USING ASSOCIATIVE NETWORKS TO REPRESENT ADOPTERS' BELIEFS IN A MULTIAGENT MODEL OF INNOVATION DIFFUSION

A lot of agent-based models were built to study diffusion of innovations. In most of these models, beliefs of individuals about the innovation were not represented at all, or in a highly simplified way. In this paper, we argue that representing beliefs could help to tackle problematics identified for diffusion of innovations, like misunderstanding of information, which can lead to diffusion failure, or diffusion of linked inventions. We propose a formalization of beliefs and messages as associative networks. This representation allows one to study the social representations of innovations and to validate diffusion models against real data. It could also make models usable to analyze diffusion prior to the product launch. Our approach is illustrated by a simulation of iPod™ diffusion.Agent-based modeling, diffusion of innovations, knowledge representation

Tweet, Tweet, and Repeat: How College Students and Social Media Bring You the News

This study examined what college students tweet about, how that information is presented, and if age and/or social media experience play a role in the tweets. The researcher followed 118 college student participants on Twitter in the fall of 2012 to determine if use the social media network to communicate news and found that the college students in the study did use Twitter to communicate news and receive the news. Their main topics of Twitter conversation included sports, politics, and arts and entertainment, and they tweeted more opinionated tweets than pure factual tweets. Additionally, the researcher found students in the study enjoyed their tweets being retweeted because they felt someone else either agreed with their opinion or found their tweet interesting or amusing enough to share with other individuals. Also, students do not respond often in tweets, preferring instead to give their own opinion regarding a news event. They want to contribute their opinion, but they are less interested in responding than they are creating their own content regarding news information

Diffusion of Innovation in Small-world Networks with Social Interactions

학위논문 (박사)-- 서울대학교 대학원 : 협동과정 기술경영·경제·정책전공, 2015. 2. 이종수.인터넷과 무선통신, 사회연결망서비스 등의 등장으로 사회 네트워크가 발전하면서, 예전보다 소비자들은 서로 더 자주, 신속하게 정보를 교환하고 서로의 제품 구매에 영향을 미치고 있다. 그러나, 기존에 널리 이용되어 온 배스 모형을 비롯한 여러 확산 예측 모형들은 이론적 기반이 취약할 뿐만 아니라, 시장 수준에서의 분석만을 행하고 있기 때문에 이러한 소비자간 상호작용의 효과를 통합적이고 한정적으로 반영하고 있어 최근의 사회 현상을 제대로 설명하지 못하는 한계가 있다. 그 대안으로 등장한 행위자 기반 모형들은 개인 단위 분석을 가능하게 한 장점은 있으나, 여전히 이론적 기반이 취약하고 총 시장 수준 자료를 통한 분석은 요원한 실정이다. 이 연구에서는 소비자들이 서로의 선택으로부터 영향을 받는 사회적 효용함수가 있다고 가정하고, 이와 같은 사회적 상호작용으로부터 오는 효용을 개개인의 효용구조에 직접적으로 반영시켜 이것이 확산 곡선에 어떤 영향을 미치는 지를 상호작용 기반 확산 모형이라 명명된 새로운 모형을 통해 보고자 한다. 또한 기존의 대표적인 확산 모형과의 비교를 통해 실제로 본 모형이 최근의 사회 현상을 설명하는 데 적합한 지 살펴보고자 한다. 더불어, 혁신 확산을 잘 설명하기 위해서는 위해서는 기존에 널리 사용되었던 세포자동자 격자구조보다는 작은 세상 연결망 사회구조가 더 적합함을 밝힌다. 이 연구를 통해, 개인의 효용과 상호작용에 기반한 경제학적 확산 모형을 얻을 수 있었을 뿐만 아니라, 기존 확산 모형과 달리 상호작용의 이질성까지 반영할 수 있는 확산 모형을 구축할 수 있었다. 또한 본 연구는 개인 단위의 모형이 시장 전체 수준의 수요 예측에 활용될 수 있는 새로운 접근방식을 제안하고 있으며, 실제 자료의 분석에 대해서도 모형이 충분히 활용가능 할 수 있음을 보였다. 이 연구에서 제시하는 모형은 경제학적 이론에 기반을 두었기 때문에 연구 대상에 따른 확장이 용이하다는 장점이 있으며, 이러한 일반적인 모형 구축은 향후 확산 과정에 대한 이해를 더욱 확장시켜 줄 수 있을 것으로 기대한다.The advent of the Internet, mobile communications, and social network services has stimulated social interactions among consumers, allowing people to affect one anothers innovation adoptions by exchanging information more frequently and more quickly. Previous diffusion models, such as the Bass model, however, face limitations in reflecting such recent phenomena in society. These models are weak in their ability to model interactions between agentsthey model aggregated-level behaviors only. The agent-based model, which is an alternative to the aggregate model, is good for individual modeling, but it is still not based on an economic perspective of social interactions so far. This study assumes the presence of social utility from other consumers in the adoption of innovation and investigates the effect of individual interactions on innovation diffusion by developing a new model called the interaction-based diffusion model. By comparing this model with previous diffusion models, the study also examines how the proposed model explains innovation diffusion from the perspective of economics. In addition, the study recommends the use of a small-world network topology instead of cellular automata to describe innovation diffusion. This study develops a model based on individual preference and heterogeneous social interactions using utility specification, which is expandable and, thus, able to encompass various issues in diffusion research, such as reservation price. Furthermore, the study proposes a new framework to forecast aggregated-level market demand from individual-level modeling. The model also exhibits a good fit to real market data. It is expected that the study will contribute to our understanding of the innovation diffusion process through its microeconomic theoretical approach.Abstract iii Contents v List of Tables vii List of Figures viii Chapter 1. Introduction 1 1.1 Research Background 1 1.2 Objective of the Study 3 1.3 Outline of the Study 4 Chapter 2. Literature Review 7 2.1 Overview of the Diffusion of Innovation 7 2.2 Diffusion Models 12 2.2.1 Aggregate Models 13 2.2.2 Agent-Based Models 20 2.2.3 Diffusion models based on individual behavior 26 2.3 Interaction-Based Model 31 2.4 Research Motivation 36 Chapter 3. Interaction-Based Diffusion Model 41 3.1 Utility Model 41 3.2 Structure of Social Network 52 3.3 Diffusion Process 61 Chapter 4. Interpretation of the Interaction-Based Diffusion Model 65 4.1 Specification of Simulation 65 4.2 Simulation Results 74 4.2.1 Effect of Price Coefficients 78 4.2.2 Effect of Social Interactions 87 4.3 Summary 92 Chapter 5. Empirical Availability of the Interaction-Based Diffusion Model 99 5.1 Adjustment of the Model for Fitting 99 5.2 Analysis of Real Market Data 111 5.2.1 Fitting Procedure 111 5.2.2 Analysis Results 114 5.3 Summary 119 Chapter 6. Conclusion 121 6.1 Concluding Remarks 121 6.2 Contributions and Limitations 122 6.3 Future Research Topics 125 Bibliography 127 Appendix I: Raw data of mobile communication subscriptions in three countries. 143 Abstract (Korean) 145Docto

Consumer behavior, social influence, and smart grid implementation

To achieve the goals of German energy transition especially in renewable energy shares, the smart grid will play a key role in managing the demand able to match more volatile supply and optimizing the entire electricity system. Even though the system transformation is technically feasible, the successful transition cannot live without end users willing to transform their way of using energy. This thesis has explored possible roles of individual consumers in the smart grid implementation and in detail analyzed their influential factors. An online survey was conducted to capture preferences and behaviors of energy consumers during the time period of November 2013 to January 2014. The three roles of private electricity consumers - as consumers consuming electricity through appliances, as citizens holding attitudes towards smart grid applications, and as potential producers of electricity - are targeted. Constructs from the theory of planned behavior were tested by using a sample of 517 German citizens. Structural equation models of individual’s electricity saving behavior, their intention to participate in smart grid applications and investment behavior in solar panels were built. It was found that determinants of attitude, perceived norm, and perceived behavioral control together explain 32%-56% of the variance in the three behaviors. Attitude was found to be the most influential factor of individual electricity saving behavior, as well as of citizens’ intentions to participate in smart grid applications. For solar panel investment, it is perceived behavioral control that has the highest impact on the behavior. As the smart grid concept is not well understood by common people, education program and information campaigns are needed, in which social norm marketing is worth more attention, ascribable to the considerable impact caused by the diffusion of norms through social networks. To examine this social influence effect, empirically founded agent-based models for the above-mentioned three behaviors were created to estimate possible behavior changes brought by social norms at the aggregate level. Simulation results show that a reduction of total consumptions by 20% could be achieved in the virtual community due to behavior conformity induced by identified adopters. The potential impact of social norms on home generation and load shift are also promising

Planning the market introduction of new products

In einem zunehmend dynamischen Wettbewerbsumfeld bildet die Fähigkeit zur erfolgreichen Vermarktung von neuen Produkten eine entscheidende Grundlage für den langfristigen Erfolg von Unternehmen. Quantitative Modelle der Verbreitung (Diffusion) von Innovationen in einem sozialen System sind daher sowohl für Wirtschaftswissenschaftler als auch für Manager, die Unterstützung bei der Entwicklung von Markteinführungsstrategien benötigen, von besonderem Interesse. Erste Modelle zur mathematischen Beschreibung von Diffusionsverläufen wurden bereits in den 1960er-Jahren entwickelt. Ziel dieser Modelle ist die empirische Generalisierung von typischen Diffusionsmustern auf aggregierter (d.h. Markt-) Ebene, um den wahrscheinlichen Verlauf der Adoption durch Konsumenten mittels Extrapolation aus frühen Verkaufszahlen abzuschätzen. Für zuverlässige Schätzungen benötigen diese Modelle allerdings Daten über den Großteil des Produktlebenszykluses. Überdies berücksichtigen aggregierte Modelle weder die Heterogenität von Konsumenten noch die Struktur ihrer sozialen Interaktionen. Schließlich eignen sich diese Modelle nur bedingt zur Erprobung des Einflusses von Marketing-Entscheidungsvariablen auf den Diffusionsverlauf. Das Hauptziel dieser Dissertation ist die Entwicklung eines Diffusionsmodells das Entscheidungsträger bei der Planung einer Markteinführungsstrategie für neue Produkte unterstützen kann. Zu diesem Zweck wird agentenbasierte Modellierung und Simulation, eine Methode die in den letzten Jahren in den Sozialwissenschaften zunehmende Verbreitung gefunden hat, eingesetzt. Diese Methode begreift die Diffusion von Innovationen als komplexes emergentes Phänomen, das durch soziale Interaktionen und individuelle Adoptionsentscheidungen von heterogenen Individuen zustande kommt. Ein solcher Bottom-Up-Ansatz ermöglicht es, die prinzipbedingten Einschränkungen von aggregierten Ansätzen zu überwinden und eröffnet damit neue Forschungsmöglichkeiten. Insbesondere können Aspekte wie Adoptionsentscheidungsfaktoren auf Mikroebene, beschränkte Rationalität, unvollständige Information sowie die Heterogenität von Konsumenten hinsichtlich ihrer Präferenzen, ihres Verhaltens und ihrer Verbindungen im sozialen Netzwerk berücksichtigt werden. Die Dissertation zeigt eine Forschungslücke zwischen abstrakten theoretischen Modellen einerseits und angewandten Modellen für bestimmte, sehr spezifische Einsatzbereiche andererseits auf und zielt darauf ab zur Schließung dieser Lücke beizutragen. Der agentenbasierte Ansatz bietet ausgezeichnete Möglichkeiten zur Entwicklung eines generischen und vielseitig einsetzbaren Modells das es erlaubt, aktuelle Forschungsinteressen wie etwa die Diffusion von Innovationen in einem kompetitivem Wettbewerbsumfeld, die räumliche Diffusion von Innovationen oder die Analyse auf Produktebene anstatt auf Branchenebene zu verfolgen. Insbesondere trägt die Dissertation durch (i) die Modellierung aller Stufen des Adoptionsentscheidungsprozesses, (ii) die Erfassung des gesamten Marktes anstatt der Beschränkung auf Erstadoptoren, (iii) die Modellierung der Diffusion einer Innovation in einem Markt mit mehreren Mitbewerbern, (iv) die Erweiterung der zeitlichen Betrachtung von Diffusionsprozessen durch eine räumliche Dimension, (v) die Modellierung eines räumlich definierten sozialen Netzwerkes und (vi) die Einbeziehung von Konsumentenpräferenzen hinsichtlich mehrerer Produktattribute zur Diffusionsforschung bei. Die Eignung des Modells zur Entscheidungsunterstützung in realen Problemstellungen wird anhand eines Anwendungsfalls zur Diffusion eines Biokraftstoffs der zweiten Generation auf dem österreichischen Markt illustriert. Anhand von Simulationsszenarien wird demonstriert, wie das Modell die Planung der Markteinführung einer solchen Innovation unterstützen kann. Ergebnisse zeigen, dass ein wettbewerbsfähiger Preis, wie erwartet, ein wichtiger Adoptionstreiber ist. Zudem weisen die Ergebnisse aber auch darauf hin, dass ein gewisses Marktpotential auch bei einem Preis oberhalb des Niveaus von konventionellen Kraftstoffen besteht. Die Simulation erlaubt potentiellen Investoren die Erprobung unterschiedlicher Strategien zur Auswahl von Vertriebsstellen unter Berücksichtigung von beschränkter Produktionskapazität, lokaler Verfügbarkeit von Rohstoffen und der geographischen Verteilung von Konsumenten. Außerdem ermöglicht es die Simulation, Preisstrategien unter unterschiedlichen Annahmen hinsichtlich zukünftiger Entwicklungen auf dem Rohölmarkt zu testen. Der Anwendungsfall zeigt damit, dass das entwickelte agentenbasierte Diffusionsmodell Entscheidungsträgern wertvolle Unterstützung bei der Entwicklung von Markteinführungsstrategien in einem kompetitiven Marktumfeld bietet.In today’s competitive business environment, firms’ ability to create and maintain competitive advantage and secure long-term survival are critically dependent upon their ability to successful market innovations. Quantitative models of innovation diffusion have therefore attracted strong interest both from management scholars and from practitioners responsible for new product marketing decisions. Pioneering efforts to describe the diffusion of innovations mathematically were made during the 1960s. The aim of these models is to provide empirical generalizations of prototypical diffusion patterns at the aggregate (i.e., market) level in order to estimate the likely diffusion of a new product through extrapolation from early sales; to this end, they typically require considerable amounts of data covering most of the product’s lifespan. Aggregate models cannot account for heterogeneity and social structure and are limited in their potential to evaluate likely effects of decision variables on the diffusion process. The main objective of this thesis is to introduce a diffusion model that can support decision-makers in the process of planning the market introduction of new products. To this end, agent-based modeling and simulation, a methodological innovation that has increasingly been adopted in the social sciences in recent years, is applied to overcome inherent limitations of phenomenological aggregate-level approaches. This bottom-up approach conceives the diffusion of innovations as a complex social phenomenon that emerges from the aggregated individual behavior and the interactions between individuals. It opens up new research opportunities because it can easily incorporate micro-level drivers of adoption, bounded rationality, and imperfect information as well as individuals’ heterogeneity in terms of attributes, preferences, behavior, and linkages in the social network. This thesis identifies and aims at a research gap between purely abstract models of innovation diffusion aimed at general theoretical insights on the one hand, and highly specialized models tailored to a particular practical application on the other hand. The agent-based approach offers excellent opportunities to develop a generic and versatile model that can be applied to a wide range of specific problems. Furthermore, it allows us to pursue cutting-edge research interests including spatial diffusion, diffusion in a competitive context, product-level rather than industry- level analysis, and managerial diagnostics. In particular, the thesis contributes by (i) modeling all stages of the innovation-decision process, (ii) modeling sales rather than exclusively focusing on initial adoption, (iii) modeling the competitive diffusion of multiple products, (iv) complementing the temporal focus with the spatial dimension, (v) incorporating a spatially explicit social network model, and (vi) incorporating multi-attribute consumer decision-making. The capability of the model to tackle real world problems is illustrated by means of a particularly interesting, empirically grounded application study on the diffusion of a second generation biofuel at the Austrian market. Various simulation scenarios demonstrate how the model can be used to plan the market introduction of this innovation. Findings suggest that while a competitive price is unsurprisingly an important driver for adoption, there is a limited market potential for a high quality second generation biofuel at a higher price level than that of conventional fuels. The simulation enables potential investors to assess the effectiveness of various approaches towards selecting gas stations for distribution while accounting for limited production capacity, availability of rich sources of biomass, and the geographic concentration of consumers. It also allows a decision-maker to evaluate the effectiveness of pricing strategies under varying assumptions about future energy market developments. The sample application illustrates how the agent-based model introduced in this thesis can provide managers with valuable decision support in the process of developing product launch strategies in a competitive setting