INTERNATIONAL JOURNAL OF ORGANIZATION THEORY AND BEHAVIOR, 16 (4), 521-572
WINTER 2013
SOCIALLY DISTRIBUTED COGNITION AND INTRA-ORGANIZATIONAL
BANDWAGON: THEORETICAL FRAMEWORK, MODEL, AND SIMULATION
Davide Secchi and Emanuele Bardone*
ABSTRACT. Bandwagon refers to the adoption of popular ideas, thoughts, or
practices. Although the inter-organizational (macro) dynamics of the
phenomenon have been widely studied, its intra-organizational (micro)
aspects have received limited attention. The paper presents a theoretical
framework and a model that address intra-organizational aspects of
bandwagon drawing on distributed cognition, social relationships, and other
elements of the organizational structure such as culture and defensive
routines. The analysis of simulated data from the model suggests that the
phenomenon is likely to decrease with highly informal culture, promotion of
advice taking and giving, low levels of distrust, strong social ties, and
minimal defensive routines.
INTRODUCTION
Bandwagon refers to the diffusion of a thought, behavior, or
practice, as a result of its popularity. From Leibenstein (1950)
onwards, analyses and models of bandwagon have proliferated. As a
particular type of imitative behavior (Chiang, 2007; Pangarkar, 2000)
bandwagon has been studied from economic (e.g., herding behavior;
Banerjee, 1992), sociological (e.g., Granovetter, 1978; Chiang, 2007;
Spears Zollman, 2010), management (e.g., Staw, & Epstein, 2000;
Abrahamson, & Rosenkopf, 1997), marketing (e.g., consumer
behavior, Rohlfs, 2003), cognitive (Bardone, 2011), and biological
------------------------* Davide Secchi, Ph.D., is Research Lead and Senior Lecturer, Department
of Human Resources & Organisational Behaviour, Bournemouth University
(U.K.). His current research efforts are on socially-based decision making,
rational processes in organizations and individual social responsibility.
Emanuele Bardone, Ph.D., is Marie Curie Fellow, Institute of Informatics,
Tallinn University (Estonia). His research focuses on chance seeking
behavior, decision making heuristics, and affordance.
Copyright © 2013 by Pracademics Press
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(e.g., Boyd, & Richerson, 2002; Whitehead, & Richerson, 2009)
perspectives. Despite all of these efforts, a micro-foundation of
bandwagon as it emerges and evolves within organizations has yet to
be developed (Røvik, 2011).
This study presents a theoretical framework to understand and
analyze intra-organizational bandwagons, a phenomenon mostly
overlooked in the current literature. The paper pursues this goal in
bridging the gap between the micro and macro levels of analysis, in
an attempt to connect individual cognition to typical macro
organizational characteristics such as organizational culture,
trustworthiness, and routines. Mathematical modeling is used to
build up a theory of intra-organizational bandwagons. In summary, the
following questions are addressed: (1) How is the emergence of
bandwagons sensitive to individual cognitive processes? (2) Which
organizational variables reduce or increase the emergence of
bandwagons and how?
THEORETICAL FRAMEWORK
Fiol and O’Connor (2003) propose a micro-level explanation of
bandwagons based on the dichotomy ‘mindfulness-mindlessness’
(Langer, 1989; Langer, & Moldoveanu, 2000) or, respectively, the
state of active, conscious, and deliberate thinking as opposed to its
contrary. They suggest that the decision maker’s mindfulness “can
moderate [the] potentially dysfunctional effect of formal decision
structures, thus contributing to greater discriminatory behavior in the
face of bandwagons” (Fiol, & O’Connor, 2003, p. 55). They also argue
that “discriminatory behavior, i.e., a choice-based decision, is what
directly follows from a conscious and active state of mind” (p. 55).
This approach can be taken as a starting point, as it links three topics
that appear to be particularly important for the model presented in
this paper.
First, individual motives (or justifications) to join the bandwagon
may depend on how active or passive is one’s state of mind. Contrary
to many concepts in organizational behavior that address latent
characteristics (i.e., they can be inferred but not directly observed;
e.g., De Vellis, 2012), bandwagon is apparent and can be observed
through people’s behavior or thinking (e.g., Granovetter, 1978). This
rules out a series of conjectures or measurement problems that are
SOCIALLY DISTRIBUTED COGNITION AND INTRA-ORGANIZATIONAL BANDWAGON
523
typical of other organizational measures but does not entail the
understanding of what the reasons are for people to join the
bandwagon. We know that people join because the phenomenon is
popular (e.g., Abrahamson, & Rosenkopf, 1993; David, & Strang,
2006; Angst et al., 2010), but there are cognitive and psychological
mechanisms, some of them rational, some irrational (see below), that
may lead individuals to act far quicker than others. These attitudes
towards bandwagon are the latent aspects that call for closer
attention. In the following subsection, we detail why and how a switch
in the focus on the individual to the organization (or community) may
help unveil some of these aspects.
The second aspect that the concept of mindfulness helps to
disentangle is the fact that bandwagons have been usually
characterized as dysfunctional (Fiol, & O’Connor, 2003). Although
bandwagon may reveal itself to be dysfunctional for the organization
as a whole, this element is defined at the individual level because
falling short of mindfulness is the underlying cause. This approach
seems to rest on the assumption that the fully conscious and active
mind state leads to behaviors that make sense, or that make more
sense as opposed to those coming out of an unconscious and passive
state of mind. However, a growing body of research reveals that, for
example, less information (Goldstein, & Gigerenzer, 1996, 2008) and
emotions (Hanoch, 2002) still allow people to make sensible
decisions. This means that complete mindfulness is not always
necessary.
Third, we see mindfulness as a bridging concept where employee
cognitive statuses are not determined in isolation but rather,
embedded within the existing social environment of the given
organization. This element of mindfulness has been under-explored
and overlooked in the past but it is increasingly important as scholars
have started reflecting on the distributed nature of cognition
(Hutchins, 1995; Michel, 2007). Although the word ‘distributed’ has
two meanings, these cannot be treated separately. Firstly, when
solving problems or making decisions, individuals make use of
external resources (artifacts, tools, other people), which shape and reshape their cognitive capabilities (Clark, & Chalmers, 1998; Clark,
2008). The cognitive process is not limited to one’s brain, but rather,
includes every resource, no matter where located, that affects the
process. It is distributed amongst resources, not limited to one’s brain
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SECCHI & BARDONE
(Clark, 2008). The second meaning of the word ‘distributed’ refers to
the fact that the output of a certain process cannot be attributed to a
single individual, but to an institutional agent (Gabbay, & Woods,
2005), in which a number of several components work together in
solving a particular problem. For example, a scientific experiment is
often a process involving distributed cognition in that there are
several people working on a problem, with a particular set of tools
and artifacts (Giere, 2007). The following subsections detail these
three topics---motives, dysfunctional elements, and distributed
cognition---and attempt to explain the theoretical framework
underneath the model.
Table I organizes selected literature on six dimensions: (1)
cognition and mindfulness, i.e., there is an emphasis on the cognitive
mechanisms and an individual’s (rational or irrational) motives; (2)
the bandwagon effect, i.e., studies on the effects on the population as
a whole; (3) micro-level of analysis, i.e., the study is mostly on
individuals; (4) macro-level of analysis, i.e., the organization is the
unit of analysis; (5) dysfunctional outcomes, i.e., bandwagon is
qualified or modeled as having unfavorable consequences; (6) peer
pressure, i.e., adoption is mostly caused by the social environment or
pressure from peers. These do not qualify the publication per se, but
are aimed at defining some aspects analyzed in that publication that
can be useful to this study. The column ‘research opportunity’
highlights one or two areas that constitute a sort of ‘what can we take
from the literature’ listed on the left column should one want to focus
on intra-organizational bandwagons. The final column on the right
(labeled ‘factors’) presents the variable or parameter used in our
model to study the phenomena highlighted in the preceding column.
Tackling Bandwagon Effects
There are several factors that could foster or limit the emergence
of the so-called bandwagon effect and explain how imitation spreads
in any given group, team, community, organization, or society. Despite
the fact that the expressions ‘bandwagon’ and ‘bandwagon effect’
have been used as a synonym in the past literature (e.g., Granovetter,
1978; Leibenstein, 1950; Chiang, 2007; Angst et al., 2010; van
Herpen et al., 2009; Corneo, & Jeanne, 1997; Rohlfs, 2003), we
differentiate between the two and use the latter to highlight the
dynamic of social interactions observed in a defined social
environment, such as a community, group, or organization. In other
SOCIALLY DISTRIBUTED COGNITION AND INTRA-ORGANIZATIONAL BANDWAGON
525
words, the effect is something observable only when the social
environment or collectivity (Abrahamson, & Rosenkopf, 1997) is
specified so that the effect entails the system, while the bandwagon
is referred to a single individual. This distinction is useful in the case
of this paper because we are trying to bridge the two levels, i.e.
individual vs collective/organizational, and we need to have
terminology that differentiates between the two.
There are several reasons that can be taken into consideration to
explain the emergence and/or persistence of bandwagons. For
example, one may consider formal vs. informal decision structures
(Fiol, & O’Connor, 2003), heterogeneity vs. homogeneity of individual
preferences (Chiang, 2007), network structure (Granovetter, 1978),
mindfulness vs. mindlessness (Fiol, & O’Connor, 2003), weak vs.
strong social ties (Granovetter, 1973, 1978), cost vs. benefit
evaluations (Abrahamson, & Rosenkopf, 1997), network coreperipheral relations (Abrahamson, & Fombrun, 1994; Abrahamson, &
Rosenkopf, 1997), or social pressure (Abrahamson, & Rosenkopf,
1993). Ambiguity and uncertainty are two further concepts related to
what triggers individuals to join the bandwagon. Besides the technical
definition of these two concepts, which is not relevant here (see
Abrahamson, & Rosenkopf, 1997: 291-292, for details), bandwagon
is often seen as a response to some threat or problem when
information availability and its representation are an issue
(Abrahamson, & Rosenkopf, 1997), and/or cognitive capabilities are
constrained (Fiol, & O’Connor, 2003). In these cases, bandwagons
are seen from the perspectives of those joining them as an attempt to
overcome these difficulties. However, the motives are usually seen as
strictly related to the individual that is cognizant and all external
stimuli are relevant in understanding his or her behavior and/or
thinking. Using the distinction introduced above, these are all
elements of the bandwagon, which are extremely useful to define how
and why one joins, but they may or may not explain bandwagon
effects, (i.e. what makes something a widespread or increasingly
popular phenomenon). In other words, some basic questions remain
unanswered: How do other people (e.g., co-workers, managers, etc.)
create ‘viral’ adoptions (e.g., Strang, & Tuma, 1993; Rogers, 1983) of
practices, or ideas? How can imitation (e.g., Chiang, 2007) be
modeled to take into consideration popularity of practices, ideas, or
behaviors? A way to address these issues is that of modeling
bandwagon as a phenomenon that entails a community, so that
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SECCHI & BARDONE
‘motives’ are somehow understandable as distributed to a number of
actors rather than understood via a simple sum of individual
behaviors and/or attitudes. With this perspective in mind, the focus
shifts from individual motives to organizational (or community) factors
that foster or limit the spread of bandwagons (now termed ‘effects’).
As presented below, the bandwagon phenomenon becomes a
mediator of meaning and information (Weick, & Roberts, 1993).
These studied aspects can provide an insight regarding the measure
of how much of their cognitive effort individuals actually share
(socially distributed cognition or SDC, Table 1, row 1) in the modeling
effort, together with a measure of how strongly or weakly tied are
employees (i.e., social relationships; Table 1, rows 4 and 5). Details
are provided in the following section.
TABLE 1
Topics Analyzed by Selected Literature on Bandwagon and Diffusion,
Outline of Research Opportunities, and Study Variables and
Parameters
Literature
Dimensions
C
Fiol & O’Connor (2003);
Abrahamson & Rosenkopf (1997)
BE
+
Abrahamson & Rosenkopf
(1993, 1997); Rosenkopf &
Abrahamson (1999);
Bikhchandani, Hirshleifer, &
Welch (1998)
David & Strang (2006); Strang &
Macy (2001); Strang & Tuma
(1993)
Staw & Epstein (2000);
Abrahamson (1991, 1996,
2011); Abrahamson & Eisenman
(2008); Abrahamson & Fairchild
(1999); Nicolai, Schulz, &
Thomas (2010)
Granovetter (1978); Leibenstein
(1950); Chiang (2007); Angst,
Agarwal, Sambamurthy, & Kelley
(2010); van Herpen, Pieters, &
Zeelenberg (2009); Corneo &
Jeanne (1997); Rohlfs (2003)
Research
Opportunity
Mi Ma Dy PP
+
+
+
+
+
+
+
+
+
+
Factor(s)
+
+
cognition as a
SDC
basis to tackle
+
bandwagons;
social contagion
individuals driven openness to
advice
by rationalistic
motives and/or
peer pressure
diffusion driven by
institutionalized
+
structural
elements
management fads
become
institutionalized or
‘structured’
organization
culture,
organization
routines
organization
routines
diffusion via
social contagion
SDC, social
relationships
+
SOCIALLY DISTRIBUTED COGNITION AND INTRA-ORGANIZATIONAL BANDWAGON
527
TABLE 1 (Continued)
Literature
Dimensions
C
BE
+
+
9
7
Factor(s)
Mi Ma Dy PP
Pangarkar & Klein (1998);
Pangarkar (2000)
McNamara (2008); Terlaak
(2007); Xia, Tan, & Tan (2008);
Banerjee (1992)
Total Numbers of References per
Dimension
Research
Opportunity
1
3
21
Organizational
courses of action
that derive from
peer pressure
strategic
+ implications of
bandwagon
1 17
3
+
openness to
advice,
organization
routines
SDC,
trust/distrust
Notes: C = cognition/mindfulness; BE = bandwagon effect; Mi = micro-level analysis /
individual; Ma = Macro-level analysis / inter-organization; Dy = dysfunctional
(something in need of fixing); PP = peer pressure; SDC = socially distributed
cognition.
Dysfunctional and Functional Bandwagons
A second element associated with the bandwagon phenomenon,
is the fact that it is ‘dysfunctional’ (Fiol, & O’Connor, 2003: 55) or
‘bad’ for organizations and management because it implies that the
decision maker falls short of carefully evaluating potential outcomes
following his/her behavior. Joining the bandwagon is associated with
routine—defined as formal decision processes (March, 1994) that are
tacit (Nelson, & Winter, 1982)—in that these prevent the decision
maker from assessing active cognitive abilities, but allow the
individual to mechanically proceed towards action. This suggests that
an individual can make evaluations of whether to join a bandwagon
based on a logic of appropriateness more than one of consequences
(March, 1994). The first logic relates to personal and organizational
identity, while the second follows a more structured rational decision
making process (Levitt, & March, 1995). The appropriateness of any
given action—such as joining bandwagons—does not necessary imply
that a specific evaluation of outcomes has been performed. Rather,
the decision maker has made a decision based on the felt need to
align their behavior to the context in which s/he operates. By contrast,
other theories explain bandwagons on the basis of costs and benefits
(e.g., Rohlfs, 2003) or ‘profitability’ (Abrahamson, & Rosenkopf,
1997) and thus imply that there is a partial rational evaluation of
alternatives, which is closer to the logic of consequences. Table 1
(rows 1, 2, and 4) summarizes a selection of authors that have
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considered this phenomenon as dysfunctional. The following sections
explain the choice of factors in detail; for the present moment
however, it is worth noting that organizational aids (e.g., routines) and
individual characteristics (e.g., advice taking and cognition) may
affect the selection of rational alternatives.
On these premises, we argue that (a) whilst the logic of
appropriateness may not rest on a full mindful status this does not
necessarily imply dysfunctional decisions, (b) a complete and
continuous mindful status is not possible over time since the line that
separates mindful vs. mindless statuses is not always clear
(Levinthal, & Rerup, 2006; Esposito, 2011), and that (c) decisions to
adopt ideas, practices, behaviors based on their popularity cannot be
eliminated from individual behavior or thinking. In the latter case we
assume they can be kept to a minimum, but to avoid them completely
may result in cognitive and behavioral problems as a result of the fact
that our cognition leans on mental leaps or shortcuts (e.g.,
Gigerenzer, & Selten, 2001), imitation being one such shortcut
(Richerson & Boyd, 2002). Hence, it seems that individual cognition is
key: How can we represent the link between individual cognition and
socially distributed phenomena? What is the cognitive mechanism
that allows individuals to (more or less implicitly) discount or overassess the meaning of ideas, practices, behaviors that become
popular? The model tries to address these questions with the use of
socially-based decision making and the so-called ‘docility’ (Simon,
1993).
Bridging the Gap: Micro and Macro Levels
Third, bandwagons manifest themselves at the micro- and macrolevels. The latter is what has been studied in terms of practices,
know-how, processes, and knowledge transfer between companies. In
particular, studies of innovation and technology diffusion abound
(e.g., Abrahamson, 2011; Abrahamson, & Rosenkopf, 1993, 1997;
Lee, Smith, & Grimm, 2003; Rosenkopf, & Abrahamson, 1999; Van
de Ven, & Hargrave, 2004; Rohlfs, 2003). Social network analysis has
recently demonstrated how the organizational social structure favors
imitative behaviors (Chiang, 2007; Abrahamson, & Rosenkopf, 1997).
The former area of study—micro-level analysis—focuses on factors
that influence the individual to behave or think in the same ways as
the majority of group members. Studies of decision making,
rationality, social cognition, and social psychology (e.g., Fiol, & Huff,
SOCIALLY DISTRIBUTED COGNITION AND INTRA-ORGANIZATIONAL BANDWAGON
529
1992; Sutcliffe, 1994; Festinger, 1957) explain why and how
individuals ‘fall victim’ to bandwagons. Usually, these two
perspectives remain separate, as few attempts have been made to
cover the existing gaps between micro- and macro-level theories. The
work of Fiol and O’Connor (2003) is one of these attempts. They
present the case of consolidation in the health care market to analyze
how (micro) individual decision making processes have the potential
to affect mergers, integrations, and other crucial management
decisions at the (macro) corporate level.
One of the dimensions that is usually overlooked in this approach
is that formal and informal norms of behavior may affect
bandwagons. These can be summarized by studies on how culture
affects the adoption of ‘something’ and diffusion processes in
institutions and organizations (e.g., Strang, & Meyers, 1993). Table 1
(rows 1, 2, 3, 6, and 7) provides an indication on what studies
focusing on the macro or on a combination of micro-macro effects of
diffusion and bandwagon suggest where attention should be
channeled. In addition to some of the factors already considered
above, we propose that a measure of trust or distrust should render
the study of more opportunistic motives easier to model.
Issues in Modeling Intra-Organizational Bandwagons
One question effectively encapsulates the aim of this paper: How
can organizations decrease bandwagons to a functional level and
increase individual and organizational mindfulness? In our attempt to
define a theoretical model of intra-organizational bandwagons, we
use mathematical modeling techniques and apply them to
organizational management studies (Adner et al., 2009). We propose
a mathematical model and discuss it via numerical analysis and
simulation. The methodological choice is consistent with the literature
(e.g., Rosenkopf, & Abrahamson, 1999; Chiang, 2007; Strang, &
Tuma, 1999) and it unveils relations that may otherwise remain
hidden (Adner et al., 2009). We try to support further theoretical
developments adopting an equation-based model as a tool (Gilbert, &
Troitzsch, 2005).
In the following section, we present and discuss variables and
parameters that define our model (Table 1, right end column).
Subsequently, we focus on a numerical analysis and simulation of
how parameter variations affect the relation between dependent and
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SECCHI & BARDONE
independent variables.
ORGANIZATIONAL DETERMINANTS
A Socially Distributed Model of Bandwagon
In an article discussing bounded rationality mechanisms, Laland
(2001) suggests that imitation is key to social learning. In the
bandwagon literature, some scholars argue that there is transfer of
information when adopters’ behavior is observed (Bikhchandani et
al., 1992; Terlaak, & King, 2007). However, exactly what kind of
information is shared on the bandwagon? We answer the question
with reference to advice taking. The growing body of literature on the
topic (see the review by Bonaccio, & Dalal, 2006) suggests that
advice giving and taking is not marginal among groups and
organizations. Scholars in this field usually consider only active advice
(e.g., Dalal, & Bonaccio, 2010), i.e., when people know that what they
are about to receive is advice (e.g., Yaniv, 2003), and other
individuals willingly provide suggestions on a specific problem (Dalal,
& Bonaccio, 2010). In the case of bandwagon, mass-behavior works
as a sort of collective advice available to the individual that can be
defined as passive because those who take the advice do not
recognize it as such. Furthermore, in such a process, nobody is
explicitly and actively providing the advice. The population rather,
provides this passive advice via collective behavior. In short, no active
exchange among the new adopter and other people transpires.
Adopting Chiang’s (2007), Equation (1) below helps to frame how the
passive (or mass) advice mechanism works. This is what assists to
define the dynamic of the dependent variable (y) for our model.
Let p [0, 1] be the probability that an individual will adopt the
idea, behavior, or something else increasingly popular within the
organization. This probability p increases with the number of people
in the organization u that have already joined the bandwagon (the
independent variable in Equation 1), and depends on individual
attitudes q [0, 1] towards taking passive advice. This individual
attitude q is what others term the ‘threshold’ level (Abrahamson, &
Rosenkopf, 1997). The value of q is a measure of ‘uniqueness’, the
closer it gets to 1 the more unlikely it is that individuals take passive
advice; on the other hand, the closer it gets to 0, the more likely it is
that individuals will take passive advice. In the dynamic of Equation
(1), higher values of q are related to higher numbers of individuals
SOCIALLY DISTRIBUTED COGNITION AND INTRA-ORGANIZATIONAL BANDWAGON
531
that one may see before joining a given bandwagon. The letter N,
indicates the limit towards which the model tends. If N is set to 1 then
u may be read as a percentage of the number of individuals that have
already joined the bandwagon.
The individual only joins the bandwagon when this personal
attitude matches a certain number of adopters. This means that
every individual may have a different threshold (attitude) depending,
for example, on the issue, perception of organizational culture, role,
and experience. In addition to that, the end result is the probability p
that the individual will adopt the practice, idea, or behavior. Adoption
is a dummy variable that could take values 1, if adoption occurs, and
0 if it does not. The probability to join the bandwagon is one of the
variables that affects ultimate adoption but it is not adoption (Ajzen,
& Fishbein, 2005). In other words, the probability to join quickly
increases as the number of adopters increase only if the decision
maker’s attitude (threshold) is low enough (i.e., q is closer to 0 than to
1).
(1)
Since the number of individuals that join bandwagon u varies with
time u(t), hence p also varies with time p(t). Organizational
bandwagons are given by a cumulative function y that is the expected
value for the sum of individual attitudes towards bandwagons, or pi:
(1.1)
where y is the cumulative function of individual attitudes towards
the bandwagon, i is the individual, N the total number of
individuals in the organization, and pi is the function for one
single individual (that is specified by Equation 1 above).
The diffusion function is the cumulative outcome of these
individual ones, assuming that when the probability reaches a given
threshold, an individual would join the bandwagon. The cumulative
function is the expected (mean) overall tendency that an individual
would join the bandwagon.
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What we identified as the source of advice has a cognitive
counterpart termed docility, i.e. “the willingness to be taught” in its
Latin root (‘docilis,’ from ‘docere,’ to teach). As Herbert Simon puts it,
docility is the tendency “to depend on suggestions,
recommendations, persuasion, and information obtained through
social channels as a major basis for choice” (1993, p. 156). A recent
version of docility also includes the active component, as in advice
giving (Secchi, & Bardone, 2009). It is apparent that the coefficient q
reflects this attitude, as advice taking can be considered a symptom
or byproduct of docility (Secchi, 2009: 577-578). In this simple model,
the closer q is to 1, the higher the probability p that individuals will
join organizational bandwagons.
Docility attitudes can be related to altruism (Simon, 1993;
Knudsen, 2003), and to other individual attitudes such as social
responsibility (Secchi, 2009). The idea of docility is linked to the
distribution and exploitation of cognitive resources that are located
outside of one’s brain (e.g., Clark, & Chalmers, 1998), i.e. other
individuals are treated as external social resources (e.g., Magnani,
2007). As it mirrors distributed cognitive activities oriented towards
other individuals, it is easy to relate levels of docility to the
bandwagon. As we know, bandwagon concerns the tendency of the
individual to observe a given phenomenon, and to adopt it if judged
popular (i.e., if the number of adopters < personal threshold) from the
decision maker (Angst et al., 2010; Bikhchandani et al., 1998).
Docility can be defined as the average attitude that individuals have
to exchange information with other individuals, and to make decisions
on the basis of that information. We argue that bandwagon can be
explained when passive docility prevails among individuals.
It is now possible to reframe the subject matter from this cognitive
angle. Bandwagons, interpreted as part of organizational dynamics,
are processes where: (a) individuals show passive attitudes toward
the environment (or where the social pressure prevails on rational
analysis; Fiol, & O’Connor, 2003; McNamara, Haleblian, & JohnsonDyke, 2008), (b) this attitude is facilitated by distributed cognitive
processes (e.g., Clark, 2003; Hutchins, 1995), and (c) there is a need
to expand individual cognition to include more active external social
resources (Magnani, 2007). Therefore, an individual decision to
engage in a passive or in an active-passive docile exchange of
SOCIALLY DISTRIBUTED COGNITION AND INTRA-ORGANIZATIONAL BANDWAGON
533
information depends on the subjective level of docility and on other
peoples’ docility (Simon, 1993).
Distributed Cognition and Docility
Drawing on docility research (e.g., Knudsen, 2003; Secchi, 2009;
Miller, & Lin, 2010), we hypothesize that every individual uses
distributed cognitive mechanisms with a given frequency, thus
implicitly defining the level of docility. It then becomes easier to
measure it as the amount of decisions that are based upon, depend
on, or relate to information originating from social channels. The
cumulative average docility that individuals display in an organization
could be thought of as a measure of exploitation of socially
distributed cognitive channels (e.g., Magnani, 2007), in short socially
distributed cognition (SDC) for a given organization.
The higher the level of SDC (x), the lower the possibility for
bandwagons to emerge. The rationale for this derives from the way we
defined docility and from what constitutes distributed cognition.
Hutchins (2000) explains that “cognitive processes may be
distributed across members of a social group […] in […] that the
operation of the cognitive system involves coordination between
internal and external (material or environmental) structures.
[Moreover] processes may be distributed through time in such a way
that the products of earlier events can transform the nature of later
events” (pp. 1-2). According to this explanation, human cognition is
shaped by external resources on the basis of a continuous and useful
interchange between resources in- and outside the brain (Clark and
Chalmers, 1998 refer to this as a ‘smart interplay’). Therefore,
imitation and joining bandwagons are typical cases of how cognition
can be distributed. The resources associated with distributed
activities are particularly relevant. To further illustrate this point, we
can consider the act of speaking as an example. Speaking requires
the externalization of words, and sentences, in specific tones of voice
(Donald, 2001; Love, 2004). Those words that an individual
externalizes (Magnani, 2006) are resources used to formulate further
thoughts, i.e., to continue with one’s stream of thinking, or to obtain
something from somebody else. We can find resources that an
individual exploits for an extensive range of actions. Without external
resources, our cognition cannot perform its tasks properly. The point
is not that of assigning a specific resource to a specific action, but to
highlight the fact that cognition could not function as we know it
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without this synergy between external-internal resources (Clark,
2003).
Drawing on distributed cognition, adopting something because of
its popularity is compatible with the passive side of docility—the
individual tendency to take from social channels (i.e., resources, if we
were to use distributed cognition jargon) with no meaningful
interactions. The observation of other peoples’ behavior is a passive
process, and the bandwagon can emerge when individual SDC is low,
which means that individuals are in a ‘take-only mode.’ We see this
as close to mindlessness (Fiol, & O’Connor 2003), although our
proposal concerning the way individuals respond to bandwagon builds
on a different ground. Instead of focusing on the mindlessnessmindfulness tendencies of the individual, we suggest that these
depend on how cognitive processes of the other members of the
organization are structured. From this perspective, and consistently
with what Levinthal and Rerup (2006) argue, it is unlikely that an
individual is always attentive and actively engaged in mindful
cognitive processes, but it is more likely that the individual
‘distributes’ or shares his/her attentiveness with others (Giere,
2002). Our claim is that it is precisely the social environment that
supports individuals when it comes to mindfulness. In this case, we
interpret being mindful in a particular way. Building on Varela et al.
(1991), we argue that the process of becoming mindful is not an
abstract and disembodied activity, in which supposedly cold
reasoning helps us select the best option. Instead, we are referring to
an experience, which is highly influenced by the situation in which the
reasoner finds her/himself, and the external resources s/he has at
disposal. Such an experience is mindful insofar as the reasoner
adopts an attitude that is open to possibilities and chances that can
emerge in the interaction with the social environment, rather than
exclusively relying on past habits.
When the level of attention falls below a certain threshold
because of the natural limits of our cognition, there is a more active
exploitation of external resources. It is not only the single individual
responsible for bandwagons, but also wider social relations and
organizational processes (Hutchins, 1995; DiMaggio, & Powell,
1983). Hence, a variation in level of docility x explains the increase
and/or decrease of bandwagons y(x):
SOCIALLY DISTRIBUTED COGNITION AND INTRA-ORGANIZATIONAL BANDWAGON
535
Assumption 1. The increase or decrease of bandwagons (y) in an
organization is related to the SDC (x) that individuals show, on
average, in that environment.
Equation (1.1) does not capture what is in Assumption 1 as it is
an individual representation of the likelihood that joining the
bandwagon may occur. Furthermore it is too simple in that it does not
consider some of the variables that may affect the relation between
the emergence of bandwagons and individual cognitive attitudes and
behavior. In the following pages, we explore and define the
relationship between average SDC (or the level of docility) and
bandwagon through some of the variables that organizational studies
suggest could be involved in the process: (i) organizational culture, (ii)
distributed cognitive traits, such as information sharing, and (iii) the
quality of social ties or human relationships. Notation of variables and
parameters is provided in Table 2.
TABLE 2
Model Notations
Variables
y
x
n [0,…100,
…]
p
q [0,1]
u
c [0,1]
i [0,5]
w [0,5]
Description
measure of bandwagons (e.g., equation 1)
socially distributed cognition (SDC or docility intensity) which
represents the average number of decisions made on the
basis of information originating from social channels (i.e. other
human beings)
n is a measure of culture formality or informality. High values
of the parameter indicate a culture that changes at a very fast
pace, while low values indicate a formal and stable culture
probability that the individual joins the bandwagon
attitude/likelihood of the individual to take passive advice
number of individuals that have already jumped on a
bandwagon
c summarizes openness to advice and cultural permeability. In
other words, it represents how slow or fast socially distributed
cognition (SDC) affects culture
i is the tendency of people to build strong relations based on
friendship and goal sharing (altruism)
w is the level of distrust that people have on each other
(selfishness)
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TABLE 2 (Continued)
Variables
r [0,1]
Description
r stands for routines and measures the amount of routines
that are not ‘defensive.’ When r values are close to zero then
a significant part of routines are ‘defensive;’ and when it is
close to 1, this means that there are few defensive routines in
the organization
Organizational Culture and Cognition
Hutchins (1995) postulates that an organization’s behavioral and
normative structure (Scott, 2003; Meyer, & Scott, 1983) may support
or limit distributed cognitive processes. One of the widely studied
variables that encapsulates this social structure is organizational
culture (Scott, 2003). This is a very broad concept that needs to be
defined carefully because, depending on the traits of the shared
organizational culture, individuals may feel encouraged to be docile
and/or to join bandwagons. Abrahamson, & Rosenkopf (1997) and
Chiang (2007) show that the structure of a network of relationships
may affect diffusion processes, and we argue that culture is part of
that structure (Strang, & Meyers, 1993). The attempt to relate docility
and the propensity to join the bandwagon to the stability/instability,
formality/informality of organizational culture takes the network
structure one step farther.
There are many definitions of organizational culture (Ott, 1989;
Schein, 1990; Davies, Nutley, & Mannion, 2000) and there is little
consensus regarding its founding elements (DiMaggio, 1997). We
found that Schein’s (1990) definition summarizes many of the traits
that can be found in other definitions (Scott et al., 2003). According to
Schein (1990, p. 111), organizational culture is “(a) a pattern of basic
assumptions, (b) invented, discovered, or developed by a given group,
(c) as it learns to cope with its problems of external adaptation and
internal integration, (d) that has worked well enough to be considered
valid and, therefore (e) is to be taught to new members as the (f)
correct way to perceive, think, and feel in relation to those problems”.
In particular, we would like to stress “the rote of a shared belief
system in integrating the various components of the social system”
(Schein, 1996, p. 233). In a recent review of the management and
health care literature “[e]ighty-four articles appeared to report the
SOCIALLY DISTRIBUTED COGNITION AND INTRA-ORGANIZATIONAL BANDWAGON
537
development or use of organizational culture assessment
instruments” (Scott et al., 2003, p. 297). When a culture is more
formal—i.e., when behavior is strictly based on rules and norms—,
then artifacts, values, and assumptions become more apparent; we
call this evidence of a culture its recognizable traits. However, these
resources are mostly static since it is unlikely that organizational rules
change overnight or during a regular business period. Hence,
considering that “culture manifests itself [in terms of] (a) observable
artifacts, (b) values, and (c) basic underlying assumptions” (Schein,
1990, p. 111), and that there are organizational cultures where
formality is widespread, we argue that:
Assumption 2. A highly formalized organizational culture has more
potential to promote bandwagons (y) than a less formalized
culture, where a culture’s recognizable traits (n) define
formalization.
Broadly speaking, we argue that a strong/strict (more formal)
organizational culture manifests itself from the behavior of its
members and especially on limits set in the use of external resources.
When mechanical rules operate over individual choices, it is more
likely to ‘fall victim’ to bandwagons. This factor is an attempt to
include what Strang and Meyer (1993) indicate as central to diffusion
processes, and this is very close to how ambiguity and uncertainty
shape bandwagons (Abrahamson, & Rosenkopf, 1997).
Let n [0, 1, 2, …] be the extent to which individuals in a given
organization prefer stability and formalization to overcome
uncertainty over a more informal and dynamic environment at a given
point in time (T). This process leads to the emergence of two distinct
issues. First, it is very hard to discriminate between behaviors and
norms that are part of a culture from those that are not. Second,
culture is an evolving phenomenon, and it is particularly hard to
crystallize it at a given point in time. We can overcome the first
difficulty with techniques commonly used in the organizational
behavior field to define cultural patterns (Price, 1997).
How do cognition and individual behavior relate to the level of
formality (n) in a given organizational culture? The first answer can be
that of considering norms and rules as external resources to which
individual cognition must deal with or conform. Habits and common
patterns of behavior usually affect individual cognition (Kunda, 1999).
However, a more robust answer can be presented by taking into
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consideration social relations among organizational members
(discussed in the following subsection).
Organizational culture consists of routines, which can be
considered part of a culture’s formalizing process (Wezel, & SakaHelmhout, 2006). However, not all routines are equally important for
the emergence of bandwagons. We focus on a special set of routines
(r in Equation 2) that account for how individuals compensate when
too many people tend to imitate without learning: this is the case
where individuals tend to develop defensive routines resulting in antilearning cognitive procedures that are “policies or actions that
prevent the organization from experiencing pain or threat and
simultaneously prevent learning how to correct the causes of the
threat in the first place” (Argyris, 1986, p. 541). Equations 1 and 1.1
illustrate that bandwagon is not supported by average levels of SDC
(x). Only low levels of x support y, (i.e. when social relations are yet to
emerge and organizational culture is still in formation). This situation
may be compatible with organizations in the early stages their life
cycle, or during periods of crisis.
Assumption 3. Defensive routines (r) emerge when SDC (x) is
particularly low so that bandwagons (y) are more likely to
increase.
Another parameter, c [0, 1], indicates the type of individual
relations in a given organization. This parameter takes into
consideration what Saint-Charles and Mongeau (2009) report in their
study on social networks. They found that depending on the threat,
people choose different social channels. For example, when
ambiguity is present, the individual tends to rely on friends, while in
times of uncertainty, individuals prefer expert advice. This means that
a culture that serves its organization well, needs to convey specific
sentiments to its members. While n denotes the general level of
formality-informality, c is more a measure of individual propensity
toward advice giving-taking (or openness to advice) on the one hand,
and a measure of how friendly social relations are perceived on the
other (or cultural permeability). We adopt the term ‘openness to
advice’ to summarize both aspects. Of course, there are relations
among these factors, and parameter c is connected to n in that they
are mutually affected each other.
The relations described above can be represented mathematically
by Equation 2. The curve that is drawn according to these relations
SOCIALLY DISTRIBUTED COGNITION AND INTRA-ORGANIZATIONAL BANDWAGON
539
summarizes assumptions 2 and 3. This curve is a modification of a
typical diffusion model (e.g., Dreyer, 1993) adapted to reflect how
formality, defensive routines, and cultural permeability affect docility
to gauge the likelihood that bandwagon emerges. The equation also
contains parameter i, which has an important role curbing cultural
uncertainty avoidance n. The dependent variable y has been
introduced above (Equation 1); and can take the form:1
(2)
Social Relations
Parameter r[0, 1] varies according to w (i.e., distrust; Table 1) and
defines yet another aspect of the struggle between formal (i.e., the
normative structure) and informal relations i (i.e., quality of social
relations in general). In this paper, social relations refer to the ties
that bind a community or a group of human beings (Granovetter,
1973, 1985; Chiang, 2007). Social relations are defined through (1)
the frequency of interactions among organization members and (2)
their intensity, which designates their quality (Strang, & Meyer, 1993,
p. 490). To include these items in our model, we have parameters w
[0, 5] and i [-2, 2]. These two define the strength and persistency of
relations (which Chiang, 2007 and Granovetter, 1973 consider as
‘ties’) in the organization. We consider i to address the tendency of
people to build strong relations in terms of the frequency and quality
of contacts in the organization, and w the level of distrust (which
relates closely to selfishness, as defined by Simon, 1993). Otherwise
stated, the former parameter i is a docility/SDC enhancer in that it
supports information exchanges that come together with frequent
social relations (Secchi, 2009) while the latter is a docility/SDC
discounter (w) in that it considers an individual’s skepticism toward
other organization members and organizational norms. For
Kavanaugh et al. (2005), trust is very important in social relations as
it is “a feature of social capital [and it] increases as people get to
know each other, learn who is trustworthy, and experience things
together” (p. 120). Moreover, trust can be ‘thick’ or ‘thin’ depending
on its association with relatively strong or weak social relations or ties
(Kavanaugh et al., 2005; Newton, 1997). The two parameters
together express these relations.
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The combination of c—the type of relation (formal or informal)
between organization members—and i also defines the so-called
docility effect, that is to say “the level of docility the individual has.
The more docile individuals are, the more they are able to get from
the general social environment” (Secchi, & Bardone, 2009, p. 358).
This enhances their fitness in a collaborative and friendly social
environment.
Assumption 4a. Formal social structures (n) favor the emergence
of bandwagons (y) when organization members show distrust
(w), frequency and quality of relations are low (i), and
friendship is limited (c).
Assumption 4b. When the quality of social relations (i) prevails
over distrust (w), lower levels of SDC (x) are required to
prevent/control bandwagon effects (y).
Social network studies provide evidence that organizational culture
influences the nature of social relations (Pahor, Škerlavaj, &
Dimovski, 2008; Krackhardt, & Kilduff, 2002). Broadly speaking, it is
interesting to note that in the model, all other parameters being
equal, bandwagon is affected by relations that may emerge between
organization members.
(3)
With a very limited number of passages from equation (2) to
include all parameters relative to social relations, equation (3) is
derived. In summary, the model takes into account the following
elements: (a) trust/distrust w relates to the social structure of the
organization, i.e., recognizable traits (parameter n); (b) social relations
improve or worsen depending on how well the organization fits the
individual’s cognitive needs (c and i as they interact with docility); (c)
the propensity to be open-minded and to lean on social channels (i.e.,
the extent to which people show their docility/SDC, x) may vary in time
and intensity and undermines relations (x relates to c, again, and to
w); (d) when culture is strong enough, individuals are capable of
structuring docility in the organization (i.e., the docility effect) to
guarantee social relations and to prevent the system to collapse
(taking c and i together); (e) organizations develop defensive routines
(factor r).
SOCIALLY DISTRIBUTED COGNITION AND INTRA-ORGANIZATIONAL BANDWAGON
541
Following our argument, the model incorporates the fact that a
strong and formal culture facilitates imitative behaviors. This is a key
point in our cognitive-based approach, since strong cultures,
characterized by a wide range of shared values, norms, and
behaviors, present their members with predefined cognitive resources
that must be exploited (Hutchins, 2011). This mechanism leads to (a)
limiting or inhibiting externalization processes because passive and
sometimes trivial cognitive effort is required and, (b) imposing
cognitive, social, and political fines to those who deviate from the
norm.
THE MODEL: NUMERICAL ANALYSIS AND SIMULATION
We used several sources for equation modeling. Although
Equation (3) is not strictly tied to any of those consulted, we found
interesting hints from differential equation (e.g., Dreyer, 1993), and
applied mathematical modeling (e.g., Shier, & Wallenius, 2000).
Equation (3) is just an example of how to represent the relation
between bandwagon, level of docility, and the above-mentioned
parameters.
Method
To investigate some of the implications of this model, this section
presents a numerical analysis. This method has been successfully
employed in other fields of management (e.g., Carrillo, & Gaimon,
2000; Terwiesch, & Xu, 2004). What we find to be a convenient way
to proceed is to present a base example and then modify the value of
parameters to see how each one affects the relation between
bandwagon (y) and SDC (x). All modifications of single parameters are
presented on a three-dimensional landscape (Figure 1), where y
assumes the values indicated by Equation (3), as the level of SDC (x)
increases, and given the modification of one parameter at a time,
represented as the z-axis. A graphical analysis is performed.
The base example works as a benchmark for the following
analyses. The procedure is that of letting each parameter take 50
different values on the range of their variation so that we can
estimate how directly or indirectly an organization’s SDC influences
the emergence, dynamics, and decline of bandwagons.
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SECCHI & BARDONE
The next step of analysis involves considering the derivative of
equation (3) to have a clearer idea of how each parameter affects
bandwagon as the level of SDC increases. Once that information is
provided, we try to show how various configurations of organizational
structures which compare differently to the base example, may relate
to increasing docile attitudes among organizational members. All
tests and graphics have been computed using the software R version
2.14.2 (R Development Core Team, 2012). The appendix provides the
code and further details for those interested in replicating our model
and results.
For multiple parameter variations, we used a procedure for
random number generation that led to 1,000 and then 50 random
combinations of how SDC affects bandwagons. Graphical
representation has been made through bubble charts (further
information is provided below). All figures appear without numerical
marks on the axes. This is done to let the reader focus on the trend
instead of the value that is indicative only. The range of variation for
each parameter is indicated in Table 2.
Analysis of the Base Example
We think of the base example as a condition where upper levels
of SDC (x) in the organization are capable of neutralizing bandwagons
(y), given the individual probability of joining a bandwagon (p, as
defined by Equation 1). Thus, all other conditions being equal, an
increase in the tendency of individuals to make decisions on the basis
of information originating from social channels—i.e., distributing their
cognition on those social resources—helps reduce potentials of
bandwagons. When the probability of joining the bandwagon gets
close to its highest point (i.e., to 1), then the individual would do
whatever the other members of the organization are doing. This last
aspect is crucial in our model since we consider the propensity of
individuals to join bandwagons, not their actual behavior.
In Figure 1 low levels of docility represent an organization where
individuals do not depend on each other to make decisions. In that
case, bandwagons develop quickly because social interaction (and
learning) is substituted by social imitation. When levels of SDC tend,
on average to increase, then population bandwagons are restored to
their ‘physiological’ level, (i.e., they grow at a slower pace when
related to a growth in SDC). Both low and high levels of SDC do not
SOCIALLY DISTRIBUTED COGNITION AND INTRA-ORGANIZATIONAL BANDWAGON
543
make bandwagons likely to occur. This is consistent with the
assumptions of the model, as social imitation needs a minimal level
of social interaction among organizational members. In the absence
of a cognitive state that is close to passive docility, imitation and
joining the bandwagon cannot occur. In addition, when SDC is very
high, individuals are engaged in significant and meaningful cognitive
activity, and are more independent (Bardone, 2011).
FIGURE 1
The Base Example: Bandwagon Variations (Y) As a Function of
Individual Probability of Joining the Bandwagon (P) and Socially
Distributed Cognition/Docility (SDC, x)
One of the key elements in Equation (3) that is worth analyzing
through the base example, relates to the values attributed to
parameters that affect docility (or SDC), i.e., i (= 1.5), w (= 2.6), and c
(= 0.08). What the base example tells us is that employees present a
good mix of decisions based on friendships and other people’s
expertise; the docility effect, (i.e., c = 0.08 and i = 1.5) is embedded
in the organizational structure so that a significant part of
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SECCHI & BARDONE
organizational norms and rules (e.g., the observable traits, or n =
100) reflect and enhance docile attitudes. However, high levels of
docility are required to overcome the potential emergence of
bandwagons (pseudo-bell curves, Figure 1).
Last but not least, the defensive routines coefficient r (= 0.3)
describes how individuals are prone to adopt change and learn more
than merely adopting defensive routines. It is proaction versus
defensiveness. In our base example, the value associated with this
parameter is taken at one of its lowest values, indicating that the
organization has defensive routines in place. The organization
controls levels of bandwagon through docility even when a number of
defensive routines are in place. Social ties (i) and distrust (w), social
structure (n), and cultural permeability (c) take care of defensive
routines.
The base example defines the potential emergence of bandwagon
compared to any given level of SDC in what is the paradigmatic
example for this study. We need to be clear on this point. The analysis
does not reveal anything with regards to change in bandwagon and
docility over time; this is a static model. What we can do to define how
changes in docility affect bandwagons is the derivative of Equation 3
(see below).
In the following examples we let one parameter assume values in
its range, all others remaining equal, and then have combinations
that mimic different organizational structures.
Example Set 1: Single Parameter Variations
As previously mentioned, parameter n [0, …, 100, …] represents
recognizable traits, or the level of formality of organizational culture.
Starting from the base example, we let it vary assuming 50 values
among {0, 500}. Figure 2 illustrates what happens when we pass
from a more formal (lower values of n) to a less formal culture (higher
values of n), the other parameters being equal.
Bandwagon is more persistent when the organization is rigid and
too many norms sclerotize relations among individuals. This may
reflect a well-known individual tendency, that of using norms (and
routines) to engage in less mindful activities (Fiol, & O’Connor, 2003).
The only case when SDC (x) has a direct relation with the bandwagon
SOCIALLY DISTRIBUTED COGNITION AND INTRA-ORGANIZATIONAL BANDWAGON
545
FIGURE 2
Bandwagon (y) as a Function of SDC (x) for Values of Culture
Formality/Rigidity (N = 0, 10, 20, …, 500)
(y) is when there is a rigid and more flexible culture (higher values
taken by n). This means that—all other parameters being equal—a
strictly formal organization cannot rely on docility to limit bandwagon.
Together with n, the extent to which defensive routines grow in the
organization is particularly important; they are exemplified by
parameters r [0, 1]. Figure 3 presents 50 values taken by this
parameter on its range. The landscape is ‘opened’ by the different
values associated with this parameter. At very low values of r (i.e. with
almost all routines being defensive) the bandwagon (y) spreads
throughout the organization and it seems to increase independent of
SDC (x). There are several factors that seem to be important here.
First, when an organization is not proactive and it does not adapt to
external and internal pressures and change (e.g., r [0…0.2]), then
taking part in bandwagons can be perceived by members as an easy
way out. Second, levels of SDC in the organization do not change the
increasing influence and emergence of bandwagons. In that case,
bandwagons continue to exist independent of the level of SDC.
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SECCHI & BARDONE
FIGURE 3
Bandwagon (y) as a Function of SDC (x) for Values of Defensive
Routines (r = 0.1, 0.2, …, 1)
FIGURE 4
Bandwagon (y) as a Function of SDC (x) for Values of Quality of
Relations (i = -1, …, 2)
SOCIALLY DISTRIBUTED COGNITION AND INTRA-ORGANIZATIONAL BANDWAGON
547
In the numerical analysis, social relations (i) that takes values ranging
between [-1, 2]. Figure 4 shows how SDC affects bandwagons when
the quality of social relations moves from nonexistent or negative (i =
-1, …, 0) to positive (i = 1, …, 2). Bandwagons are more likely to
emerge when there are no significant social ties among members and
when SDC is particularly low. As SDC levels start to rise, social ties are
established and bandwagons remain marginal behaviors.
As far as distrust w [0, 5] is concerned, we have a completely
different situation. From Figure 5 we can observe that a lack of trust
may be a pervasive phenomenon, affecting the persistence of
bandwagon in the organization. This is apparent from our graph,
when w takes higher values (w > 4). As distrust grows, social relations
weaken, and the level of SDC needed to revert this condition back to
acceptable levels is extremely high. According to the literature
(Kramer, 1999), distrust is affected by social ties and presents issues
that are not easy to overcome; this is reflected in the model. The
problem becomes that of defining which of the two effects (between
social ties i and distrust w) is more important for an organization.
When distrust is low among organization members, then SDC gains
the potential to affect bandwagons. The next example may help
further elaborate upon this dynamic.
It is apparent from Figure 5 that increasing levels of SDC may help
reduce bandwagons only when values of i or w are respectively at
their higher or lower ends, ceteris paribus. Of course, we may have
several combinations of the two parameters here and some of them
are analyzed in the following subsection.
The last parameter to analyze is c, defined as culture
permeability, openness to advice, or the tendency of docility to
become a widespread cognitive phenomenon in the organization
through its culture. Depending on its levels (c [0, 1]), this factor
affects the dependency of bandwagons on docility when it is
significantly low, ceteris paribus (c = 0, …, 0.2). This is apparent from
Figure 6 where c ranges from 0 to .4—for values of c higher than .4,
the landscape repeats itself and therefore it has been omitted. When
the parameter ranges in its lower end, the bandwagon effect
becomes a widespread phenomenon. On the one hand, this means
that it does not matter how high the level of SDC is when people do
not take advice and/or the culture is not permeable enough:
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SECCHI & BARDONE
FIGURE 5
Bandwagon (y) as a Function of SDC (x) for Values of Distrust
(W = 0, …, 5)
FIGURE 6
Bandwagon (y) as a Function of SDC (x) for Values of Permeability of
Culture (c = 0, …, 0.4)
SOCIALLY DISTRIBUTED COGNITION AND INTRA-ORGANIZATIONAL BANDWAGON
549
bandwagons abound. On the other hand, when c is at higher levels of
SDC there is a lower occurrence of bandwagons.
To complete the analysis of single parameter variations, we want
to test the impact of organizational structure, for incremental
variations of SDC. In other words, the model needs to be checked as
to determine which among the parameters that constitute
organizational structure, makes bandwagon increase or decrease,
when the level of SDC increases. This aspect can be easily revealed
by a first-order derivative of Equation 3. This should be able to
indicate whether the quality of relations, distrust or any other
parameter is the most likely to affect the variation of bandwagons (y)
for increasing values of SDC (x).
As discussed above, while one parameter was set free to
fluctuate, all remaining parameters were anchored to the values of
the base example. The first-order derivative demonstrates that the
level of distrust (w) is what determines the widest range for variation
of (y), for infinitesimal increments of SDC (x). This probably relates to
the fact that distributed cognitive phenomena need trust and strong
social relations to occur, develop, and maintain their status over time
(e.g., Kramer, 1999; Hutchins, 1995).
What this type of analysis is not able to tell, however, is what
happens to the relation between bandwagon and cognition when all
parameters are set free to fluctuate. In other words, a good test is
that of stressing variables and seeing what happens to bandwagon
when parameters take different values. This is further discussed
below.
Example Set 2: Simulation on Multiple Parameter Variations
The model should be tested over multiple variations of
parameters to verify whether certain combinations of parameters
increase or decrease the likelihood of organizational bandwagons.
The method used in this set example is that of stressing parameters.
This time, instead of the semi-arbitrary selection of numbers for each
parameter, we let random numbers indicate the shape that
bandwagon may take to mimic realistic and unpredictable
organizational dynamics. Using R’s random number generators, all
parameters are allowed to take any value within their range.
Parameters w, c, r, and n are assigned random values on a uniform
distribution within their range, while parameter i—the only one that
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SECCHI & BARDONE
could take negative values—is assigned values on a normal
distribution (mean = 0.5, st. dev. = 0.5). The computer generates
1,000 random numbers for each parameter. The next step is that of
letting the parameters assume 50 out of the 1,000 generated values,
and substitute these values in Equation 3. This would create 50
curves with 50 random configurations representing states where a
hypothetical organization could find itself located. The likelihood of
bandwagon is defined by the dimension curve, (i.e. the shape it takes
on the xy plot). Since a bandwagon outcome is more likely when the
curve is wider (i.e. when the first s is taller and the bell-like shape is
larger) then the area underneath the curve may be taken as a proxy
of emergent bandwagons. A curve delimiting a wider area also points
towards an organization that sees the greater likelihood of
bandwagons emerging. This also means that there is a mix of
parameters that has greater chances of attracting individuals to join
the bandwagon. Integration is calculated for each of the 50
configurations and results are plotted utilizing bubble charts (Figure
7). The dimension of the circle is the likelihood of organizational
bandwagons to emerge as measured by the area behind the curve,
while combinations of parameters appear on the x/y axes and could
be easily related to the size of the circle.
For example, Figure 7a shows levels of culture permeability (c)
and culture conformity (n) associated with the occurrence of
bandwagons in 50 cases. Since all five parameters and the cognitive
variable SDC (x) help define bandwagons, the combination of the two
selected parameters points at how much they contributed to the
bandwagon outcome. Figure 7a indicates that an organization is more
likely to tend towards bandwagons when individuals rarely take advice
from each other (c), and when organizational culture is very stable
and highly formalized (n).
From the above, we know that distrust (w) has a significant effect on
determining how bandwagon emerges from organizational behaviors.
If we consider Figures 7b, 7c, 7e, and 7f, we can confirm that effect
since larger circles have the tendency to be found as we find higher
levels of distrust. On the one hand, from Figures 7c and 7e it is not
clear whether the quality of social ties (i) and defensive routines (r)
play a role on discounting higher levels of distrust. On the other hand,
culture permeability (c) and formalization (n) seem to reduce the
SOCIALLY DISTRIBUTED COGNITION AND INTRA-ORGANIZATIONAL BANDWAGON
551
strong effect caused by distrust on the emergence of organizational
bandwagons.
Another interesting finding from the simulation is that high/low
levels of defensive routines seem unassociated to bandwagons
(Figures 7d, 7h, and 7i), as there are other parameters that have
stronger effects. As a check on relations among parameters and
bandwagon, we can run a linear regression. In short, the analysis tells
FIGURE 7
Bandwagon Likelihood Given Variations of All Parameters
b. Variation of culture permeability (c) and distrust (w)
0.4
0.3
0.0
0
0.1
0.2
culture permeability (c)
300
200
100
culture formality (n)
400
0.5
500
a. Variation of culture formality (n) and culture permeability (c)
0.0
0.1
0.2
0.3
0.4
0.5
0
2
3
4
5
c. Variation of social ties (i) and distrust (w)
d. Variation of culture permeability (c) and defensive routines (r)
0.4
0.3
0.2
0.1
0.0
0.5
1.0
culture permeability (c)
1.5
0.5
distrust (w)
0.0
-0.5
social ties (i)
1
culture permeability (c)
0
1
2
3
distrust (w)
4
5
0.0
0.2
0.4
0.6
0.8
defensive routines (r)
1.0
552
SECCHI & BARDONE
FIGURE 7 (Continued)
4
3
0
1
2
d istru st (w )
3
2
0
1
d istru st (w )
4
5
f. Variation of culture formality (n) and distrust (w)
5
e. Variation of defensive routines (r) and distrust (w)
0.0
0.2
0.4
0.6
0.8
1.0
0
100
defensive routines (r)
300
400
500
h. Variation of culture formality (n) and defensive routines (r)
0 .8
0 .6
0 .4
0 .0
-0 .5
0 .2
0 .0
0 .5
1 .0
d e fe n s iv e ro u tin e s (r)
1 .5
1 .0
g. Variation of culture formality (n) and social ties (i)
s o c ia l tie s (i)
200
culture formality (n)
0
100
200
300
culture formality (n)
400
500
0
100
200
300
culture formality (n)
400
500
SOCIALLY DISTRIBUTED COGNITION AND INTRA-ORGANIZATIONAL BANDWAGON
553
FIGURE 7 (Continued)
j. Variation of social ties (i) and culture permeability (c)
0.4
0.3
0.0
0.0
0.1
0.2
culture permeability (c)
0.8
0.6
0.4
0.2
defensive routines (r)
1.0
0.5
i. Variation of social ties (i) and defensive routines (r)
-0.5
0.0
0.5
1.0
1.5
-0.5
0.0
social ties (i)
0.5
1.0
1.5
social ties (i)
us that the model explains the occurrence of organizational
bandwagons (as expected, R-squared is .94 and very close to 1.0)
and, among parameters, distrust (β = 3.259385, SE = 0.145926, pvalue < 0.001) and culture permeability (β = -13.318855, SE =
1.790792, p-value < 0.001) work as good bandwagon predictors.
IMPLICATIONS AND CONCLUSIONS
A model was introduced to help explain the bandwagon
phenomenon through the absence or presence of SDC explained
through the distributed cognition approach. One of the major features
of the model is related to the fact that it is based on the analysis of
bandwagons as a micro- and intra-organizational phenomenon.
Factors that relate the independent (x) to independent variable (y) are
parameters in Equation 3. Bandwagon may be reduced by higher
levels of docility when there is a favorable mix of these parameters.
That is to say that an open and dynamic culture that copes with
uncertainty n, an SDC facilitator c, a low incidence of the so-called
defensive routines r, together with strong social relations i, and
554
SECCHI & BARDONE
minimal levels of distrust w among members of the organization help
to keep bandwagons at a functional and workable state.
These relations derive from the five assumptions of the model.
The multiple set of numerical examples help to uncover relations
between the two variables. Overall, explaining organizational
bandwagons through distributed cognition leads us to consider: (1)
the complexity of bandwagon as a distributed phenomenon, (2)
implications for management, and (3) limitations of the model and
future research.
The Bandwagon Outcome as a Distributed Phenomenon
There are at least two important contributions made by our
model. Firstly, our model portrays bandwagon as a diffused
phenomenon that depends on organizational social settings.
Secondly, the model suggests ways to monitor and control the
phenomenon through a process that we may term distributed
mindfulness.
The assumption that bandwagon is very similar to the way
cognitive mechanisms work has been specified through some factors
typical of organizational life. This implies that statements such as (a)
“bandwagon is cognitively inexpensive” and (b) “if widespread, it has
the potential to become detrimental for organizations” make little
sense if (c) the individual is not considered together with contextual
organizational cognitive factors (Langley et al., 1996). The model
keeps these three propositions together in the way it relates how
much organizational members rely on each other (docility/openness
to advice) to the raising of the bandwagon. At the beginning of the
paper we posed the question “what prevents or sustains bandwagons
in an organization?” The model helps us state a simple answer to this
question by merging together two sets of literatures, namely
individual mindfulness (e.g., Langer, 1979; Dane, 2011) and
organizational structures (e.g., Scott, 2003, Strang, & Meyer, 1993).
In so doing, our model points out that individual cognitive capabilities
are not a sufficient means to limit bandwagons. Rather, there is a
need to tie these abilities to what constitutes human interactions.
More specifically, we need to tie them to the social characteristics of
the organization. This idea, we advance, is connected to those factors
that may support individual cognitive awareness. The model shows
that significant levels of distrust among organizational members (high
SOCIALLY DISTRIBUTED COGNITION AND INTRA-ORGANIZATIONAL BANDWAGON
555
w, low i), rigid structures that support defensive routines (low n, low r),
and a low general level of docility (low c) may support bandwagons. In
short, the following holds for the individual: (A) the status of
mindfulness is consistent with his/her distributed cognitive processes
(Magnani, 2007); (B) the docile individual is less likely to follow
bandwagons when organizational social settings are favorable (e.g.,
Strang, & Meyer, 1993; (C) a working level of bandwagon is
‘physiological’ (Esposito, 2011) for organizations even when
conditions are particularly favorable for docile individuals, since this is
part of how people learn, know, interact, and make logical reasoning
that seem good (i.e., fallacies; Woods, 2004); (D) the link between
SDC and bandwagon is non-linear, unless particular configurations of
parameters are in place.
The idea to define a level of intensity for docility (what we called
SDC) serves two purposes. The first is the above-mentioned point (B):
docile individuals need to find this characteristic in others as well
(docility effect; Secchi, 2011). The second is that we are not
suggesting that the idea of mindfulness presented in Fiol and
O’Connor’s paper (2003) should be abandoned. On the contrary, it
holds true if we try to understand how it evolves only when sociocognitive and organizational processes are considered.
A second set of findings relate to what emerges from the analysis.
We can, furthermore, use the model to define what most facilitates
bandwagons. Organizational structures are very complex and the five
parameters isolated and implemented in the model constitute only a
subset of a wider set of constructs (Scott, 2003). However, even
within this subset (w, i, c, r, n), the model points out that distrust may
be far more important than other parameters affecting the likelihood
of organizational bandwagons. If the width of curves is considered,
the first-order derivative shows this result in a very clear way.
However, this result only forms one side of the coin. What emerges
from the model is that a decrease of distrust is equally important and
beneficial to the reduction of bandwagons. The incremental analysis
works both ways. As a matter of fact, this is also reflected in the
multiple analyses and configurations of parameters as they emerge
from the simulation. The model implies that (A) trust/distrust is a key
issue in organizational structures, and suggests it could be related to
the emergence of bandwagons, and (B) there are cases in which
highly dynamic organizations (n and c), high quality of social relations
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SECCHI & BARDONE
(i), and wide openness to advice or culture permeability (c) may limit
the impact of rather selfish and individualistic behavior (Figure 7).
SDC plays a key role in the analysis of bandwagons. Overall,
higher levels of SDC bring, on average and with a favorable
combination of parameters (i.e., favorable organizational structure),
result in the decreased probability of bandwagon emerging. In some
cases, organizational structures set bandwagons so high that it
becomes widespread. Only very high levels of SDC could bring that
amount of bandwagon down, however what is the cost of all
individuals being significantly dependent on social channels? What is
the risk of having too many individuals being super-docile? If that is
the case, the risk may be that of bringing the organization towards
something where everybody is expert, leader, role model, and highly
creative (Bardone, 2011; Secchi, & Bardone, 2009). In short, such an
organization defeats the likelihood of the bandwagon phenomenon at
the cost of decreasing the level of manageability. This point may need
further consideration but, we believe, it highlights the fact that even
distributed mindfulness (as it originates from SDC) has its limits.
Organizations and managers should not rely on only one factor to
eliminate issues caused by the bandwagon. The model points this out
very clearly.
Implications for Management Studies
The model presents a number of features that may be helpful for
managers having problems defining and limiting bandwagons. We
have not tried to isolate assumptions having a particular type of
organization in mind. Rather, the purpose of the study has been that
of making sense of bandwagons independent of the type of
organization. This gives the model extensive flexibility as it may adapt
to many different organizations.
This adaptability (or fit) of the model to many different
circumstances is defined though (1) organizational culture, its
formalization, its routines, and its measures (e.g., Price 1997);
together with (2) the circles of distrust, social ties, and their measures
(social network analysis is particularly useful here; e.g., Krackhardt, &
Kilduff, 2002, Strang, & Meyer, 1993); and (3) SDC measures at both
the individual and average organizational levels (although there is no
docility scale besides the so-called c factor for collective intelligence
found by Woolley et al., 2010). These three elements of the model
SOCIALLY DISTRIBUTED COGNITION AND INTRA-ORGANIZATIONAL BANDWAGON
557
may help define bandwagon in any organization. The first implication
of this adaptability is that it helps define the curve that leads to a
given level of bandwagon. Depending on the value of each parameter,
we may have different curves (the numerical analysis above
demonstrates this point exactly), and each curve depicts the potential
for bandwagons to emerge for each level of SDC. As it is arguable that
we do not have a stable equilibrium for SDC levels in an organization,
so it should be better to select an interval of points of the curve that
relate to an approximate level of docility. In other words, the model
can be used as a descriptive tool because it defines the actual state
of organizational bandwagon, depending on parameters and variables
involved.
A second managerial implication is that of using the model to
understand the levels at which managers should operate when they
intend to foster or to decrease bandwagons. Parameters included in
the model are not easily modifiable, and not all of them have a direct
impact on bandwagon outcomes. From this angle, what the model
tells is that in order for docility to be effective over bandwagons,
certain conditions should be met. With the only exception of
significant numbers of defensive routines r, high levels of docility
prevent bandwagons to reach a dysfunctional level. The task then
becomes how managers can enhance the level of docility; that is to
say, how can they promote more distributed cognitive processes
within the organization? Although there is no definite answer to this
question, recent studies have started to address the problem (e.g.,
Michel, 2007).
A third implication is that of using the model to predict when a
level of docility intensity is detrimental or dysfunctional to the
organization’s performance. Studies on how bandwagons affect
productivity or performance (e.g., Fiol, & O’Connor, 2003) do not
provide any definitive evidence, although it is accepted that it is not
helpful when it comes to individual cognitive processes (i.e.,
mindlessness). What we have developed here may provide useful
information on threshold levels of organizational docility under which
bandwagons are more likely to emerge.
The paper outlines a model of organizational bandwagon that
depends on several parameters and one variable that related to
individual attitudes towards socially-distributed cognition (SDC, or
docility).
558
SECCHI & BARDONE
Limitations of the Model and Conclusions
The first limitation relates to the analytical methodology adopted
in this paper. In an attempt to reach a wider audience, we tried to
limit the number of tests and checks that appear in the paper. This
may weaken the presentation of the model itself and highlight the
possible lack of rigor while broadening the number of those that could
access the information provided. This methodological constraint may
be addressed in further research that solves these uncertainties and
fully develops the model in its mathematical rigor and scope. Also, the
model emphasizes institutional and cognitive parameters (Equation
3) where bandwagon is described from a separate algorithm
(Equation 1). In using Equation 3 to explain bandwagon (y) as it
derives from Equation 1 face the risk of over-emphasizing institutional
and cognitive phenomena and overlook the basic peer imitative
aspect of bandwagon. However, we take that as a basic description of
the independent variable and we claim that its variance is better
explained
by
institutional-based
and
cognitive-distributed
organizational phenomena.
The second limitation, depending on the point of view adopted, is
potentially a strength. We emphasize that the model is set up
specifically for organizations, although it can be used to study group
dynamics, (i.e., it may not be organization-specific). We do not exclude
the possibility that our model may be successfully applied to
understanding group dynamics. We are working to address this point
in further studies.
The attempt to link cognition to behavior is slippery and may be
somewhat tricky. This relation is not mechanical and may not be
automatic; it describes what ought to be and not what it is actually in
place. Although some of the parameters account for behavior (e.g.,
culture, distrust), the model is not set up to include this fundamental
difference and therefore requires fine tuning. Including time as a
variable may help solve this problem.
Another limitation is that we started from theory as opposed to
practice. This is contrary to the positivistic and behavioral tradition
characteristic of organization studies. We acknowledge that the
empirical validation may lead to a different model, and even to the
complete rejection of our initial proposition. However, we also believe
that any empirical validation must start from theoretical assumptions.
From an orthodox scientific perspective, no improvement can be
SOCIALLY DISTRIBUTED COGNITION AND INTRA-ORGANIZATIONAL BANDWAGON
559
made without theory, since empirical analysis is but a fundamental
corollary of initial theory. Despite the limitations above, we still claim
that the model is suitable for being tested as the literature provides
measures for most of the parameters and variables mentioned above
(e.g., Cameron and Quinn, 2011 for culture; intellectual openness
measures by Jackson et al, 2000; Costa & McCrae, 1992;
friendliness from Hofstee et al, 1992 and/or altruism/benevolence
from Colquitt et al, 2011; while measures of distrust may be taken
from Conn & Rieke, 1994 or as cognitive-based and affect-based
trust from Colquitt et al, 2012).
In short, the paper has provided the grounds to analyze
bandwagons as both an individual and organizational phenomenon.
To decrease the likelihood of bandwagons, organizations should
make efforts to fine tune their structures in a way that fits the socially
distributed mechanisms of individual cognition. This happens to be
dependent upon low levels of uncertainty avoidance, diffused advice
taking and giving, low levels of distrust, quality social ties, and
minimal defensive routines.
NOTES
1. Please contact the authors if you wish to have details on the
logical and mathematical steps that lead to this (eq. 2) and the
next equation (eq. 3).
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APPENDIX
All calculations, graphs, and examples have been tested using R
version 2.14.2, the R Foundation for Statistical Computing © 2012 (R
Development Core Team, 2012). This appendix provides a succinct
guide to the codes and procedures that we followed in order to
recreate (and test) the model. R is open source software and most of
its procedures and codes are made freely available online and/or on
the packages that come bundled with the software. As an
introduction to R, covering basic commands and procedures, we
suggest Venables, W. N., Smith, D. M., and the R Development Core
Team, 2005. An introduction to R (3rd ed.). Bristol: Network Theory.
For simple and advanced graph procedures, we have adopted
Maindonald, J., and Braun, J. 2007. Data analysis and graphics using
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University Press.
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Replica of the base model (Equation 3) --- variation of one parameter,
ceteris paribus
The following is to replicate Figure 4; all other figures can be
derived restoring i to its base level and having parameters vary one at
a time, all others remaining equal.
> w <- 2.6
> c <- 0.08
> r <- 0.3
> n <- 100
> i <- c(seq(from=-1, to=2, length=50))
> x <- c(seq(from=0, to=500, length=50))
> f <- function(x,i){
+ t1 <- x^w
+ t2 <- n^i
+ t3 <- exp(c*x)
+ t4 <- x^(1-r)
+ t1/(t2+t3/t2)+t4}
> y <- outer(x,i,f)
> persp(x,i,y, theta=115, phi=15, r=1, d=5, expand=0.5,
ltheta=70, lphi=180, shade=0.5, ticktype="simple", nticks=5,
col="green").
Multiple parameter variation
Parameters w, c, n, and r are assigned values on the basis of a
uniform distribution, i is simulated using a normal distribution. Here is
the procedure for random number generation:
> w <- runif(1000,0,5)
> i <- rnorm(1000,mean=0.5,sd=0.5)
> r <- runif(1000,0,1)
> c <- runif(1000,0,0.5)
> n <- runif(1000, 0, 500)
> x <- c(seq(from=0, to=500, length=1000))
The equation is as follows:
> K <- 1
> yK <- (x)^w[K]/(n[K]^i[K]+(exp(c[K]*x)/n[K]^i[K])+x^(1-r[K]))
K is assigned different values, from 1 to 50. Every time K is assigned
a different value, then equation, standard deviation, mean, and
integrals are computed, as follows:
SOCIALLY DISTRIBUTED COGNITION AND INTRA-ORGANIZATIONAL BANDWAGON
571
> yK1 <- (x)^w[K]/(n[K]^i[K]+(exp(c[K]*x)/n[K]^i[K])+x^(1-r[K]))
> sd(yK1)
> mean(yK1)
> yK1 <- function(x){ (x)^w[K]/(n[K]^i[K]+(exp(c[K]*x)/n[K]^i[K])+
x^(1-r[K]))}
> integrate(yK1,lower=1, upper=Inf)
The integral needs a function(){} to be computed. The first line of
commands below provides a graphical account of linear relations
between y and x. We need to make sure that results are saved and
stored in a vector. Then, a cumulative function with all of the integrals
of y needs to be assembled (as it provides a generic idea of the
phenomenon). Here is an example with the random data provided:
> lines(yK5~x)
> density <- c(0.05580783, 176665.7, 1013236, … )
It may be convenient to have more homogeneous values, with
density = 1 as the max value, if needed:
> d.perc <- density/max(density)
We decided to stick with the original ‘density’ as calculated above.
Then, a data frame with all parameters and variables needs to be
created. A regression and relative diagnostic plots helped to check for
outliers and refine and interpret results. This is the code to obtain
these results on a range of 50 values; where number of values
considered can be expanded at will:
> reg <- data.frame(density, w[1:50], i[1:50], n[1:50], c[1:50],
r[1:50], x[1:50])
> lm.density <- lm(log(density)~w+i+n+c+r+x, data=reg)
> summary(lm.density)
To check for outliers, standard procedures apply:
> par(mfrow=c(2,2)) ## see four graphs in one screenshot
> plot(lm.density)
> plot(d.perc, type="h", subset=w>2.6) ## if you want to see only
a section of data
The next step is that of creating a data frame with all of the variables:
> data.mat <- c(d.perc, x[c(1:50)],
w[c(1:50)],i[c(1:50)],r[c(1:50)],c[c(1:50)],n[c(1:50)])
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> colnames(data.mat) <- c("d.perc", "doc", "distrust", "soc.ties",
"def.rout", "eff", "unc.avoid")
> attach(data.mat)
From library(ggplot2) there are several commands that lead to Figure
7; here is how it works (after some readjustments) for one of the subgraphs, after segmenting R’s Quad for graphics. What we used is
adapted from N. Yau (http://flowingdata.com/2010/11/23/how-tomake-bubble-charts/):
> radius <- sqrt(sqrt( bandwagon[-c(8,11)]/ pi ))
> par(mfrow=c(3,3), pty="s")
> symbols(data.mat$soc.ties[-c(8,11)], data.mat$eff[-c(8,11)],
circles=radius, inches=0.5, fg="black", bg=rgb(227,227,227,
100,maxColorValue=255), xlab="social ties (i)", ylab="openness
to advice (c)", cex.axis=1, cex.lab=1.2, main="j. Variation of
social ties (i) and culture permeability (c)", cex.main=1).