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Digital Marketing: Algorithms, Applications and Business Sustainability

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Economic and Business Aspects of Sustainability".

Deadline for manuscript submissions: 1 April 2024 | Viewed by 4844

Special Issue Editors

Department of Big Data Analytics & AI Business Research Center, Kyung Hee University, Seoul 02447, Korea
Interests: recommender systems; big data analytics; machine learning; deep learning
Special Issues, Collections and Topics in MDPI journals
Graduate School of Business IT, Kookmin University, Seoul 02707, Republic of Korea
Interests: AI/ML applications in marketing and finance; knowledge engineering; IS adoption or use
Special Issues, Collections and Topics in MDPI journals
Graduate School of Business, Seoul National University, Seoul 08826, Korea
Interests: AI-based data analytics; electronic commerce; platform business models; online retail business strategies; digital transformation

Special Issue Information

Dear Colleagues,

Diverse demographics and customer experiences are changing consumer expectations and consumption trends. In addition, purchasing channels are becoming more diverse and complex. This change in the business environment demands continuous innovation from companies. One of the promising innovations is the application of digital marketing. Digital marketing uses digital technology to market products or services over the Internet, social media, smart phones, or other digital media.

Digital marketers today can combine the Internet and big data analytics to collect and analyze data about various customer behaviors or user engagements. As a result, we can provide more personalized content and advertisements to our customers for better customer engagement, corporate profits, and business sustainability. Some companies use big data accumulated inside and outside the company to provide personalized recommendation services by discovering new trends and analyzing customer preferences and consumption patterns.

The purpose of this Special Issue is publish papers developing algorithms for digital marketing, examining applications of personalization services, and analyzing the impact of the use of big data analytics in digital marketing on the sustainability of businesses.

For this Special Issue, we invite paper contributions related to sustainable business models of digital marketing and digital marketing technologies such as big data analysis, marketing channel analysis, unstructured data analysis, recommender systems, case studies, or other quantitative, qualitative, or mixed-methods on this topic.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Marketing technology;
  • Marketing automation;
  • Customer relationship management;
  • Intelligent marketing services and applications;
  • Social media marketing;
  • Personalized service/advertisement;
  • Recommender systems;
  • Customer experience design/digital experience design;
  • Customer data and privacy;
  • Digital platform business;
  • Digital contents business;
  • Big data analytics;
  • AI-based data analytics in E-commerce.

We look forward to receiving your contributions.

Prof. Dr. Jae Kyeong Kim
Prof. Dr. Hyunchul Ahn
Prof. Dr. Byungjoon Yoo
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

  • digital marketing
  • data analysis
  • personalized service
  • marketing technology
  • big data
  • recommender systems
  • business sustainability

Published Papers (2 papers)

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Research

19 pages, 674 KiB  
Article
Exploring the Interactive Relationship between Retailers’ Free Shipping Decisions and Manufacturers’ Product Sales in Digital Retailing
by Hu Wang, Di Li, Changbin Jiang and Yuxiang Zhang
Sustainability 2023, 15(17), 12762; https://doi.org/10.3390/su151712762 - 23 Aug 2023
Viewed by 874
Abstract
In the realm of multichannel digital retailing, free shipping has gained popularity as a promotion strategy. However, few studies have investigated how retailers make decisions regarding free shipping. Furthermore, concerns have arisen regarding the sustainability of free shipping promotions for manufacturers. This research [...] Read more.
In the realm of multichannel digital retailing, free shipping has gained popularity as a promotion strategy. However, few studies have investigated how retailers make decisions regarding free shipping. Furthermore, concerns have arisen regarding the sustainability of free shipping promotions for manufacturers. This research employs a simultaneous equation model with fixed effects to explore the determinants of market structure concerning the proportion of retailers offering free shipping and its impact on manufacturers’ product sales. As per our current knowledge, this research is pioneering in establishing a causal relationship between the percentage of free-shipping retailers and manufacturers’ product sales. Specifically, an increase in the percentage of retailers employing free shipping leads to higher product sales, while lower product sales drive increased retailers to adopt free shipping. Our findings indicate that competition among products has a significant positive effect on the percentage of retailers offering free shipping in the interactive relationship. Furthermore, increased competition among retailers results in more retailers adopting free shipping strategies. These results affirm the efficacy of free shipping as a promotional approach to increase manufacturers’ product sales, particularly in highly competitive markets. Full article
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19 pages, 2728 KiB  
Article
An Empirical Investigation of Personalized Recommendation and Reward Effect on Customer Behavior: A Stimulus–Organism–Response (SOR) Model Perspective
by Jaeho Jeong, Dongeon Kim, Xinzhe Li, Qinglong Li, Ilyoung Choi and Jaekyeong Kim
Sustainability 2022, 14(22), 15369; https://doi.org/10.3390/su142215369 - 18 Nov 2022
Cited by 2 | Viewed by 3185
Abstract
With the continuous growth in the Home Meal Replacement (HMR) market, the significance of recommender systems has been raised for effectively recommending customized HMR products to each customer. The extant literature has mainly focused on enhancing the performance of recommender systems based on [...] Read more.
With the continuous growth in the Home Meal Replacement (HMR) market, the significance of recommender systems has been raised for effectively recommending customized HMR products to each customer. The extant literature has mainly focused on enhancing the performance of recommender systems based on offline evaluations of customers’ past purchase records. However, since the existing offline evaluation methods evaluate the consistency of products on the recommendation list with ones purchased by customers from the test dataset, they are incapable of encompassing components such as serendipity and novelty that are also crucial in recommendation. Moreover, the existing offline evaluation methods cannot measure rewards such as discount coupons that may play a vital role in strengthening customers’ desire for purchase and thereby stimulating their purchase with a provision of a recommendation list. In this study, we used an SOR model to verify the effect of personalized recommendation stimulus on a customer’s response in an actual online environment. The results indicate that the customers’ response rate was higher with a provision of personalized recommendations than that of bestseller recommendations, and higher when being offered with cash discounts than earning redeemable points. Meanwhile, the response rate to the recommendation with higher volumes of rewards was not as high as expected, while the point pressure mechanism did not work either. Full article
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