Special Issue "Crowd-Powered e-Services"

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: 30 September 2021.

Special Issue Editors

Dr. Ioanna Lykourentzou
E-Mail Website
Guest Editor
Department of Information and Computing Sciences, Utrecht University, Domplein 29, 3512 JE Utrecht, Netherlands
Interests: Computer-supported cooperative work; Crowd computing; Online communities
Dr. Angeliki Antoniou
E-Mail Website
Guest Editor
Department of Informatics and Telecommunications, University of Peloponnese, Terma Karaiskaki, 22100 Tripolis, Greece
Interests: Technologies and applications for cultural heritage; educational games (formal, non-formal and informal learning); augmented reality; user profiling; personalization; group adaptation; social networks; crowdsourcing
Special Issues and Collections in MDPI journals
Prof. Dr. Martín López-Nores
E-Mail Website
Guest Editor
Department of Telematics Engineering, University of Vigo, EE Telecomunicación, Campus Universitario s/n, 36310 Vigo, Spain
Interests: Applied Artificial Intelligence; knowledge modeling; semantic reasoning; interactive storytelling
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Crowdsourcing is a model in which individuals or organizations obtain goods and services from a large, open and rapidly-evolving group of Internet users. The idea of dividing work between participants to achieve a cumulative result has been applied successfully in many areas, from biology and linguistics to engineering and cultural heritage. As a particular branch of crowdsourcing, crowd computing (also known as “human computation” or “human-centered AI”) systematizes the intertwining of human intelligence with artificial intelligence, aiming to solve tasks that are hard for individuals or computers to do alone. The key principles include (i) automation: machines do non-creative and repetitive work, providing a cascade of knowledge for humans to evaluate; (ii) micro-tasking: work is broken into small tasks that are easier to complete by humans chosen specifically on the grounds of their expertise; and (iii) mixed crowd: a greater volume of work, and of greater value, can be completed when specialists and open communities work together.

This Special Issue seeks to become a forum to publish broad, interdisciplinary research about human-in-the-loop intelligent e-services, human-AI interaction, and techniques for augmenting the abilities of individuals and communities to perform whichever tasks. We thereby solicit papers presenting theoretical contributions or practical uses of crowdsourcing and crowd computing models in any domains of application. Topics of interest include, but are not limited to, the following:

  • Crowdsourcing/crowd computing case studies:
    • digital humanities,
    • economy,
    • education,
    • health,
    • journalism,
    • software engineering,
    • tourism,
    • urban data collection,
  • Crowdsourcing/crowd computing theory and techniques:
    • algorithm design,
    • collective knowledge,
    • human-AI interaction,
    • incentives to collaboration,
    • intellectual property,
    • macro- and micro-tasking,
    • mixed crowd,
    • psychological and emotional aspects of crowd involvement,
    • quality control,
    • task assignment,
  • Uses of crowdsourcing/crowd computing:
    • games,
    • knowledge bases,
    • fact verification,
    • information retrieval,
    • machine learning,
    • optimization,
    • virtual labor markets,

Dr. Ioanna Lykourentzou
Dr. Angeliki Antoniou
Prof. Dr. Martín López-Nores
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • crowdsourcing
  • crowd computing
  • human computation
  • human-centered AI
  • IT-mediated crowds

Published Papers (2 papers)

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Research

Article
The Impact of Organic Traffic of Crowdsourcing Platforms on Airlines’ Website Traffic and User Engagement
Sustainability 2021, 13(16), 8850; https://doi.org/10.3390/su13168850 - 07 Aug 2021
Viewed by 438
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Crowd-Powered e-Services)
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Article
Harvesting Crowdsourcing Platforms’ Traffic in Favour of Air Forwarders’ Brand Name and Sustainability
Sustainability 2021, 13(15), 8222; https://doi.org/10.3390/su13158222 - 23 Jul 2021
Viewed by 447
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Crowd-Powered e-Services)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Crowdsourcing Airlines - Exploring Crowdsourcing Organic Traffic as a Means of Influencing Airline Website Traffic and User Engagement
Authors: Dimitrios P. Rekleitisa; Damianos P. Sakas
Affiliation: School of Applied Economics and Social Sciences, Department of Agribusiness and Supply Chain Management, Agricultural University of Athens
Abstract: Recent digital trends have revealed a surge in the total number of clients, principally corporate clients, relying on crowdsourcing services to meet their demands. This new practice has resulted in clients experiencing an upsurge of website traffic on their sites. This trend is also translated in the airline industry, wherein large airline companies increasingly seek to rely on crowdsourcing websites in order to attract more visitors on their digital platforms. The growing use of crowdsourcing websites intrinsically generates a large amount of Big Data, both on the client’s website as well as the crowdsourcer’s site, which can be examined by marketers and strategists to acquire an understanding of the factors influencing the client’s organic traffic and user engagement. The opportunity for such an analysis is considerable as it allows businesses to comprehend, based on Big Data, how the airline’s website is influenced by its organic traffic and user engagement. However, in order for such an analysis to be carried out, all the possible correlations between the variables which influence a company’s organic traffic and user engagement should be examined. The present work proposes a three-stage data-driven framework to examine the organic traffic and user engagement patterns found on selected airline websites. The purpose of this study is to consider strategies which can optimize these factors. In this light, the first section of this paper will gather data from 5 airline companies’ websites and 5 crowdsourcing websites over a continuous time period of 180 days. The data gathering stage will allow for the initial examination and measurement of all the relevant variable relating to organic traffic and user engagement. The second stage develops a diagnostic exploratory model by relying on Fuzzy Cognitive Mapping in order to provide a visual illustration of the cause-and-effect correlations between the examined metrics. The third and final stage of the present work will consist of the creation of a predictive micro-level agent-based model with a view of simulating potential optimization strategies that can be deployed by the examined airline websites in order to improve their respective organic traffic and user engagement. Thus, this paper provides a practical and market-focused examination of the implications of digital marketing strategies on organic traffic and user engagement. Further, this work provides tangible digital marketing strategies, which combine knowledge experience and data-driven decision-making, that can be used by airline companies to improve the influence of their digital marketing strategies.

Title: Crowdsourcing Platforms’ Traffic Contribution to Air Forwarders’ Website Traffic Through Impacting Their Brand Name
Authors: Nikolaos Th. Giannakopoulosa; Damianos P. Sakasb
Affiliation: School of Applied Economics and Social Sciences, Department of Agribusiness and Supply Chain Management, Agricultural University of Athens
Abstract: In modern digitalized era, the total number of clients, such as businesses of organizations, utilizing crowdsourcing services has risen, leading to an increase of their website traffic. Many big companies, included those of air forwarding sector, seek to or already use crowdsourcing services. In this way, there’s plenty of space for marketers and strategists to capitalize big data from both theirs’ and the crowdsourcer’s website in order to comprehend factors affecting their profit and consequently how website visitors are influenced by that. The above emerge from need of examining new possible variables impacting companies’ profit and brand name and thus, the way their traffic is affected. In our paper, for the measurement and optimization of gross profit, as a variable of brand name, from crowdsourcing activities and web traffic coming from gross profit’s variation, we suggest a three staged context based on website data. The first of the stages includes the retrieval of web data analytics and metrics from 5 air forwarding and five crowdsourcing websites in 210 observation days. At stage two, we deployed a diagnostic-exploratory model so as to underline the potential correlations among the chosen web metrics. In this stage, the Fuzzy Cognitive Mapping was applied to depict cause-and-effect relationships among the referred metrics. In the last stage, we deployed an agent-based model, for the prediction and simulation of variation of web traffic and keywords due to variation of gross profit coming from web traffic of platforms they crowdsource tasks. To sum up, the contribution of this paper is to offer realistic and well-informed insights to marketers and strategists, with a view to developing web marketing strategies and SEM, combining expertise in the domain and decision-making based on accurate data.

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