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The determinants of holistic work practices: A fuzzy-set analysis Conrad Schulze-Bentrop

(First draft. Comments are welcome!)

Beitrag zum 12. Kolloquium zur Personalökonomie, Universität Wien, 5. und 6. März 2009

Conrad.Schulze-Bentrop@notes.upb.de

Abstract

Advances in production technology, information technology and human capital are believed to foster holistic work practices such as team work, job rotation, decentralization of responsibility and multitasking. However, the same factors have been considered as leading to a larger division of labor, more specialization and tayloristic work practices. I argue that the choice between holistic and tayloristic work practices crucially depends on contingency factors. In particular, it is speculated that the influence of customers and suppliers on the production process essentially influences whether a firm chooses holistic work practices. This idea is explored applying a fuzzy-set qualitative comparative analysis (fsQCA) on data from the NIFA workplace panel, a dataset representative of the German mechanical engineering industry. In contrast to most studies applying configurational comparative methods, my analysis involves a large number of cases (some 500 observations). This reduces some of the problems inherent in QCA such as limited diversity and the need of simplifying assumptions. The fuzzy-set analysis shows that only workplaces combining the influence of customers or suppliers with certain complementarity-enabling characteristics have implemented holistic work practices. For example, the joint presence of a strong influence of customers on the one hand, and computerized systems of production planning and control and NC/CNC-machines on the other, is one sufficient combination of conditions leading to the presence of holistic work practices. The analysis also shows that computerized systems of production planning and control and NC/CNC machines act as functional equivalent to multi-skilled workers; either condition is necessary for the implementation of holistic work practices.

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1 Introduction

Adam Smith detected the productivity gains of workers’ specialization on narrowly defined

tasks in the 18th century. Gains from specialization are attended by a hierarchical

organization of work, where employees specialize either in production or conception tasks.

Since Frederick W. Taylor was the first who promoted the total separation of execution and

conception tasks, this work organization is called tayloristic. Braverman (1974: 239-241)

identified a rapid increase of the proportion of supervising and managing employees in

American firms in the first half of the 20th century as evidence for a growing division of

conception and execution of production. However, in the last decades economists recognize

the productivity gains from the execution of versatile tasks and complementarities through a

joint execution of production and conception tasks. Workers performing multitasking can

benefit from inter-task learning. This has resulted in a shift within firms to a more

decentralized, holistic organization of work involving team work, job rotation, flatter

hierarchies, continuous learning, multi-skilled employees, decentralization of responsibilities

and greater autonomy of workers (Lindbeck & Snower 2000: 354; Littek & Charles 1995: 4).

Firms apparently try to exploit the productivity gains either from specialization or from

multitasking. Thus firms permanently optimize their work organization. The Daimler

corporation, for example, changed from a division of labor in narrowly defined tasks and

short cycle times at the assembly line to larger timing devices as well as a larger quantity of

tasks – and back again (Hawranek 2008). Hence, two equilibria of work organization co-exist.

An entrepreneur faces a trade-off between the returns from specialization of the workers and

the returns from task complementarities.

The organization of work yielding the largest productivity gains might depend on contingency

factors, the combination of changing aspects of the environment and properties of the

production, e.g. the influence of external stakeholders on the production process. If customers

and suppliers are entrenched in the production process, as is the case in the just-in-time

system, a holistic work organization with autonomous workers allows using decentralized

information about customers and suppliers and their demands. By adapting quickly to

changing needs, a holistic work organization can minimize the internal transaction costs

(Kräkel 2007: 117).

In this paper, I explore which combination of conditions leads to multitasking and the

implementation of holistic work practices. Holistic work practices are measured by the extent

of team work on an ordinal scale. Linear methods such as regression analysis are less capable

of capturing contingency factors as these factors are characterized by strong

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complementarities. Therefore a configurational comparative method (Rihoux & Ragin 2009),

in particular the fuzzy-set qualitative comparative analysis (fsQCA) (Ragin 2000; Ragin

2008) is applied in the present analysis.

In section 2, an overview of the “traditional-versus-modern” work organization discussion is

briefly given and I justify why I diverge from the unidirectional “from-tayloristic-to-holistic”

approach. In section 3, two hypotheses are derived. In section 4, initially a regression analysis

is conducted to identify in an explorative manner the conditions relevant for holistic work

practices in addition to those identified by the theory. Then a fsQCA is conducted on

extensive data for properties of the production process, the entrenchment of customers and

suppliers in the production process, the product type and the extent of holistic work practices

in work groups among 500 firms of the German mechanical engineering industry in 1998. I

find that various configurations are linked to a holistic work organization in work groups. The

most important configuration leading to holistic work practices is a high number of

computerized functions of production planning and control (PPC) and a predominant

application of CNC-machines in combination with a high influence of customers on the

production process. Computerized PPC and NC/CNC-machines act as functionals equivalent

to multi-skilled operators. These three features can be understood as complementarity-

enabling characteristics. Implications of these findings are highlighted in the concluding

section 5.

2 Literature Review

It has become a generally accepted argument that many firms are reorganizing their work

organization from a traditional tayloristic towards a “modern” holistic approach (Lindbeck &

Snower 1996; 2000). However, authors investigating the influence of a cost degression in

information technologies on the division of labor usually find a resulting increase in returns

from specialization and thus an increasing productivity of tayloristic firms (Becker & Murphy

1992, Bolton & Dewatripont 1994). Others see especially advances in human capital as

driving firms to decentralization and “new” work practices (Caroli, Greenan & Guellec 2001).

Carstensen (2002a, 2002b, 2002c) shows that two polar types of work organization coexist

and that the tayloristic firm is the more widespread form. She also quantifies the returns from

task complementarities. In particular, marginal returns from multitasking are negative in

tayloristic firms and positive in holistic firms, i.e. multitasking is only valuable in holistic

organized firms. This is in line with research on human resource “bundles”. Innovative work

practices do not have a positive effect on firm performance when implemented in isolation but

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need to be integrated in a consistent bundle or system of work practices (MacDuffie 1995;

Ludewig 2006). Carstensen (2001) also investigates empirically whether systematic

productivity differences exist between the two distinct types of work organization. She shows

that multiskilling is productivity-enhancing but holistic reorganization as a whole does not

increase labor productivity – at least not in the short run. By contrast an increasing empirical

literature postulates that work organizations with innovative high-involvement/ high-

commitment/ high-performance work practices generate productivity-enhancing effects –

being higher than those from work organizations with traditional work practices (Arthur 1994,

MacDuffie 1995, Huselid 1995, Ichniowski, Shaw & Prennushi 1997, Bartel, Ichniowski &

Shaw 2004). Arvanitis (2004), for example, finds positive effects for team-work and

autonomous problem solving of production workers but negative effects of job rotation and an

overall shift of decision competences from managers to employees in Swiss manufacturing.

Overall, the findings from previous studies illustrate that neither a tayloristic nor a holistic

work organization universally will lead to higher productivity. I therefore assume that the

productivity effect crucially depends on the appropriate fit between work organization and

particular basic conditions. The shift from tayloristic jobs to holistic jobs, for example, has

been attributed to need for a more flexible production (Kieser 2006: 100-101). Flexible

production is one basic condition, or contingency factor, that may work in favor of the

productivity-enhancing effect of holistic work practices. As a result, firms with flexible

production are more likely to adopt holistic work practices. So far the literature lacks a

systematic empirical analysis of the factors that lead to the implementation of holistic work

practices and holistic work organization, respectively.

3 Theory

Lindbeck & Snower (2000: 353) identify three main driving forces fostering a holistic work

organization: advances in information technologies, advances in production technologies and

advances in human capital. When these conditions exceed a particular threshold, it pays off to

switch from a tayloristic to holistic work organization (Carstensen 2002c: 6). However, the

same factors have been considered as fostering specialized work and a tayloristic work

organization.

Advanced information and communication technologies: An increasing division of labor that

entails numerous separate tasks causes a greater exchange of information because the

activities of the specialized workers have to be coordinated. New information technologies

reducing these coordination costs foster a more complex division of labor (see: Bolton &

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Dewatripont 1994). But advances in information technologies can also lead to “informational

complementarities” between single tasks and thus bring forward a rather small division of

labor. Borghans & ter Weel (2006) examine how the use of computers affects the optimal

division of labor. They find a promotion of flatter organizational structures, larger autonomy

for workers or workgroups and the application of innovative work practices.

Advanced production technologies: The introduction of new production technologies is

accompanied by the ongoing division of conception and execution tasks. But advanced

production technologies can also foster a holistic organization of work through technological

task complementarities. The introduction of flexible machine tools and programmable

equipment enables firms to execute a larger variety of tasks and thus shorten production

cycles. “Returns to scale” are lowered, whereas “returns to scope” can be increased (Lindbeck

& Snower 2000: 355). Shared product components or jointly used factors lead to

complementarities. Furthermore, flexible production facilities have lower set up costs and

decrease the costs of product change (Hardes 1999: 274).

Advanced human capital: The education of employees is constantly improved resulting in a

steady growth of human capital per worker. This leads to a “capital deepening”, i.e. the single

worker achieves better performance at particular tasks, and to a “capital widening”, i.e. the

worker is more multi-skilled and versatile (Lindbeck & Snower 2000: 355). “Capital

deepening” raises efficiency at specialized work, “capital widening”, on the contrary, at

versatile work.

If the same factors apparently foster specialized as well as versatile work, and since tayloristic

as well as holistic work organization coexist, it is obvious that the type of work organization

and the utilization of a certain productivity gain must depend on other factors. A unilateral

trend in favor of one work organization is not observable despite the three driving forces

described above. After all, both task specialization and task complementarities generate

utility. The work organization, then, depends on contingency factors such as the flexibility of

production. The flexibility of production is highly based on the involvement of the firm in the

value chain. Thus the choice between holistic and tayloristic work organization might

crucially depend on such contingency factors.

I argue that the influence of customers and suppliers on the production process essentially

influences whether a firm chooses holistic work practices. If customers and suppliers are

entrenched in the production process, as is the case in the just-in-time system, a holistic work

organization with autonomous workers allows using decentralized information about

customers and suppliers and their demands. By adapting quickly to changing needs, a holistic

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work organization can minimize the internal transaction costs (Kräkel 2007: 117). Thus, a

close interaction with customers is supposed to support a holistic work organization

(Carstensen 2001: 5). Multitasking goes along with a technologically caused increase in the

flexibility of production and an increase in research cooperation with customers and suppliers.

Hence, production and innovation cycles are shorter in holistic firms (Carstensen 2001: 7, 19).

MacDuffie (1995: 197) states that “innovative” work practices contribute most to productivity

when “they are integrated with manufacturing policies under the ‘organizational logic’ of a

flexible production system.” That means: a flexible (or lean) production system as opposed to

a mass production system.

Hypotheses

A decentralized, holistic organization needs to realize productivity gains from

complementarities between tasks. The enabling of complementarities seems to be a

precondition for a profitable holistic work organization. Different routes to the realization of

complementarities exist. The execution of multiple tasks enables workers to benefit from

complementarities. Hence, multi-skilled workers are a necessary condition for

decentralization of decision-making and multitasking. The exploitation of complementarities

between partial tasks within the production process inevitably involves multi-skilling

(Carstensen 2001: 2). The described advances in information and production technologies

offer two other possibilities to exploit complementarities. First, the use of computerized

information and communication systems is a possibility to realize informational

complementarities between tasks. Second, advanced production technologies can enable

technological task complementarities through the introduction of flexible machine tools and

programmable equipment. As the productivity gains of a tayloristic work organization result

from returns to specialization, the productivity gains of a holistic organization result primarily

from the exploitation of complementarities. Hence, the enabling of informational and

technological complementarities – through computer assistance or multi-skilling – seems to

be a necessary condition for the implementation of holistic work practices. Schneider (2008:

28) presumes that computerized production planning and control partially supersedes the

coordination of multi-skilled workers. This may also be true for NC/CNC-machines. The

function is in each case the enabling of complementarities; only the structure differs (see for a

discussion of functional equivalents: Gresov & Drazin 1997).

.

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Hypothesis 1: Multi-skilled workers, computerized systems of production planning and

control, and NC/CNC-machines all allow exploiting inter-task complementarities. If

neither of these factors is present, a firm will not adopt holistic work practices.

If these factors that enable task complementarities are present, certain contingency factors

may lead to the choice of a holistic work organization. Consistent with this idea, MacDuffie

(1995) posits that holistic work practices contribute most to productivity when combined with

„manufacturing policies under the ‘organizational logic’ of a flexible production system”. In

particular, I argue that a strong influence of customers or suppliers on managerial decisions

may impose flexible production on manufacturing firms. The production process must be

changed frequently. The resulting coordination problems can only be solved if the firm has

implemented computerized systems of production planning and control, NC/CNC-machines

or employed multi-skilled workers. Therefore, if customer or suppliers influence the

production process, and at the same time at least one of the above-mentioned

complementaritiy-enabling characteristics are given, then firms are likely to implement

holistic work practices.

Hypothesis 2: Firms with complementarity-enabling characteristics will adopt holistic work

practices only if their production process is strongly influenced by customers or suppliers.

4 Empirical analysis:

Mixed method research

The empirical analysis conducts mixed method research by applying a configurational

comparative method and a regression method. Bazeley (2004: 141) distinguishes two ways of

“mixing” methods: a side-by-side use of two methods or a full integration in a single analysis

Here, I follow the second way. A logistic regression analyis is applied to identify factors

explaining the choice of holistic work practices. Then, a fuzzy-set QCA is applied in order to

explore possible routes leading to the choice of holistic work practices. The regression

method can uncover effects of single variables but only the fuzzy-set analysis will help to

uncover the complex causal conditions postulated in the hypotheses.

Previous studies usually follow the first way; they apply both methods side by side. Skaaning

(2007) who employs crisp-set QCA, fuzzy-set QCA and OLS-regression states that the

distinct methods are valuable complements. The methods are employed side by side and yield,

of course, diverging results. Katz, vom Hau & Mahoney (2005) title “fuzzy-set analysis

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versus regression analysis” and compare both methods explaining the great reversal in

Spanish America. Their fuzzy-set analysis reaches “substantively important conclusions”

whereas the regression analysis “generates findings that are not meaningful”.

Data and calibration

Data from the NIFA workplace panel, a dataset representative of the German mechanical

engineering industry, is analyzed with the two distinct methods for a closer look at the factors

leading to holistic work practices. The NIFA workplace panel contains exceptionally detailed

information about work organization. The extent of the holistic organization of work is

measured on the basis of detailed information about the work organization in groups. In the

fuzzy-set analysis it is determined in which combination relevant conditions are sufficient for

a holistic work organization at the shop-floor level. The phenomenon to be defined is the type

of work organization. The outcome variable is defined by the number of holistic work

practices in the work groups of production. A high number of holistic work practices indicates

a holistic work organization, a low number indicates a tayloristic work organization. Such

work practices are, for example, the qualification of all group members for all tasks, job

rotation and decentralization of decision-making.

In the NIFA workplace panel the use of advanced information technologies in the firm is

operationalized through the number of computerized functions of PPC. The use of advanced

production technologies is operationalized by the extent of the application of machines with

computerized numerical control (CNC).

The human capital is measured by a polyvalence variable. Polyvalence is “the capability of a

worker to shift from position to position within a given productive organization” (Maurice,

Sellier & Silvestre 1986: 68). The width and depth of human capital on shop floor level is

operationalized by the polyvalence and multi-skilling respectively of the machine operators

(see for the operationalization of the polyvalence variable: Schneider 2008: 23). A high value

of the polyvalence variable indicates a “wide” human capital, i.e. versatile operators. A low

value indicates “deep” human capital, i.e. specialized operators.

The extent of the influence of customers and suppliers on the production process or

managerial decisions is taken directly in the NIFA workplace panel for various divisions of

the firm. For example, in the questionnaire is asked for both customers and suppliers the

influence on planning of operational procedures, organizational structure, quality assurance or

application of processing procedures. Out of it an index is built.

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For the fsQCA, each index is calibrated into a four value fuzzy-set. That means each variable,

both the causal conditions and the outcome, can take the fuzzy values 0, 0.33, 0.67 and 1.

Regression analysis

The regression analysis is conducted to uncover in an explorative manner, in addition to the

above mentioned theoretically specified variables, further relevant conditions influencing the

outcome. The impact of the company size is investigated by means of the number of

employees. The ratio of specialists is defined as the proportion of engineers, craftsmen and

master craftsmen. A high proportion might support a holistic work organization. The plant

may be subject to centralized policies of the firm. Therefore, a dummy variable depicting

whether the plant belongs to a company with several plants is included. The impact of

differences in the production process is controlled via four dummy variables characterizing

the product type: complete systems, single machines/aggregates, modules and single parts.

The ordinal scaled dependent variable requires to estimate an ordered logistic regression

(ordered logit model). Two models are estimated. The first comprises the five theoretically

determined variables, the second includes additionally the control or “explorative” variables.

The results of the estimations are presented in Table 1. All theoretically determined variables

show a positive coefficient. In model 1, the variables polyvalence, computerized PPC and

NC/CNC-machines are statistically significant. In model 2, in addition the influence of

suppliers on managerial decisions becomes significant. Furthermore from the control

variables the variable complete systems has a highly significant negative effect on the

implementation of holistic work practices.

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Table 1 Determinants of holistic work practices: an ordered logistic regression (508 observations)

Independent variables (1) (2) Polyvalence

0.0750** (2.00)

0.1024*** (2.67)

Computerized PPC

0.0397** (2.59)

0.0279* (1.73)

NC/CNC-machines

0.4516** (2.46)

0.3139* (1.68)

Influence of customers

0.0445 (1.02)

0.0227 (0.51)

Influence of suppliers

0.0400 (0.91)

0.0766* (1.69)

Employees

0.0004 (1.56)

Ratio specialists

-0.0276 (1.35)

Company with more than one plant

-0.1262 (0.72)

Complete systems

-0.7776*** (4.24)

Single machines/Aggregates

-0.1687 (0.97)

Modules

0.2941 (1.55)

Single parts

0.2467 (1.23)

LR-Statistic 24.62*** 59.81***

McFaddens Pseudo R² 0.0141 0.0344 Modulus of z-statistics in parentheses Level of significance: *10%; ** 5%; ***1% Calculations with Stata 9.2, logit

The regression results indicate that the variables of interest are related to the outcome.

Furthermore, the plants’ product type also influences the outcome. Single part or module

producers are more likely to implement holistic work practices than complete systems or

aggregate producers. Therefore, the product type is taken into account in the fsQCA. The

productivity of holistic work practices might increase in particular market sectors. For the

fsQCA out of the four product type variables a single fuzzy-set variable is constructed. A

single part manufacturer is coded 1, a producer of modules 0.67, single machines/aggregates

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0.33 and complete systems 0. This calibration reflects the degree of a firm’s vertical

integration within the value chain. Some firms in the dataset produce more than one product

type. In such a case the maximum fuzzy value is chosen for the product type variable.

Methodology: Fuzzy-set QCA

Both the technique and the results of a fuzzy-set qualitative comparative analysis (fsQCA),

introduced by Ragin (2000), completely diverge from conventional statistical methods. It

allows discovering various links between combinations of (interraleted) conditions and an

outcome. Not marginal effects are uncovered as in the regression analysis but the effect of a

condition in combination with others. By contextualizing effects and displaying causal

complexity “it becomes easier to go beyond global and typically vague statements about

effects” (Fiss 2007: 1195).

In the following a short overview of the technique is given. For a fsQCA, all variables are

calibrated as fuzzy values ranging from 0 to 1, and these boundaries constitute the ideal type

values of a variable (Verkuilen, 2005). My data suggests a four value fuzzy-set comprising

the fuzzy values 0, 0.33, 0.67 and 1 (Ragin 2008: 31). That means, for example, that firms

having machine operators performing all or almost all tasks (multitasking) the polyvalence

variable assumes the value 1. For firms having machine operators performing only one or two

tasks (singletasking) the polyvalence variable assumes the value 0. Furthermore, the fuzzy

value 0.67 indicates that machine operators are performing more multitasking than

singletasking (five to six tasks), and a fuzzy value of 0.33 indicates that more singletasking

than multitasking is done (three to four tasks). By defining, for example, that the condition

polyvalence is present when at least more multitasking than singletasking is done and absent

when more singletasking than multitasking is done, it is possible to display each case as a

combination of causal conditions and to construct a truth table out of fuzzy conditions (Ragin,

2008: 124-44).

To illustrate the main idea of fsQCA, imagine the simple example of three causal conditions.

With these, 2³ = 8 different combinations of causal conditions are theoretically possible. This

concept of a case as a particular configuration implements the idea, implicit in configurational

thinking, “that a single difference between two cases may constitute a difference in kind”

(Ragin, 2000: 73). The procedure to model the links between configurations and outcome uses

Boolean logic. Two Boolean symbols are used to illustrate the causal structure. A plus sign

(+) indicates a logical “or”, whereas a multiplication sign (*) symbolizes a logical “and”. Let

P denote an outcome, and A, B, and C three causal conditions. Then the values of the

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characteristics are indicated by capital and lower case letters. “A” capitalized, for example,

stands for the presence of polyvalence (i.e. multitasking), and “a”, for the absence of

polyvalence (i.e. singletasking). If the cases under examination, for example, reveal three

different sufficient combinations of causal conditions leading to the outcome, this can be

displayed in the following manner: A*b*c + A*B*c + A*B*C → P. Hence, three causal

configurations, indicating equifinality, have been uncovered by the analysis. Note that eight

combinations would have been theoretically possible. Hence, the data show “limited

diversity” with five unobserved combinations as logical remainders. The equation can be

simplified according to a reduction rule: “If two Boolean expressions differ in only one causal

condition yet produce the same outcome, then the causal condition that distinguishes the two

expressions can be considered irrelevant and can be removed to create a simpler, combined

expression” (Ragin, 1987: 93). In the example, A*b*c combined with A*B*c reduces to A*c.

Likewise, A*B*c combined with A*B*C reduces to A*B. This procedure is automatized in

the fsQCA software by the truth table algorithm.

When conditions are joined by a logical “and”, the fuzzy value for a case is the minimum

fuzzy value of the joined conditions. Assume, for example, that the fuzzy value of condition

A is 0.67; that of condition B is 1. Then the fuzzy value of the combination A*B is 0.67, i.e.

the intersection of the two conditions. The fuzzy value of this case in a*B is 0.33 because this

is the complementary fuzzy value of 0.67. In our example, only in three out of eight

configurations do the observed cases show values above 0.5. Likewise, when conditions are

joined by a logical “or”, the value for each case is the maximum fuzzy value of the joined

conditions (Ragin 2000: 173-76).

Before simplifying the logical expressions through the reduction rule, it is determined whether

any of the three observed combinations of conditions can be regarded as sufficient to lead to

the outcome P. To do this, the fuzzy values are taken into account. A condition or a

combination of causal conditions is defined as sufficient if the instances of a causal condition

(or a combination of causal conditions) constitute a subset of the instances of an outcome.

Hence, a combination of causal conditions can be considered sufficient to lead to the outcome

if the fuzzy-set values of the causal condition X are less than or equal to the fuzzy-set values

of the outcome Y across all observations (Ragin 2000: 235). Figure 1 illustrates this point for

a continuous fuzzy set (not a four value fuzzy set) by depicting the causal condition along the

X-coordinate and the outcome along the Y-coordinate. All observations are located above the

line.

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Figure 1: Sufficient condition for an outcome

Conversely, a condition is necessary if the instances of an outcome constitute a subset of

instances of a causal condition (Ragin, 2006: 297). In other words, a causal condition X can

be called necessary if the fuzzy-set values of the outcome Y are always lower than the fuzzy-

set values of the causal condition X. In Figure 2, the observations are all located below the

line.

Figure 2: Necessary condition for an outcome

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As explained above a case is described as a combination of causal conditions. Assigning each

case, in this analysis each firm, to an ideal type allows detecting the quantity of firms

exhibiting each theoretically possible ideal type combination. For example, complementarity

enabling features in combination with a low customer or supplier influence or a high

integration into the value chain should in no case lead to holistic work practices. Instead

complementarity enabling features should be combined with various combinations of the

three mentioned conditions.

FsQCA captures the concept of conjunctural causation: the idea that combinations of causal

conditions, rather than one condition alone, lead to an outcome. Since I argue that it is the

appropriate fit between complementarity enabling features and the three conditions that

triggers the efficient implementation of holistic work organizations the method is suitable for

the analysis. Furthermore, it is obvious that various combinations will be sufficient to explain

the implementation of holistic work practices in groups. FsQCA is able to uncover this

equifinality.

Fuzzy-set analyses are predominantly applied with country-level data and thus a smaller

number of cases (Jackson 2005; Kogut & Ragin 2006; Kvist 2007; Pajunen 2008). In this

article, in contrast, the fuzzy-set analysis is applied with firm-level data and a larger number

of cases (see for a discussion of the applicability on firm-level data: Fiss 2007; Kogut,

MacDuffie & Ragin 2004; Braumoeller 2003). Only few studies provide examples with larger

datasets, e.g. in the fields of strategic management research with 2.841 cases (Greckhamer,

Misangyi, Elms & Lacey 2008) and of educational research with 17.000 cases (Cooper 2005).

Especially the problem of limited diversity can be essentially reduced through a larger number

of cases (see for examples and a discussion of limited diversity in large datasets: Ragin 2008:

158). However, the application on firm-level can contribute to management research: “Rather

than estimating the relative importance of different strategies across all cases, set-theoretic

methods allow us to better examine which strategies make sense for which kinds of firm”

(Fiss 2007: 1195).

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Analysis of necessary conditions (Hypothesis 1):

Initially it is tested whether any of the conditions can be considered as necessary condition for

the outcome. A causal condition is called “necessary” if the instances of an outcome

constitute a subset of instances of a causal condition (Ragin, 2006: 297). This implies that, for

each case, the set membership value of the outcome Y is lower than the set membership value

of the causal condition X. The data usually does not fully meet that rule. In probabilistic

versions of fsQCA, therefore, consistency measures are used to quantify the degree to which

the observations conform to the strict rule. In this analysis the consistency score suggested by

Ragin (2006) is adopted.i It measures the extent to which cases do not meet the rule, and it

values large misses more strongly than short misses. A consistency score of 1 indicates that

the combination of causal conditions fulfills the criterion across all the cases. The more cases

fail to meet the consistency criterion and the larger the distance from meeting the criterion,

the further the consistency score will fall below 1. Conventionally, a condition, or a

combination of conditions, is called “necessary” or “almost always necessary” if the

consistency score exceeds the threshold of 0.9. No one of the six examined causal conditions

or their negation exceeds this threshold (see Table 2). Hence, no condition can be regarded as

necessary to account for holistic work practices in work groups. The negation of the supplier

variable, i.e. the absence of influence of suppliers on managerial decisions assumes the

highest consistency value of 0.85. That is surprising as the suppliers influence on the

production process has a significantly positive impact on holistic work practices. This oddity

can be seen as a first indication for the complex interactions of causal conditions, i.e. causal

complexity. The second highest value of 0.82 assumes the polyvalence variable.

Table 2: Analysis of necessary conditions Condition Consistency Coverage

POLY 0.82 0.79 CNC 0.76 0.87 PPC 0.71 0.83 CUSTOM 0.50 0.83 SUPPLIER 0.27 0.87 poly 0.43 0.89 ppc 0.53 0.81 cnc 0.58 0.89 custom 0.68 0.75 supplier 0.85 0.71 POLY+PPC 0.93 0.76 POLY+CNC 0.90 0.78 +: presence of either condition or of both conditions

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Multi-skilled workers are productive due to informational task complementarities. This

function can also be generated through computerized systems of PPC. A third possibility to

generate productivity are technological task complementarities induced by NC/CNC-

machines. The function of all the three structures is similar. Thus they may act as functional

equivalents. Functional equivalents can be explored by assessing “substitutable necessary

conditions” in fsQCA (Ragin, 2006). Technically, this implies a test whether two conditions

joined by a logical “or” form a necessary condition for the outcome. Polyvalence and

NC/CNC machines, for example, are functionally equivalent necessary conditions if the

expression (POLY + CNC) is a consistent superset of the outcome. Table 1 contains the

results of substitutable necessary conditions for two expressions: (POLY + PPC) and (POLY

+ CNC). These expressions achieve a consistency score of 0.93 and 0.90, respectively. This

indicates that both informational task complementarities and technological task

complementarities are necessary for holistic work practices. In other words, multi-skilled

workers act as functional equivalent to the two features enabling informational and

technological task complementarities, computerized systems of PPC and NC/CNC-machines.

These necessities are non-trivial and relevant, respectively. A necessary condition would be

theoretically and empirically trivial or irrelevant if it occurs in all cases independent from the

presence or absence of the outcome. For example, a workers will to execute multiple tasks

would be a trivially, irrelevant necessary condition for the implementation of holistic work

practices. The measure to evaluate the relevance of a necessary condition is the coverage

rate.ii A trivially necessary condition would yield a coverage rate near 0 (Ragin 2006: 302-

303; Schneider & Wagemann 2007: 214-215). The coverage rates of (POLY + PPC) and

(POLY + CNC) are 0.76 and 0.78, respectively. This implies that both substitutable necessary

conditions are non-trivial and relevant for the implementation of holistic work practices.

The findings support hypothesis 2. Complementarities – informational and technological – are

necessary for the implementation of holistic work practices in groups.

17

Analysis of sufficient conditions (Hypothesis 2):

As explained above, a causal condition can be considered sufficient to lead to the outcome if

the fuzzy-set values of the causal condition X are less than or equal to the fuzzy-set values of

the outcome Y (Ragin 2000: 235). The same applies for a combination of conditions such as

X*Z. A consistency measure is invoked that assess how consistent each combination of

condition conforms to this rule. iii Combinations of conditions exceeding an appropriate cut-

off consistency score are categorized as sufficient. The outcome is therefore assigned a value

of 1. Conversely, combinations with a consistency below or at the cut-off value are not

sufficient, and the outcome is assigned a value of 0.

The consistencies of the configurations range between 0.93 and 1. Two reasons account for

the high consistency values: The large number of cases and the four value fuzzy-set. Ragin

offers two rules of thumb to set an appropriate cut-off value of consistency. He suggests a

consistency of at least 0.85, and argues that – when combinations are ordered by their

consistency score – a substantial gap between the consistency values provides an additional

clue to differentiating consistent combinations from inconsistent ones (Ragin 2005: 10-11).

The largest gap, albeit a rather small one, lies between the values of the 19th and 20th

combination. Furthermore, drawing the line between these rows implies the best theoretical

plausibility. Hence, the cut-off value is set at 0.96.

57 out of 26 = 64 logical possible combinations of conditions are empirically observable. The

number of cases following a particular combination ranges from 1 to 60. To account for the

importance of a combination, a frequency cut-off is set allowing only those combinations

entry into the reduction process exceeding a minimum number of cases. The high number of

cases allows this freedom and makes this procedure reasonable. By setting a frequency cutoff

of at least 3 cases per combination, 29 combinations still remain. The phenomenon of limited

diversity occurs but for a configurational comparative analysis with six conditions it is

negligible. In Table 3, the truth table, the observed combinations are presented.

18

Table 3: Truth table

poly  ppc  cnc  custom  supplier  prod  holi  number  consist 1 0  1 1  1 1 0 1 6  1 2 1  1 1  0 1 0 1 4  0.9897 3 1  1 1  1 1 0 1 19  0.9859 4 1  1 0  1 0 0 1 4  0.9826 5 1  0 1  1 1 1 1 4  0.9812 6 1  1 1  1 0 0 1 23  0.9808 7 1  1 1  1 1 1 1 17  0.9801 8 0  1 1  1 0 0 1 11  0.9783 9 1  1 0  1 0 1 1 6  0.9780 

10 0  1 1  1 0 1 1 5  0.9777 11 0  1 1  1 1 1 1 5  0.9747 12 1  1 0  0 0 1 1 9  0.9747 13 1  0 1  0 0 1 1 24  0.9725 14 1  0 0  1 0 1 1 6  0.9724 15 1  0 1  1 1 0 1 4  0.9714 16 1  0 0  0 0 1 1 19  0.9710 17 1  0 1  1 0 0 1 8  0.9661 18 1  1 1  1 0 1 1 28  0.9646 19 1  0 0  1 0 0 1 8  0.9645 20 1  1 0  0 0 0 0 24  0.9597 21 0  1 1  0 0 0 0 29  0.9570 22 0  1 0  0 0 1 0 10  0.9551 23 1  1 1  0 0 1 0 49  0.9537 24 1  0 1  1 0 1 0 10  0.9530 25 0  0 1  0 0 0 0 6  0.9466 26 0  1 1  0 0 1 0 23  0.9455 27 1  1 1  0 0 0 0 60  0.9391 28 1  0 1  0 0 0 0 43  0.9373 29 1  0 0  0 0 0 0 25  0.9269 

number: frequency of observations; consist: consistency score

Most cases follow the configuration displayed in line 26. The combination consists of the

presence of all complementarity-enabling characteristics and the absence of all contingency

factors and does not achieve the consistency threshold, but, rather, assume a comparatively

low consistency. This provides an indication for the truth of Hypothesis 2. Even when

polyvalence, computerized PPC, and NC/CNC-machines are present that does not suffice for

the implementation of holistic work practices. The second largest cluster comprises the 49

cases represented by the configuration in line 23. These cases combine the presence of all

complementarity-enabling conditions and the absence of customer and supplier influence on

managerial decisions with the presence of the product condition, (i.e. the firm is single

product or module producer). That leads to the assumption that the impact of the market

19

sector on the outcome might by complex. In the presence of all complementarity-enabling

conditions and the absence of a high involvement of customers and suppliers the market

sector of the firm does not matter.

Nearly all combinations exceeding the consistency threshold combine complementarity-

enabling characteristics with the presence of supplier or customer influence on managerial

decisions. Exceptions are the configurations in line 12, 13 and 16. They show the presence of

one or two complementarity-enabling characteristics and the absence of supplier or customer

influence on managerial decisions combined with the presence of the product variable.

Hypothesis 1 is further tested by applying the truth table algorithm. The complex solution is

chosen. Due to the large number of cases no simplifying assumptions have to be made to

achieve a short solution. The minimization process reduces the 29 combinations to the 6

combinations presented in Table 4.

Table 4: Sufficient combinations of conditions for holistic work practices

frequency cutoff: 4

consistency cutoff: 0.96

raw coverage

unique coverage

consistency

complex solution

1 PPC*CNC* CUSTOM + 0.40 0.05 0.96 2 POLY*cnc *supplier *PROD + 0.35 0.04 0.96 3 POLY*CUSTOM*supplier*prod + 0.28 0.03 0.95 4 POLY*ppc *custom*supplier*PROD + 0.25 0.02 0.95 5 POLY*CNC*CUSTOM*SUPPLIER + 0.21 0.01 0.96 6 POLY*PPC*CNC *SUPPLIER *prod 0.14 0.01 0.98

solution coverage: 0.61 solution consistency: 0.93

Calculation with the fsQCA 2.0 software (www.fsqca.com)

The six configurations differ concerning their empirical importance. Empirical importance

refers to the degree to which the outcome is covered by a particular combination of

conditions. Two measures, the raw coverage and the unique coverage assess the empirical

importance. Raw coverage refers to the size of the overlap between configuration set and

outcome set relative to the size of the outcome set (Ragin, 2006: 301).iv The unique coverage

is derived because single cases are often explained by more than one configuration. The

unique coverage controls for overlapping explanations by partitioning the raw coverage. It is

calculated for a certain configuration by subtracting the joint raw coverage of the remaining

configurations from the joint raw coverage of all configurations including the one of interest.

20

The raw coverage for the configurations ranges from 0.40 to 0.14. The unique coverage

ranges from 0.05 to 0.01. Hence, each configuration provides a unique contribution to the

explanation of holistic work practices.

In the first, most relevant configuration, computerized PPC and NC/CNC-machines is

combined with a high influence of customers on the production process. Hence, these

conditions lead to the presence of holistic work practices in work groups independent of the

remaining three conditions. In other words, this configuration represents firms that have

adopted advanced information and production technologies and that have a production

process strongly influenced by the customers.

The next four, less relevant configurations do not show the combined presence of

computerized PPC and NC/CNC-machines but they all employ multi-skilled operators.

Configurations 2 and 4 are special in three aspects: First, they represent firms with a low

influence of suppliers on the production process. Second, they combine polyvalence of the

operators with the absence of NC/CNC-machines (configuration 2) and the absence of

computerized PPC (configuration 4). Third, the firms represented by these configurations are

solely single part or module producers.

The last, least relevant configuration comprises all three complementarity-enabling features.

This combination indicates firms that produce complete systems or aggregates and are highly

reliant on the suppliers.

Overall, the findings provide conditional support for Hypothesis 2. Basically, firms with

complementarity-enabling characteristics adopt holistic work practices if their production

process is strongly influenced by customers or suppliers. Furthermore, those firms adopt

holistic work practices that follow the two described “special” paths.

5 Conclusions

The analysis delivers strong support that complementarity-enabling characteristics are

necessary but not sufficient for the presence of a holistic work organization in work groups.

However, the findings are limited to work groups of the German mechanical engineering

industry.

The regression analysis alone would provide evidence for the “from-tayloristic-to-holistic”

argument. The fuzzy-set analysis supports the argument that advances in information

technology, communication technology or human capital enable a holistic work organization.

However, the choice of work organization, i.e. which one outperforms the other, depends on

contingency factors such as the need for a flexible production. Thus, rather than technological

21

progress, a changing involvement of customers or suppliers in the production process

determines a trend. In future studies, contingency factors deserve further attention.

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i Ragin (2006) defines the consistency score of a necessary condition X (or combination of conditions)

as:

=

=

≤ =∑

∑1

1

min( , )( )

I

i ii

i i I

ii

X YConsistency Y X

Y. Y denotes the outcome, and I the number of cases.

ii The coverage of a necessary condition X (or combination of conditions) is defined as:

=

=

≤ =∑

∑1

1

min( , )( )

I

i ii

i i I

ii

X YCoverage Y X

X

. Y denotes the outcome, and I the number of cases.

iii The consistency score of a sufficient condition X (or combination of conditions) is defined as:

∑

∑

=

==≤ I

ii

I

iii

ii

X

YXYXyConsistenc

1

1),min(

)(

.

Y denotes the outcome, and I the number of cases.

iv The coverage of a sufficient causal condition X (or combination of conditions) is defined as:

∑

∑

=

==≤ I

ii

I

iii

ii

Y

YXYXCoverage

1

1),min(

)(

.

Y denotes the outcome, and I the number of cases.