Socially distributed cognition and intra-organizational bandwagon: Theoretical framework, model, and simulation

SOCIALLY DISTRIBUTED COGNITION AND INTRA-ORGANIZATIONAL BANDWAGON: THEORETICAL FRAMEWORK, MODEL, AND

SIMULATION

Davide Secchi and Emanuele Bardone* International Journal of Organization Theory and Behavior 16(4):521-572

ABSTRACT. The 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 the bandwagon phenomenon 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 bandwagon 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 (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). ------------------------- * 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.

SECCHI & BARDONE 2013

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; 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 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,

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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 re-shape 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 (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.

Tackl ing 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 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.

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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 core-peripheral 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 ‘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 and 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 I, row 1) in the modeling effort, together with a measure of how strongly or weakly tied are employees (i.e., social relationships; Table I, rows 4 and 5). Details are provided in the following section.

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

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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 I (rows 1, 2, and 4) summarizes a selection of authors that have 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 over-assess 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 macro-levels. 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 field 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, 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 I (rows 1, 2, 3, 6, and 7) provide 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

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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 I, right end column). Subsequently, we focus on a numerical analysis and simulation of how parameter variations affect the relation between dependent and independent variables.

ORGANIZATIONAL DETERMINANTS

A Social ly Distr ibuted 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 suggests (see the review by Bonaccio, & Dalal, 2006) 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) 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 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:

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(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 (as specified in equation 1).

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.

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 phenomenon. 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, & Johnson-Dyke, 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 information depends on the subjective level of docility and on other peoples’ docility (Simon, 1993).

Distr ibuted Cognit ion and Doci l i ty

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

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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 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 mindlessness-mindfulness 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):

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 Description

y measure of bandwagons (e.g., equation 1)

x

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)

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Variables Description

n [0,…100, …] 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

p probability that the individual joins the bandwagon

q [0,1] attitude/likelihood of the individual to take passive advice

u number of individuals that have already jumped on a bandwagon

c [0,1] c summarizes openness to advice and cultural permeability. In other words, it represents how slow or fast socially distributed cognition (SDC) affects culture

i [0,5] i is the tendency of people to build strong relations based on friendship and goal sharing (altruism)

w [0,5] w is the level of distrust that people have on each other (selfishness)

r [0,1]

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 Cognit ion

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 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,

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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 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, & Saka-Helmhout, 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 anti-learning 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 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

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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.

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).

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,

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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.

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).

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 member 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 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 Variat ions (Y ) As a

Function of Individual Probabil i ty of Joining the Bandwagon (P ) and Social ly Distr ibuted Cognit ion/Doci l i ty

(SDC, x )

One of the key elements to analyze in equation (3) through the base example, is related to the values attributed to parameters that affect docility, 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

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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 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, 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 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 Variat ions

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.

FIGURE 2


Bandwagon (y ) as a Function of SDC (x ) for Values of Culture Formality/Rigidity (N = 0, 10, 20, …, 500 ) , Ceteris

Paribus (see the base example)

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 (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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FIGURE 3 Bandwagon (y) as a Function of SDC (x) for Values of Defensive Routines (r = 0.1, 0.2, …, 1 ) , Ceteris Paribus

(see the base example)

FIGURE 4 Bandwagon (y) as a Function of SDC (x) for Values of

Quality of Relations ( i = -1, …, 2 ) , Ceteris Paribus (see the


base example)

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 argue 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 (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: bandwagons abound. On the other hand, when c is at higher levels of SDC there is a lower occurrence of bandwagons.

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Distrust (W = 0, …, 5 ) , Ceteris Paribus (see base example)


Permeabil i ty of Culture (c = 0, …, 0.4 ) , Ceteris Paribus (see the base example)


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 parameters that constitute the 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). We have drawn a curve for each parameter varying in its range (Figure 7) to compare effects of parameters in the equation.

As discussed above, while one parameter was set free to fluctuate, all remaining parameters were anchored to the values of the base example. Figure 7 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

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below.

FIGURE 7 Graphical Representation of First -Order Derivative for

Bandwagon’s Inf initesimal Variat ion of SDC (X ) ; Parameter Fluctuations in their Range

Example Set 2: Simulation on Mult iple Parameter Variat ions

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 could take negative values—is assigned values on a normal

dy - basewin (dotdash)c (longdash)r (two dash)


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 8). 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 8a shows levels of openness to advice (c) and uncertainty avoidance (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 8a 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).

Figure 7 indicates that distrust (w) has a significant effect on determining how bandwagon emerges from organizational behaviors. If we consider Figures 8b, 8c, 8e, and 8f, 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 8c and 8e 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, openness to advice (c) and high uncertainty avoidance/formalization (n) seem to reduce the 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

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(Figures 8d, h, and i), 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 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, p-value = 2e-16) and openness to advice (β = -13.318855, SE = 1.790792, p-value = 3.01e-09) 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 IV (x) to the DV (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 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.


FIGURE 8 Bandwagon Likel ihood Given 50 Random Levels of Al l Parameters

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The Bandwagon Outcome as a Distr ibuted 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 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 socio-cognitive 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, Figure 7 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 (i), and wide openness to advice (c) may limit the impact of rather selfish and individualistic behavior (Figure 8).

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

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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 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).

L imitations 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

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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 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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Socially distributed cognition and intra-organizational bandwagon: Theoretical framework, model, and simulation