A Consumer Memory-based Model of New Product Diffusion within a Social Network Johannes Kottonau Jan Burse Claudia Pahl-Wostl Swiss Federal Institute for Environmental Science and Technology (EAWAG) Department of System Analysis, Integrated Assessment, and Modelling (SIAM) Ueberlandstrasse 133 P.O. Box 611 CH-8600 Duebendorf Switzerland
[email protected] http://www.internal.eawag.ch/~kottonau/
ABSTRACT:
Explicit processes of persuasion, resistance and acceptance are generally not incorporated in theories of new product diffusion. It is supposed that the deficiency of a dynamic cognitive foundation limits the use of diffusion models for scheduling advertising campaigns. An agent-based computational model is presented which encompasses the whole attitude formation and decision-making process. Product sellers, the mass media, and the consumers are connected in a social network. Running the simulation yields realistic communication about the product alternatives, and the belief exchange leaves episode traces in the consumer memories. A new memory model draws on the spreading activation metaphor and produces four separated responses to the communication episodes. These response types result from crossing the automatic-controlled and the holistic-analytic dimensions of information processing. KEY WORDS: Agent-based social simulation, diffusion theory, social networks, persuasion, habit breaking spreading activation memory model, automaticity, holistic processing
Submitted to the 10th meeting of the Annual Workshop on Computational and Mathematical Organisation Theory, CMOT, Computational Social Organisational Science Conference, CASOS, CMU – Pittsburgh, PA, July, 21-24
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Introduction Current standard models of product diffusion processes lack an explicit decision-theoretical foundation on the micro level (Rogers 1983; Mahajan, Muller et al. 1990; Gatignon and Robertson 1991). The main equation of the standard Bass model (Bass, 1969) operates totally on the level of macro variables: it traces the total number of adopters and non-adopters within the population over time. The two parameters in the equation might be interpreted as the processes of how the first adopters are impersonally “persuaded” and how non-adopters “imitate” the first adopters (Mahajan & Wind, 1986). Although some refinements have been added to the equations in the last few years (Koschnick, 1997; Strang & Tuma, 1993), step-bystep specifications of the adoption process at the micro level are still missing. We suppose that this is the main reason why applications in marketing and advertising planning are extremely limited (Dockner & Jorgensen, 1988): if typical problems (like scheduling an advertising campaign) occur, the Bass formalism yields rather general answers without much useful content for practitioners1 (Klophaus, 1995). In search of theories about underlying motivational processes of product diffusion, the modeller faces a vast body of literature about consumer responses to persuasion activities. Many of the most elaborated and empirically well-tested ideas draw on models of persuasion developed in the field of social psychology (Chaiken, Liberman, & Eagly, 1989; Chaiken & Trope, 1999; Kruglanski & Thompson, 1999; Petty & Cacioppo, 1986). However, a totally procedural framework of attitude formation and product diffusion has not yet been developed. The current work proposes the novel Episode-based Social Persuasion (ESP) Model as a first step in this direction. It includes a computational architecture of the consumer’s cognition which is as simple as possible, but as complex as necessary. As a first criterion of indispensable complexity, the resulting artificial consumer should be open to the symbolic aspects of products associated with ego-defence and impression-management. These product aspects have clearly shown to be as important as the instrumental aspect for consumer decision-making. (e.g. McCracken, 1988). As a second criterion, the architecture should consider both human inertia and habit breaking processes. Sticking to old behavioural scripts consumers are prevented from trying (or even perceiving) new product alternatives even if these are heavily marketed (e.g. Ram & Sheth, 1989). The proposed consumer model has to encompass rich motivational components mediating the part of available information actually acquired and used as knowledge in the adoption process. The long-term goal of this research to improve the understandings of the consumer responses to specific communication patterns scheduled in advertising campaigns. Since the emergent dynamics of consumer responses in the social network cannot be trivially forecasted, the pragmatic question is rather: how does the campaign optimally react to the ongoing dynamics of the diffusion process? What has to be done if certain consumer groups, which were intuitively expected to take the role of innovators, resist adopting the product? How to adapt the communication strategy if unexpected negative product beliefs are devastatingly spreading through the social network? How to tackle severe consumer resistance due to habits in product use?
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The models produce reliable forecasts of potential sales only if at least eight data points of the diffusion trace are known and the point of inflection (the point of maximum penetration rate) is included in the data set (Heeler & Hustad, 1980; Srinivasan & Mason, 1986).
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The paper is organised in the following way: Chapter 1 is a general outline of the ESP model. The central theoretical components are presented in brief to provide an overview of the implemented structure and processes. In chapter 3, the model is applied to a specific domain of product diffusion. The specifications required to adapt the general model to the case of the diffusion of the Car Sharing concept are presented in detail. If the principles of Car Sharing are unknown to the reader, chapter 2 provides a useful short introduction. Although the focus of this paper is clearly on the theoretical foundation of the model, chapter 4 summarises some first experiments with the ESP model, which are discussed in chapter 5. Finally, the appendix supplies some rationale of the step-by-step social psychology approach. 1.
Theoretical Components
Several concepts of empirical and theoretical research in consumer psychology serve as the corner stones of the ESP model. In section 1.2, a first brief sketch of the model serves as a guide through the following chapters. In the following sections, the elements required to account for habitual consumer behaviour are introduced. An important element is the distinction of the habitual versus the deliberative mind-set is deduced from habit and habit breaking research (sections 1.2 and 1.3). Section 1.4 captures some ideas of how to specify these mind-sets referring to the automaticity dimension and the strategy dimension. These dimensions lead to the core concepts of the ESP model described in section 1.5. Four basic types of consumer responses are introduced. They serve as sources of the broader attitude construct. The differentiation of the attitude into three different response accounts allows a new approach to the recently investigated concept of attitude strength (Petty & Krosnick, 1995). Section 1.6 introduces the episode memory model, which underlies the temporal dynamics of the response accounts. Chapter 1.7 intermediately describes the formation of confidence in the attitude leading to an implementation intention and the final purchase decision. The last section 1.8 relates the ESP model to the recently proposed Uniform model of persuasion (Kruglanski & Thompson, 1999). 1.1
Overview
The ESP model is based on the idea that attitudes towards innovations are mainly socially constructed since (as true innovations) they are unrelated to direct consumer experience. The model aims to trace the whole chain from elementary beliefs about the product aspects up to the final consumer decision. As a first step consumers actively seek or are passively provided with beliefs about the products (the standard and the alternative, say). The result is a set of communication episodes leaving traces in the consumer memory. Depending on the degree of involvement into the product category of the innovation, these traces are continuously integrated forming more or less controlled (vs. automatic) and analytic (vs. holistic) responses. These responses are the underlying structure of the resulting attitude valence containing additional information of its strength or its affective component. The final purchase decision is made if confidence in the attitude exceeds some threshold (e.g. number of days being positive) and the purchase intention had time to grow. Consumers serve as source of second-order beliefs and communicate their attitudes to other consumers. This re-entry produces highly unforeseeable effects of the beliefs currently spreading through society. This overview in mind (see also Fig. 4), several basic concepts of the ESP model are presented through this chapter now.
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Fig. 1 General outline of the Episode-based Social Persuasion (ESP) Model. The model is a step-by-step simulation of the attitude formation process starting from first-order beliefs from informants and other consumers. 1.2
Habits
Bargh and Gollwitzer (1994) define habits as goal-directed automatic behaviours2 (cf. Ouellette & Wood, 1998; Ronis, Yates, & Kirscht, 1989). That is, a particular situation automatically activates suitable goals and a sequence of appropriate actions. Habits are supposed to be mentally represented as triple associations between a specific situation, situation-specific goals, and goal-specific actions (cf. Aarts & Dijksterhuis, 2000). The primary steps towards building a new habit are passing through the “deliberative mind-set” and then repeating the “implementation mind-set” (Gollwitzer, 1993). Thereby, the triple associations are deliberately formed and subsequently strengthened by recurring implementation. The result of this loops through different action phases is the “habitual mind-set” (Verplanken & Aarts, 1999). 1.3
Habit breaking and mind-sets
Regarding processes of habit formation arises the question of how inappropriate habits are broken. If a habit is inappropriate, a particular situation triggers inappropriate instrumental acts. Naturally, these “wrong” instrumental acts were once considered the “right” ones. There has been a deliberative attitude formation process, which has evaluated the wrong acts as the “best” ones available. However, if at a later point in time the evaluation of the “best” instrumental acts exceeds a certain threshold of dissonance, the deliberative mind-set is activated again and a new 2
The automatic character of habits is supported by the work of (Verplanken, Aarts, Knippenberg, & Knippenberg, 1994; Verplanken, Aarts, Knippenberg, & Moonen, 1998). They could find that the stronger the habit the weaker the relationship between deliberately formed intentions and subsequent behaviour. If habits are strong, the attitude-behaviour relationship is relatively weak.
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attitude formation process starts. Thereby, new chains of different instrumental acts are assessed. For example, if somebody moves from a rural region to the city, the quality of local and national transport can abruptly increase. The former habit of using a private car for commuting3 might therefore be regarded as increasingly inappropriate. During this “waking up” phase, the deliberative mind-set becomes active. In the following persuasion process, alternatives like public transport systems are carefully evaluated. Given this evaluation is found to remain positive for a longer period, the consumer might sell her/his car. In this way, using public transport for the purpose of commuting can become a new habit. The goal of the next section is to go beyond the concepts of habit and mind-sets and to deduce the concepts of automaticity and strategy as core elements of the ESP response model of information processing. 1.4
The automaticity x strategy interaction
Many theories of encoding, storing, and retrieving information refer to the dimension of automaticity (Schneider & Shiffrin, 1977; Shiffrin & Schneider, 1977) to differentiate modes of information processing (see Tab. 1). In this paper the dimension of automaticity is relevant to demarcate central characteristics of the deliberative and the habitual mind-set. Tab. 1 Aspects of the end points along the automaticity dimension and related dichotomies. The labels of the two columns represent the extremes of a continuum pointing to a rich palette of intermediary forms. Controlled processing
Automatic processing
conscious
unconscious
explicit intended, effortful voluntarily initiated and terminated deliberative
implicit unintended, effortless involuntarily initiated and terminated evocative
(Jacoby, Toth, & Yonelias, 1993) (Graf & Schacter, 1985) (Bargh, 1996) (Kihlstrom, 1984) (Dulany, 1991)
In the deliberative mind-set extreme, mental activity is supposed to be under strict control. Specific processes can be voluntarily initiated and terminated. Memory retrieval, representation, and encoding are conscious mental acts. In the habitual mind-set extreme, the habitual behaviour is supposed to run automatically, i.e. under weak control and monitoring. Habitual processes are not intentionally initiated and terminated; they are automatically evoked. Memory retrieval, representation, and encoding are unconscious mental acts. Besides the automaticity continuum, the ESP model is based on a second dimension separating different types of information processing. This dimension demarcates two extremes of strategy. They reach from a completely holistic to a completely analytic thinking approach (Foard & Kemler Nelson, 1984; Lockhead, 1972). The central idea is that if the deliberative mind is active, the subject is not only able to control the automaticity but also the strategy of thinking. People have a great deal of experience about what they consider to be the best 3
Choosing the travel mode is a typically repetitive habit or routine (Banister, 1978; Goodwin, 1977; Verplanken, Aarts, Knippenberg, & Knippenberg, 1994).
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combination between rather holistic and rather analytical thinking. As can be seen in Tab. 2, attitude formation under the one extreme of analytic strategy includes for example relying on the evaluative, data-driven, “knowing-that”-response. Attitude formation under the other extreme of the holistic strategy encompasses for example relying on the affective, conceptually driven, “feeling-of”-response. In contrast, in the habitual mind-set, the strategy of processing cannot be controlled. It is presumed that the default strategy in the habitual mind-set is holistic (Kemler Nelson & Smith, 1989). Tab. 2 Aspects of the end points along the strategy dimension and related dichotomies. Analytic strategy
Holistic strategy
data-driven
conceptually driven
knowing-that response evaluative response
feeling-of response affective response
changes slowly and resistant to change more highly integrated appraisals of past events experienced actively
changes more rapidly and more easily more crudely integrated “vibes” from past events experienced passively
(Bobrow & Norman, 1975) (Dulany, 1991) (Breckler & Wiggins, 1991) (Epstein, 1991)
There is now ample evidence that the main determinant of the mind-sets is the involvement construct (Kroeber-Riel & Weinberg, 1996). Used for the first time in the 40’s in the field of attitude psychology, it now figures prominently in theories of persuasion. Involvement can be broadly defined as the perception of relevance (or importance) of a person, object or event (Muncy and Hunt 1984, Park and Mittal 1985, Mittal 1995, Thomson, Borgida et al. 1995, Kroschnick 1997). In the field of consumer research, the perception of relevance has been found to correlate with (Laurent & Kapferer, 1985): -
product interest positive reinforcement, fun and gratification when deciding and consuming possibilities of identification and symbolic expression, risk of the wrong decision costs of the wrong decision
The ESP model proposes that involvement is the result of a slowly or rapidly growing cognitive dissonance following an increasingly inappropriate habit related to product use. If the dissonance exceeds a certain threshold, involvement for the product category increases dramatically. The consumer is resolutely decided to change the situation. To fulfil the picture of the motivational model of the consumer the section 1.5.2 provides some additional refinements. Biases and congruities capture the characteristics of the filtering mechanism turning mere information from communication episodes into valuable knowledge used in product evaluation. To avoid redundancy the basic structure of beliefs and communication episodes have to be clarified first in the following section 1.5.1.
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Episode-based Social Persuasion
In the case of innovations, people obviously lack direct experience with a new product. Attitudes towards innovations are for the most part socially constructed since various beliefs from different sources are integrated (e.g. sellers, mass media, and other consumers). Each of these sources is supposed to hold a bundle of beliefs about the product (see section 3.2.2). Consumers either contact the sources actively (e.g. seeking information, looking for advice from friends, reading specific magazines) or they are passively influenced by them (e.g. exposure to printed or electronic advertisements, picking up some beliefs from accidental small talk). 1.5.1
Episodes as context-rich beliefs
Whether consumer behaviour is active or passive, in both cases the result is a communication episode. In the ESP model an episode contains the original content of the belief, and additionally the most important context variables. The unspecific belief content comprises of (for examples, see Tab. 3) -
the target product of the belief the predicate towards the product: very good, good, bad, or very bad the conditions of being relevant to the consumer
Tab. 3 Some examples out of the belief bundles held by the seller of Car Sharing, sellers of private cars, and the mass media. Source
Target product
Predicate
Condition (s)
Interpretation
Car Sharing seller, Belief Nr. 5
Car Sharing
+2
Importance of greenness = high
“If environmental protection is important for you, Car Sharing is very good (savings of white and grey energy, less area consumption).”
Media, Belief Nr. 8
Car Sharing
-2
Distance of Car Sharing station = low
“If the Car Sharing location is very far away from your household, forget about Car Sharing.”
Car seller, Belief Nr. 2
Private car
+1
Importance of autonomy = high
“If autonomy is important for you, cars are very good (purchase act, full property rights, no reservation necessary, no constraints of usage pattern):”
After the communication act, the following specific context variables are added: -
the moment in time the communication occurred the credibility the consumer maintains toward the source
These composite beliefs are the fundamental units of which the holistic and the analytic response are built (for an overview, see Fig. 4).
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1.5.2
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Processing differences between mind-sets: biases and congruities
In the following sections the distinctions between the three responses are now further detailed along the dimensions of biases and congruities. The ESP model makes use of the concept that in the deliberative mind-set biases are consciously suppressed. Conversely, the congruity between episode content and the consumer’s situation and motivation is fully assessed. In the habitual mind-set, biases are entirely active, whereas congruity is rather roughly considered. The next sections provide further elaboration of these concepts. They are necessary to turn the mind-sets into fully specified constructs. The ESP model makes use of the concept that in the deliberative mind-set biases are consciously suppressed. Conversely, the congruity between episode content and the consumer’s situation and motivation is fully assessed. In the habitual mind-set, biases are entirely active, whereas congruity is restricted to the symbolic congruity referring to the highly accessible self-concept. Confirmation bias People often hang on to their favoured hypotheses and show considerable opposition towards new evidence. This kind of inertia is called the confirmation bias (Klayman, 1995). It usually leads to holding overly restricted hypotheses (Klayman & Ha, 1987; Wason, 1960), because of a tendency of not testing broader or opposite hypotheses. That is, consumers place too little weight on beliefs which are inconsistent with their current attitude and preferably place too much weight on beliefs which are attitude consistent. In the ESP model, beliefs in opposition to the consumer’s present general attitude are distrusted (the episode credibility is lowered), whereas beliefs fitting the general attitude are considered to be more believable (the episode credibility is elevated). Status quo bias Samuelson and Zeckhauser (1988) define the status quo bias as a strong tendency towards “(…) doing nothing or maintaining one’s current or previous decision.” This effect is related to the endowment effect (Thaler, 1980) and can be shown to be a special case of the more general loss aversion bias (Kahnemann & Tversky, 1984). It arises if people would demand more to give up an object than they would pay to acquire it (Kahnemann, Knetsch, & Thaler, 1991). Another source of the status quo bias is the psychology of sunk costs. It is the “(…) tendency to continue an endeavour once an investment in money, effort, or time has been made.”(Arkes & Blumer, 1985). Translated into the ESP model, consumers increase the credibility of beliefs recommending their possessions (or advising against their non-possessions, respectively) and decrease the credibility of beliefs which advise against their possessions (or recommending their nonpossessions, respectively). Negativity bias Research in knowledge integration has found a consistent body of evidence that, roughly speaking, people are more sensitive towards bad news than towards good news (for a review, see Kanouse & Hanson, 1972). In the same vein, prospect theory (Kahneman & Tversky, 1979) incorporates evidence that anticipated losses are weighted roughly twice as heavily as anticipated gains.
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This is directly implemented in the ESP. Warning beliefs with negative predicates have twice as much impact on judgement formation as recommending beliefs with positive predicates. Instrumental Congruity Products remove or avoid functional problems (Park & Mittal, 1985), depending on the fit into a specific functional niche of the consumer’s household. This kind of congruity represents highly intended and consciously controlled objectivity of the consumer when s/he is considering what is truly useful for her/him. Therefore, in the ESP model it is solely in the deliberative mind-set that the impact of an episode is weighted with the predicted congruity between functional niche and conditions of the episode. Symbolic congruity Products provide social or aesthetic utility (Belk, 1988; Levy, 1959; MacInnis & Jaworski, 1989; McCracken, 1988; Park & Mittal, 1985; Veblen, 1899). They protect and enhance selfconsistency, self-esteem, social consistency and social approval if they match the symbolic niche or the consumer’s self-concept (Gardner & Levy, 1955; Grupp & Grathwohl, 1967; Sirgy, 1982; Sirgy, 1985). Integrating values into the ESP model builds heavily on research on meansend chains (Gutman, 1982) and attitude functions (Katz, 1960; Maio & Olson, 2000). According to this research, human values are important constructs when it comes to understanding the attitude-behaviour relationship and aspects of attitude strength. In the ESP model, symbolic congruity is considered to be active in both the deliberative and the habitual mind-set. The self-concept is regarded as a highly accessible mental representation, which is permanently activated in both the habitual and the deliberative mind-set (Burnkrant & Unnava, 1995). After this brief introduction into some theoretical background, it is now possible to offer definitions of the four core concepts modelled in the ESP model (see Fig. 2). 1.5.3
The automatic holistic response
If the consumer’s involvement is low, all episodes related to a particular product are processed in a primarily automatic and holistic way. In the unconscious, their symbolic congruity is continuously monitored, whereas their instrumental congruity is not. Adoption impediments like a low income or an unsuitable niche for the product in the household are largely ignored. That is, the measure of relevance is restricted to the congruity between current values of the consumer and the episodes. Episodes are integrated forming an intuitive response including general characteristics, feelings, and impressions4. In view of the fact that control in the habitual mind-set is low, the negativity bias, the confirmation bias, and the status quo bias are not suppressed and fully active.
4
The content of the holistic response is very close to the notion of product image. Jain and Edgar (1976) define the typical constituents of product images as general characteristics, feelings, or impressions. Poiesz (1989) proposes to reserve the image concept for low involvement impression formation, since attitude measuring techniques often invoke unnatural medium or high involvement.
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Fig.2
Response types underlying the specific attitudes under low and high involvement regarding the product category. The level of involvement is the main determinant of the motivational filter used in a particular integration mode. If the involvement is low, only the strongly biased automatic holistic response is active. If the involvement is high, the controlled analytic response is active in an additional manner. This mode is less biased and considers the instrumental congruity. The superposition of the two processing modes is supposed to be the most rational (and mostly used) strategy of consumers under high involvement.
1.5.4
The controlled analytic response
If the consumer’s involvement is high, all available episodes related to a particular product are processed in a controlled and reflexive way. Both their instrumental and symbolic congruity related to both the current motivation and the current situation of the consumer is continuously monitored. This procedure ensures that adoption impediments are recognized and only strictly relevant episodes influence the controlled response. 1.5.5
The controlled holistic response
High involvement and control does not imply that the obligatory strategy of processing is restricted to the analytic extreme. Rather, there is a lot of evidence from everyday life that holistic thinking is experienced as a preferred pattern for approaching careful problem solving and decision making (Bokoros, Goldstein, & Sweeney, 1992; Hunt, Krzystofiak, Meindl, & Yoursy, 1989; Myers & McCaulley, 1985). This is particularly true if available information is overly multifaceted and ill structured. For instance, consumers may engage in careful
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introspection to elucidate and refine intuitions and spend time for incubation or relaxation allowing feelings to mature. 1.5.6
The controlled analytic/holistic superposition response
The result is a controlled superposition of both the analytic and the holistic strategy. Whereas in the controlled analytic response the negativity bias, the confirmation bias, and the status quo bias are suppressed, in the controlled holistic response biases are still strong (as in the automatic holistic response). This composite response can be regarded as an attitude integrating the cognitive and affective response5. It is one of the merits of the ESP model to be able to catch this rich content of the attitude construct. 1.6
The memory model
A core model element of the ESP model is the simulation of the activation level of each available episode, i.e. there is a memory model. The implemented processes are built on the principles of the spreading activation memory metaphor (Anderson, 1983). That is, the activity of every episode gradually decays following the potential law of forgetting (Anderson & Schooler, 1991; Wixted & Ebbesen, 1991). The process of forgetting is supposed to be relatively fast if the mind-set is habitual at the moment of communication. Conversely, if the mind-set is deliberative regarding the moment of communication, the process of forgetting has been shown to be relatively slow (e.g., Park & Hastak, 1994). 1.6.1
Activity, noise level, and use probability
However, holistic and analytic responses do not directly form from integrating differently activated episodes. According to Anderson (1993), there is noise resulting in fluctuations around the mean activity. Integrating noise into the memory model leads to the construct of the use probability. The mean level and the standard deviation of the fluctuations combined with a certain activity threshold define the probability of the episode to be used in the formation of the responses (see section 3.3 and Fig. ). In other words, the more recent the episode, the higher its remaining activity, and the easier it exceeds the threshold of being used.
5
Social psychologists commonly differentiate between affective, cognitive and behavioral responses (cf. Breckler & Wiggins, 1991).
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Fig. 3 Episode activities are assumed to be normally distributed fluctuating according to a specific noise level noise σ m given in a particular mind set m . The smaller the gap between the mean activation level Ai (t ) and the use threshold τ m , the bigger the probability pi of being used in the episode integration. 1.6.2
Mental accounting, attitude strength, and uncertainty
The memory is organised using a hierarchical account structure (Brendl, Markman, & Higgins, 1998; Henderson & Peterson, 1992). The top accounts are the two products in the market. Each product provides four activity sub-accounts for the very-good-predicate, the goodpredicate, the bad-predicate, and the very-bad-predicate. The sub-accounts are further divided into an account for the holistic and the analytic response. The activity of each account results from an additive superposition of all available episodes referring to a certain product, containing a certain predicate, and being formed in a particular strategy (see figure 1). As will be shown in greater detail later (see section 3.3), keeping the four predicate activities separated provides the necessary data to find a measure for attitude uncertainty or, conversely, attitude strength (Petty & Krosnick, 1995; Smith & Haugtvedt, 1995). If one of the predicate activations dominates the other three, uncertainty about the “correct” attitude is low. On the other extreme, if all activities are equal, uncertainty is at the maximum. 1.7
Product Niche, Confidence, and Decision-Making
If an attitude towards a new product remains positive for a longer time, the consumer starts forming an implementation intention (Ajzen, 1985; Ajzen, 1991; Gollwitzer, 1993). In the ESP model these processes are kept very simple: As a first step towards implementation, the actual situation is carefully re-examined to verify a truly suitable niche for the product. In this reexamination, the quality of the anticipated niche fit must exceed a certain threshold for the focused product.
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As a second step, the attitude towards the product is tested against time. As soon as there is a given set of consecutive steps that have resulted in the attitude “good” or beyond, enough confidence has been gathered to proceed to the purchase decision. Beliefs, episodes, memory traces, response types, attitudes and decision-making can now be linked to the overall picture of the ESP model (see Fig. 4 below).
Fig. 4 From belief communication up to consumer decisions: General overview of structures and processes implemented in the ESP model. Grey fillings point to the influence of cognitive biases.
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Relationship between the ESP model and the Uniform Model
The habitual and the deliberative mind-sets are modelled differently. For example, there is different memory decay depending on different involvement levels. Furthermore, specific biases come into play in the habitual response. Full congruity measures (both instrumental and symbolic) are restricted to the analytic response. This duality of processes points to some analogy to standard dual-mode persuasion models like the Heuristic-Systematic Model (HSM) (Chaiken, Liberman, & Eagly, 1989) or the Elaboration Likelihood Model (ELM) (Petty & Cacioppo, 1986). Nevertheless, the author follows the main criticism towards these models recently formulated in the unimodel of persuasion (Kruglanski & Thompson, 1999; Kruglanski, Thompson, & Spiegel, 1999). In contrast to the HSM and the EML, the unimodel uncouples the systematic/central route from its exclusive relation to message content and the heuristic/peripheral route from its exclusive relation to message cues. As in the unimodel, both mind-sets in the ESP model use episode credibility as a basic peripheral cue apply congruity measures related to the message content as a central cue are based on the evidence concept: The episode and its conditions (major belief) are assessed to fit to the situation and motivation of the consumer. The better the fit, the more the major belief is weighted, and the higher the impact of the episode for the response.
-
2.
Validation: Car Sharing in Switzerland
The simulation will be validated against empirical data from the problem domain of mobility, namely the current diffusion of Car Sharing (CS) in Switzerland (Muheim, 1998). CS is defined as the common use of vehicles by various users in succession and independent of each other. The main idea of the new mobility form is to uncouple car use and car ownership (Baum & Pesch, 1994). In Europe, about 100‘000 people participate in a Car Sharing organization today (Harms & Truffer, 1998); in Switzerland there are currently 33’800 members and a fleet of 1’350 cars (Mobility Schweiz, personal communication, June 2000). At each time of usage, CS members make a reservation via phone or Internet, fetch the car from the car location, and bring it back within the reservation time window. They pay a monthly charge dependent on the kilometers driven, the duration of use, and the type of car. The membership is guaranteed by an annual administration fee. Due to the reservation system and the charge dependent on kilometers and duration, CS is not suitable for daily commuting and long (holiday) trips. Rather the main benefit is the opportunity for car-free households to remain car-free (Petersen, 1995). If the household is connected to well-developed local and public transport network, CS simply supplements the public transport system, say, for transporting big and heavy shopping items, going out late during the weekend or irregular rare trips into regions where public transport is inefficient. Stabilizing car-free households6 has several positive environmental effects (Muheim, 1998). CS users save 30% white energy and its emissions. Furthermore, they decrease the number of cars occupying the limited urban public space. Large future market potentials are predicted (ForschungsgemeinschaftMobilität, 1996). In Switzerland, Muheim (1998) estimates a potential of 613'000 clients.
6
Note that non-car-free households sometimes employ CS instead of a rarely used second car.
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Disruptions and mind-set changing
However, the mere existence of Car Sharing alone does not drive consumers to renounce their private car, because there are strong habits to be broken first (Harms & Truffer, 2000). A recent empirical study shows that switching to CS is frequently accompanied by slowly growing or abrupt disruptions in the personal situation of the consumer (e.g. divorce, changes of employment, birth of a child, etc.), and/or changes in the public transport system (e.g., moving from A to B) (Meijkamp, 1998; Müller&Romann, 1999). The results suggest that consumers have to be externally compelled to dissolve the habitual mind-set and to engage in the deliberative mind-set (Harms & Truffer, 2000). Without a disruption there is no behavioural change to be expected (Nisbett & Ross, 1980). Habits are strong enough to stabilize truly maladapted mobility patterns as long as the maladaptivity does not exceed a certain threshold (Ritov and Baron 1992). 2.2
Testing intuitions about market dynamics with ESP
The ESP model is developed to simulate scenarios of marketing strategies combined with different policy interventions. The main goal is to test their strengths and limitations as well as to detect inherent fallacies due to inappropriate intuitions of the modeller and the marketing practitioner about market and consumer dynamics. At the moment, the project has just finished the implementation phase. Nevertheless, some first experiments are reported in section 4. 3.
Implementation of ESP structures and processes
This chapter provides the details of the specific way the theoretical framework has been implemented into Quicksilver, an agent-based modelling and simulation environment. At first, Quicksilver is portrayed in brief. 3.1
Quicksilver
The Quicksilver modelling and simulation environment is an ongoing development of the system analysis, integrated assessment, and modelling department at the EAWAG. The project started two years ago and Quicksilver has already found various applications in the area of integrated assessment (Pahl-Wostl, Schlumpf, Schönborn, Büssenschütt, & Burse, 2000; Schlumpf, Pahl-Wostl, Schönborn, Jaeger, & Imboden, 2000) and water management (Tillman, Larsen, Pahl-Wostl, & Gujer, 1999). The Quicksilver modelling and simulation environment cooperates with the Java platform. Computer models and simulations can be packed as Java applets and delivered over the internet. The Java applets can be further integrated with hypertext information systems. Such enhanced information systems have already been used in participatory processes such as focus groups (Pahl-Wostl, Schlumpf, Schönborn, Büssenschütt, & Burse, 2000; Schlumpf, Pahl-Wostl, Schönborn, Jaeger, & Imboden, 2000). The specific agent population used for a computer model and simulation can be derived programmatically from the predefined software components that come with Quicksilver. The editor of Quicksilver allows the interactive creation and composition of agent populations. The browser of Quicksilver allows the interactive testing of agent populations. For the purpose of data analysis, Quicksilver supports the modeller with software components for the visualisation of data. Soon it will be complemented with a Monte Carlo Simulator component for the stochastic analysis of model runs.
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3.2
Summer Academy on Technology Studies 2000
Availability of Episodes: Communication Activity in the Social Network
The structure of the social network is kept as simple as possible. However, it is as complex as required to capture some minimal analogy from the problem domain (see chapter 2). Firstorder informants (see section 3.2.1) represent the primary source of beliefs (see section 1.5.1). The content of the beliefs is independent of the dynamics of the simulation, i.e. they are the main input variables of the model. The targets of the first-order beliefs are the consumers (see section 3.2.3) integrating them into attitudes (see section 3.3). Yet, consumers are not restricted to the role of passive receptors. As second-order informants, they communicate their current attitudes as encapsulated second-order beliefs. The content of these beliefs is dependent on the dynamics of the simulation. 3.2.1
Products
The simplest possible market is chosen to limit the complexity of the model. There are only two products the consumer is confronted with: The private car, and its alternative, the Car Sharing membership. They can be purchased or sold. At a given moment in time, consumers can own both, one of them, or none of them. 3.2.2
Mobility Informants
Every informant holds a certain number of beliefs about each product. These beliefs represent the knowledge aimed to diffuse through the social network. The principal informants in the problem domain are, not surprisingly, the sellers of the two products. These are the private car industry and the Car Sharing company called “Mobility CarSharing”. Both are advertising in the mass media and in the public, and/or maintain direct customer contacts. Due to several areas of co-operations between “Mobility CarSharing” and various public transport systems, an additional informant is the company called SBB (“Schweizerische Bundesbahnen”, Swiss Federal Railways). The fourth belief source is the mass media. They publish reports about both products from a (presumably) neutral perspective. At some steps of the simulation7, each informant randomly selects a particular belief from the belief bundle and sends it to all target consumers. Over time, the result of this communication activity is a dense belief patchwork woven by the informants in the mind of the consumer. Besides the differences regarding the content of the belief bundles, there is another crucial difference between informants. Not all of them enjoy the same credibility8 (O'Keefe, 1990). The consumers express individual credibilities towards the four informant types and towards other consumers by a value between 0 and 1. Values near 0 symbolize a low credibility level, values near 1 express a high credibility level. 3.2.3
Mobility Consumers
The three global characteristics of a specific mobility consumer are her/his social character, her/his mobility constraints, and her/his motivation. These three aspects form the total knowledge the consumer has about herself/himself.
7
according to the informant’s specific advertising frequency Generally, consumers are supposed to judge the credibility according to expected knowledge biases (i.e. lack of source expertise), the expected report bias (Eagly, Wood, & Chaiken, 1978) (ending up with a lack of trust), and overall mental similarity
8
of the source (McCroskey,
Richmond, & Daly, 1975).
New Product Diffusion within a Social Network
17
The social character contains information about age, education, employment, income, and the number of children. The mobility constraints report the ownership of relevant means of transportation, the availability of the car, the quality of the local and the national public transport systems, and the distance from the Car Sharing place. Furthermore, the typical mobility pattern of the consumer is sketched in terms of trip frequency x trip duration data couples for working, little shopping, a lot of shopping, and spare time. The motivation is represented by seven general values and three specific goals relevant in the domain of mobility. The value goals are autonomy/dependence, greenness, affiliation, power/achievement, conspicuous rationality, security/risk, and hedonism. Five of them are selected from the ten cross-cultural value universals found in empirical surveys (Schwartz, 1992; Schwartz & Bilsky, 1987). The values of greenness and of conspicuous rationality are added to represent specific motivations that are supposed to be decisive in the Private Car vs. Car Sharing domain (Einert & Schrader, 1996; Empacher, 1997). The minimal set of rather pragmatic goals that have repeatedly been found to be crucial regarding the motivation to choose a particular travel mode are: saving time, saving financial costs, and saving effort (Bundesamt_für_Statistik & GVF, 1996; Franzen, 1997; Held, 1982). At some steps of the simulation9, each consumer encapsulates her/his current global attitude (see section 3.3.2) into a new belief and sends it to all peers (see section 3.5). This leads to the spreading of second-order beliefs, which cannot be directly controlled by the the first-order informants. Memory Parameters The characteristics of the consumer’s memory are encoded in a set of variables held constant during the simulation. Even beyond, they do not differ between consumer types. As a first simple approximation, they are regarded as kinds of anthropological constants. The following three parameters are required to compute the current use probability of each episode (see section 3.3): -
-
-
the general quality of memory decay inhibition independent of the mind-set. High levels point out a good memory; low levels imply rapid decay and bad retrieval performance. the two levels of the involvement in the habitual and in the deliberative mind-set (see section 0), respectively. Values near 0 symbolise the absolute absence of any interest in mobility questions; values near 1 stand for the maximal attention towards finding a solution to a mobility problem as soon as possible. the two levels of the usage threshold and activation noise (see section 1.6.1). Values near 0 denote that even the faintest memory trace of an episode contributes to the judgement; values near 1 indicate that only highly activated episodes can jump into section of consideration.
Bias strengths are also regarded as memory variables. The strengths of the confirmation bias, the status quo bias, and the negativity bias (see section 1.5.2) range from 0 to 1. Values near 0 represent a weak bias, whereas values near 1 represent a strong bias.
9
according to her/his communication frequency
18
3.3
Summer Academy on Technology Studies 2000
Episodal Activity and Usability: Knowledge Integration
The first step towards a global attitude (see section 3.3.2) is calculating the activities of the accounts of the four predicates. This requires finding the activities of all episodes in the consumer’s memory. The form of forgetting is supposed to follow a potential law (Anderson & Schooler, 1991; Wixted & Ebbesen, 1991). Let Ai (t ) be the activation of episode i if ∆t + 1 is the time that has passed since the episode was formed10. The speed of forgetting depends on the parameter bi . To adapt Ai (t ) to specific units of time, the factor 1 a is applied. It can be interpreted as a kind of “decay inhibition”.
∆t + 1 Ai (∆t + 1) = a
− bi
(1)
It is proposed that the speed of forgetting is inversely proportional to the involvement I i at the moment of the belief communication (e.g. Park & Hastak, 1994). If t is defined as ∆t + 1 this yields the expression
t Ai (t ) = a
−1
Ii
(2)
Assuming activities to be normally distributed, the probability pi of being used in the
episode integration depends on the noise level σ m , the mean activation level Ai (t ) and the use threshold τ m that are given in a particular mind set m :
A (t ) − τ m if Ai (t ) − τ m ≥ 0 pi (t ) = 0.5 + φ i σm
(3)
A (t ) − τ m if Ai (t ) − τ m < 0 pi (t ) = 0.5 − φ i σm
(4)
3.3.1
Habitual Mind-Set and the Implementation of Biases
If the mind-set is habitual, there is no control to avoid that negative predicates about a product have double impact on the judgement (see Tab. 4). Tab. 4 The loss aversion bias: Bad news weight twice as much than good news
10
(
predicate
-2 (very bad)
-1 (bad)
1 (good)
2 (very good)
Biased predicate
-4
-2
1
2
(
)
The term ∆t + 1 provides that if ∆t = 0 , Ai ∆t + 1
)
Ai ∆t + 1 converges to 0.
is at the maximum value of 1. If ∆t grows towards infinity,
(5)
New Product Diffusion within a Social Network
19
The confirmation and the status quo bias both affect the credibilities ci of every episode i . The confirmation bias β c has the following simple effect on ci (see Tab. 5): Tab. 5 The effect of the confirmation bias on the credibility of episode i is dependent on the correspondence between incoming predicate and the consumer’s current attitude. Current global attitude
Predicate of incoming belief
The status (see Tab. 6):
quo
bias
(6)
>0
0
ci (β c ) = ci ⋅ (1 + β c )
ci (β c ) = ci ⋅ (1 − β c )
1.0 reaches a certain confidence threshold
τ d , the product is adopted. The time passing between ngood = 0 and ngood = τ d can be interpreted as a period where the intention to adopt is growing. 3.5
Communication in the Social Network
The central source of the dynamics in the ESP model is the generation of episodes. For a new episode to be formed, an informant sends a first-order belief to a consumer within the target set, or a consumer sends a second-order belief to another consumer within the peer set. In the current first version of ESP, a random generator produces the temporal pattern of these communication acts. The generator maintains the individual advertising and communication frequencies (see sections 3.2.2 and 3.2.3) over the long-term. The concept of target sets and peer sets allows the controlled specification of every tie in the social network. All target and peer sets taken together represent the full structure of the social network. Due to the random generation of communication patterns, the simulation of belief exchange is still in an embryonic phase. Yet, the ESP model is principally open to test more realistic patterns of communication strategies designed by marketers. Replacing the random generator in later versions, every conceivable temporal sequence of communication acts can be used as the input of the model. 4.
First Experiment
The focus of this paper has been on the development and implementation of a theoretical model. Nevertheless, a first experiment is presented here in brief. Since the Monte-CarloSimulation (MCS) component of Quicksilver is not ready yet, the simulations had to be started and stopped by hand. Thus, their number does not allow for statistical analyses. It is for this reason that the following experiment is rather rudimentary and lacks some scientific rigour. The first step when it comes to running simulations is to select input parameters to be varied, output parameters to be monitored, and model variables held constant. In the present experiment, these constants are the following:
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Summer Academy on Technology Studies 2000
-
the consumer memory parameters (see section 3.2.3) the consumer-to-informant credibilities the consumer-to-consumer credibilities the levels of the biases the contents of the belief bundles of the informants, and the advertising and communication frequencies.
The advertising frequency of the Car Sharing company and the entrance time of the disruption in the consumer’s mobility context serve as the input of the simulation. This leads to the experimental design depicted in Tab. 7. According to the advertising frequencies of the informants, a random pattern of episodes is produced in each run of the model. For each field in the table, 20 such simulation runs were produced. That is, the consumers were confronted with 20 distinct random sequences of beliefs from the informants. At each run, it was monitored which consumers had finally decided to adopt or not to adopt one of the products during the simulated time window. Tab. 7 Results of a first tentative experiment with the ESP model. In one part of the simulations, advertising for Car Sharing was restricted to the source of the SBB and the mass media. The numbers in the central 4x5 fields denote the sum of adopted cars and new Car Sharing users given a certain disruption entrance time. If the disruption is missing (bottom row), the habitual mind-set is not dissolved, and no consumer adopts a new product. Advertising frequency of the Car Sharing company11 Entrance time of the disruption
0.01
0.00
Adoptions Car Sharing
Adoptions Private Car
Adoptions Car Sharing
Adoptions Private Car
500
13
1
6
3
470
4
6
2
13
350
4
5
2
4
200
11
1
5
9
never
0
0
0
0
Consumers were divided in three types differing with respect to the situation and motivation variables. Each type was represented by four copies. Thus, twelve consumers formed the total social network. Each consumer had three links to other consumers. Two were set within the same type and one pointed to another type. A single simulation runs over 1’000 steps, which is equalled with 1’000 days, or three years, respectively. The disruption enters according to the varied entrance time and affects all consumers simultaneously. The effect is currently restricted to an abrupt increase of the involvement variable. All other variables are nor affected. The numbers in the central 4 x 5 fields of Tab. 7 are the sum of car adoptions and new Car Sharing members. The results clearly show that habit breaking is required to trigger product 11
If the advertising frequency of the Car Sharing seller equals zero, pro Car Sharing arguments exclusively originate from the mass media and the SBB informant.
New Product Diffusion within a Social Network
23
adoptions. As expected, missing pro-Car Sharing-arguments lower the number of new members, and increase the number of car adoptions. Varying the entrance time of the disruption reveals that only little shifts produce a completely different adoption behaviour. Whereas results from the settings corresponding to the entrance time = 500 and entrance time = 200 are very similar, entrance time = 470 leads to a completely different outcome. If consumers are hit by the disruption only one month in advance, the trajectories of the model can strongly diverge. 5.
Discussion
One of the merits of the ESP model is that its results are not the output of a simple black box random generator representing the attitude formation process of the consumer. For example, since the four responses of the attitude are simulated separately (see sections 1.5.3 to 1.5.6), the ESP allows carefully tracing and analysing possible reasons for the adoptions or non-adoptions occurred in the simulation. This fine-grained structure is precisely the reason why the ESP model offers better understandings of social persuasion. Simulating the whole human attitude-behaviour chain starting from beliefs, episodes, responses, attitudes, and intentions up to the final decisions involves some irreducible complexity. In our view, the level of complexity required to simulate consumer behaviour in the product diffusion process must be related to the complexity of the real system. Bearing in mind the tremendous complexity of social systems, the ESP model is a simple model. However, for the ESP model (like for every model), the criterion for the implemented structures and processes remains their parsimony related to their explanatory power. It is important to note that in the ESP model nearly every process can be switched off by setting its value to zero. Switching processes on and off will lead to further experiments finding the optimal level of model complexity. 6.
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Appendix: Step-by-step social psychology
This is not the place to go into a thorough analysis of the social simulation approach (cf. Conte, Hegselmann, & Terna, 1997; Gilbert & Conte, 1995; Gilbert & Troitzsch, 1999; Hegselmann, Müller, & Troitzsch, 1996; Liebrand, Nowak, & Hegselmann, 1998). Since the merits and limitations of the method are largely unknown, this paragraph nevertheless presents a brief overview. The main goal of using the simulation approach in social science is to gain more explicit and precise understandings of the most important processes and mechanisms underlying the behavior of the target system (Axelrod, 1997)12. The majority of models (e.g. in consumer psychology) are essentially statistical models informing about correlations between variables being true for the short time window during measurement. In these models processes are suggested in a more or less implicit form using descriptions, definitions, conceptual schemes like box-arrow-diagrams commented in textual form (Hanneman & Patrick, 1997). In contrast, social simulation focuses directly on the explicitness of mechanisms: building a model is to a big part implementing a set of mechanisms. The problem is that the implementation process often necessitates going beyond empirically validated theory pieces. The modeller has often to be explicit where the theory has been implicit. Often there is no way out from supposing rather ambiguous ad-hoc assumptions. However, this contingency of the model architecture can be regarded from the opposite viewpoint as well: adding procedural details into known theory structures can reasonably guide steps towards theory completing, refining, and formalisation. To find the quality of the relationship between the target system and the model is a delicate procedure without an orthodox set of criteria. Architectural analogy and behavioural similarity between scenarios and data from the target system can be taken as minimal evidence for the validity of implemented structures and processes and the start parameterisation of the variables. Yet, the model alone cannot proof the “true” or “real” underlying mechanisms, it can only point to a restricted group of tested and empirically plausible possibilities of a theoretical infinity of possibilities (Halfpenny, 1997). From a more pragmatic view, running scenarios is a kind of severe training for theory experts and problem domain practitioners if they are confronted with actual outcomes of the simulation and have to compare them with their pre-run expectations and intuitions. Trying to explain counterintuitive dynamics represented in the data trace from the model run advances thinking within the complexity of the real system and fosters understanding the internal processes. The result is refined knowing-why-knowledge about the procedural behaviour of the target system when is passing.
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Quantitative predictions are rather an inappropriate use of the method. Forecasts of the behaviour of complex non-linear systems like social systems are principally restricted in accuracy since the outcomes are very sensitive to the initial setting of the parameters. As (Byrne, 1997) puts it: “(…) simulation is clearly a tool which helps us not know what will happen, but what can be made happen (section 5.3).” The focus of the method is on observing qualitative dynamics stepping through the model and trying to understand them.
