scholarly journals Harvesting Crowdsourcing Platforms’ Traffic in Favour of Air Forwarders’ Brand Name and Sustainability

2021 ◽  
Vol 13 (15) ◽  
pp. 8222
Author(s):  
Damianos P. Sakas ◽  
Nikolaos Th. Giannakopoulos
Keyword(s):  
Data Analytics ◽  
Cognitive Mapping ◽  
Brand Name ◽  
Web Data ◽  
Factors Affecting ◽  
Agent Based ◽  
Data Prediction ◽  
Last Stage ◽  
Prediction And Simulation ◽  
Exploratory Model

In the modern digitalised era, the total number of businesses and organisations utilising crowdsourcing services has risen, leading to an increase of their website traffic. In this way, there is plenty of space for marketers and strategists to capitalise big data from both their own and the crowdsourcer’s websites. This can lead to a comprehension of factors affecting their brand name, sustainability (gross profit) and consequently visitor influence. The first of the three staged contexts, based on web data, includes the retrieval of web data analytics and metrics from five air forwarding and five crowdsourcing websites in 210 observation days. At stage two, we deployed a diagnostic-exploratory model, through Fuzzy Cognitive Mapping (FCM), and in the last stage, an Agent-Based Model is deployed for data prediction and simulation. We concluded that crowdsourcing referral traffic increases air forwarders’ top 3 keywords volume, and decreases social traffic and total keywords volume, which then boosts their global web rank and gross profit. The exact opposite results occur with crowdsourcing search traffic. To sum up, the contribution of this paper is to offer realistic and well-informed insights to marketers about SEO and SEM strategies for brand name and profit enhancement, based on harvesting crowdsourcing platform traffic.

scholarly journals Big Data Contribution in Desktop and Mobile Devices Comparison, Regarding Airlines’ Digital Brand Name Effect

2021 ◽  
Vol 5 (4) ◽  
pp. 48
Author(s):  
Damianos P. Sakas ◽  
Nikolaos Th. Giannakopoulos
Keyword(s):  
Big Data ◽  
Mobile Devices ◽  
Cognitive Mapping ◽  
Agent Based Modeling ◽  
Marketing Strategies ◽  
Digital Marketing ◽  
Brand Name ◽  
Agent Based ◽  
Exploratory Model ◽  
Observation Period

Rising demand for optimized digital marketing strategies has led firms in a hunt to harvest every possible aspect indicating users’ experience and preference. People visit, regularly through the day, numerous websites using both desktop and mobile devices. For businesses to acknowledge device’s usage rates is extremely important. Thus, this research is focused on analyzing each device’s usage and their effect on airline firms’ digital brand name. In the first phase of the research, we gathered web data from 10 airline firms during an observation period of 180 days. We then proceeded in developing an exploratory model using Fuzzy Cognitive Mapping, as well as a predictive and simulation model using Agent-Based Modeling. We inferred that various factors of airlines’ digital brand name are affected by both desktop and mobile usage, with mobile usage having a slightly bigger impact on most of them, with gradually rising values. Desktop device usage also appeared to be quite significant, especially in traffic coming from referral sources. The paper’s contribution has been to provide a handful of time-accurate insights for marketeers, regarding airlines’ digital marketing strategies.

scholarly journals The Effects of Cryptocurrency Trading Websites on Airlines’ Advertisement Campaigns

2021 ◽  
Vol 16 (7) ◽  
pp. 3099-3119
Author(s):  
Damianos P. Sakas ◽  
Nikolaos T. Giannakopoulos ◽  
Dimitrios P. Reklitis ◽  
Thomas K. Dasaklis
Keyword(s):  
Statistical Analysis ◽  
Cognitive Mapping ◽  
Positive Impact ◽  
Marketing Strategies ◽  
Digital Marketing ◽  
Stage Model ◽  
Web Analytics ◽  
Factors Affecting ◽  
Agent Based ◽  
Agent Based Modelling

In future years, airline companies will be leaning more and more towards cryptocurrencies to implement their digital marketing strategies as leaders seek to gain an understanding of the factors affecting airlines’ visibility parameters. Cryptocurrency investment websites are currently experiencing rising demand, making them an appropriate site for paid advertisements. The above factors suggest the need for airlines to harvest cryptocurrency investment and platform users in their favour. To this end, it can be beneficial for airlines’ web promotions to link certain web analytics metrics to cryptocurrency trading site metrics. For research purposes, web analytics data were monitored and gathered for 2 consecutive years from 10 globally leading cryptocurrency trading companies and 10 airline websites. A three-stage model was adopted by the authors. In the first stage, statistical analysis was implemented using cryptocurrency and airline metrics, followed by fuzzy cognitive mapping and agent-based modelling stages. The findings of the study indicate that engagement with cryptocurrency trading websites has a positive impact on airline websites’ global ranking and visibility parameters. The outcomes of this research provide noteworthy digital marketing strategies which can be addressed by airline companies to increase their website visitors and optimise visibility parameters with the assistance of cryptocurrency trading websites.

scholarly journals The Impact of Organic Traffic of Crowdsourcing Platforms on Airlines’ Website Traffic and User Engagement

2021 ◽  
Vol 13 (16) ◽  
pp. 8850
Author(s):  
Damianos P. Sakas ◽  
Dimitrios P. Reklitis
Keyword(s):  
Cognitive Mapping ◽  
Marketing Strategies ◽  
Digital Marketing ◽  
Diagnostic Model ◽  
User Engagement ◽  
Factors Affecting ◽  
Agent Based ◽  
Second Stage ◽  
Micro Level ◽  
The Impact

With airline companies increasingly relying on crowdsourcing websites to deploy their digital marketing strategies, marketeers and strategists seek to acquire an understanding of the factors affecting airlines’ organic traffic and user engagement. Such an understanding is acquired through the consideration of variables that influence a company’s organic traffic and user engagement and their correlation to each other. A three-stage data-driven analysis is used to examine the correlation between the foregoing variables and to consider strategies that can be implemented to optimize organic traffic and user engagement. The first section gathers data from five airline companies’ websites and five crowdsourcing websites over an interval of 180 days. The second stage creates an exploratory diagnostic model, through Fuzzy Cognitive Mapping, to visually illustrate the cause-and-effect correlations between the examined metrics. Finally, a predictive micro-level agent-based model simulates optimization strategies that can be used to improve organic traffic and user engagement. The results of this study, reveal that crowdsourcing organic traffic increases airline websites’ user engagement through paid campaigns, while a limited correlation was found to exist between the average duration of a user to organic traffic. The results of this study provide tangible digital marketing strategies which can be used by airline companies to improve the influence of their digital marketing strategies on their users.

scholarly journals Agent-Based Modeling Framework for Electric Vehicle Adoption Transition in Indonesia

2021 ◽  
Vol 12 (2) ◽  
pp. 73
Author(s):  
Dita Novizayanti ◽  
Eko Agus Prasetio ◽  
Manahan Siallagan ◽  
Sigit Puji Santosa
Keyword(s):  
Electric Vehicles ◽  
Data Analytics ◽  
Agent Based Modeling ◽  
Analysis Framework ◽  
Modeling Framework ◽  
Agent Based ◽  
Twitter Data ◽  
Decision Making Processes ◽  
Electric Vehicle Adoption ◽  
Technical Features

Currently, the adoption of electric vehicles (EV) draws much attention, as the environmental issue of reducing carbon emission is increasing worldwide. However, different countries face different challenges during this transition, particularly developing countries. This research aims to create a framework for the transition to EV in Indonesia through Agent-Based Modeling (ABM). The framework is used as the conceptual design for ABM to investigate the effect of agents’ decision-making processes at the microlevel into the number of adopted EV at the macrolevel. The cluster analysis is equipped to determine the agents’ characteristics based on the categories of the innovation adopters. There are 11 significant variables and four respondents’ clusters: innovators, early majority, late majority, and the uncategorized one. Moreover, Twitter data analytics are utilized to investigate the information engagement coefficient based on the agents’ location. The agents’ characteristics which emerged from this analysis framework will be used as the fundamental for investigating the effect of agents’ specific characteristics and their interaction through ABM for further research. It is expected that this framework will enable the discovery of which incentive scheme or critical technical features effectively increase the uptake of EV according to the agents’ specific characteristics.

scholarly journals Special issue: Mobile web data analytics (part I)

2018 ◽  
Vol 16 (2) ◽  
pp. 89-90
Author(s):  
Zili Zhang ◽  
Li Liu ◽  
Li Li ◽  
Xiangliang Zhang
Keyword(s):  
Data Analytics ◽  
Special Issue ◽  
Web Data ◽  
Mobile Web

Introduction to the Theme Issue on Dynamics of Health Behavior: Revisiting Systems Science for Population Health

2020 ◽  
Vol 47 (2) ◽  
pp. 185-190
Author(s):  
Jessica G. Burke ◽  
Jessica R. Thompson ◽  
Patricia L. Mabry ◽  
Christina F. Mair
Keyword(s):  
Health Behavior ◽  
Health Behaviors ◽  
Population Health ◽  
Complex Dynamics ◽  
Agent Based Modeling ◽  
Systems Science ◽  
Science Methods ◽  
Factors Affecting ◽  
Agent Based ◽  
Public Health Professionals

Systems science can help public health professionals to better understand the complex dynamics between factors affecting health behaviors and outcomes and to identify intervention opportunities. Despite their demonstrated utility in addressing health topics such influenza, tobacco control, and obesity, the associated methods continue to be underutilized by researchers and practitioners addressing health behaviors. This article discusses the growth of systems science methods (e.g., system dynamics, social network analysis, and agent-based modeling) in health research, provides a frame for the articles included in this themed issue, and closes with recommendations for enhancing the future of systems science and health behavior research. We argue that integrating systems sciences methods into health behavior research and practice is essential for improved population health and look forward to supporting the evolution of the field.

scholarly journals Hedonic Retail Beef and Pork Product Prices

2007 ◽  
Vol 39 (1) ◽  
pp. 29-46 ◽  
Author(s):  
Joseph L. Parcell ◽  
T.C. Schroeder
Keyword(s):  
Grocery Stores ◽  
Pork Meat ◽  
Hedonic Models ◽  
Store Brands ◽  
Brand Name ◽  
Product Size ◽  
Factors Affecting ◽  
Beef Meat ◽  
Pork Product

Consumer-level hedonic models are estimated to determine factors affecting retail pork and beef meat cuts. Results indicate that brand premium and discount varies across private, national, and store brands and that brand premium varies across meat cuts carrying the same brand name. Product size discounts are linear for beef and nonlinear for pork, meat items on sale are significantly discounted to nonsale items, specialty stores typically will not garner higher prices than supermarket/grocery stores, and warehouse stores typically have premium prices relative to supermarket/grocery stores.

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