MBA students. We gave them paper copies of the
questionnaire and asked them to write any comments about the design, instructions, or wording
of the questionnaire. We held a joint discussion with all respondents immediately after they
answered the questionnaire regarding clarity and relevance of items. Finally, we corrected the
wording and finalized the measurement instruments to the ones presented as follows.
First, we measure the extent of numerical pragmatism with the following claims (on a 1-5 point
Likert-type scale, where 1 means ‘strongly disagree’ and 5 means ‘strongly agree’):
- q20a: Top management in general interprets measurements (numbers) roughly, as indicators
of dynamics, not as accurate assessments.
- q20b: Top management prefers reports with crude measurement of multiple perspectives
more than the accurate measurement of a specific narrow topic.
- q20c: Top management believes that for decision-making it is more important to also have
qualitative assessments than just precise and more complex numerical calculations, as
numbers cannot completely reflect the underlying business conditions.
Numerical pragmatists would score high on all characteristics.
Second, we measure the extent of analytical enthusiasm with the following claims (on a 1-5 point
Likert-type scale, where 1 means ‘strongly disagree’ and 5 means ‘strongly agree’):
- q20d: Top management believes in the benefits of quantitative modelling and makes
decisions based on results from highly analytical calculative practices.
- q20e: Top management requires regular building, maintaining and improving the
‘robustness’ and accuracy of our analytical models.
- q20f: Top management believes that the quality of calculative practices is good only if
advanced quantification methodologies are used.
Analytical enthusiasts will score high on all characteristics.
4 The questionnaire will be administered to Croatian companies in further research.
10
Moreover, we included also a control question (q26a) that incorporates both dimensions to test
the consistency of responses. We expect that consistent respondents scoring highly on items
q20a – q20c (numerical pragmatism) will also score highly on question q26a, while those
scoring high on items q20d – q20f (analytical enthusiasm) will score low on question q26a.
3. SAMPLING AND DATA COLLECTION
We collected data with an on-line version of the questionnaire. We e-mailed the first round cover
letter with a link to the questionnaire to a sample of medium and large Slovenian companies
(n=1.386) in mid-December 2014.5 Our targeted respondents in medium companies were general
managers, while in large companies we e-mailed the questionnaire to CFO/CRO6 or to the
general manager if the CFO was not an Executive Board member. We sent two reminder e-mails
in mid-January 2015 and mid-February 2015. Follow up and reminder calls were performed until
mid-March 2015. We finished the data collection phase by the end of March 2015. We collected
136 responses, which correspond to an approximately 10% response rate; there were 124
complete responses for the items measuring calculative cultures and we use them in the
following statistical analyses. The distribution of responses by industry is shown in Table 1.
Table 1: Sample structure by industry
Industry Frequency Percent
Mining and quarrying industry 1 0.81
Manufacturing / processing industry 39 29.84
Gas, electricity and water supply 6 4.84
Construction industry 2 1.61
Trade (retail and wholesale) 16 13.71
Services (e.g. tourism and catering industry) 6 4.84
Transport and storage 5 4.03
Communications 2 1.61
Information technology 8 6.45
Financial intermediation and other financial services 23 18.55
Real estate activities 4 2.42
Other 12 11.29
Total 124 100.00
5 According to Slovenian legislation valid in 2014, a ‘medium’ company is a company fulfilling two of the three
following criteria: the average number of employees does not exceed 250, annual net sales account for less than
€29.2 million, and average assets at the end of business year do not exceed €14.6 million. Companies exceeding
these criteria are classified as ‘large’ companies. 6 CRO – Chief Risk Officer; CFO – Chief Financial Officer. As one of the preparatory steps, we have contacted all
large companies from the initial sample to collect information about the most appropriate respondent in the company
to whom we would e-mail the invitation to take the survey. For medium companies, we used contact data provided
by a commercial database of business subjects GVIN.
11
The survey also included questions on contextual variables to corroborate statistically that
calculative culture constructs are distinct from them. Beside company’s industry, size, age, and
its ownership structure, we collected data on its strategy type, business environment uncertainty,
(geographic) market orientation, and perceived organizational performance relative to
competitors. Company size is measured by the number of employees (q30) and also as a
categorical variable (q30_g) with four size groups (<10 employees ‘micro,’ 10 – 49 employees
‘small,’ 50 – 249 employees ‘medium’, >250 employees ‘large’). Company age is measured as a
categorical variable with five groups (q29): less than 6 years, 6-10, 11-15, 16-20, and more than
20 years. The ownership structure of the company is measured through the % owned by
managers (q40), % owned by institutional investors (q41), and % owned by foreign investors
(q42). We measure the strategy type (q23) with a scale proposed by Shortell and Zajac (1990)
based on Miles & Snow’s (1978) typology (prospector, analyser, defender, and reactor). Based
on Gordon and Narayanan (1984), Govindarajan (1984), and Miller (1997), we use the scale for
measuring the uncertainty in seven components of business environment (q21a – q21g):
technology, products, demand, suppliers, competitors’ actions, internal environment, and
external environment. The level of uncertainty is measured on a 5-point scale where 1 is ‘low
uncertainty’ and 5 is ‘high uncertainty.’ We asked the respondents about their company’s
primary (geographic) market orientation (q22): national, regional, European, or global. We
measure the perceived organizational performance relative to competitors with a 5-point scale
based on Govindarajan (1984) for the following performance indicators (q27a – q27e):
profitability, sales growth, market share, new product development, and customer satisfaction.
4. STATISTICAL ANALYSIS AND STATISTICAL EVIDENCE OF THE CONSTRUCT
The last phase in the scale development is the statistical analysis and evidence of the construct.
We first assess the dimensionality of the measure, then its reliability, its construct validity, and
finally the measurement and structural equivalence (invariance) across sub-groups of population.
3.1. Dimensionality
The dimensionality of the measure refers to the homogeneity of the items in the construct and it
is defined as the number of common factors needed to account for the correlation among the
items (Netemeyer et al. 2003). A uni-dimensional measure has a single dimension or facet and
the items reflect one underlying factor; a multi-dimensional measure has multiple dimensions
and the items reflect multiple factors. Our qualitative research has led us to the conclusion that
there are two dimensions of calculative culture: ‘analytical enthusiasm,’ which refers mainly to
the enthusiasm about advanced analytical methods, and ‘numerical pragmatism,’ which refers
mainly to interpretation of numbers. We have thus developed two uni-dimensional scales, one
12
for measuring ‘analytical enthusiasm’ and one for measuring ‘numerical pragmatism.’ In this
step, we assess statistically the dimensionality of calculative culture through an inspection of
pairwise item correlations, as well as through exploratory and confirmatory factor analysis (EFA
and CFA, respectively).
Clark and Watson (1995) suggest that interitem correlations for a uni-dimensional measure
should fall between 0.15 and 0.50, clustering narrowly around the mean value. Table 2 shows the
pairwise correlations of the 6 items measuring calculative cultures (q20a – q20f) and the
additional control item (q26a). Interitem correlations between q20a – q20c range from 0.22 to
0.51 and are all statistically significant. Also, items q20d – q20f correlate positively and
statistically significantly among each other, with coefficients ranging from 0.59 to 0.62. These
results support the uni-dimensionality of each scale. Items from the first group (q20a – q20c) are
not correlated with the items from the second group (q20d – q20f) in a statistically significant
way, their coefficients are small in absolute values (0.008 to 0.13) and out of 9 coefficients 7
have a negative sign. This supports the view that there are two dimensions of calculative culture
and analytical enthusiasm and numerical pragmatism are not simply the opposite poles of one
construct. If the latter would be true, we should observe significant negative correlations between
items q20a – q20c and items q20d – q20f, but this is not the case (Table 2). Additionally, items
q20a – q20c correlate positively with the control question q26a and items q20d – q20f correlate
negatively with q26a (apart from q20a and q20c, all significant at p>0.05), which supports the
construct validity of our measurement instruments. These results jointly offer initial support for
our hypothesis of two dimensions underlying the construct of calculative culture.
Table 2: Pairwise correlations between items measuring the dimensions of calculative cultures
q20a q20b q20c q20d q20e q20f q26a
q20a 1
q20b 0.5130* 1
q20c 0.2218* 0.4087* 1
q20d -0.0747 -0.0742 0.1045 1
q20e -0.0227 -0.1266 -0.0274 0.6089* 1
q20f 0.0083 -0.0626 -0.0744 0.6161* 0.5936* 1
q26a 0.1565 0.2122* 0.0538 -0.4132* -0.5532* -0.4362* 1
Notes: n = 124, * denotes significance at p<0.05
q20a – q20f are items measuring the dimensions of calculative cultures (all on Likert-type scale 1 – 5, where
1 means ‘strongly disagree’ and 5 means ‘strongly agree’), q26a is a control item (measured on a scale 1 – 4,
where 1 and 2 correspond to analytical enthusiasts, and 3 and 4 correspond to numerical pragmatists).
Since our research is explorative and only tentatively confirmative, we first run an EFA to
statistically investigate the potential number of factors underlying our measurement instrument.
13
The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy is 0.65, which is a mediocre,
but acceptable result for conducting EFA. Presented in Table 3 are the results of EFA run in
Stata 13.1 and with an orthogonal varimax factor rotation. The analysis revealed two factors with
an eigenvalue greater than 1, which implies that two factors best describe the sample’s pattern of
item covariances (Panel A). The items q20d – q20f load on the first factor (explaining 75% of the
total variance) and the items q20a – q20c load on the second factor (explaining 46% of the total
variance). The first factor matches our proposed measurement scale for analytical enthusiasm
(AE) and the second factor matches our proposed measurement scale for numerical pragmatism
(NP) There are no cross loadings above 0.4 and all the loadings on the two factors are higher
than the conventional cut off value 0.35. Unique variances are higher for items q20a – q20c,
which implies that the measurement error is higher for quantitative scepticism than for
quantitative enthusiasm. The Bartlett test of sphericity for each of the two factors is significant
(p=0.000) demonstrating that the items within factors are significantly inter-correlated. All this
supports the two-dimension (i.e. two-factor) solution for measuring calculative cultures. The
screeplot analysis, however, pointed to a three-factor solution, but since the third factor has low
loadings of all items we discarded this solution (Panel B: the highest loading on the third factor
is 0.22 for item q20c which cross loads on the second factor with 0.48).
Table 3: Exploratory factor analysis
PANEL A: PANEL B:
Item Factor1 (AE) Factor2 (NP) Uniqueness Factor1 Factor2 Factor3 Uniqueness
q20a 0.5829 0.6599 -0.1355 0.5672 -0.1680 0.6317
q20b 0.7025 0.5003 -0.2184 0.6723 -0.0212 0.4998
q20c 0.4898 0.7596 -0.0766 0.4843 0.2202 0.7111
q20d 0.7688 0.4090 0.7541 0.1494 0.1225 0.3940
q20e 0.7359 0.4541 0.7341 0.0835 -0.0330 0.4530
q20f 0.7448 - 0.4442 0.7361 0.1180 -0.1069 0.4328
Eigenvalue 1.72 1.05 1.7214 1.0515 0.1047
Cronbach’s alpha 0.65 0.82
Notes: n=124; EFA with orthogonal varimax factor rotation (Stata 13.1.). All items are measured on a Likert-type scale 1 – 5,
where 1 means ‘strongly disagree’ and 5 means ‘strongly agree’. KMO is mediocre (0.6449) and Bartlett test of sphericity is
highly significant for each factor (p=0.000). Panel A: 2-factor solution, only loadings higher than 0.4 shown. Panel B: 3-factor
solution, all loadings shown. AE – ‘quantitative enthusiasm, NP – ‘quantitative scepticism.’.
After EFA, we proceed with CFA to assess the two-factor measurement model with items q20a –
q20c loading on one factor (NP) and items q20d – q20f loading on the second factor (AE). We
include the covariance between the factors in the model, but we do not specify any error
covariances between the items (Figure 1).
14
Figure 1: A two-factor measurement model of calculative cultures
The results are presented in Table 4. The estimation method in CFA is ML, which assumes a
joint normal distribution of the variables. Since the univariate skewness and kurtosis of items are
well below the criteria suggested in Kline (2011), i.e. skewness below 3 and kurtosis below 10,
problems due to non-normality of joint distribution are not likely (Table A3 in the Appendix).
The fit of the model according to conventional criteria is good: the chi-square test is not
significant (p=0.2676) supporting the exact fit hypothesis; the ratio of chi-square value and
degrees of freedom is below 2 (1.25); the RMSEA is below 0.05 (0.044) and the probability that
RMSEA is below 0.05 is 0.478; the CFI and TLI measures are close to 1 (CFI=0.99 and
TLI=0.98); the SRMR is below 0.08 and close to 0 (0.032).7 All factor loadings are significant
and all standardized loadings are above 0.4. We analysed also the standardized residuals for
correlations to discover potential localized areas where the model fit might be worse, yet there
are no standard residuals higher than 2, which also confirms that the fit of the model is good. The
only modification, suggested by software, which would have a significant effect on parameters,
is an error covariance between items q20d and q20c. We reject the inclusion of this modification
in the model because we do not find an underlying theoretical explanation for this connection.
Overall, the statistical analysis provides good support for the proposed two-factor measurement
model of calculative culture.
7 RMSEA is the Root Mean Square of Approximation (Browne & Cudeck, 1993), TLI is the Tucker-Lewis Index
(Tucker & Lewis, 1973), CFI is the Comparative fit index (Bentler, 1990), and SRMR is the Standardized Root
Mean Residual (Bentler, 1995).
NP
q20a
1
q20b
2
q20c
3
AE
q20d
4
q20e
5
q20f
6
15
Table 4: Confirmatory factor analysis
Unstandardized
coefficient SE z-score p-value
Standardized
coefficient CRI AVE sqrt AVE
Factor 1 (AE) 0.822 0.607 0.779
q20d 1 0.794***
q20e 0.893*** 0.117 7.61 0.000 0.769***
q20f 0.959*** 0.125 7.67 0.000 0.773***
Factor 2 (NP) 0.737 0.536 0.732
q20a 1 0.515***
q20b 2.007*** 0.725 2.77 0.006 0.995***
q20c 0.618*** 0.155 4 0.000 0.411***
cov(NP,AE) -0.049 0.046 -1.07 0.285 -0.112
Notes: n= 124; ML estimator. Goodness-of-fit: chi-square 9.964; degrees of freedom 8; p-value 0.2676; chi-square ratio 1.25;
RMSEA 0.044 (90 percent C.I. is 0.000 − 0.12 prob. RMSEA < 0.05 is 0.478); CFI 0.990; TLI 0.981; SRMR 0.032. All items are
measured on Likert-type scale 1 – 5, where 1 means ‘strongly disagree’ and 5 means ‘strongly agree’. AE – ‘quantitative
enthusiasm, NP – ‘quantitative scepticism.’ CRI – Construct Reliability Index; AVE – Average Variance Extracted.
3.2. Reliability
Scale reliability refers to the consistency of measurement i.e. the proportion of true score
variance to total observed variance of the measure (Brown, 2006) and is most commonly
assessed with internal consistency (Clark & Watson, 1995) i.e. the homogeneity of items within
the scale. The most commonly used measure of internal consistency is Cronbach’s alpha (1951),
which we report in Table 3 for the two factors: the coefficient is 0.82 for the first factor (AE) and
0.65 for the second factor (NP). Hair, Black, Babin, and Anderson (2014) recommend that the
generally accepted lower limit for Cronbach’s alpha (0.7) be decreased to 0.6 in exploratory
research. Our results thus demonstrate a very good reliability of the scale for measuring
quantitative enthusiasm (AE), and an acceptable reliability of the scale for measuring quantitative
scepticism (NP). Internal consistency can be assessed also with item-to-total correlation and
inter-item correlations, which according to Hair at al. (2014), should be above 0.5 and 0.3,
respectively. Table 5 shows the correlations between latent factor scores and items: all are above
0.5, except the correlation between q20c and NP (0.41). The interitem correlations are shown in
Table 2: all except one are above 0.3, the exception is the correlation between q20c and q20a
(0.22). The interitem correlations are higher for the items measuring AE, which again indicates
that this measurement scale is more reliable than the scale for NP, however also the latter is
acceptable considering the explorative stage of our research.
16
Table 5: Pairwise correlations between factor scores predicted from CFA, factor items, and a
control item.
PANEL A:
q20d q20e q20f Factor 1 (AE) q26a
q20d 1
q20e 0.6089* 1
q20f 0.6161* 0.5936* 1
Factor 1 (AE) 0.8750* 0.8473* 0.8526* 1
q26a -0.4132* -0.5532* -0.4362* -0.5444* 1
PANEL B:
q20a q20b q20c Factor 2 (NP) q26a
q20a 1
q20b 0.5130* 1
q20c 0.2218* 0.4087* 1
Factor 2 (NP) 0.5178* 1.0000* 0.4125* 1
q26a 0.1565 0.2122* 0.0538 0.2128* 1
Notes: n = 124, * denotes significance at p<0.05.
AE – ‘quantitative enthusiasm, NP – ‘quantitative scepticism.’ q20a – q20f are items measuring the
dimensions of calculative cultures (all on Likert-type scale 1 – 5, where 1 means ‘strongly disagree’ and 5
means ‘strongly agree’), q26a is a control item (measured on a scale 1 – 4, where 1 and 2 correspond to
analytical enthusiasts, and 3 and 4 correspond to numerical pragmatists).
3.3. Construct validity
Construct validity is commonly assessed through convergent and discriminant validity:
convergent validity refers to the extent to which independent measures of the same construct
converge or are highly correlated and discriminant validity refers to the extent to which two
measures designed to measure similar, but conceptually different constructs are related
(Netemeyer et al. 2003). EFA and CFA results are usually used to assess these two aspects of
construct validity. Hair et. al (2014) suggest significant loadings and standardized loadings above
0.5 demonstrate convergent validity. All factor loadings in Table 4 are significant for our two
factors, the standardized loadings for the first factor (AE) are all above 0.7 (good convergence),
while two of the standardized loadings for the second factor (NP) are above 0.5 (acceptable
convergence) and one is 0.41. Additionally, average variance extracted (AVE) above 0.5 (Fornell
& Larcker, 1981) and construct reliability index (CRI) above 0.6 (Bagozzi & Baumgartner,
1994) for both factors confirm adequate convergence validity. Discriminant validity, on the other
hand, is demonstrated by the absence of high cross loadings between items in different factors
and, additionally, by the square root of AVE higher than intra-construct correlations (Tables 4
and 5). Finally, the correlation between latent factors is low and not significant (-0.11, p =
0.257), which again demonstrates discriminant validity of our measures (Table 4: the
17
standardized covariance between factors is equal to correlation because there are no specified
error covariances and all items load only on one factor).
Additionally, we examine the equivalence of indicator loadings of each factor (i.e. tau-
equivalence; see Table A4 in the Appendix), and the results indicate that scores of items
measuring AE (q20d-q20f) can be considered equivalent, and so simply summated or averaged in
order to measure AE, while this is not justified for NP.
3.4. Measurement and structural equivalence over sub-groups
The next step is an analysis of measurement and structural equivalence (invariance) over sub-
groups of population. We test if scores from the operationalization of the constructs have the
same meaning under different conditions (Meade & Lautenschlager, 2004) – e.g. different
industries or size classes of the respondent company. Since multiple groups CFA is difficult to
run on 12 industrial sub-groups, even more so when the group size is small, we first identify
which groups are ‘worthwhile’ for such inspection. Considering that the concept of calculative
culture was originally developed in the context of banking industry, it is of a particular interest
whether the scale can be considered equivalent (invariant) across financial and non-financial
industries. We run an ANOVA analysis on predicted scores of latent variables AE and NP over
the 12 industrial sub-groups (results not tabulated). Overall, the differences in AE and NP over
the industrial sub-groups are not significant (F = 1.71, p = 0.0806; F=1.26, p = 0.2546,
respectively). However, the mean score of AE is statistically significantly higher in ‘Financial
intermediation and other financial services’ industry compared to all other non-financial
industries (one-tailed p = 0.011). On the other hand, the ‘Service’ industries have a significantly
lower mean score of AE compared to all other non-service industries (one tailed p = 0.000). We
tested also the differences for the larger industrial sub-groups (‘Trade’ industries vs. all other and
‘Manufacturing’ industries vs. all other), but the t-tests did not reveal any significant differences
for AE or NP (results not tabulated).
We ran ANOVA on predicted latent scores of AE and NP, and also across different size groups
(micro, small, medium, large), across different strategic type (defender, analyst, prospector,
reactor), across five levels of uncertainty in technology, products, demand, suppliers,
competitors’ actions, internal environment, external environment (1-low uncertainty, 5-high
uncertainty), and across market orientation (national, regional, European, global) – there were no
statistically significant differences in scores for AE and NP (results not tabulated).
Based on these preliminary results, we then performed a multiple group CFA only for two
industrial sub-groups: ‘Financial’ industries (coded 1, n = 23) and ‘Non-financial’ industries
(coded 0, n = 101). We follow the stepwise testing strategy suggested by Kline (2011) and
18
Brown (2006): first we test for equal form (configural invariance) and if this holds, we proceed
with more and more restrictive solutions, each evaluated with nested χ2
diff. If the more restricted
model’s fit is not significantly worse (χ2
diff not significant), the equality constraint is seen as
justified and as evidence of equivalence (invariance) across groups. We start with tests for metric
invariance (equal factor loadings and intercepts), and then continue with testing structural
parameters describing population heterogeneity (equal latent factor variances and means). The
results are presented and explained in Table A5 in the Appendix. The results confirm that factor
loadings and intercepts can be considered invariant across the two groups and the comparison of
groups on the latent mean is interpretable. In other words, this means that the slopes and
intercepts can be considered equal across both groups. We can also confirm that indicators have
a similar measurement precision across the two groups and that the amount of within-group
variability (dispersion) of the constructs does not significantly differ across groups. The
relationship between AE and NP is not significantly different across groups. Finally, we test the
equality of latent means which is a CFA analogue to t-tests or ANOVA of observed group means
that takes into account the measurement error8 (Brown, 2006). Due to the nature of business, we
would expect that companies from financial industries have a higher score on AE. Table A5
(Appendix) shows that latent means of NP are equivalent across groups (χ2
diff (1) = 1.029, not
significant), but indeed latent means of AE are not: ‘Financial’ industry has on average a 0.455
higher estimated latent mean score on AE compared to ‘Non-financial’ industry (χ2
diff (2) =
6.055, significant at p = 0.048).
We conclude that there is good statistical evidence supporting our proposed measurement
instruments: both scales show good or at least adequate reliability, convergent and discriminant
validity, and also strong factorial invariance (equal loadings and intercepts) between financial
and non-financial industries. These measurement instruments also produced differences (or no
differences) in latent mean scores where expected: e.g. the AE scale produced a higher latent
mean score for companies in financial industries compared to companies in non-financial
industries; on the other hand, the NP scale did not reveal such difference. Taken all together, this
means that they can be used as measuring instruments for calculative culture.
5. A PROPOSED TYPOLOGY OF CALCULATIVE CULTURE
5.1. Conceptual development of the proposed model
Douglas and Wildavsky (1982) use cultural analysis to show how a given cluster of values and
beliefs makes sense out of the various positions people take and the practices they employ. We
8 Traditional ANOVA assumes perfect reliability. In our case the reliability of AE seems to be good (based on
Cronbach's alpha), but the reliability of NP is not so high, therefore we employ multiple group CFA instead of just
ANOVA to test for group differences on latent means.
19
argue that also calculative culture connects people and shapes their collective preferences for the
use of numbers and analytical methods to make decisions. Based on the identified two
dimensions of calculative culture, we can propose a typology and represent it with a 2-by-2
diagram, where the horizontal axis measures enthusiasm about analytical methods and the
vertical axis measures pragmatism in numbers’ interpretation. Each of these dimensions can have
a low or high score, and we can thus distinguish between four combinations – types of
calculative culture (see Figure 29).
The vertical axis reflects the level of pragmatism in numbers’ interpretation. We label this axis
‘numerical pragmatism.’ One end of the axis represents an emphasis on pragmatism in number
interpretation. Here, managers interpret initial numerical measurements in the light of their past
experience and intuition, and possess the ambition to participate in the discussion of non-
measurable strategic uncertainties. They regard numbers as attention-directing devices with no
intrinsic claims to represent reality (Power, 2007). Collier et al. (2007) found that managers in
their survey predominantly used subjective methods for risk management, particularly
experience. The other end of the axis represents a focus on idealism in number interpretation.
Here, top management members lack the ambition to participate in the discussion of non-
measurable strategic uncertainties.
The horizontal axis reflects the level of enthusiasm about analytical methods (models). We label
this axis ‘analytical enthusiasm.’ One end of the axis represents scepticism (low enthusiasm)
about analytical models. Here, we expect managers to prefer highly judgemental information
systems. This cultural orientation takes a more cautious, incredulous approach to the benefits of
quantitative modelling. The other end of the axis represents high enthusiasm about analytical
models. Here, the logic of calculation is very close to a highly abstract, analytical activity that
draws heavily on advances in statistics and financial economics (Mikes, 2011). Therefore, we
expect managers to prefer highly analytical information systems. This corresponds to an
‘analytics-friendly management culture’ (Mikes, 2009). Douglas and Wildavsky (1982) claim
that some forms of institutional life ‘encourage the idea that all formal procedures are prone to
error because they are too mechanical’, but other, more hierarchical, forms rely on them because
they allow to transform political issues of choice into administrative (computational) problems.
From the juxtaposition of these two dimensions, four types of cultural orientations emerge: (1)
smart enthusiasts, (2) extreme sceptics, (3) irrational sceptics, and (4) extreme enthusiasts. Each
of the four types of cultural orientations is described below.
9 Note: numbers and percentages in every quadrant pertain to Section 4.2.
20
The first group culture (in the upper right quadrant in Figure 2) is called ‘smart enthusiasts.’
Here, managers score high on analytical enthusiasm and high on numerical pragmatism. Smart
enthusiasts appreciate analytical models, but still rely on human judgement when interpreting the
numbers. In contrast to extreme enthusiasts, smart enthusiasts look at the ‘bigger picture.’ Such a
calculative culture could be described as ‘judicious’ as the advantages of advanced analytics are
appreciated, however the outputs (numbers) of analytical procedures are pragmatically
interpreted – as indicators of trends – and coupled with qualitative information to make
decisions.
Figure 2: A proposed typology of calculative culture
The second group culture (in the upper left quadrant in Figure 2) is called ‘extreme sceptics.’
Here, managers score low on analytical enthusiasm and high on numerical pragmatism. It
reflects a culture where analytical techniques are not appreciated, nor trusted to provide
important numerical information for decision-making. Decision-making is heuristic, based on
qualitative information and experience, rather than on analytical reports. Extreme sceptics use
‘soft instrumentation’ (Mikes, 2011), referring to decision-making methods that do not privilege
measurement. The decision-maker’s mental models, prior experience, beliefs, and values are
important complementary elements of decision-making. Soft instrumentation privileges concepts
Analytical
enthusiasm
Analytical
scepticism
Idealistic interpretation
of numbers
Pragmatic interpretation
of numbers
(2) Extreme sceptics
33 (26.6%)
(4) Extreme
enthusiasts
33 (26.6%)
(1) Smart enthusiasts
28 (22.6%)
(3) Irrational
sceptics
30 (24.2%)
21
of adaptive control, robustness, scenario planning and worst-case analysis (Kleindorfer, 2010).
They operate with simpler models than smart or extreme enthusiasts. They see calculative
practices as a ‘learning machine’ (see: Burchell, Clubb, Hopwood, & Hughes, 1980). This group
corresponds to Power’s (2007) description of calculative pragmatists and Mikes’ (2009)
description of quantitative sceptics.
The third group culture (in the lower left quadrant in Figure 2) is called ‘irrational sceptics.’
Here, managers score low on analytical enthusiasm and low on numerical pragmatism. It reflects
a culture where analytical techniques are not very appreciated, however the numerical data and
information is trusted and taken at face value for decision-making. This cultural orientation of is
somewhat paradoxical (that is why we call them ‘irrational’), but it is at least hypothetically
possible.
The fourth group culture (in the lower right quadrant in Figure 2) is called ‘extreme enthusiasts.’
Here, managers score high on analytical enthusiasm and low on numerical pragmatism. Top
management team greatly appreciates analytics, numerical outputs are taken at their face value,
and the numbers themselves become beacons in decision-making. Extreme enthusiasts regard the
outputs of models as a close proxy to the underlying economic reality. They do not question the
assumptions that went into the models. Non-quantifiable issues are not as important for them as
they do not have tools to frame them into a quantitative model. This group corresponds to
Power’s (2007) description of calculative idealists and Mikes’ (2009) description of quantitative
enthusiasts.
The four cultures described above should be thought of as ideal types defined by our framework.
Organizations are unlikely to reflect only one culture. More likely, we can find combinations of
each cultural type, with some types more dominant than others (Denison & Spreitzer, 1991). We
can also find paradoxical combinations of values in organizations (Cameron, 1986). Our
typology is based on group culture pertaining to top management team. As this is supposed to be
the ‘strongest’ and the most ‘influential’ group in the organization, we can expect that their
calculative culture would spread throughout the organization. However, this was not the subject
of our study and has to be further empirically tested. Our typology groups cultures into broad
categories based on general characteristics shared by the top management team (group).
22
5.2. Statistical analysis of the proposed model
We identify and analyse also statistically the four types of calculative culture in our sample.
First, we split the sample into four groups based on the latent scores of AE and NP10
. The unit of
measurement for latent scores is standard deviation from the mean. The cut-off value for both
variables is 0, which represents the mean. Responses with above average score for AE and NP
are coded 1 (‘Smart enthusiasts’); responses with below average score for both dimensions are
coded 3 (‘Irrational sceptics’); responses with above average score for AE and below average
score for NP are coded 4 (‘Extreme enthusiasts’); and responses with below average score for AE
and above average score for NP are coded 2 (‘Extreme sceptics’). Based on this classification,
the sample consists of 30 (24.2%) irrational sceptics, 33 (26.6%) extreme enthusiasts, 33 (26.6%)
extreme sceptics, and 28 (22.6%) smart enthusiasts.11
Second, we analyse the differences in
construct items average scores among these four groups to cross-check their ‘identity’ (Table A6
in the Appendix, panel A). According to expectations, groups 1 and 2 scored significantly higher
on items q20a-q20c than groups 3 and 4, and groups 2 and 3 scored significantly lower on items
q20d-q20f than groups 1 and 4.12
Third, we analyse demographic and contextual variables over these four groups to identify
possible confounding influences. Is ‘extreme enthusiastic’ type of culture in fact overlapping
with the financial industry (because of specific nature of business and regulation)? Is an ‘extreme
sceptic’ type of culture just a reflection of highly uncertain environment? We answer these
questions based on ANOVA and association tests (the results are tabulated in the Appendix,
Table A6 - panel B and Table A7)13
. There is no statistically significant difference among groups
(at p<0.05) with respect to different uncertainties (q21a-q21g), perceived performance relative to
competitors (q27a-q27e), or ownership structure (q40-q42). Also, there seems to be no
statistically significant difference (at p<0.05) with respect to strategy type (q23), market
orientation (q22), firm size (q30, q30_g), firm age (q29), and industry (q28). However, there is a
difference between financial and non-financial companies (Fin_dummy: Pearson χ2 significant at
p=0.037, Fisher’s exact test significance = 0.021). Financial firms are over-represented in group
4 (‘extreme enthusiasts’) and under-represented in group 2 (‘extreme sceptics’), compared to
statistically expected frequencies. We also tested for differences between manufacturing vs. non-
10
Summated scales or average scores would be more intuitive to interpret, but we use the latent scores because
statistical analysis showed that only AE can be summated, while this is not warranted for NP. The drawback of this
approach is that we do not know the absolute value of the mean. 11
It has to be noted that another selection of the cut-off criteria would result in a different distribution over the four
ideal types of calculative cultures. 12
The exception is group 4’s score on q20c which is not low enough to be statistically different from groups 1 and 2.
The item refers to the importance of qualitative assessments compared to precise and complex numerical
calculations. 13
This ANOVA analysis refers to the types of calculative culture (combinations of high/low scores on both
constructs, AE and NP), and it differs from the ANOVA of each of the constructs presented in previous section.
23
manufacturing firms and trade vs. non-trade firms – in these cases there are no statistically
significant differences over the four groups (Man_dummy: Pearson χ2 significant at p= 0.913,
Fisher’s exact test significance = 0.937; Trd_dummy: Pearson χ2 significant at p=0.786, Fisher’s
exact test significance = 0.785).
Also, we detect statistically significant differences for the control question q26a over the four
calculative cultures types (Pearson χ2 significant at p=0.007, Fisher’s exact test significance =
0.009). The pattern of associations corresponds to the findings from ANOVA: there is a clear
overrepresentation of groups 1 and 4 in the bottom two categories of the control question (1 and
2, AE), and a clear overrepresentation of group 2 in the upper two categories (3 and 4, NP),
however, group 3 (‘irrational sceptics’) is not significantly over/underrepresented in any category
of q26a. This could indicate that of the four groups, group 3 has the least clear ‘identity’ and/or,
on the other hand, that the control question failed to capture the difference/equivalence between
this group and others.
This analysis reveals two things: i) our types of calculative cultures are not proxies or reflections
of the tested contextual variables, but are in fact measuring something that is different from these
variables, and ii) financial industry is indeed over-represented in extreme enthusiastic group, but
more importantly, it does not define it, as demonstrated by the fact that companies from other
large industries (manufacturing and trade) are not significantly under-represented in this group.
This again confirms that the scales can be used beyond risk management and banking domains,
where they originate from. They can be applied to the broader context of MCS implementation
and use.
CONCLUSION
In the paper we develop and validate an instrument for measuring calculative culture. Until now
the concept was empirically presented with cases in financial industry in the context of risk
management. Our study investigates calculative cultures in the broader context of MCS, where
calculative cultures are relevant as they shape managerial predilections towards MCS practices,
such as performance measurement systems, budgeting, or capital budgeting decision-making.
Providing a measuring instrument for calculative culture in the context of MCS research is
important as qualitative research suggests that calculative cultures are constitutive of, and also
are constituted by, the particular forms and uses of the control systems observed. Thus,
introducing the construct of calculative cultures in the context of MCS can help future
researchers to explain why and how some MCS practices are more successful than others.
Since calculative culture could affect MCS design, and hence performance, we believe it is
important to operationalize it for further survey-based research. We develop the measurement
24
instrument through several stages, from a theoretical description of the construct’s domain to the
statistical validation. Our conclusion is that calculative culture is a two-dimensional construct; it
can be described in terms of ‘analytical enthusiasm’ and ‘numerical pragmatism.’ Analytical
enthusiasm refers mainly to the enthusiasm about advanced analytical methods, while numerical
pragmatism refers mainly to the interpretation of numbers. Based on the reliability and construct
validity criteria, we find the proposed measurement scales for both dimensions acceptable for use
in further research, which is the main contribution of our paper to management control literature
and research. We develop a framework for calculative cultures and find that the proposed types
of calculative cultures are not proxies or reflections of the tested contextual variables, but are in
fact measuring additional contextual variable. This is a very important finding in the light of
contingency-based research in management accounting as calculative culture can explain a part
of the contextual effects on MCS implementation and use. Thus, it has the potential to become
one of the contingency variables.
The main limitation of our study is that measuring of calculative culture was included in a
broader survey, and therefore we were limited in the number of measuring items that could be
included in the questionnaire. If more items were included, perhaps additional cultural
dimensions would be revealed through statistical analysis. Similarly to other quantitative studies
investigating cultural effects (e.g. Henri, 2006), we didn’t ask all members of top management to
describe calculative culture. We asked one member about the ‘top management culture’ and take
this as a proxy for the group. Another limitation refers to the ‘arbitrarily’ determined cut-off
value of the two dimensions when analysing the proposed cultural types. Also, due to a cross-
sectional study design, we cannot capture the evolution of the calculative culture over time. Our
study is exploratory in its nature. Thus, our findings and suggested typology should be
considered as preliminary, rather than conclusive, evidence. Even if measurement instrument has
reflected satisfying reliability and validity, we acknowledge that culture is a broad concept for
which richness cannot be fully captured with only a survey instrument. Further studies will
reveal its value in other research settings.
A number of directions for further research emerge from this study. First, our study offers the
basis for future methodological triangulation to further elaborate the construct of calculative
culture (see also: Bamber, 1993; Bhimani, 2003; Birnberg, Shields, & Young, 1990). Second, we
suggest investigating how calculative cultures influence the fit between MCS and organizational
context (see also: Berry, Coad, Harris, Otley, & Stringer, 2009). As Bhimani (2003) finds,
crucial to the perceived success of a MCS is the alignment between the cultural premise of the
new control system and the predilections of intended users for the particular numerical and
procedural approach. Third, it would be useful to examine the relationship among different
calculative cultures (types) and the use of MCS (for example, diagnostic vs. interactive; see
Simons, 1995, 2000). Fourth, further studies can look into finding possible solutions to change
25
group calculative culture for better fit with the organizational context. Here, future researchers
can use the logic from the context of organizational culture (e.g. see Denison & Spreitzer, 1991).
As calculative culture is subject to change, it is possible that another management team or a turn
in the institutional pressures brings a calculative culture change. Hence, longitudinal studies of
cultural change would be welcomed as well.
26
APPENDIX
A1. List of initial items for ‘quantitative scepticism’ and ‘quantitative enthusiasm’
(adopted from Mikes, 2009)
List of initial items for ‘quantitative scepticism’ (17):
1. Top management believes risk numbers have low ability to reflect the underlying risk profiles.
2. Top management prefers highly judgemental information systems.
3. Top management has a cautious, incredulous approach to the benefits of quantitative modelling: cautious
about the interpretation of their measurements and questions the accuracy of risk models.
4. Top management has a highly sceptical attitude to risk quantification.
5. Top management underplays the computational role of risk techniques and emphasis falls on their use as a
learning tool.
6. Top management believes the purpose of risk management is to restrain excessive risk-taking.
7. Precise risk numbers receive low top managerial attention and do not influence the decision making
agenda.
8. Top management uses risk numbers for learning (to understand dynamics, trends, to learn from
mistakes…).
9. Top management is characterised by judgemental decision making (judgmental risk assessments).
10. Top management sees a lot of limitations of highly analytical calculative practices in an organization.
11. Top management has low ‘trust in numbers’.
12. Top management regard risk figures as trend indicators, which they seek to complement, and often
overwrite by senior managerial discretion, experience and judgment; risk control is not considered as an
‘answer machine’.
13. Top managers prefer crude metrics, trend indicators rather than accurate risk measures per se.
14. Top management believes the precise amount of risk is not so important. However, the trend and the big
picture are interesting.
15. Calculations, although deemed indicative of risk exposure trends, are judged by top management as
insufficient to reflect the absolute risk profile.
16. Our top management’s motto is: ‘If you want to manage risk, risk numbers are not enough.’
17. Top management has doubts about the benefits of using quantitative models.
List of initial items for ‘quantitative enthusiasm’ (19):
1. Top management believes risk numbers have a high ability to reflect the underlying risk profiles.
2. Top management highly relies on risk numbers in the process of negotiating intra-group capital allocations
3. Top management prefers highly analytical (data-driven techniques) information systems
4. Top management encourages a highly analytics-friendly management culture.
5. Top management has a trusting approach to the benefits of quantitative modelling.
6. Top management has highly enthusiastic attitude to risk quantification.
7. Precise risk numbers receive high top managerial attention and influence the decision making agenda.
8. Top management is characterised by computational decision making where risk is managed ‘by the
numbers’.
9. Top management perceives highly analytical calculative practices in an organization as useful.
10. Top management tends to agree that risk measures are capable of reflecting the underlying economic reality
well enough to induce requisite economic behaviours in the light of these.
27
11. Top management puts a high priority on building, maintaining and improving the ‘robustness’ and accuracy
of analytical models.
12. Top management has high ‘trust in numbers’.
13. Our top management’s motto is: ‘If you want to manage risk, you have to quantify it.’
14. Top management appreciates the accuracy of the risks that are recognised more than a comprehensive
report on all possible risks.
15. The quality of risk management is assessed by judging how advanced the quantification methodologies are.
16. There is a separate body devoted to discussing and updating the risk measurement methodologies in use,
and there are a lot of debates on risk methodology because of concerns that they are technically lagging
behind competitors.
17. Top management thinks “We can’t afford having any of the analysts or anyone else saying we have a bad
methodology.”
18. Top management demands that the risk function also provides the necessary analytics to make quantifiable
risks subject to limit setting and control.
19. Top management has confidence in the reliability of the risk models.
A2. List of items after interviews and focus group
Characteristic Item Sceptics (11)
Enthusiasts (12)
Trust in
numbers
1. Top management in general has low ‘trust in numbers’.
2. Top management in general thinks numbers have low ability to reflect the underlying
economic reality. 3. Top management thinks risk numbers have low ability to reflect the underlying risk profiles.
4. Our top management’s motto is: ‘If you want to manage risk, risk numbers are not enough.’
5. (R) Top management in general has low ‘trust in numbers’.
6. (R) Top management in general thinks numbers have low ability to reflect the underlying
economic reality.
7. Top management believes risk measures are capable of reflecting the underlying risk profiles well enough to induce appropriate economic behaviours in the light of these.
8. Our top management’s motto is: ‘If you want to manage risk, you have to quantify it.’
X
X
X
X
X
X
X
X
Importance of
accuracy
1. Top management in general interprets measurements (numbers) roughly, as indicators of
dynamics, not as accurate assessments.
2. Top management thinks risk measurements (numbers) indicate risk exposure trends, but are insufficient to reflect the absolute risk profile.
3. Top management prefers comprehensive risk reporting with crude measurement of all types of
risk (including non-quantifiable risks) more than the accurate measurement of fewer (quantifiable) risk types.
X
X
X
Trust in models 1. Top management is in general sceptical about the benefits of quantitative modelling.
2. Top management in general sees a lot of limitations of highly analytical calculative practices in an organization.
3. Top management attitude towards the reliability of risk models is sceptical. 4. (R) Our management culture is analytics-friendly.
5. Top management in general believes in the benefits of quantitative modelling.
6. Top management in general perceives highly analytical calculative practices in an organization as useful.
7. Top management has confidence in the reliability of risk models.
8. Our management culture is analytics-friendly.
X
X
X X
X
X
X
X
Importance of
cutting-edge
1. Top management in general puts a high priority on building, maintaining and improving the
‘robustness’ and accuracy of our analytical models.
2. Top management assesses the quality of risk management by judging how advanced the quantification methodologies are.
3. There is a lot of discussing and updating of the risk measurement methodologies in use.
4. There is a lot of debate on risk measurement methodology because of concerns that we are technically lagging behind competitors.
X
X
X
X
Notes: (R) denotes items that are intentionally written in ‘reversed’ form.
28
A3. Descriptive statistics of variables
Panel A: Constructs’ measurement items
Variable Obs Mean Std. Dev. Min Max Skewness Kurtosis
q20a 124 3.298 1.082 1 5 -0.420 2.291
q20b 124 3.169 1.124 1 5 -0.302 2.031
q20c 124 3.790 0.839 1 5 -0.919 3.795
q20d 124 3.105 0.994 1 5 -0.261 2.381
q20e 124 3.669 0.917 1 5 -1.015 3.949
q20f 124 3.403 0.979 1 5 -0.720 3.403
q26a 124 2.427 0.921 1 4 0.183 2.215
Notes: q20a – q20f are items measuring the dimensions of calculative cultures (all on Likert-type
scale 1 – 5, where 1 means ‘strongly disagree’ and 5 means ‘strongly agree’), q26a is a control
item (measured on a scale 1 – 4, where 1 and 2 correspond to quantitative enthusiasts, and 3 and 4
correspond to quantitative sceptics).
Panel B: Contextual variables
Variable Obs Mean Std. Dev. Min Max
q21a Technology 124 2.83 1.11 1 5
q21b Demand 124 3.10 0.94 1 5
q21c Product 124 2.91 1.13 1 5
q21d Competitors 124 3.14 0.97 1 5
q21e Suppliers 124 3.31 0.79 2 5
q21f Internal environment 124 2.81 1.03 1 5
q21g External environment 124 2.86 0.95 1 4
q27a Profitability 120 3.52 1.00 1 5
q27b Sales growth 121 3.41 0.93 1 5
q27c Market share 121 3.36 0.88 1 5
q27d New product development 121 3.38 0.96 1 5
q27e Customer satisfaction 122 3.71 0.81 1 5
q30 Number of employees 114 569.82 1200.65 2 7000
q40 % own. managers 101 16.40 34.10 0 100
q41 % own. institutional 102 20.51 36.40 0 100
q42 % own. foreign 101 27.46 41.16 0 100
Notes: q21a – q21g are items measuring the uncertainty in the business environment (1 = ‘strongly
disagree’, 5 = ‘strongly agree’); q27a – q27e are items measuring the last year’s perceived
performance relative to competitors (1 = ‘well below’, 5 = ‘well above’); q30 is the number of
employees; items q40 – q42 measure the % owned by managers, institutional investors, and
foreign investors, respectively.
29
A4. Model fit of benchmark, tau-equivalent, and parallel solutions of the model
χ2 df χ2 diff Δdf RMSEA (90% CI) CFit SRMR CFI TLI
Model 1:
Benchmark model
9.96
8
0.044 (0.000-0.120)
0.478
0.032
0.99
0.981
Model 1a:
Tau-equivalent: AE 10.72 10 0.756 2 0.024 (0.000-0.102) 0.618 0.039 0.996 0.994
Model 1b:
Tau-equivalent: AE & NP 28.57** 12 17.85** 2 0.106 (0.056-0.156) 0.036 0.094 0.913 0.891
Model 2a:
Parallel: AE 11.434 12 0.714 2 0.000 (0.000-0.088) 0.734 0.054 1 1.004
Notes: χ2
diff is nested χ2 difference;
** significant at p < 0.05.
Equivalent loadings for the first factor – AE (the χ2diff of the model 1a is not significant at p<0.05), but not for the
second factor – NP (the χ2diff of the model 1b is significant at p<0.05): AE can be measured by averaging the items,
NP cannot be. Parallel indicators of AE (the χ2diff of the model 2a is not significant at p<0.05): they measure the
construct AE with the same level of precision.
A5. Tests of measurement invariance and population heterogeneity
χ2 df χ2 diff Δdf RMSEA (90% CI) Cfit SRMR CFI TLI
Single group solutions
Non-finance 10.285 8
0.053 (0.000-0.135) 0.416 0.037 0.985 0.972
Finance 8.204 8
0.033 (0.000-0.248) 0.458 0.080 0.994 0.989
Measurement invariance
Eq. form 22.470 20
0.045 (0.000-0.122) 0.494 0.070 0.987 0.980
Eq. loadings and intercepts 27.546 24 5.076 4 0.049 (0.000-0.118) 0.471 0.067 0.981 0.976
Eq. indicator error variances 37.306 30 9.760 6 0.063 (0.000-0.121) 0.353 0.145 0.961 0.961
Population heterogeneity
Eq. factor variance (AE) 40.683 31 3.377 1 0.071 (0.000-0.126) 0.274 0.236 0.948 0.950
Eq. factor variance (AE & NP) 41.168 32 3.862 2 0.068 (0.000-0.123) 0.300 0.240 0.951 0.954
Eq. factor covariance 42.330 33 1.162 1 0.068 (0.000-0.122) 0.302 0.240 0.950 0.955
Eq. latent mean (NP) 43.359 34 1.029 1 0.067 (0.000-0.120) 0.309 0.237 0.950 0.956
Eq. latent mean (NP & AE) 48.385** 35 6.055** 2 0.079 (0.000-0.128) 0.195 0.285 0.929 0.939
Notes: χ2
diff is nested χ2 difference;
** significant at p < 0.05.
The equal form solution shows good model fit and the equal factor loadings and intercepts solution did not produce
a significantly worse fit (χ2
diff (4) = 5.076): factor loadings and intercepts can be considered invariant across the two
groups. The equal indicator error variance solution did not produce a significant degradation in fit (χ2diff (6) = 9.760):
indicators have a similar measurement precision across the two groups. The equal factor variances solution did not
significantly degrade the fit (χ2
diff (2) = 3.862): the amount of within-group variability (dispersion) of the constructs
does not significantly differ across groups. The equal factor covariances solution did not degrade the fit (χ2diff (1) =
1.162): the relationship between AE and NP is not significantly different across groups.
30
A6. Mean scores and ANOVA for groups
1 2 3 4 ANOVA
Panel A:
construct items
Smart
enthusiasts
Extreme
sceptics
Irrational
sceptics
Extreme
enthusiasts F - stat
Bartlett
(sig)
Bonferroni
(sig. pairs)
q20a Rough numbers 3.86 3.55 2.9 2.94 6.37*** 0.039 3,4<1,2;
q20b Crude measurement 4.21 4.12 2.2 2.21 130.14*** 0 3,4<1,2;
q20c Qualitative assessments 4.11 4 3.43 3.64 4.57*** 0.155 3<1,2
q20d Model benefits 3.93 2.24 2.53 3.79 51.98*** 0.241 2,3<1,4;
q20e Model development 4.36 2.88 3.4 4.12 28.21*** 0 2,3<1,4; 3>2
q20f Model condition 4 2.79 2.73 4.12 32.63*** 0.001 2,3<1,4;
N 28 33 30 33
1 2 3 4 ANOVA
Panel B:
contextual variables
Smart
enthusiasts
Extreme
sceptics
Irrational
sceptics
Extreme
enthusiasts F - stat
Bartlett
(sig)
Bonferroni
(sig. pairs)
q21a Technology 2.79 3.03 2.83 2.67 0.61 0.586 all ns
q21b Demand 3.06 3.33 2.83 3.15 1.58 0.813 all ns
q21c Product 2.89 2.94 2.63 3.15 1.12 0.402 all ns
q21d Competitors 3.07 3.18 3.17 3.12 0.08 0.58 all ns
q21e Suppliers 3.32 3.27 3.37 3.27 0.1 0.932 all ns
q21f Internal environment 2.86 2.95 2.87 2.7 0.19 0.679 all ns
q21g External environment 2.93 2.79 2.87 2.88 0.11 0.971 all ns
q26a Calculative culture(AE/NP) 2.18 2.97 2.5 2.03 7.68*** 0.862 2>1,4
q27a Profitability 3.43 3.48 3.61 3.55 0.16 0.014 all ns
q27b Sales growth 3.5 3.19 3.45 3.52 0.8 0.502 all ns
q27c Market share 3.11 3.32 3.35 3.64 1.94 0.168 all ns;
q27d New product development 3.46 3.16 3.43 3.48 0.8 0.952 all ns
q27e Customer satisfaction 3.86 3.63 3.62 3.76 0.58 0.31 all ns
q30 Number of employees 713 897 300 360 1.68 0 all ns
q40 % own. managers 15.87 24.07 10 14.33 0.77 0.096 all ns
q41 % own. institutional 19.41 15.97 27.17 20.78 0.41 0.634 all ns
q42 % own. foreign 17.24 29.6 36.35 25.44 0.83 0.757 all ns
N 28 33 30 33
Note: *** significant at p < 0.01
31
A7. Frequencies and association tests for groups
1 2 3 4 Association tests
Nominal
contextual variables
Smart
enthusiasts
Extreme
sceptics
Irrational
sceptics
Extreme
enthusiasts Total
Pearson
χ2 (df) sig
Fisher's
exact
q23 Strategy type 6.8527 (9) 0.652 0.651
Defender 4 4 7 3 18
Analyzer 16 16 11 20 63
Prospector 8 11 10 8 37
Reactor 0 2 2 2 6
q28 Industry
35.9941 (33) 0.33 0.164
Mining and quarrying 0 0 0 1 1
Manufacturing 8 10 9 12 39 Gas, electricity and
water supply 0 4 0 2 6
Construction 0 1 1 0 2
Trade 5 3 4 4 16
Services 0 3 3 0 6
Transport and storage 2 0 2 1 5
Communications 1 1 0 0 2
Information technology 1 4 2 1 8
Financial services 6 1 6 10 23
Real estate activities 1 1 1 1 4
Other 4 5 2 1 12
q22 Market orientation 8.9422 (9) 0.443 0.476
National 8 13 7 14 42
Regional 8 9 7 7 31
European 8 5 12 5 30
Global 4 6 4 7 21
q30_g Size 8.3039 (9) 0.504 0.541
Micro 2 0 1 0 3
Small 1 5 5 5 16
Medium 12 13 13 16 54
Large 10 13 7 11 41
q29 Age 11.6187 (12) 0.477 0.413
less than 6 1 1 1 0 3
6 – 10 2 1 2 0 5
11 – 15 4 3 5 2 14
16 – 20 2 7 2 3 14
more than 20 17 19 17 27 80
q26a Calculative culture 22.7474 (9) 0.007 0.009
1 (A: Enthusiast) 5 1 4 9 19
2 14 10 11 16 51
3 8 11 11 6 36
4 (B: Sceptic) 1 11 4 2 18
Fin_dummy 8.4736 (3) 0.037 0.021
0 22 32 24 23 101
1 6 1 6 10 23
Man_dummy 0.5266 (3) 0.913 0.937
0 20 23 21 21 85
1 8 10 9 12 39
Trd_dummy 1.0611 (3) 0.786 0.785
0 23 30 26 29 108
1 5 3 4 4 16
N 28 33 30 33 124
Note: Statistically significant results at p>0.05 in bold typeface.
32
REFERENCES
1. Bagozzi, R. P., Baumgartner, H. (1994). The evaluation of structural equation models and
hypothesis testing. In R. P. Bagozzi (Ed.), Principles of marketing research (pp. 386–422).
Cambridge, MA: Blackwell.
2. Bamber, E. M. (1993). Opportunities in behavioural accounting research. Behavioural
Research in Accounting, 3, 1–29.
3. Bentler, P. M. (1990). Comparative fit indices in structural models. Psychological Bulletin,
107, 238–246.
4. Bentler, P. M. (1995). ENP structural equations program manual. Encino, CA: Multivariate
Software.
5. Berry, A. J., Coad, A. F., Harris, E. P., Otley, D. T., Stringer, C. (2009). Emerging themes in
management control: A review of recent literature. British Accounting Review, 41(1), 2–20.
6. Bettis, R. A., Thomas, H. (1990). Risk, Strategy, and Management. Greenwich, Conn: Jai
Press.
7. Bhimani, A. (2003). A study of the emergence of management accounting system ethos and
its influence on perceived system success. Accounting, Organizations and Society, 28(6),
523–638.
8. Birnberg, J. G., Shields, M. D., Young, S. M. (1990). The case for multiple methods in
empirical management accounting research. Journal or Management Accounting Research, 2,
35–65.
9. Bozeman, B., Kingsley, G. (1998). Risk Culture in Public and Private Organizations. Public
Administration Review, 58(2), 109–118.
10. Brown, T. A. (2006). Confirmatory Factor Analysis for Applied Research. New York, NY:
The Guilford Press.
11. Browne, M. W., Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen
& J. S. Long (Eds.), Testing Structural Equation Models (pp. 136–172). Newbury Park, CA:
Sage Publications.
12. Burchell, S., Clubb, C., Hopwood, A., Hughes, J. (1980). The roles of accounting in
organizations and society. Accounting, Organizations and Society, 5 (1), 5–27.
13. Cameron, K. S. (1986). Effectiveness as paradox. Management science, 32, 87–112.
14. Churchill, G. A. Jr. (1999). Marketing Research: Methodological Foundations. Fort Worth:
The Dryden Press.
15. Clark, L. A., Watson, D. (1995). Constructing validity: Basic issues in objective scale
development. Psychological Assessment, 7(3), 309–319.
16. Collier, P. M., Berry, A. J, Burke, G. T. (2007). Risk and Management Accounting: Best
Practice Guidelines for Enterprise-wide Internal Control Procedures. Burlington, MA: CIMA
Publishing.
33
17. Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika,
16(3), 297–334.
18. DeVellis, R. F. (2003). Scale development: theory and applications (2nd ed. Vol. 26).
Thousand Oaks, CA: Sage Publications.
19. Denison, D. R., Spreitzer, G. M. (1991). Organizational culture and organizational
development: A competing values approach. Research in organizational change and
development, 5, 1–21.
20. Dillman, D. A., Smyth, J. D., Christian, L. M. (2009). Internet, mail, and mixed-mode
surveys: the tailored design method (3rd ed.). Hoboken, N.J.: John Wiley & Sons.
21. Douglas, M., Wildavsky, A. (1982). Risk and culture. Berkeley, CA: University of California
Press.
22. Erez, M., Earley, P. (1993). Culture, self-identity and work. New York, NY: Oxford
University Press.
23. Farrell, J., Hoon, A. (2009). What is Your Company’s Risk Culture?, Directorship.com,
http://www.kpmg.com/MT/en/IssuesAndInsights/ArticlesPublications/Documents/Risk-
culture.pdf.
24. Flamholtz, E., Das, T., Tsui, A., 1985. Toward an integrative framework of organisational
control. Accounting, Organizations and Society, 10(1), 35–50.
25. Fornell, C., Larcker, D. F., 1981. Evaluating Structural Equation Models with Unobservable
Variables and Measurement Error. Journal of Marketing Research 18 (1), 39–50.
26. Gordon, L. A., Loeb, M. P., Tseng, C.-Y. (2009). Enterprise risk management and firm
performance: A contingency perspective. Journal of Accounting and Public Policy, 28, 301–
327.
27. Gordon, L. A., Narayayan, V. K., (1984). Management accounting systems, perceived
environmental uncertainty and organization structure: An empirical investigation.
Accounting, Organizations and Society, 9 (1), 56–69.
28. Govindarajan, V. (1984). Appropriateness of accounting data in performance evaluations: An
empirical examination of environmental uncertainty as an intervening variable. Accounting,
Organizations and Society, 2, 125–135.
29. Hair J. F., Black, W. C., Babin, B. J., Anderson, R. E. (2014). Multivariate data analysis (7th
ed.). Harlow: Pearson Education.
30. Hardesty, D. M., Bearden, W. O. (2004). The use of expert judges in scale development:
Implications for improving face validity of measures of unobservable constructs. Journal of
Business Research, 57(2), 98-107.
31. Haynes, S. N., Richard, D. C. S., Kubany, E. S. (1995). Content validity in psychological
assessment: A functional approach to concepts and methods. Psychological Assessment,
7(3), 238-247.
32. Henri, J.-F. (2006). Organizational culture and performance measurement systems.
Accounting, Organizations and Society, 8, 77–103.
34
33. Kleindorfer, P. R. (2010). Reflections on decision-making under uncertainty. In F. X.
Diebold, N. A. Doherty, R. J. Herring (Eds.), The known, the unknown and the unknowable
in financial risk management. Princeton and Oxford: Princeton University Press.
34. Kline, R. B. (2011). Principles and Practice of Structural Equation Modelling (3rd ed.). New
York, NY: The Guilford Press.
35. Meade, A. W., Lautenschlager, G. J. (2004). A comparison of item response theory and
confirmatory factor analytic methodologies for establishing measurement
equivalence/invariance. Organizational Research Methods, 7, 361–388.
36. Miles, R. E., Snow, C. C. (1978). Organizational strategy, structure and process. New York:
McGraw-Hill.
37. Miller, K. D. (1997). Measurement of Perceived Environmental Uncertainties: Response and
Extension. Purdue CIBER Working Papers. Paper 123.
http://docs.lib.purdue.edu/ciberwp/123
38. Mikes, A. (2009). Risk management and calculative cultures. Management Accounting
Research, 20(1), 18–40, http://dx.doi.org/10.1016/j.mar.2008.10.005.
39. Mikes, A. (2011). From counting risk to making risk count: Boundary-work in risk
management. Accounting, Organizations and Society, 36, 226–245.
40. Netemeyer, R. G., Bearden, W. O., Sharma, S. (2003). Scaling procedures: Issues and
applications. Thousand Oaks, CA: Sage Publications.
41. Nunnally, J. C., Bernstein, I. H. (1994). Psychometric theory (3rd ed.). New York: McGraw-
Hill.
42. Power, M. K. (2007). Organized Uncertainty: Designing a World of Risk Management.
Oxford: Oxford University Press.
43. Power, M. K., Ashby, S., & Palermo, T. (2013). Risk Culture in Financial Organisations: A
Research Report. London: LSE Academic Publishing
44. Sackmann, S. A. (1992). Culture and Subcultures: An Analysis of Organizational
Knowledge. Administrative Science Quarterly, 37(1), 140–161.
45. Schein, E. H. (1999). The Corporate Culture Survival Guide. San Francisco: Jossey-Bass.
46. Shortell, S. M., Zajac, E. J. (1990). Perceptual and archival measures of Miles and Snow’s
strategic types: A comprehensive assessment of reliability and validity. Academy of
Management Journal, 33 (4), 817–832.
47. Simons, R. (1995). Levers of Control: How Managers Use Innovative Control Systems to
Drive Strategic Renewal. Boston: Harvard Business School Press.
48. Simons, R. (2000). Performance Measurement and Control Systems for Implementing
Strategy. Upper Saddle River: Prentice Hall.
49. Slavec, A., Drnovšek, M. (2012). A Perspective on Scale Development in Entrepreneurship
Research. Economic and business Review, 14(1), 39–62.
50. Soin, K., Collier, P. (2013). Risk and risk management in management accounting and
control (Editorial). Management Accounting Research, 24, 82–87.
35
51. Tucker, L., Lewis, C. (1973). A reliability coefficient for maximum likelihood factor
analysis. Psychometrika, 38, 1–10.