A Multi-Scale Model of Cultural Distinctions in Technology Adoption

Transcription

1 E. Briscoe, E. Trewhitt, C. Blunt, C. Hutto, D. Folds, L. Weiss, E. Whitaker. (2010). A Multi-Scale Model of Cultural Distinctions in Technology Adoption. Advances in Cross-Cultural Decision Making. Schmorrow, D. and Nicholson, D., Eds. CRC Press A Multi-Scale Model of Cultural Distinctions in Technology Adoption ABSTRACT Erica Briscoe, C.J. Hutto, Carl Blunt, Ethan Trewhitt, Lora Weiss, Elizabeth Whitaker, Dennis Folds Georgia Tech Research Institute Atlanta, GA , USA This chapter presents research on the benefits of multi-scale modeling to capture cultural and behavioral influences, using the adoption of new technology as a domain of demonstration. The spread of new technology depends on the decisions of individuals with diverse beliefs, traditions, and dispositions, which can be modeled using various approaches. The work here uses an agent-based model to represent factors at an individual level, and a system dynamics model to represent societal influences on technology acceptance. These models exchange information by having the society-level model generate outputs that are trends and which are used as inputs to the individual-level agent-based model, while the aggregation of individual behaviors supply reciprocal input to the higher-level society model. This type of federated modeling construct provides an analysis tool for examining the potential implications of economic and technology-related policies that may affect human behavior rising from one s cognition, social interactions, and culture. The approach is also extensible to modeling decision-making by individuals in other activities beyond technology acceptance. Keywords: multi-scale modeling, agent-based modeling, system dynamics, culture, e-commerce INTRODUCTION When modeling individuals as part of a group or society, a single modeling paradigm may be inadequate to capture the nuances and interactions within the

2 domain. Individual behaviors are often modeled using agent-based models, which are able to represent causal relationships among individuals using various methods, such as collections of rules (Bonabeau, 2002). This allows personality and other internal attributes of an individual to be incorporated within the model. Societylevel interactions, on the other hand, are often modeled using paradigms that focus on trends among large numbers of people and can be incorporated using methods such as system dynamics modeling (Sterman, 2000). This allows high-level behaviors to be represented that are not apparent among individuals or even groups of individuals. Each modeling paradigm is suited for a particular level of interaction, so that an ideal system integrates multiple approaches to model at multiple scales. This chapter describes a system that uses an agent-based model to encompass the detailed behavior of individuals and their immediate network of relationships, as well as an influence-based system dynamics model to show how society-level influences function interactively and with respect to the individual agent. The system combines these two model types with specific interaction points to create a single integrated model of human behavior and culture. The integrated model is then applied to the adoption of a technology to determine how the characteristics of individuals affect the use and prevalence of a new technology in a society. Modern society is distinguished by the constant and rampant introduction of new technology, including cell phones, laptops, and the Internet. Many factors contribute to the choice to observe, slowly accept, or quickly adopt a new technology, and these factors often derive from longstanding values, beliefs, and traditions, (or culture ). As such, technology adoption and usage often exhibit varying patterns (e.g., Straub, Keil, & Brenner, 1997). The spread of a new technology typically depends on the decisions of varied individuals, each with his own characteristic set of beliefs, traditions, and dispositions. Although the decision process has often been described at the intra-individual level of cognition, it is subject to social and cultural influences at both the interpersonal level as well as the societal level. Thus, it is a confluence of several factors at each of these levels of human socio-cognitive and cultural interaction which contribute to the overall decision process associated with an individual (and aggregately, society) adopting the new technology. Relevant literature in cognitive psychology, social psychology, economics, and sociology is abundant, independently describing and modeling, at various levels of detail, the processes and mechanisms for intra-individual, interpersonal, and societal levels of human socio-cognitive and cultural behavior. In comparison, there is a relatively small body of literature that attempts to develop a multi-scaled view of the phenomena. A critical factor in technology adoption is whether the product provides a substantial benefit, but culture also plays an important role. Behavioral norms may resist change and eventually override any technological effects (Watson, Ho, & Raman, 1994). Also significant are societal factors and individual characteristics such as user appeal (Carr, 1999). Numerous theories have been developed to explain and model the adoption of technological innovations. Macro-level theories focus on broad, population-wide influences where innovation encompasses a wide

3 Multi-Scale Models of Technology Adoption range of technologies and economic practices. Micro-level theories focus on individual adopters of a specific innovation. Although prominent theories exist to explain the spread of innovations (e.g., the technology adoption lifecycle, [Beal, Rogers & Bohlen, 1957; Rogers, 1962] and the Technology Acceptance Model [Davis, 1986]), it has been noted that many theories lack an adequate account of the various types of influence (e.g., Davis, et. al., 1989). Newer, composite theories may prove to be more predictive. The Unified Theory of Acceptance and Use of Technology (UTAUT) combines eight of the most prominent technology acceptance models (Venkatesh, et al., 2003). Starting with the UTAUT as the theoretical basis for integration, the research in this chapter presents a multi-scale model to represent influences on the adoption of technology at three interrelated levels of human socio-cognitive and cultural behavior. At the intra-individual level, a person s decision making process is captured using an innovative approach called the Cognitive Network Model (CNM), in which cognitive belief structures are represented as a network in an agent-based model. The Socio-Cognitive Network Model (SCNM) extends the CNM to the interpersonal level, capturing influences resulting from interactions between individuals in a multi-agent model. At the societal level, a system dynamics model is used to represent macro-level influences of technology acceptance. These models are linked so as to exchange information by having macro-level influences feed into the micro-scale agent-based models, while the aggregation of individual behaviors supply input to the macro-scale system dynamics model. This interaction between component models at multiple scales is significant as it allows for the manipulation of variables specific to particular cultures at both an individual and societal level. THE COGNITIVE NETWORK MODEL The Cognitive Network Model (CNM) approach is an innovative method of quantitatively characterizing specific cognitive mechanisms associated with human social-cultural decision making at the individual agent level. The fundamental tenet of CNM is the application of network analysis techniques as a basis for characterizing and understanding human decision making resulting from the diffusion of information. In essence, CNM represents decision making in terms of the emergent interactions of a set of parameters associated with beliefs. Thus, beliefs are the foundational element of cognitive network modeling. Beliefs are often represented as propositions and can be thought of as subjective probability estimates of an object having a particular attribute (Fishbein and Ajzen 1975). In processing information (e.g., raisins are a healthy snack ), a belief exists when the human agent assesses the probability that the information is true. From a hierarchical category perspective, raisins might be defined as inclusive of the category healthy (raisins are healthy) or exclusive of the category (raisins are not healthy). From a network perspective, beliefs might be represented as network nodes, with links representing the relationships between nodes. In the CNM

4 approach, a belief proposition is described as a pairing of cognitive concepts to which a degree of perceived truth is assigned. For example, the proposition raisins are healthy pairs the concept of raisin with healthy. Although this is consistent with (and derived from) existing theory, the model presented here differs in some important respects. To represent beliefs and provide insight into how to predict the influence of new information, this chapter represents beliefs through three main quantitative parameters: Veracity: This represents the general strength of the belief held by an agent. Quantitatively, veracity is represented as a value between 0 and 1 indicating the degree to which an agent accepts a given belief proposition as being true, where 0 indicates the agent believes the proposition is not true at all (proposition is completely rejected), and 1 indicates complete belief in the truth of the proposition. Epsilon (ε): Here, each belief has an associated epsilon representing the malleability of the veracity of the belief; epsilon is the degree of acceptable variance/differences in the veracity value of a belief that an agent will accept without arousing the defense. Thus, epsilon captures the interval limits on either side of current veracity for which new values of veracity are outright accepted/rejected (new values of veracity for given belief propositions might be based on information received from an external source such as another agent or contextual evidence) 1. Epsilon may not be uniform in each direction; changes in veracity may be more readily accepted for one direction versus the other. Defense: Defense measures the degree to which the agent tolerates/considers changes in veracity that fall outside epsilon; it includes how strongly one might argue for the veracity of a belief (or against its veracity if an alternative belief is supported). Quantitatively, defense is a value between 0 and 1, where 0 indicates a weak strength of defense value (high degree of tolerance for beliefs outside of ε) and 1 indicates a strong defense (intolerance for beliefs outside of epsilon). In other words, the strength of the defense of the belief is a value that indicates the degree to which a person rejects beliefs that are different from (conflict with) their current belief and fall outside its associated epsilon range. If the agent s defense of the belief is low, differing/conflicting information is less likely to be rejected outright without first modifying one or more belief parameters. The defense value affects the degree to which an existing belief is amenable to change via (a) adjusting epsilon, (b) adjusting the strength of veracity, (c) adjusting the strength of the defense, or (d) a combination of belief parameter adjustments 2. The defense value is 1 When considered in conjunction with veracity, is a partial indication of confidence: small values correlate with higher confidences; larger values correlate with lower confidences. 2 The strength of defense is influenced not only by the belief to which it is attached, but by relationships within the belief network (i.e., the degree to which a change in the relevant belief influences change in other beliefs) as well as individual difference variables that operate more generally (e.g., we might expect a person high in the personality attribute of openness to be more accepting of changes in beliefs and therefore have an overall tendency of lower defense values across the belief network).

5 Multi-Scale Models of Technology Adoption the mechanism by which the CNM is able to model both rational and irrational cognitive behavior. If beliefs are conceived as estimates of the accuracy of information, then the model needs to support changes to targeted beliefs within an individual agent s belief system (termed a Cognitive Belief Network), either through shifts in strength of veracity of the belief ( maybe raisins are not as healthy as I thought ), shifts in epsilon surrounding the veracity of the belief, or changes in the strength of the defense of the belief. Thus, the belief parameters of veracity, epsilon, and defense are the mechanisms by which cognitive network modeling quantitatively characterizes many of the cognitive processes associated with human decision making. Among the means for making adjustments to these belief parameters is the introduction of interventions (alternative beliefs) which effect a shift in proposition referents (e.g., raisins are poisonous ). THE COGNITIVE BELIEFS NETWORK The lowest level of analysis in CNM comes from studying the emergent properties associated with the Cognitive Beliefs Network (CBN). The CBN (FIGURE 1) is conceptualized according to precepts familiar to areas of graph theory and network science: vertices (nodes/points) represent individual beliefs held by an agent, and edges (links/lines/arcs) represent connections/relationships between beliefs; they are usually weighted and may be directed (see Diestel (2005) for an introduction to graph theory). The CBN is the collection of all beliefs and associated parameters held by an individual agent. The CBN structure evolves over time, as new beliefs are added and/or parameters associated with prior beliefs change. Following the work of Bourdieu (1977) and Kameda, Ohtsubo, and Takezawa (1997), we introduce the concept of cognitive capital as the emergent features of interest at this level of network modeling. Within CNM, cognitive capital refers to the relative influence power of a belief node in the CBN; it is a composite score as measured by network metrics for FIGURE 1. Conceptual rendering of the Cognitive Beliefs Network (CBN) cognitive prestige (a compound score based on various measures of network centrality), and cognitive importance as measured by the eigenvector centrality (weighted according to relative scores for cognitive prestige of related vertices;

6 connections to nodes with higher cognitive prestige scores contribute more the importance score of the node in question) 3. In this manner, the CNM becomes a framework for studying decision making resulting from agents' processing of information. Cognitive network modeling is then a matter of (a) selecting the beliefs of interest for the chosen domain, (b) defining the relevant belief parameter values, and (c) describing the governing functions for dependencies between beliefs as well as other intra-personal influences 4. From this framework, it follows that CNM is able to capture the concept of culture by adjusting one or more of the following in the CBN: (1) the relative strengths of individual belief parameters for belief nodes determined to be culturally germane, (2) the weighted magnitude of the CBN edges, and (3) the relationships defining the intra-network and external influence forcing functions. THE SOCIO-COGNITIVE NETWORK MODEL The Socio-Cognitive Network Model (SCNM) is the next level of the multi-scale modeling spectrum described in this chapter. Socio-cognitive network modeling is extremely relevant to the study of human social-cultural decision making: just as the CBN does not function in isolation within an individual agent, neither do agents act in isolation a number of external environmental and social influences interact with the agent to affect the agent s CBN. At the SCNM level, network vertices are individual human agents, and network edges are the cognitive ties, communication links, and social relationships between them. Within the current context, cognitive ties generally refer to the extent of agreement between the CBNs of multiple agents. A central premise of SCNM is that the strength of the cognitive ties (degree of agreement between agents CBNs) affect the degree to which agents influence and are influenced by one another s beliefs during social interactions. The SCNM relies heavily on the principles of social cognitive theory (Bandura, 1986), which subscribes to a model of emergent interactive agency (i.e., triadic reciprocal causation whereby personal factors, behavioral factors, and environmental events all operate as interacting determinants of human decision making). Additionally, Bandura s (2001) characterization of the role of social cognitive theory in mass communications is particularly relevant to modeling the governing functions of information exchange within the SCNM. 3 Other emergent properties of the CBN that have greater relevance in other contexts include: network size, network density, and network cohesion. 4 An individual human agent s CBN does not function in isolation, but with other, noncognitive personal determinants such as personality, transient affective states (moods), biological factors (e.g., age, gender), and personal history (e.g., education, work). Although not detailed herein, they are considered an integral part of the CNM, and have been notionally represented in the agent based model described in this chapter.

7 Multi-Scale Models of Technology Adoption The SCNM and CNM are represented physically within an agent based model (see Bonabeau (2002) for a description of agent models). By representing the relevant beliefs for a given domain, as well as the forcing functions associated with the modification of those beliefs, the model can monitor the changes in beliefs (both at the individual and at the aggregate levels) to determine the effects of information as it propagates through the network. The domain of demonstration selected for this chapter is cultural determinants of human decision making within the context of technology adoption (e-commerce), that is, the decision to use e- commerce technology to make an on-line purchase. The set of interrelated beliefs and governing functions selected for modeling purposes are those familiar to researchers in the area of technology acceptance. The specific beliefs that are represented are based on the work by Venkatesh, Morris, Davis, & Davis (2003). Their UTAUT model combines eight of the most prominent technology acceptance models observed in the literature and provides a definitive list of variables that are critically relevant to an individual s decision to adopt a new technology. Additionally, cultural distinctions in technology adoption at this level of modeling are manifested in varying algorithmic functions associated with culturally relevant determinants of interpersonal influence, as well as six cultural value-dimensions as described by Zakour (2004). SOCIETAL MODEL A system dynamics approach is used to capture high-level factors that contribute to the adoption of technology (specifically, e-commerce). A system dynamics model is a type of executable model used to represent and understand the dynamic behavior of a complex system over time (Sterman, 2000). Complex systems, whether social, technical, or some combination, often exhibit highly non-linear behavior where the relationship between cause and effect is not intuitively evident. System dynamics models use stocks and flows to represent system elements and their relative influences upon each other. Stocks represent an inventory of accumulated entities (e.g., inventory of cell phones) and are indicated in the e- commerce adoption model using rectangular boxes (see Figure 2). Flows are indicated by double-lined arrows, and show how entities move between stocks or between a stock and a cloud. Clouds, which may take the place of stocks, indicate the world outside the scope of the model and act as either a source or a sink. Figure 2 depicts a system dynamics model that captures some of the critical variables involved with the adoption of e-commerce. This particular model concentrates on two areas: economic variables related to selling a product on the internet, and the effect of marketing by e-commerce retailers on purchasing trends. This society-wide model is valuable in that it allows for the exploration of feedback relationships within the system. For example, in this model the demand that exists for a product drives the desire for retailers to offer that product through a particular medium (in this example, through the online e-commerce medium). Because retailers wish to sell goods online, they increase their marketing efforts to

8 make that service known to the buying public, which in turn increases the familiarity that the public feels for the retailer s online presence. As potential customers become more familiar with a retailer s online presence, they become more likely to desire to purchase a product from that retailer, thereby increasing online demand, which completes the loop. FIGURE 2: A system dynamics model of selected variables related to e-commerce Significant cultural variables are captured within the system dynamics model. The actual entities that exist within the model, in terms of stock, flows and influences, are representative of relevant aspects of the particular culture that is being modeled. For example, in some cultures, privacy is more highly regarded than others, and differences in the perceived privacy of different retail options may be amplified in society. MODEL INTEGRATION AND E-COMMERCE EVALUATION The most obvious benefit to developing and executing an integrated model is that it provides the potential for multiple levels of analysis. For example, one may wish to

9 Multi-Scale Models of Technology Adoption test a scenario of technology adoption to evaluate the effect that a specific change in individual behaviors might have on societal variables and vice versa. Individuals live in, react to, and affect their environment (society). A scenario could be constructed to investigate how an advertising campaign targeting a particular demographic will influence the e-commerce retailing of a given product. Characteristics about the campaign could be represented in the model in terms of the type of psychological persuasion tactics (marketing) used. The effect of this campaign could then be evaluated at both the individual level (e.g., how the campaign altered the individuals beliefs), at the inter-personal level (e.g., how the influence of the campaign translated into influence between individuals), and at the societal level (e.g., did the demand for the product cause an increase in the number of retailers competing for e-commerce customers). Of special interest are the relationships between each of these levels. Namely, the influence of individual beliefs on society and culture, and how culture and society work to encourage, reinforce or suppress beliefs at the level of the individual agent. Perhaps the greatest benefit afforded by this type of model is the flexibility to tailor it to particular populations through the manipulation of cultural variables. Each level of the model provides a representation of cultural variables. The integrated model allows for a demonstration of not only how these variables influence individuals and society independently, but how they interact. For example, an ad campaign targeted at the societal level may concentrate on the increased level of privacy afforded by e-commerce (see FIGURE 2). In this case, privacy is represented in the societal model because it is a relevant characteristic of business interactions in the modeled culture. The expectation of privacy as a society becomes a relevant variable in the individual model and is an influence on an individual s belief about his own acceptable levels of privacy. This in turn affects the opinions concerning the adoption of e-commerce that are communicated toward others. The amount and type of communication with other individuals is also highly dependent on culture. For example, in some cultures it is impolite to speak of personal financial decisions. The type and manner in which relevant information circulates within the social network influences an agent s decision to use an internet retailer. The decision to use e-commerce, as well as the decisions of the network of peers, is aggregated to feed into the high-level model as demand, which, ultimately, affects how marketing campaigns are designed. This interaction among cultural variables would be difficult to capture using a single-scale model. CONCLUSION This chapter has presented an integrated, multi-scale modeling approach that captures human cognitive, social, and cultural components of decision-making behavior using the adoption of e-commerce as a domain of demonstration. The exploration of technology adoption grounds the model in a realistic environment and incorporates well-researched psychological models. The federated approach provides an analysis tool for examining the potential implications of economic and

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