Modelling the Rise and Fall of Two-Sided Mobility Markets with Microsimulation

In this paper, we propose a novel modelling framework to reproduce the market entry strategies for two-sided mobility platforms. In the MaaSSim agent-based simulator, we develop a co-evolutionary model to represent day-to-day dynamics of the two-sided mobility market with agents making rational decisions to maximize their perceived utility. Participation probability of agents depends on utility, composed of: experience, word of mouth and marketing components adjusted by agents every day with the novel S-shaped formulas - better suited (in our opinion) to reproduce market entry dynamics than previous approaches. With such a rich representation, we can realistically model a variety of market entry strategies and create significant network effects to reproduce the rise and fall of two-side mobility platforms. To illustrate model capabilities, we simulate a 400-day evolution of 200 drivers and 2000 travelers on a road-network of Amsterdam. We design a six-stage market entry strategy with consecutive: kick-off, discount, launch, growth, maturity and greed stages. After 25 days the platform offers discounts, yet it starts gaining market share only when the marketing campaign launches at day 50. Campaign finishes after 50 days, which does not stop the growth, now fueled mainly with a positive word of mouth effect and experiences. The platform ends discounts after 200 days and reaches the steady maturity period, after which its greedy strategy leads to collapse of its market share and profit. All above simulated with a single behavioral model, which well reproduces how agents of both sides adapts to platform actions

The effect of customer satisfaction on parcel delivery operations using autonomous vehicles: An agent-based simulation study

The quality of Third-Party Logistics (3PL) services represented by delivery time decides the outcome of customer satisfaction. The result of this satisfaction judges the type of Word of Mouth (WoM) that, if positive, plays a vital role in attracting non-customers who are willing in 3PL services to join as customers. In this paper, we investigate the effect of an essential factor represented by Word of Mouth on the number of customers in 3PL companies. Therefore, an agent-based model for parcel delivery is developed to investigate the impact of social factors such as WoM and other operational factors, including vehicle number and speed, on customer number and satisfaction, average service time, and vehicle utilization. As a methodology, state charts of Vehicle, Customer, Hub agents are developed to mimic the messaging protocols between these agents under the WoM concept. A case study based in 3PL in Jordan is used as a test bench of the developed model. A sensitivity analysis study is conducted to test the developed model's performance, including different levels of influential model parameters such as targeting non-customers parameters by Loyal/Unhappy customers. Key results reveal that the best scenario is achieved when the WoM value equals 10, the vehicle number equals 30, and the vehicle speed equals 60 km/h. These model parameters result in higher customer numbers of 873, vehicle utilization equals 63%, and customer satisfaction equals 99%. Video of our proposed model showing it in action can be found at: https://www.youtube.com/watch?v=3rR4l130-QU

A Review of Approaches for Sensing, Understanding, and Improving Occupancy-Related Energy-Use Behaviors in Commercial Buildings

Buildings currently account for 30–40 percent of total global energy consumption. In particular, commercial buildings are responsible for about 12 percent of global energy use and 21 percent of the United States’ energy use, and the energy demand of this sector continues to grow faster than other sectors. This increasing rate therefore raises a critical concern about improving the energy performance of commercial buildings. Recently, researchers have investigated ways in which understanding and improving occupants’ energy-consuming behaviors could function as a cost-effective approach to decreasing commercial buildings’ energy demands. The objective of this paper is to present a detailed, up-to-date review of various algorithms, models, and techniques employed in the pursuit of understanding and improving occupants’ energy-use behaviors in commercial buildings. Previous related studies are introduced and three main approaches are identified: (1) monitoring occupant-specific energy consumption; (2) Simulating occupant energy consumption behavior; and (3) improving occupant energy consumption behavior. The first approach employs intrusive and non-intrusive load-monitoring techniques to estimate the energy use of individual occupants. The second approach models diverse characteristics related to occupants’ energy-consuming behaviors in order to assess and predict such characteristics’ impacts on the energy performance of commercial buildings; this approach mostly utilizes agent-based modeling techniques to simulate actions and interactions between occupants and their built environment. The third approach employs occupancy-focused interventions to change occupants’ energy-use characteristics. Based on the detailed review of each approach, critical issues and current gaps in knowledge in the existing literature are discussed, and directions for future research opportunities in this field are provided

Word of Mouth, Viral Marketing and Open Data: A Large-Scale Simulation for Predicting Opinion Diffusion on Ethical Food Consumption

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.This paper presents the first results of a large-scale-Agent-Based Simulation devoted to simulate individual behaviour inside a medium sized city (600,000 inhabitants). Humans are simulated as Intelligent Individual entities characterized by several attributes created from the Open Data available by means of a multi-layer approach. The work presented is divided into two main parts: the first part aims to describe the multi-layer approach adopted with the inclusion of the social network layer devoted to capture how social networks can be correlated with human activities and how an “Individual Opinion” can changes based on social interactions. The second part is devoted to present a preliminary case study for simulating the propagation dynamics of the individual opinion in the form of an ethical value function. The basic idea is to capture the changes in the individual opinion based on the social interactions predicted by the simulation. Finally, a food choice model for predicting individual choices based on the individual opinion function is presented; the model is based on three parameters: accessibility of ethical shops, price difference with standard products, and ethical value propagation

Effects of social networks on innovation diffusion and market dynamics

The main goal of this thesis is to incorporate part of the flourishing literature on network structures in a marketing context. Most of the results we have obtained and presented generate several implications. First of all we hope that the reader, after going through these chapters is convinced that often networks do play a role, that they can explain different market dynamics and that studying networks can be used to develop marketing strategies. Most of the theoretical implications derive from the following metaphor: a new product that diffuses into a society of consumers is like an epidemic that spreads into a population of susceptible individuals. Inspired by this metaphor, we believe that marketing can gain useful insights studying, adjusting and adopting epidemic models. This is what we explicitly do in chapters 2, 3 and 4. We build different network structures of consumers with their preferences and their attributes and we study how the diffusion dynamics of different products vary. Although we believe that the diffusion of a new product might look like the spread of an epidemic, we are also aware that these two processes are not completely the same. A substantial part of the work presented here consists of adapting the epidemic models to a marketing framework that can include product characteristics, personal preferences and social influence

Seeding as Part of the Marketing Mix:Word-of-Mouth Program Interactions for Fast-Moving Consumer Goods

Seeded marketing campaigns (SMCs) have become part of the marketing mix for many fast-moving consumer goods (FMCG) companies. In addition to making large investments in advertising and sales promotions, these firms now encourage seed agents or microinfluencers to discuss brands with friends and acquaintances to create further value. It is thus critical to understand how an FMCG seeding program interacts with traditional marketing tools when estimating the effectiveness of such efforts. However, the issue is still underexplored. The authors present the first empirical analysis of this question using a rich data set collected on four brands from various European FMCG markets. They combine advertising and sales promotion data from FMCG brand managers with sales and retail variables from market research companies as well as firm-created word-of-mouth variables from SMC agencies. The authors analyze the data using several approaches, confronting challenges of endogeneity and multicollinearity. They consistently find that firm-created word of mouth through SMC programs interacts negatively with all tested forms of advertising but positively with promotional activities. This phenomenon has significant implications for understanding the utility of SMCs and how they should be managed. The analysis implies that SMCs may increase total sales by approximately 3%-18% throughout the campaigns

The Contribution of Social Simulation in the Advancement of Marketing Issues and Challenges

For some years now, marketers have been praising for a more holistic approach of a company’s marketing efforts across all areas. However, traditional models show serious limitations to address the complexities of managing all of a company’s touch points with a customer. Agent-based modeling (ABM) has opened the door to explore the unfolding behaviors and outputs of an increasingly connected and interactive marketplace. The contribution of this paper is twofold. On the one hand, it provides researchers with a state-of-the-art repository for this strand of research. This facilitates the identification of relevant gaps in the literature and future research avenues. Second, it contributes to assess the way ABM has improved our understanding of the dynamics of markets and its participants when marketing strategies are implemented. Both goals aim at showing the various ways that social simulation has expanded our understanding of marketing and the future research opportunities for both, marketing and computer scientists