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Sustainable Development of Social Commerce in the New Era

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

Deadline for manuscript submissions: closed (31 July 2021) | Viewed by 12595

Special Issue Editors


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Guest Editor
School of Information Management and Artificial Intelligence, Zhejiang University of Finance and Economics, Hangzhou 310018, China
Interests: digital transformation; social commerce; sustainable business model innovation; omnichannel-management; smart business
Special Issues, Collections and Topics in MDPI journals
School of Information Management and Artificial Intelligence, Zhejiang University of Finance and Economics, Hangzhou 310018, China
Interests: social commerce; sustainable social media user behavior; government social media
Centre for Lifelong Learning, Universiti Brunei Darussalam, Gadong BE1410, Brunei
Interests: sentiment analysis; big data analytics; information systems; sustainable social media user behavior; sustiablbe energy; e-learning; digital innovation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As an emerging business model that integrates social media and e-commerce, social commerce has greatly changed the methods of traditional online business, such as online marketing, selling, and the purchasing of products and services [1,2]. In the era of social commerce, consumers can use various social media (e.g., Facebook, Twitter, WeChat, and Weibo) to support each other online by obtaining and sharing valuable and reliable information about products and services [3]. Such social interactions and user generated content in social media networks also add value to firms by helping them improve their performance [4].

Despite social commerce attracting extensive attention from both researchers and practitioners [3,5], there is a gap in the literature for understanding how this emerging business model could be developed sustainably. For instance, what are the key factors that influence consumers to continue shopping on a social commerce platform? How do new forms of social media (e.g., live-stream platforms) affect consmers’ behaviors of seeking, shopping, and sharing products and services? What are the key components of the social commerce ecosystem, and how can one build a sustainable social commerce ecosystem?  

The purpose of this Special Issue therefore is to focus on studies that enhance and broaden the understanding of the sustainable development of a business under the background of social media. We are inviting researchers who are interested in this topic to submit their high-level original research works from various perspectives, such as from that of the consumer, retailer, channel, technology, and ecosystem of social commerce. Articles, including research articles, reviews, communication, and concept papers, should address, but are not limited to, the following:

  • Social commerce repurchasing behaviors
  • Marketing strategies for sustainability of social commerce
  • Social commerce forms in the new era
  • Co-create value in social commerce
  • Social commerce and AI
  • Online reviews and social commerce
  • Customer journey management for sustainability of social commerce
  • The channel integration quality between social media and e-commerce
  • Social interaction model and its impact on social commerce
  • The sustainable social commerce ecosystem
  • Economic recession and COVID-19
  • Public behaviour and COVID-19
  1. Chen, J.; Shen, X.-L. Consumers' decisions in social commerce context: An empirical investigation. Support Syst., 2015, 79, 55–64.
  2. Horng, S.-M.; and Wu, C.-L. How behaviors on social network sites and online social capital influence social commerce intentions. Manag., 2020, 57, 103–176.
  3. Bhattacharyya, S.; Bose, I. S-commerce: Influence of Facebook likes on purchases and recommendations on a linked e-commerce site. Support Syst., 2020, 138, 113–383.
  4. Hajli, N. Social commerce and the future of e-commerce. Hum. Behav., 2020, 108, 106–133.
  5. Fu, J.; Sun, Y.; Zhang, Y.; Shuiqing, Y. Does Similarity Matter? The Impact of User Similarity on Online Collaborative Shopping. IEEE Access, 2020, 8, 1361–1373.

Dr. Shuiqing Yang
Dr. Yixiao Li
Dr. Atika Qazi
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 submissions that pass pre-check are 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 2400 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

  • social commerce
  • e-commerce
  • social media
  • online reviews
  • social shopping
  • consumer behaviour
  • sentiment analysis
  • renewable energy

Published Papers (4 papers)

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16 pages, 6603 KiB  
Article
Prediction of the Infectious Outbreak COVID-19 and Prevalence of Anxiety: Global Evidence
by Daniyal Alghazzawi, Atika Qazi, Javaria Qazi, Khulla Naseer, Muhammad Zeeshan, Mohamed Elhag Mohamed Abo, Najmul Hasan, Shiza Qazi, Kiran Naz, Samrat Kumar Dey and Shuiqing Yang
Sustainability 2021, 13(20), 11339; https://doi.org/10.3390/su132011339 - 14 Oct 2021
Cited by 5 | Viewed by 2059
Abstract
Forecasting disease outbreaks in real-time using time-series data can help for the planning of public health interventions. We used a support vector machine (SVM) model using epidemiological data provided by Johns Hopkins University Centre for Systems Science and Engineering (JHU CCSE), World Health [...] Read more.
Forecasting disease outbreaks in real-time using time-series data can help for the planning of public health interventions. We used a support vector machine (SVM) model using epidemiological data provided by Johns Hopkins University Centre for Systems Science and Engineering (JHU CCSE), World Health Organization (WHO), and the Centers for Disease Control and Prevention (CDC) to predict upcoming records before the WHO made an official declaration. Our study, conducted on the time series data available from 22 January till 10 March 2020, revealed that COVID-19 was spreading at an alarming rate and progressing towards a pandemic. The initial insight that confirmed COVID-19 cases were increasing was because these received the highest number of effects for our selected dataset from 22 January to 10 March 2020, i.e., 126,344 (64%). The recovered cases were 68289 (34%), and the death rate was around 2%. Moreover, we classified the tweets from 22 January to 15 April 2020 into positive and negative sentiments to identify the emotions (stress or relaxed) posted by Twitter users related to the COVID-19 pandemic. Our analysis identified that tweets mostly conveyed a negative sentiment with a high frequency of words for #coronavirus and #lockdown amid COVID-19. However, these anxiety tweets are an alarm for healthcare authorities to devise plans accordingly. Full article
(This article belongs to the Special Issue Sustainable Development of Social Commerce in the New Era)
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20 pages, 4059 KiB  
Article
A Multi-Criteria Approach for Arabic Dialect Sentiment Analysis for Online Reviews: Exploiting Optimal Machine Learning Algorithm Selection
by Mohamed Elhag Mohamed Abo, Norisma Idris, Rohana Mahmud, Atika Qazi, Ibrahim Abaker Targio Hashem, Jaafar Zubairu Maitama, Usman Naseem, Shah Khalid Khan and Shuiqing Yang
Sustainability 2021, 13(18), 10018; https://doi.org/10.3390/su131810018 - 07 Sep 2021
Cited by 25 | Viewed by 3746
Abstract
A sentiment analysis of Arabic texts is an important task in many commercial applications such as Twitter. This study introduces a multi-criteria method to empirically assess and rank classifiers for Arabic sentiment analysis. Prominent machine learning algorithms were deployed to build classification models [...] Read more.
A sentiment analysis of Arabic texts is an important task in many commercial applications such as Twitter. This study introduces a multi-criteria method to empirically assess and rank classifiers for Arabic sentiment analysis. Prominent machine learning algorithms were deployed to build classification models for Arabic sentiment analysis classifiers. Moreover, an assessment of the top five machine learning classifiers’ performances measures was discussed to rank the performance of the classifier. We integrated the top five ranking methods with evaluation metrics of machine learning classifiers such as accuracy, recall, precision, F-measure, CPU Time, classification error, and area under the curve (AUC). The method was tested using Saudi Arabic product reviews to compare five popular classifiers. Our results suggest that deep learning and support vector machine (SVM) classifiers perform best with accuracy 85.25%, 82.30%; precision 85.30, 83.87%; recall 88.41%, 83.89; F-measure 86.81, 83.87%; classification error 14.75, 17.70; and AUC 0.93, 0.90, respectively. They outperform decision trees, K-nearest neighbours (K-NN), and Naïve Bayes classifiers. Full article
(This article belongs to the Special Issue Sustainable Development of Social Commerce in the New Era)
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13 pages, 474 KiB  
Article
How CEO Political Connections Induce Corporate Social Irresponsibility: An Empirical Study of Tax Avoidance in South Korea
by Ji-Hee Kim and Ji-Hwan Lee
Sustainability 2021, 13(14), 7739; https://doi.org/10.3390/su13147739 - 11 Jul 2021
Cited by 6 | Viewed by 3097
Abstract
Building upon prior literature on the role of executives in tax payments, this study investigates the relationship between a CEO’s political connections and tax avoidance behavior as a typical type of social irresponsibility of a corporation. We propose that CEOs who are well [...] Read more.
Building upon prior literature on the role of executives in tax payments, this study investigates the relationship between a CEO’s political connections and tax avoidance behavior as a typical type of social irresponsibility of a corporation. We propose that CEOs who are well connected to politicians through family, academic, and professional ties tend to adopt riskier strategic choices such as tax avoidance. We employ a multi-faceted method to quantify political connections in a more comprehensive and delicate way. Empirical results from 4,706 firm-year observations in South Korea between 2003 and 2014 provide support for our predictions. In addition, we test competing hypotheses on the moderating role of CEO tenure and find that such a tendency diminishes as the focal CEO stays in the position longer. Full article
(This article belongs to the Special Issue Sustainable Development of Social Commerce in the New Era)
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5 pages, 195 KiB  
Data Descriptor
Airfares Data in New Zealand Domestic Aviation Market
by Tin H. Ho, Dat T. Nguyen, Thanh Ngo and Tu D. Q. Le
Sustainability 2021, 13(16), 8916; https://doi.org/10.3390/su13168916 - 09 Aug 2021
Cited by 3 | Viewed by 2446
Abstract
Price competition has been a growing concern of worldwide researchers and managers. In the aviation market, especially with the help from e-commerce platforms such as Expedia, TripAdvisor, and SkyScanner, airfares are now available to customers in the easiest and quickest way. It thus [...] Read more.
Price competition has been a growing concern of worldwide researchers and managers. In the aviation market, especially with the help from e-commerce platforms such as Expedia, TripAdvisor, and SkyScanner, airfares are now available to customers in the easiest and quickest way. It thus allows airlines to match their fares immediately and simultaneously upon any changes of their rivals, given that customer’ choices are made with regard to their incomes. This study provides a dataset on domestic airfares in New Zealand that could be useful for future studies in the fields of marketing, business and economics, transportation and aviation, or management. The dataset covers 12 trunk routes and 40 secondary routes in New Zealand from 19 September 2019 to 18 December 2019, a total of 90 days. It provides a rich dataset of more than 162,000 observations regarding the airfare, departure time, arrival time, flight duration, airline, departure airport, arrival airport, transiting airport, and so on. There are possibilities to extend the dataset (e.g., in terms of flying distance, airport characteristics, and airline characteristics) to make it be valuable for future study. Full article
(This article belongs to the Special Issue Sustainable Development of Social Commerce in the New Era)
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