Web and social media dynamics, and evolutionary and adaptive branding: theories and a hybrid intelligent model

Effective branding is pivotal to the success of most companies. In this paper, a hybrid intelligent model and associated supporting methods, techniques and technologies are proposed to represent, simulate and analyze the adaptation, evolution and growth of corporate and product brands in the contexts of World Wide Web and social media, through integrating the strengths of complex adaptive systems theory, environment-sensitive reasoning method, system dynamics, Web analytics, intelligent software agents, and expert systems. The rationale, modeling paradigm and decision support framework for branding in online social networks are presented and discussed. Hypotheses for further research on this topic are also formulated. Keywords—social media dynamics; Web dynamics; adaptive branding; corporate & product brand building; complex adaptive systems theory; intelligent software agent; hybrid intelligent model; expert system; Web analytics; system I. NTRODUCTION AND DEFINITIONS FOR WEB & SOCIAL MEDIA DYNAMICS, WEB ANALYTICS AND BRANDS Tim Berners-Lee’s invention of World Wide Web has brought significant cutting-edge revolution and great opportunities for businesses and human social networking across the world. World Wide Web, Web sites and business environments change dynamically. According to Oxford dictionary, dynamics are “the forces or properties which stimulate growth, development, or change within a system or process”. In this paper, we define Web dynamics as the dynamic behavior of the complex Web system including online environmental changes, events, visitor/audience behavior, voice of customers, Web site usage and clickstreams, company behavior and maneuvers, competitor moves, and interactions of relevant players, participants, users and audience on the World Wide Web and associated Web sites. Social media and associated tools, such as Facebook and Twitter, are Web-based applications built upon Web 2.0 technologies. They enable and allow online interactions and communication amongst individuals and virtual communities for creating, co-creating, sharing, discussing, and amending user-produced contents [1, 2, 18]. We define social media dynamics as social media buzz, trends, growth, evolution, co-evolution, the dynamic behavior of social media contributors and users, and in particular, how they interact with each other, how they interact with online Web resources, what contents they generate, how they share information and knowledge, what they like, and what they dislike over time. Web analytics is the study of reporting and analyzing what has happened and what is happening on Web sites, tracking user behavior on the Web, assessing Web site performance and improving its usage. In order to compete, survive and succeed in the digital age, business people need to understand how Web & social media dynamics and relevant Web analytics affect and inform digital marketing decision-making and online branding activities. In this study, we focus on corporate and product branding. “Brand” can be defined as the name, design (shapes, colors, sounds, etc.), logo, symbol, or any other characteristics or attributes that distinguishes one company's product or service from those of other companies [17, 25]. Product branding highlights and focuses on individual product’s identity and value [17]. Corporate branding is concerned with establishing an image and name for a Li, Shuliang and Li, Jim Zheng (2014), ”Web and social media dynamics, and evolutionary and adaptive branding: theories and a hybrid intelligent model". In: Proceedings of the 13th international conference on artificial intelligence, knowledge engineering and data bases, 15-17th May 2014, Gdansk, Poland. Advances in Neural Networks, Fuzzy Systems and Artificial Intelligence . WSEAS, 106 -111. ISBN 9789604743797 company or organization [25]. They can be promoted separately or combined together and co-branded simultaneously. Increasing brand awareness is pivotal to the survival and success of most businesses. Effective branding may lead to improved customer loyalty, larger number of orders, better sale volume, and higher revenue and profits. There are diverse branding strategies including company names, individual branding, attitude branding, private labels and social media brands. Pont [3] argues that social media brands may be the most evolved version of brand form for the reason that they focus on both the brands themselves and the consumers or users. Brands need to evolve and adapt to dynamic competition, consumer expectations, and environmental changes. Marketing efforts and advertising campaigns should be made by companies to build positive perceptions for their corporate and product brands and thus to achieve better sales and profit margins than competitors. While World Wide Web and social media offer the great arena and avenue for branding in the digital age, there are also challenges to companies and managers’ knowledge and skills. Due to the uncertainty, disturbance, complexity and changing nature of social media, branding and brand building in this context bring difficulties to business managers and marketers. This paper aims to propose and discuss a novel hybrid intelligent model, paradigm and associated supporting methods and tools for representing, simulating and analyzing the evolution, adaptation and growth of product brands, through integrating the strengths of complex adaptive system theory, environment-sensitive reasoning method, system dynamics, Web analytics, expert systems and others. II. A HYBRID MODEL FOR ADAPTIVE CORPORATE & PRODUCT BRANDING IN THE CONTEXTS OF WORLD WIDE WEB & SOCIAL MEDIA Considering the reality that many managers lack knowledge and skills on branding in social media, and taking into account the bounded rationality of the human mind in decision making for complex situations [23], diverse methods, techniques and technologies should be employed to model, analyze and support effective branding through online social networks in the digital age. In this paper, we propose a hybrid modeling and decision support paradigm and model. Within this paradigm, Peirce’s abductive reasoning method [19] and the inductive reasoning approach are employed to detect the Web environments, make inference and find causes for relevant phenomena, events and changes. The salient feature of contextual smart software agents are utilized to monitor, analyze and react to the audience mentions, buzz, events, competitor manoeuvers, through inductive and abductive reasoning. The context-sensitive intelligent decision support systems approach [24] is also incorporated to support this. Self-organized ant foraging systems [21] are extended and applied to detect and respond to digital trails, trends, and optimize the adaptive corporate and product branding processes [9]. Research on adaptive foraging in the field of ecology [22] can also bring new ideas to adaptive branding, and customer buzz hearing [9]. System dynamics is a methodology and modeling approach towards structuring and understanding the dynamic behavior of complex systems [6]. This method mainly utilizes differential and difference equations, combined with probabilities for some variables to describe a continuously changing system. A typical system dynamic diagram may contain feedback loops, causal loops, flow rates, flows and stocks, variables and influences. The system dynamics approach is employed to describe and analyze brand evolution and associated interactions and relationships amongst participants and other variables in the Internet-enabled social networking environment. Complex adaptive systems (CAS) theory [4] provides a solid foundation and remarkable capacities for modeling and analyzing the branding problem in social media. As stated by Holland [4], a CAS system has such characteristics and properties as self-organization; parallelism of agents interacting with each other and producing signals simultaneously; If-then conditional action depending the contexts and signals; modularity with building blocks for reacting to current situations and dealing with new circumstances by applying a set of organised rules; micro level of individual agent behaviour and interactions, and macro level of emergence that cannot be predefined or predicted precisely; and adaptation, evolution and coevolution over time; implied fitness for measuring agent needs fulfilment and performance [4, 5, 7]. Specifications for relevant agents or subjects, relevant algorithms and the basic cycle for rule-based signal processing procedure can be found in Holland’s work [4, 5]. In this study, John Holland’s complex adaptive systems theory and the basic cycle [4] are extended to integrate with environment-sensitive detection and reaction [8], system dynamics simulation [6], intelligent software agents and fuzzy expert systems for adaptive branding in social media. In a CAS, system components or agents interact through reacting to the environmental events, exchanging messages, inter-communicating, learning, co-operating and competing [7, 11, 13]. Because the interactions amongst relevant agents or subjects are non-linear, some mentions or buzz may exponentially cause significant emergence to arise from online social networks. Some emergence may help improve a corporate or product name. Some may damage a brand. A branding agent can learn, evolve, respond to changes and affect its social media environment. The brand, the company, competing brand, competitors, customers, online virtual communities interact with each other randomly in the contexts of social networking and its dynamics. Patterns may Li, Shuliang and Li, Jim Zheng (2014), ”Web and social media dynamics, and evolutionary and adaptive branding: theories and a hybrid intelligent model". In: Proceedings of the 13th international conference on artificial intelligence, knowledge engineering and data bases, 15-17th May 2014, Gdansk, Poland. Advances in Neural Networks, Fuzzy