Operationalizing routines:
Linking antecedents, characteristics, and performance outcomes of routines
Markus C. Becker
University of Southern Denmark
Department of Marketing
Campusvej 55
DK-5230 Odense M
Denmark
mab@sam.sdu.dk
Draft of 18 April 2003
1
Operationalizing routines:
Linking antecedents, characteristics, and performance outcomes of routines
Markus C. Becker
Abstract
While much empirical research on routines has been carried out since Nelson and Winter (1982),
progress on the empirical front is slow. One possible reason is that the operationalization of routines
is still far from perfect. There are two problems in connection with operationalizing routines:
determining which are the important characteristics of routines that an operationalization should
capture, and making sure that these are captured. Furthermore, to the extent that different
operationalizations are used, empirical research findings will not build upon each other. The
objective of the article is to (i) review the methods for operationalizing organizational routines that
have been employed in empirical research on routines, and (ii) to identify the most important
characteristics of routines and develop hypotheses that link those to antecedents and outcomes of
routines.
2
Nelson and Winter (1982) have proposed the concept of an organizational routine1 as unit of
analysis for investigating organizational and economic change. While this idea has been inspiring to
many and has sparked much research, many open questions regarding organizational routines still
prevail. One question that is not entirely clear yet is how to operationalize organizational routines.
Reviewing the empirical literature on organizational routines (Becker, 2003b forthcoming), two
points regarding the operationalization of organizational routines become clear. First, there is not
yet a an agreed- upon and widely used operationalization of organizational routines. Different
authors operationalize organizational routines differently. The implication is that empirical results
are difficult to compare as they might refer to slightly different things. Furthermore, the
operationalizations of routines chosen by various researchers seem to be implicit, rather than
explicit. This applies mainly to some of the research employing qualitative research methods. The
implication is that such an implicit operationalization precludes systematically linking the empirical
results with variables that are of theoretical interest. For instance, it will be difficult to link different
kinds of routines to different kinds of outcomes. The objective of this paper is to tackle these
problems by (i) reviewing the methods for operationalizing organizational routines that have been
employed in empirical research on routines, and (ii) identifying the most important characteristics
of routines and develop hypotheses that link those to antecedents and outcomes of routines.
The paper is structured as follows: Section one presents the definition of routine applied here,
section two a literature review of methods of operationalizing routines, and section three develops
hypothesis on the antecedents and outcomes of recurrent interaction patterns.
1.
Routines defined
Before we begin discuss the operationalization of routines, it is appropriate to present the definition
of routines applied here. In the economics and business literatures, the notion of an ‘organizational
routine’ has come to stand for regularity in economic activity. The concept of ‘organizational
routine’ is used to capture repetitive, stable activity (Nelson and Winter 1982; Winter 1990;
Hodgson 1993; Langlois 1992; Nelson 1994; Cohen and Bacdayan 1994; Pentland and Rueter
1
Building on earlier predecessors (for more detail on the history of the concept of organizational routines see Becker
2003a forthcoming).
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1994; Langlois and Robertson 1995; Rumelt 1995; Cohen et al 1996; Pentland et al 1996; Egidi and
Narduzzo 1997; Oliver 1997; Coombs and Metcalfe 1998; Amit and Belcourt 1999; Knott and
McKelvey 1999; Costello 2000; Feldman and Rafaeli 2002; Karim and Mitchell 2000; Betsch et al.
2001). Although regularities in economic activity thus have found a widely used label in the
economics and business literature, it is not perfectly clear, however, what precisely that label refers
to. For some authors, the term refers to behavior (e.g. Winter, 1964; Winter, 1986; Gersick and
Hackman, 1990; Winter, 1990; Dosi, Teece and Winter, 1992; Coombs and Metcalfe, 1998; Dosi,
Nelson and Winter, 2000; Feldman, 2000).2 The term ‘organizational routines’ then means
‘recurrent interaction patterns’. For other authors, however, the term refers to some cognitive
representation such as rules (Simon 1947; Cohen 1991; Egidi 1992; Egidi 1996; cf. March and
Simon 1958; Cyert and March 1963). Behavioral patterns and cognitive representations are clearly
different. Therefore it is important to distinguish these different meanings associated with the term
‘routines’ (Cohen et al 1996, p. 672). In the present paper, we focus first on behavior, for the simple
reason that regularities in economic activity show as regularities in behavior. This does not,
however, mean we do not consider routines-as-representations unimportant. To the contrary.
Independent of where to focus our attention in the present article is the question what the term
‘routines’ should refer to: behavior (recurrent interaction patterns) or the representation of behavior
(some kind of rule, for instance). Much recent literature associates the term ‘routine’ with behavior
(Coombs and Metcalfe, 1998; Dyer and Singh, 1998; Amit and Belcourt, 1999; Bessant, Caffyn and
Gallagher, 2000; Dosi, Nelson and Winter, 2000; Feldman, 2000; Karim and Mitchell, 2000;
Edmondson, Bohmer and Pisano, 2001; Jones and Craven, 2001; Feldman and Rafaeli, 2002;
Jarzabkowski and Wilson, 2002). While it is not the prime objective of this paper to argue for which
of the two alternatives the term should refer to, we would like to point to the arguments provided by
Hodgson (2002 forthcoming) and Knudsen (2002 forthcoming) for why it is more appropriate to
apply the term ‘organizational routines’ to the level of representation. The present attempt to
operationalize recurrent interaction patterns is intended to provide a basis on which to subsequently
tackle identifying the relationship between routines-as-expressions and routines-as-rules, and
identify the causal mechanism(s) that bring(s) recurrent interaction patterns about. In order to be
consistent with Hodgson (2002 forthcoming) and Knudsen (2002 forthcoming), we will use the
more precise term ‘recurrent interaction pattern’ instead of the term ‘organizational routines’ in
2
In the most recent literature, the conceptualization of routines as behaviour patterns come through as dominant.
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order to be clear that we are concerned with the behavioral level on this paper (which seems a
reasonable starting point for operationalization).3
2.
Methods for operationalizing recurrent interaction patterns – A review of the
literature
How can we operationalize recurrent interaction4 patterns? Empirical studies have employed four
different methods: identifying repeated sequences, identifying fixed condition-action rules,
identifying task variety and analyzability, and identifying the content, process, and sequence of
recurrent interaction patterns.
a)
Identify repeated sequences (Cohen and Bacdayan)
The first method to identify recurrent interaction patterns is to identify repeated sequences of
patterned behaviors. This method is applied in Cohen and Bacdayan (1994). The idea is simply that
routines are repeated instances of the same behaviour (recurrent interaction patterns). While such an
operationalization is based on the perhaps most important characteristic of recurrent interaction
patterns, namely their recurrence, the problem is to decide what precisely constitutes the ‘same’
behavior. How exactly does one recognize identity – or for that purpose, close similarity – of two
instances of behavior? Nelson and Winter (1982, p. 135) write that ‘[j]ust what "resemble" means
here is an important and complex question’, and Winter is of the opinion that we are up here against
‘serious conceptual and measurement challenges’ (Winter 1990, p. 279).5 For an operationalization
based on repetition, we also need a measure of similarity (or conversely, variation) of repeated
instances of a recurrent interaction pattern, identifying the degree to which two instances of a
behaviour vary from each other (more on that below).
Where the term ‘organizational routine’ still appears, we ask the reader to bear in mind that it is understood strictly as
‘recurrent interaction pattern’.
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Routines are social, not individual, phenomena. They involve multiple actors. Repetitive activity of an individual actor
is a habit (Hodgson 1993). There has been confusion about the individual or social character of routines. Since the
recent article by Dosi, Nelson and Winter (2000), however, such confusion should not persist any more. Throughout
this article, we mean organizational routines when we speak of ‘routines’, and the activity we talk about is ‘interaction’,
i.e., between at least two agents.
5
Unfortunately, the problem is largely unsolved in psychology as well (Reber 1993).
3
5
b)
Identify fixed condition-action rules (Egidi and Narduzzo)
Building on March and Simon (1993), the second way to identify recurrent interaction patterns is to
define routinised behaviors as behaviors based on fixed condition-action rules, and to test for the
existence of systems of rules which trigger appropriate actions in response to given circumstances
(Egidi and Narduzzo 1997). This method differs from the first one in that it is not limited to the
behavioral level, but takes the link between the cognitive and the behavioral level into account. The
problem of assessing similarity is pushed from the behavioral to the cognitive level. In terms of
operationalization, one then has to compare the similarity of the rules followed by the actors, not
the behavior induced by the rules (Egidi and Narduzzo 1997).
c)
Identify task variety and analyzability (Perrow)
In his work on the influence of technology6 on organization structure, Perrow (1967) distinguishes
four different cases on the basis of two dimensions: the number of exceptions to be handled in
carrying out a task, and the analyzability of the search procedure required for carrying out the task.
The case of few exceptions and analyzable search procedure is termed the ‘routine’ case.
Accordingly, task variety and analyzability of search are proposed as measures of routinization.
Note, however, that Perrow focuses on the degree of routinization of organizational units that carry
out tasks, not on the routinization of the execution of the tasks themselves. The focus and objective
of the operationalization therefore differ slightly from ours.
d)
Identify content, process, and sequence of recurrent interaction patterns (Pentland and
collaborators)
Pentland and collaborators have criticized Perrow’s analysis and have used that critique as a starting
point for developing an alternative method: Task variety and analyzability, in their view, are ‘more
strongly connected to the content of the work than to the sequence of actions, or process with which
the work is accomplished’ (Pentland et al 1996, p. 3; emphasis in original; Pentland 2003a
forthcoming; 2003b forthcoming). They criticize these measures for being static (whereas recurrent
interaction patterns are not): ‘By failing to distinguish clearly between the content of what is
Remember that Perrow defines ‘technology’ as ‘the work done on in organizations’ or ‘the work done on raw
materials’ (Perrow 1967, p. 194).
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produced and the process of how it is produced, measures based on Perrow’s (1967) framework
appear to be inadequate to capture the complexity of work’ (Pentland et al 1996, 3). Pentland et al
also perceive a possible construct validity problem with the measurement of ‘task variety’ using
conventional, survey based methods (Pentland et al 1996). Accordingly, Pentland and colleagues
develop a new method for assessing the routineness of tasks instead of the routinisation of task
units, distinguishing work content (what is produced) from work process (how it is produced). That
also enables the direct examination of the structure of work processes (Pentland et al 1996). Instead
of breaking down routinisation into task variety and task analyzability, the routineness of processes
is broken down into lexical variety and sequential variety (cf. also Pentland 2003a forthcoming;
2003b forthcoming).
Lexical variety is the variety of the tasks a team is concerned with, and the variety of steps
employed in order to achieve those tasks. Lexical variety is measured by asking ‘How large is the
lexicon of moves?’ Thus, to measure lexical variety means to analyze the actual occurrence of the
lexical items, i.e. different moves, in question: (i) How many kinds of different moves were
observed? (ii) How far does the distribution of different moves deviate from the uniform
distribution? (Pentland et al 1996; Pentland 2003a forthcoming; 2003b forthcoming).
Once the ‘moves’ of the different processes for achieving different tasks are identified, sequential
variety can be measured. This is the variety of different combinations of those moves to achieve the
same goal. In other words, sequential variety accounts for the many ways in which actions may be
sequenced to create variations: for a given lexicon of moves, is the sequence always the same, or
can it vary depending on the circumstance? The analytical distinction between content and process
is important. Not least, sequential variety provides a rigorous methodological handle on how
informal work practices differ from formal guidelines, and calls attention to the innumerable ways
in which work flow varies from instance to instance, even within supposedly highly routinised task
units (Pentland et al 1996; Pentland 2003a forthcoming; 2003b forthcoming).
The Pentland method has four stages: identifying content, process, sequence, and combination
constraints.
Content. The starting point is to describe the content of action, the actor(s), goals, artifacts, and
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context in order to form a rudimentary understanding of how which processes are taking place
(Pentland et al. 1996).
Process: analyzing moves. The next step is to describe the way these processes are accomplished, to
break the processes down into their moves, and to taxonomise these moves in a hierarchy (Pentland
et al 1996). In developing such a hierarchy, two different but complementary principles are applied:
process specialization and process decomposition (Malone et al 1993). Whereas decomposition
means breaking down processes in their moves by uncovering ‘steps in’ relations, specialization
means representing alternative ways of achieving a process (Malone et al 1993); this can be done
for example by looking for ‘ways to’ relations (Pentland et al 1996).
Sequence: analyzing combinations (‘sequential variety’). The analytical power of the Pentland
method lies in its ability to analyze sequence. This is achieved by separately analyzing sequences of
moves and constraints to the combination of moves. The objective in analyzing sequence is to
represent possible combinations (Pentland 1994).
Combination principles: analyzing combination constraints. Identifying the relevant sources of
constraints on the lexicon and the ways in which its elements can be recombined (Pentland 1995,
550) is an economical way to represent possible combinations. Two types of combination
constraints can be distinguished: (i) constraints that emerge because of different kinds of
interdependencies between actions, and (ii) constraints arising from institutions ‘external’ to the
action (Pentland 1995). The latter kind of constraints could for instance be social, institutional,
technological, co-ordination, cultural, and cognitive structures.
Pentland (1994) identifies three kinds of dependencies between different parts of processes:
(a) Producer-consumer dependencies, where one activity produces a resource that is consumed by
another activity (Thompson’s (1967) ‘sequential interdependency’). This involves three aspects that
require co-ordination: temporal or sequential dependence (step A must be completed before step B),
usability dependence (the resource produced must be usable by the subsequent activity), and
transport dependence (the output of the producer must be physically available for the consumer).
(b) Shared input (Thompson’s (1967) ‘pooled interdependency’): two activities share the same input
(e.g. two meals must be cooked using one stove).
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(c) Shared output: two activities share the same output (e.g. when teams must interact to make sure
that their work is compatible)7.
From a methodological perspective, it might be difficult to identify constraints, because as long as
everything is running smoothly constraints are not always visible (Pentland 1994; Pentland et al
1996). It will therefore be beneficial to focus on cases where coordination is under extreme strain
and at its breaking point (Eisenhardt 1989; Szulanski 1996). Also, eliciting dependencies from
informants through purely interview-based techniques may be difficult so that additional methods
like observation are required (Pentland et al 1996).
Methods of operationalizing recurrent interaction patterns - Conclusions
The four methods identified in the literature review present different kinds of answers to the
question how we can operationalize recurrent interaction patterns. They fall into three groups. (1)
Identifying repeated sequences and the Pentland method of identifying content, process, and
sequence of recurrent interaction patterns both are ways to identify recurrent interaction patterns by
the characteristic ‘repetition’. The Pentland method also enables a detailed description of the
building blocks of recurrent interaction patterns, and thus a more fine-grained description of
recurrent interaction patterns. The other two methods, however, point beyond mere description. (2)
Identifying recurrent interaction patterns by way of identifying fixed condition-action rules entails
engaging in identifying the causal mechanism that brings about recurrent interaction patterns as
behavioural expressions of cognitive representations. (3) Identifying recurrent interaction patterns
by identifying task variety and task analysability entails linking characteristics of the task to be
executed to the ‘shape’ that recurrent interaction patterns take (described along some dimensions,
defined below). Much has been written about the link between cognitive representations (for
instance, rules) and behaviour. The present paper will not engage in that discussion. Rather, we will
pursue the avenue opened by the third group. The remainder of this paper has the objective to
propose a method that allows to identify different kinds of recurrent interaction patterns (i.e.,
develop a taxonomy of recurrent interaction patterns) and to systematically link the different kinds
of recurrent interaction patterns to different outcomes.
Thompson (1967) distinguishes an additional type of combination constraint – ‘reciprocal interdependency’ –which
occurs when, for example, two departments are in such a relationship that the outputs of A become inputs of B and the
outputs of B become the inputs of A.
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3.
Linking antecedents, characteristics, and performance outcomes of recurrent
interaction patterns
The operationalizations of recurrent interaction patterns available in the literature, as described
above, are a good starting point (in particular Pentland’s method). In order to develop them further,
it might be helpful to take into account the larger picture. Doing so will raise our awareness of what
it is that we would like the operationalization of recurrent interaction patterns to be able to do. Most
importantly, it would be beneficial if recurrent interaction patterns could be linked to (i) their
antecedents and (ii) to outcomes. In what follows, we contribute to the discussion on the
operationalization of recurrent interaction patterns but developing a first set of hypotheses on the
antecedents and outcomes of recurrent interaction patterns. This set of hypotheses serves a double
purpose. It can inform and guide further endeavours of operationalizing recurrent interaction
patterns by indicating what kind of items, for instance, should be included in a routines scale so that
this scale can be used in empirical research linking recurrent interaction patterns to antecedents and
outcomes. At the same time, the first set of hypotheses developed in the remainder of the paper can
also serve as a basis for discussing antecedents and outcomes of recurrent interaction patterns and
developing these further.
Antecedents
Characteristics
Outcomes
of recurrent
interaction
patterns
of recurrent
interaction
patterns
of recurrent
interaction
patterns
In the remainder of the paper, a section each is devoted to describing each of the elements and the
links between them: describing antecedents of recurrent
characteristics of recurrent
interaction patterns, describing
interaction patterns, linking antecedents of recurrent
interaction
patterns to characteristics of recurrent interaction patterns, describing performance outcomes of
recurrent
interaction patterns, and linking characteristics of recurrent
performance outcomes.
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interaction patterns to
3.1
Describing antecedents of recurrent interaction patterns
As Pentland (2003a forthcoming) has reminded us, work processes in general have been rather
overlooked. In analyzing what firms do (more precisely, their employees), it seems a good starting
point to relate this to what they want to do. Perrow argued that any organization is designed to
produce something, and then focused on the technology employed, i.e. the method for transforming
raw materials into some product or service (Perrow, 1967). One way to describe what firms want to
do, at a detailed level, is to describe tasks.
Task characteristics
One of the core ideas of contingency theory is that characteristics of the task to be executed
influence how the activity of executing this task is expressed. Charles Perrow (1967) in particular
has focused on routinization, and on identifying the antecedents of routinization. Perrow’s work
therefore represents a fundament on which we can build in identifying antecedents of recurrent
interaction patterns.
a)
Task complexity
The complexity of the task refers to how many different ‘elements’ are required in order to carry it
out and the number of connections between those elements. ‘Elements’ can refer not just the
number of steps in the process, but also the number of people that need to contribute to the steps,
the number of different knowledge inputs that might be held at different ‘locations’ within and
outside the firm, the number of different physical inputs and so on. For instance, developing a new
model of a car is a highly complex task, whereas preparing and serving food in a fast food
restaurant is a much less complex task.
b)
Task interdependence
Tasks differ regarding the extent to which the steps of the process are interdependent (Thompson,
1967; Lynch, 1974; Withey, Daft and Cooper, 1983). Interdependence can exist between several
steps in the process (some steps have to be carried out before others can be begun), as well as
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between steps in the process and other elements, such as physical artefacts (e.g. a machine).
Interdependencies act as combination constraints, in particular interdependencies between several
steps in the overall process (Pentland 1995). Different kinds of interdependencies have been
described in the section on the Pentland method above.
c)
Time pressure
Both empirical research in management (for instance Weick 1990, Zellmer-Bruhn 1999), and
experimental research in psychology (cf. Bet<sch, Fiedler and Brinkmann 1998; Betsch,
Haberstroh, Glöckner and Fiedler 1998; Betsch, Haberstroh and Höhle 1999; Betsch, Brinkmann,
Fiedler and Breining 1999), indicate that the degree of time pressure (i.e., the time available) under
which a task has to be carried out has an influence on the execution of the task, and thus on the
recurrent interaction pattern that it gives rise to. Under time pressure, behavior tends to be more
routinised, as experimental subjects use only one strategy to co-ordinate their actions, even if it is
inefficient (Garapin and Hollard 1999). Further experimental results have confirmed that time
pressure increases the likelihood of routine choices (as opposed to non-routine choices), even if the
inadequacy of the routine was indicated before the choice (cf. Betsch, Fiedler and Brinkmann 1998;
Betsch, Haberstroh, Glöckner and Fiedler 1998; Betsch, Haberstroh and Höhle 1999; Betsch,
Brinkmann, Fiedler and Breining 1999; see also Zellmer-Bruhn 1999).
d)
Uncertainty pertaining to the task
Routines have been credited with a role in enabling decision-making in situations that are
characterized by uncertainty (Nelson and Winter, 1982; Richardson, 1960; see also March & Simon
1958; Cyert & March 1963). The argument is that the constraints of routinized behavior (i.e.,
recurrent interaction patterns) help decision makers to cope with uncertainty.
The capacity of routines to help agents cope with uncertainty is particularly attractive because it
also extends to such forms of uncertainty where other strategies do not work well (Becker and
Knudsen, 2003 forthcoming). Briefly speaking, one can distinguish three kinds of uncertainty: risk,
uncertainty, and pervasive uncertainty. In a situation of risk, all the possible consequences and the
probability of each consequence are known, while in a situation of uncertainty, there is a set of
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possible specific outcomes, but the probability of each outcome is initially unknown (Savage,
1954). Under pervasive uncertainty, not even all the possible outcomes are known, and neither are
their associated probabilities (March and Simon, 1958; Knight, 1921).
e)
Turnover of agents
If agents who participate in the recurrent interaction pattern are of any importance, then we have to
control for turnover of agents in order to eliminate a cause for change and thus a negative influence
on persistence of recurrent interaction patterns. The turnover rate (in relation to the overall number
of agents participating in the pattern) is also of further interest in itself, because organizations seem
to have a remarkable tendency to keep interaction pattern stable even in the face of continuing
turnover in the population of individuals displaying the pattern (Winter 1991, p. 187).8
3.2
Describing characteristics of recurrent interaction patterns
a)
Frequency
As the literature review of methods of operationalizing recurrent interaction patterns at the
beginning of the paper has shown, the earliest influential operationalization of routinization was the
one proposed by Perrow. It has been applied in a number of studies and has been refined by several
authors (cf. Lynch, 1974; Withey, Daft and Cooper, 1983).
In one of those refinements (Lynch, 1974), Perrow’s technology construct includes two two factors
that operationalize routineness: ‘overall routineness’ and ‘routineness of operations’. The factor
‘overall routineness’ is a multi-item construct consisting of the following items:

monotony of the job,

job as a challenge,

job is boring,

something new happening every day,

chance to do the things one does best,
8
The turnover of agents serves as a control and will therefore not be considered on its own in the hypothesis
development that follows.
13

events that cause the work are interesting,

all of the work is routine,

you have more training and skills than needed for the job,

job is frustrating but never dull,

work keeps changing and one has to change to keep up with it,

variety in the events that cause the work,

repetitiousness of the events that cause the work (Lynch 1974).
The factor ‘routineness of operations’ consists of the following items:

search procedures used are dissimilar from one day to the next, and

work decisions are dissimilar from one day to the next (Lynch 1974).
These two multi-item constructs are quite broad in the sense that they measure (i) the perception of
a task execution as routine (items 1, 2, 3, 4, and 8); (ii) the match between skills and task (items 5
and 7), (iii) repetitiveness (items 9, 10, and 11); and (iv) similarity between different instances of
the execution of the task (items 12 and 13). Subsequent research developed more focused measures.
Withey, Daft and Cooper (1983), for instance, proposed an instrument that ‘has somewhat greater
ability to differentiate among work units and that improves the fit between the theoretical constructs
and their empirical measures’ (ibid., p. 45). The items of their new instrument, selected from
amongst six previous instruments, are:

tasks are the same from day to day,

say that work is routine,

do the same job in the same way most of the time,

perform repetitive activities,

duties are repetitious,

there is a clearly known way to do the major types of work one normally encounters,

there is a clearly defined body of knowledge of subject matter which can guide one in doing the
work,

there is an understandable sequence of steps that can be followed in doing the work,

one can rely on established procedures and practices,

there is an understandable sequence of steps that can be followed in carrying out the work
(Withey, Daft and Cooper 1983, p. 59).
14
This instrument measures (i) similarity (items 1 and 3); (ii) perception of a task execution as routine
(item 2); (iii) repetitiveness (items 4 and 5); (iv) effortlessness (items 6, 7, 8, 9, and 10). Significant
differences are that the match between task and skills has been dropped, and that effortlessness has
been added.
Following this development, we will omit the match between skills and the task from our
operationalization of recurrent interaction patterns. We will also omit the ‘purely perceptional’
items, i.e., whether participants think the task is called a routine or not. Our reason for doing so is
problems with self-reports. In several studies, Pentland has documented that when people are asked
about routineness, they focus on variety (or non-variety) of content, not on the variety (or nonvariety) of process (which is what recurrent interaction patterns are about). His studies show that
curiously, assessing routineness (as variety) by asking participants and by measuring the variety of
action sequences systematically produces opposed results (Pentland, 2003a forthcoming; Pentland,
2003b forthcoming). For this reason, we prefer to omit self-assessment of participant’s perception.
Third, we follow Pentland (2003a forthcoming; 2003b forthcoming) in measuring similarity of
recurrent interaction patterns as sequential variety (see section below). Fourth, we consider
effortlessness an outcome of a recurrent interaction pattern, and therefore consider it in the outcome
section. Fifth, we propose that repetitiveness can most precisely and simply be measured as
frequency. We define frequency as the measure of how often the ‘same’ interaction pattern (as
measured by sequential variety) has been repeated in one time period.
(b)
Sequential variety
There are two key dimensions that characterize recurrent interaction patterns: repetitiveness and
similarity. Even despite lack of agreement and ambiguity on many issues regarding routines, there
is broad agreement that routines (as well as recurrent interaction patterns) essentially refer to some
unvarying repetition of an activity. The previous section has explained how we propose to
operationalize repetitiveness. The present section makes a proposal how to operationalize, or
measure, similarity.
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As Pentland (2003a forthcoming, 2003b forthcoming) has noted, many measures of similarity (or
inversely: variety) have focused on the content of activity. This is also true for the measures
proposed for operationalizing Perrow’s routineness construct, namely task variety and job variety
(Lynch, 1974; Withey, Daft and Cooper, 1983). These do therefore not reflect variety in the
sequence of steps in which the task is carried out – but precisely that is what recurrent interaction
patterns are. For this reason, we follow Pentland (2003a forthcoming, 2003b forthcoming) in
applying sequential variety as a measure for the variety in the action sequences that make up
recurrent interaction patterns.
As elaborated in detail by Pentland, there are two methods to measure sequential variety. The first is
to directly compare sequences to each other, applying string matching techniques. This technique
comes from molecular biology, where it has been applied to compare protein sequence, but it can be
applied to compare other kinds of sequences of unequal length. The method consists of computing
the distance between two strings by counting the number of operations needed to transform one
string into the other. The second technique is to compare the set of observed sequences to a
completely random process. This is implemented by comparing a matrix formed by the observed
sequences with a matrix representing a uniform, random Markov process. In such a matrix, every
action is equally likely no matter which action has just occurred. One can then compute the distance
between the two matrices (Pentland 2003a forthcoming; 2003b forthcoming).
3.3
Linking antecedents of recurrent interaction patterns to characteristics of recurrent
interaction patterns
In particular since the publication of Nelson and Winter (1982), the notion of a routine has been
used widely. Despite a growing body of literature devoted to organizational routines, however, the
link between antecedents of recurrent interaction patterns and recurrent interaction patterns so far
has remained rather implicit.
(a)
Task complexity
There are two different arguments on the influence of task complexity on sequential variety.
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On the one hand, simple arithmetics tells us that the more complex the task at hand, the more
elements there are in the sequence of actions required to executed the task. For this reason, there
will be a higher number of possible combinations, i.e. we would hypothesize a positive correlation
between task complexity and sequential variety.
On the other hand, complexity poses limits to comprehension. A large number of elements, and a
large number of connections and interactions between these elements, have the implication that
actors lose the overview (Egidi and Ricottilli, 1997). This can lead to a condition of causal
ambiguity, where it is not clear which of the many elements are the important ones for carrying out
the task (Lippman and Rumelt, 1982). Actors attempt to cope with making decisions despite a lack
of overview by following ‘tried-and-tested’ ways, avoiding search for new ways. Under conditions
of complexity, actors are led to avoid search as long as the solution applied so far works (March,
1978). We therefore hypothesize that task complexity is negatively correlated with sequential
variety.
As the two arguments have the opposite direction, the net effect of task complexity on sequential
variety is therefore unclear and an empirical question.
We hypothesize the influence of task complexity on sequential variety to be determined by the
following argument: Because complex tasks take longer time to carry out, they will usually be
repeated less frequently (under the assumption that the time required for executing each step has not
changed). This does not mean that sub-sets of a large task (such as translating a 3D-CAD file into a
clay model, as part of the task of developing a new car model) would not be repeated at a higher
frequency. It is therefore important to specify the level of ‘granularity’ of recurrent interaction
patterns under consideration. Our hypothesis is that the correlation between task complexity and
sequential variety will be positive.
(b)
Interdependence
The very nature of interdependence between different steps of the process means that certain
combinations are impossible (for instance, producing the moulds for metal stamps before designing
the car parts they are supposed to stamp). We therefore expect interdependence between steps of the
process that executes the task to be negatively correlated with sequential variety.
17
(c)
Time pressure
The implication of time pressure on frequency and speed is straightforward and known to most of
us from common experience: a deliberate attempt to speed up the execution of a process as much as
possible. This translates into higher frequency (keeping the period of time considered constant).
Experimental research indicates that the implication of time pressure on sequential variety is
negative: under time pressure, actors do not engage in search but stick to the action sequences they
know even if they know that there are more appropriate ones (Betsch, Fiedler and Brinkmann,
1998). We therefore hypothesize that time pressure is negatively correlated with sequential variety.
(d)
Uncertainty
The link between uncertainty and sequential variety is one of the few links that have been explicitly
made in the literature. Heiner (1983) hypothesized that ‘greater uncertainty will cause rulegoverned behavior to exhibit increasingly predictable regularities, so that uncertainty becomes the
basic source of predictable behavior’ (Heiner 1983, p. 570). When considering sequences of
activities, ‘predictable behavior’ can be interpreted as lower sequential variety. Where sequential
variety is low, it is easier to predict sequences because the possibility space is smaller. Why should
higher uncertainty lead to more predictable behavior, however? The argument becomes clear when
the limitations on cognitive resources (Simon, 1965) are highlighted. Routines allow managers to
cope with uncertainty under the constraint of bounded rationality because they can be used to save
on mental efforts and thus preserve scarce capacity required to deal with non-routine events (Simon
1947; March and Simon 1958; Richardson 1960; Nelson and Winter 1982; Hodgson 1988; for an
empirical test of Heiner’s hypothesis see Becker and Knudsen, 2003 forthcoming).
3.4
Outcomes of recurrent interaction patterns
a)
Coordination
18
Routines co-ordinate (Nelson and Winter 1982; March and Olsen 1989; Gersick and Hackman
1990; Coriat 1995; Dosi, Nelson and Winter 2000). Routinisation means that tasks can be
performed smoothly (Rumelt 1995). This becomes particularly clear when co-ordinated action
breaks down because of the interruption of important routines (Weick 1990). The co-ordinative
power of routines derives from their capacity to support a high level of simultaneity and to permit
highly varied sequences of interaction (Grant 1996); from giving regularity, unity, and systematicity
to practices of a group (Bourdieu 1992); from making many simultaneous activities mutually
consistent (March and Olsen 1989); and from providing each of the actors with knowledge of the
behaviour of the others on which to base his own decisions (Simon 1947; cf. Stene 1940). Nelson
and Winter (1982) identify several aspects in which routines influence co-ordination: they embody
a truce, provide instructions in the form of programs, and contribute to order by establishing zones
of indifference (Barnard 1938).
b)
Saving on cognitive resources
As explained in section 3.1.a, the operationalization of Perrow’s ‘routineness’ construct contains a
set of items that in our opinion operationalize ‘effortlessness’ (Withey, Daft and Cooper, 1983: 59).
As mentioned above, we are of the opinion that ‘effortlessness’ is not a characteristic of recurrent
interaction patterns, but rather can be considered a performance outcome. The fact that recurrent
interaction patterns are executed without – or with diminished – attention, often subconsciously,
means that routines help economise on bounded cognitive resources (March and Simon, 1958;
Nelson and Winter, 1982; Simon, 1947; Hodgson, 1988, North, 1990). Because repetitive decisions
are dealt with by semi-conscious mechanisms, routines free up cognitive resources, which can then
be used to focus attention on exceptional events (March and Simon, 1958). In this way, routines
guide search through experience (Levitt and March, 1988).
c)
Quality
From the days of Scientific Management onwards, routines have been seen as positively correlated
to output quality. Indeed, routines were (Taylor, 1911) and still are (ISO 9000) seen as a tool to
improve output quality. The literature on operations management and quality management describes
19
at length the idea of process standardization and routinization as a means for improving product and
service quality, such as ISO 9000.
d)
Flexibility
On the other hand, highlighting a different part of the value chain, the marketing literature (Kotler et
al., 2001) emphasizes the value of flexibility and adaptability for serving customers needs.
Marketing thus has a wider notion of quality, which is not limited just to the quality of the physical
product, but also includes the quality of the supplementary services that make up the product
offering, such as installation, after-sales service, and so on (Kotler et al. , 2001). At first sight,
demanding that products can be adapted to different customer segments, and in the extreme even
individual customers (customization), seems at odds with the demand for routinization of
production for the sake of quality control. Should firms choose to routinize production or to
organize for maximum flexibility, foregoing quality and efficiency gains? We meet, once more, the
familiar dichotomy of routines vs. innovative and flexible activity (Leonard-Barton, 1995).
Such an idea is misguiding, however. Recently, Martha Feldman has rectified the idea of routines
being opposed to flexibility (and change). Rather, routines are the ‘source of continuous change’
(Feldman, 2000; Feldman and Rafaeli, 2002; Feldman and Pentland, 2003 forthcoming). For our
discussion here, the meaning of this argument is that flexibility is enabled not despite, but because
of routines. This argument finds support, in fact, in the notion of modularity (Baldwin and Clark,
1997; Sanchez, 1999). Where productive processes (including the production of services) are
organized in a modular fashion, both the quality benefits (by way of routinization of each step) and
the flexibility benefits (by way of recombination of the routinized processes) can be reconciled.
This is why flexibility is an outcome of the presence of recurrent interaction patterns.
e)
Stability enables learning
The stability that recurrent interaction patterns provide plays an important role for learning: it
enables learning by providing a stable base-line against which to assess feedback, compare, and
draw implications (Tyre and Orlikowski 1996). Recurrent interaction patterns provide a base-line
against which to compare, and thereby, more generally speaking, to learn (Langlois 1992; Postrel
20
and Rumelt 1992). Where such a base-line does not exist, it is difficult to draw inferences. Where
the base-lines is unstable (i.e., changes rapidly), there is the risk of overreacting to noise and to
foreclose the experimentation necessary for discovering good alternatives (Levinthal and March
1981; March 1988; Knudsen 2002).
3.5
Linking characteristics of recurrent interaction patterns to performance outcomes
a)
Sequential variety
Coordination. It seems more likely that recurrent interaction patterns with low, rather than high,
sequential variation yield smooth coordination. In principle, though, recurrent interaction patterns
with higher sequential variety could yield just as smooth coordination. This would, however, entail
being ‘fluent’ in a larger repertoire of sequences. For instance, rather than being able to produce fast
food only according to one fixed sequence of steps (which means there are problems when a
customer asks for an exception, like at McDonalds; cf. Pentland et al., 1996), it means being able to
vary the sequence of steps while keeping the same level of smooth coordination (such as at Burger
King, where the system is designed to take account of exceptional customer demands; cf. Pentland
et al., 1996). Sequential variety therefore is not directly correlated to coordination. An indirect
effect, however, is that higher sequential variety requires more experience, resp. exercise of the
‘other’ sequences, and therefore more ‘procedural memory’ (Cohen and Bacdayan, 1994), cognitive
resources and capabilities than recurrent interaction patterns with lower sequential variety. Under
conditions of bounded cognitive resources (Simon, 1965), one could argue that such cognitive
resources required for memorizing a larger set of sequences detracts from the resources available to
solve other coordination problems, thus diminishing the degree of coordination across the whole set
of processes that the actor in question is involved in (as opposed to the one process under
consideration).
Saving cognitive resources. As described above, the logic behind this outcome is that of bounded
cognitive resources. The less resources are required to decide between different possible courses of
action (or: sequences of action, i.e. recurrent action patterns), the more cognitive resources have
been saved for other purposes. In principle, the more fixed recurrent interaction patterns are, i.e. the
21
lower sequential variety, the stronger the effect of saving cognitive resources will be. We therefore
hypothesize a negative correlation between sequential variety and the outcome of saving cognitive
resources.
Quality. When focusing on the physical attributes of products, and thus the manufacturing aspect,
process variation (sequential variety) is conceptualized as reliability (the standard deviation of some
output parameter) and accuracy (changes in the mean value over time relative to some target).
Accuracy and reliability of outputs matter because they directly affect the economic performance of
the process. According to this argument, we would hypothesize a negative correlation between
sequential variety and with product quality.
Flexibility. From our description of flexibility above, clearly, the hypothesis is that sequential
variety and flexibility are positively correlated. Just like modules that can be combined in many
different ways allow a higher possible number of combinations, so do steps and ‘modules’ of a
process.
Enabling learning. Two considerations enter in the relationship between sequential variety and the
outcome that recurrent interaction patterns enable learning. First, because of the base-line argument
described above, the higher sequential variety, the less of a base-line is being established (or in
more precise terms: keeping the time period constant, less repetitions of each variant of sequence
will have occurred), thus enabling learning less. The hypothesized correlation between sequential
variety and the degree to which recurrent interaction patterns enable learning therefore is negative.
On the other hand, there is a countervailing effect. Sequential variety also represents more learning
opportunities (by way of being able to experiment more). The implication is that the higher
sequential variety, the more learning is enabled. The hypothesized relationship is therefore positive.
The net effect of these two effects is unclear and an empirical matter.
b)
Frequency
Coordination. There are at least two arguments for why higher frequency of the same recurrent
interaction pattern should lead to better (smoother) coordination. First, with increasing frequency,
additional means of coordination than just for instance contractual ones become available.
22
Williamson has elaborated this point at length, highlighting the fact that with increasing frequency
of transactions (in the form of recurrent interaction patterns), trust develops between the parties,
which increasingly allows to rely on informal governance structures such as bilateral governance,
rather than on formal governance mechanisms as included under the form of trilateral governance
(Williamson, 1985). Trust and transaction atmosphere are amongst the examples used by
Williamson (1985), who also uses the term ‘fundamental transformation’ to mark the additional
governance mechanisms (which can be used for providing smooth coordination) that become
available with increasing transaction frequency. The second argument for a positive correlation
between frequency and coordination is a learning curve effect. The more often some activity has
been carried out, the more smooth one will be able to perform it. This is also true for interaction.
Saving cognitive resources. The larger the sample, the better the probabilistic assessment of
whether it is safe to assume that the same recurrent interaction pattern applied to the situation before
will be appropriate again. That means it is appropriate to not engage in choosing between different
alternatives, but to stick to the previously chosen alternative without conscious deliberation. This
leads us to hypothesize a positive relationship between frequency and the outcome of recurrent
interaction patterns to save cognitive resources. Note also that this refers to each time period, and is
not just an average cost argument.
Quality. The whole wealth of the literature on learning and experimentation (cf. Dutton and
Thomas, 1985) supports the hypothesis of a positive correlation between frequency and quality.
Frequency translates into more experience, thus more experience-based knowledge, on the one
hand, and on the other hand into more possibilities for experimentation, thus increasing also the
scientific knowledge-base.
Flexibility. About the relationship between frequency and flexibility there are rich insights in the
literature that deals with habituation (cf. Hodgson, 1997; Twomey, 1998). For instance, experiments
in psychology indicate that the strength of association between a situation and a certain behavioural
option (recurrent interaction pattern) often increases as a function of frequency (Betsch, Fiedler and
Brinkmann, 1998). We therefore hypothesize a negative correlation between frequency and
flexibility.
23
Enabling learning. According to the base-line argument described above, the relationship between
frequency and the degree to which recurrent interaction patterns enable learning can only be
considered taking into account sequential variety at the same time. Where sequential variety is low
and a stable base-line therefore exists, high frequency means that learning is enabled more. The
hypothesized correlation between frequency and the degree to which recurrent interaction patterns
enable learning therefore would be positive. Where sequential variety is high and a stable base-line
therefore does not exist, the we would hypothesize that the correlation would not be positive.
4.
Conclusion
The objectives of this article were to review existing methods of operationalizing routines, and to
start developing hypotheses on the antecedents and outcomes of routines.
As for the first objective, the literature review of methods of operationalizing routines has indicated
that in particular the method developed by Pentland seems to capture one of the most important
characteristics of recurrent interaction patterns, sequential variety, while avoiding some of the
problems of the other methods (such as the problem of recognizing a rule). Sequential variety
coupled with a measure of frequency seems to be an adequate operationalization of recurrent
interaction patterns. As frequency is easy to operationalize, the important contribution is Pentland’s
(Pentland 2003a forthcoming, Pentland 2003b forthcoming) operationalization of sequential variety,
which is both very concrete and applicable. Addressing the issue that the various empirical studies
of routines do not build on each other to the fullest degree possible, operationalizing recurrent
interaction patterns by a combination of a frequency measure and Pentland’s sequential variety
measure could provide common ground.
The first set of hypotheses on the antecedents and outcomes of recurrent interaction patterns
developed in the paper serves mainly as a basis for debate and further development. The list of both
antecedents and outcomes might very well show not to be comprehensive, or the choice of what are
the most important antecedents and outcomes might show to not be the most adequate one. It is
hoped that the article can serve to spark a discussion on these issues.
24
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