Online social blogs have gained popularity recently. They provide an effective channel for word-of-mouth (WoM) marketing to promote products or service. In WoM marketing, an opinion leader, who is normally more interconnected and has a higher social standing, can deliver product information, provide recommendations, give personal comments, and supplement professional knowledge that help companies to promote their products. Many theories have been put forward about social networks, but few address the issue of opinion leader identification. This study proposes a framework to identify opinion leaders using the information retrieved from blog content, authors, readers, and their relationships, which we call BARR for short. We first build ontology for a marketing product and then collect parameters from BARR to identify ''hot topics'' related to the product. These hot topics are then associated with information disseminators, or opinion leaders. Marketers can use BARR to track blogs written by opinion leaders and identify their opinions to form effective marketing strategies.

Who is talking? An ontology-based opinion leader identification framework for word-of-mouth marketing in online social blogs

Artificial intelligence (AI) was introduced to develop and create “thinking machines” that are capable of mimicking, learning, and replacing human intelligence. Since the late 1970s, AI has shown great promise in improving human decision-making processes and the subsequent productivity in various business endeavors due to its ability to recognise business patterns, learn business phenomena, seek information, and analyse data intelligently. Despite its widespread acceptance as a decision-aid tool, AI has seen limited application in supply chain management (SCM). To fully exploit the potential benefits of AI for SCM, this paper explores various sub-fields of AI that are most suitable for solving practical problems relevant to SCM. In so doing, this paper reviews the past record of success in AI applications to SCM and identifies the most fruitful areas of SCM in which to apply AI.

Artificial intelligence in supply chain management: theory and applications

Telecommunications demand forecasting—a review

On the grounds that individuals heavily rely on the information that they receive from their peers when evaluating adoption of a radical innovation, this paper proposes a new approach to forecast long-term adoption of connected autonomous vehicles (CAVs). The concept of resistance is employed to explain why individuals typically tend to defer the adoption of an innovation. We assume that there exists a social network among individuals through which they communicate based on certain frequencies. In addition, individuals can be subject to media advertisement based on certain frequencies. An individual’s perceptions are dynamic and change over time as the individual is exposed to advertisement and communicates with satisfied and dissatisfied adopters. We also explicitly allow willingness-to-pay (WTP) to change as a result of peer-to-peer communication. An individual decides to adopt when (i) there is a need for a new vehicles; (ii) his/her WTP is greater than CAV price; and (iii) his/her overall impression about CAVs reaches a cutoff value. Applicability of the proposed approach is shown using a survey of employees of the University of Memphis. Our results show that the automobile fleet will be near homogenous in about 2050 only if CAV prices decrease at an annual rate of 15% or 20%. We find that a 6-month pre-introduction marketing campaign may have no significant impact on adoption trend. Marketing is shown to ignite CAV diffusion but its effect is capped. CAV market share will be close to 100% only if all adopters are satisfied with their purchases; therefore, the probability that an individual becomes a satisfied adopter plays an important role in the trend of adoption. The effect of the latter probability is more pronounced as time goes by and is also more prominent when CAV price reduces at greater rates. Some caveats may be inserted when considering the study results as the findings are subject to sample bias and data limitations.

Predicting the adoption of connected autonomous vehicles: A new approach based on the theory of diffusion of innovations

Purpose – The purpose of this paper is to review the academic literature on relationship marketing (RM), conduct a content analysis of the same for the purpose of classification and provides a comprehensive bibliography.Design/methodology/approach – A range of online databases were searched to review the literature on RM. Only, 209 papers had RM as the primary research topic. The full text of each paper was taken as the unit of analysis. The classification of all the units into mutually exclusive categories was done by two independent researchers.Findings – The content analysis led to classification of literature into five mutually exclusive categories viz. objectives, defining constructs, instruments, industrial applications and issues. The contribution of research papers has been on the rise across the time frame and there has been a sharp rise in RM research publication in the recent years. The study revealed many other useful findings.Research limitations/implications – The findings can be generalized...

Relationship marketing research (1994‐2006)

The intelligent customer relationship management (iCRM) tool — built using agent-based modelling techniques — aims to illustrate how CRM investments can influence a customer population, giving a clearer view of potential return on investment (ROI). Unlike conventional approaches, this model considers the communication of customer experiences between members of a social network, incorporating the powerful influence of word of mouth on the adoption of products and services. The tool is an advance on traditional techniques that rely on macroscopic behaviours and aggregated customer data, while neglecting important network and spatial effects.

Agent-Based Modelling — Intelligent Customer Relationship Management

A number of simulation techniques may be applied to strategy and decision-making. In using such techniques it is important to understand the role of models, in the context of the wider decision-making process which is largely a communal and political process; the choice of a particular approach or technique depends not only on the type of decision being considered, but also on the stage of the decision-making process (which in turn affects the type of problem under consideration).

Models which take into account the dynamics of a system can give insight into possible outcomes and indications of unintended consequences. Such models may not necessarily reproduce all aspects of a complex adaptive system; there is a continuum of modelling techniques, ranging from static equilibrium models through to agent-based approaches which can incorporate evolution and learning. The trade-offs between alternative approaches is discussed.

Complex Systems Models for Strategic Decision Making

Understanding the rate of adoption of a telecommunications service in a population of customers is of prime importance to ensure that appropriate network capacity is provided to maintain quality of service. This problem goes beyond assessing the demand for a product based on usage and requires an understanding of how consumers learn about a service and evaluate its worth. Field studies have shown that word of mouth recommendations and knowledge of a service have a significant impact on adoption rates. Adopters of Internet can be influenced through communications at work or children learning at school. In this paper we present an agent based model of a population of customers, with rules based on field data, which is being used to understand how services are adopted. Of particular interest is how customers interact to learn about the service through their communications with other customers. We will show how the different structure, dynamics, and distribution of the social networks affect the diffusion of a service through a customer population. Our model shows that real world adoption rates are a combination of these mechanisms which interact in a non-linear and complex manner. This complex systems approach provides a useful way to decompose these interactions. The paper concludes by suggesting improvements to the model and directions for future work.

Agent Based Customer Modelling

Understanding the rate of adoption of a telecommunications service in a population of customers is of prime importance to ensure that appropriate network capacity is provided to maintain quality of service. This problem goes beyond assessing the demand for a product based on usage and requires an understanding of how consumers learn about a service and evaluate its worth. Field studies have shown that word of mouth recommendations and knowledge of a service have a significant impact on adoption rates. Adopters of the Internet can be influenced through communications at work or children learning at school. The authors present an agent based model of a population of customers, with rules based on field data, which is being used to understand how services are adopted. Of particular interest is how customers interact to learn about the service through their communications with other customers. We show how the different structure, dynamics and distribution of the social networks affect the diffusion of a service through a customer population. Our model shows that real world adoption rates are a combination of these mechanisms which interact in a non-linear and complex manner. This complex systems approach provides a useful way to decompose these interactions.

David Collings

Papers

Agent-Based Modelling — Intelligent Customer Relationship Management

Complex Systems Models for Strategic Decision Making

Agent Based Customer Modelling

Agent Based Customer Modelling

Agent based customer modelling: individuals who learn from their environment