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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: mojca.marc@ef.uni-lj.si

Phone: 00386 1 5892 764

Darja Peljhan (corresponding author)

University of Ljubljana, Faculty of Economics

Kardeljeva pl. 17, 1000 Ljubljana, Slovenia

E-mail: darja.peljhan@ef.uni-lj.si

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.

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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

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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)

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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

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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.”

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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

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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

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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).

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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.

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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.

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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

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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

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