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Frontier Research of Management Sciences: Business Analytics, Prediction Markets and Customer Relationship Management

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: closed (30 November 2022) | Viewed by 7475

Special Issue Editor

Dr. Yu-Hsiang Hsiao

E-Mail Website
Guest Editor
Department of Business Administration, National Taipei University, New Taipei City 10478, Taiwan
Interests: business analytics; data and text mining; quality engineering and management; operations and supply chain management

Special Issue Information

Dear Colleagues,

The advent of the era of big data has revolutionized the way data are generated and obtained. For example, in the past, the customer relationship management of a business was on the basis of customers’ internal transactional data. Now it is gradually moving toward linking and integrating customers’ footprints from heterogeneous domains outside the business to capture their hidden preferences. Even through mobile communication technology, the customers’ real-time mobile data can be used for precise marketing with time and space considerations. These changes make it possible to achieve customized and automated management decisions based on data analytics. In addition, “prediction” is the most difficult part of all sciences and is also the biggest challenge to human intelligence; however, it is always the goal that everyone most wants to achieve. The uncertainty of future events often makes decision making difficult, and the collection of relevant data for decision making often lags behind changes in the external market or environment. Today, it is easy and low-cost to collect opinions from all parties, which creates opportunities for learning from external ideas for the management and decision making of a business. For example, a business can update the next generation of products/services or defining future products/services based on customers’ needs identified from online reviews. Therefore, facing the uncertainty and rapid evolution of the environment, relying on crowd knowledge and wisdom is a critical way to predict the future. These changes brought about by advances in information technology have made business data different from those available in the past—larger, real-time, and more diversified. This provides opportunities for researchers in management sciences to integrate various data into research and develop new modeling methods to generate a new understanding of business problems and drive better decision making.

This Special Issue focuses on exploring how various novel data sources and types and how the available information mined from the data inspire novel research on the methods and applications of management sciences for businesses. By using new modeling and analysis methods to implement diagnostic analytics, predictive analytics, and prescriptive analytics, new management insights, decisions, opportunities, and influences in various fields of the business can ultimately be generated. Topics include but are not limited to business analytics and decision support, data-driven customer relationship management, data-driven business diagnosis, data-driven business quality management, data-driven business optimization, marketing technology, prediction markets, data and text mining applications in business, machine learning applications in business, and big data applications in business.

Dr. Yu-Hsiang Hsiao
Guest Editor

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

  • management sciences
  • business analytics
  • decision making
  • prediction markets
  • customer relationship management
  • marketing technology
  • data and text analytics
  • business optimization

Published Papers (3 papers)

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Research

15 pages, 289 KiB  
Article
Integrating FSE and AHP to Identify Valuable Customer Needs by Service Quality Analysis
by Tien-Hsiang Chang, Kuei-Ying Hsu, Hsin-Pin Fu, Ying-Hua Teng and Yi-Jhen Li
Sustainability 2022, 14(3), 1833; https://doi.org/10.3390/su14031833 - 5 Feb 2022
Cited by 5 | Viewed by 1576
Abstract
In this study, we explore the needs of different valuable customer groups for service quality and how limited resources are allocated to enhance service quality. Accordingly, we propose a hybrid multi-criteria decision-making (MCDM) tool that uses fuzzy synthetic evaluation (FSE) in combination with [...] Read more.
In this study, we explore the needs of different valuable customer groups for service quality and how limited resources are allocated to enhance service quality. Accordingly, we propose a hybrid multi-criteria decision-making (MCDM) tool that uses fuzzy synthetic evaluation (FSE) in combination with the analytic hierarchy process (AHP) to help companies enhance understanding of quantitative data (the weights of the factors that affect service quality) and qualitative information to identify valuable customers. Fifty-three experts and 304 consumers at convenience stores (CVS) comprise the data set. We employed the AHP to obtain index weights in the second step of FSE and conducted FSE to determine the importance of various valuable customer groups. The results demonstrate that different valuable customer groups have dissimilar perceptions and feelings about service quality. The findings indicate that customers between “20 to 29 years old” are the most valuable customer group and that most consumers do not care much about “problem solving”. The analysis is distinct from extant work in that it examines the effect of receiving service quality from a consumer viewpoint, as we conducted a comprehensive analysis from both customer and expert perspectives. Full article
(This article belongs to the Special Issue Frontier Research of Management Sciences: Business Analytics, Prediction Markets and Customer Relationship Management)
16 pages, 6259 KiB  
Article
Exploration of Spa Leisure Consumption Sentiment towards Different Holidays and Different Cities through Online Reviews: Implications for Customer Segmentation
by Jianhong Luo, Shifen Qiu, Xuwei Pan, Ke Yang and Yuanqingqing Tian
Sustainability 2022, 14(2), 664; https://doi.org/10.3390/su14020664 - 7 Jan 2022
Cited by 6 | Viewed by 2171
Abstract
With the improvements in per capita disposable income, and an increase in work-related pressure, demand for leisure consumption such as foot bath spas is constantly increasing. Analysis of leisure consumption sentiment is of great importance for the leisure service industry—to meet customer needs, [...] Read more.
With the improvements in per capita disposable income, and an increase in work-related pressure, demand for leisure consumption such as foot bath spas is constantly increasing. Analysis of leisure consumption sentiment is of great importance for the leisure service industry—to meet customer needs, improve service quality and improve customer relationship management. However, traditional sentiment analysis approaches only aimed to ascertain the overall sentiment of the customer, which is less effective for analyzing customer satisfaction on account of customer size, different customer locations, and different leisure holidays. Sentiment analysis via online reviews can assist different businesses, including foot bath spa services, to better inform the development of customer segmentation strategies and ensure optimal customer relationship management. Hence, the objective of this paper is to explore foot bath spa leisure consumption sentiment towards different holidays and different cities by applying data mining via online reviews, so as to help optimize customer segmentation. A novel general framework and related sentiment analysis methods were proposed and then conducted through a collection of datasets from customers’ textual reviews of foot bath spa merchants in three cities in China on the Meituan social media platform. Findings confirm that the proposed general framework and methods can be used to gain insights into the swing characteristics of sentiment towards different holidays and different cities, to better develop customer segmentation according to the city-holiday emoticon face patterns obtained through sentiment tendency analysis from online social media review data. The study results can help to develop better customer and marketing strategies, thereby creating sustainable competitive advantages, and can be extended to other fields to support sustainable development. Full article
(This article belongs to the Special Issue Frontier Research of Management Sciences: Business Analytics, Prediction Markets and Customer Relationship Management)
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19 pages, 4792 KiB  
Article
Constructing a Sustainable and Dynamic Promotion Model for Fresh Foods Based on a Digital Transformation Framework
by Tsung-Yin Ou, Guan-Yu Lin, Chin-Ying Liu and Wen-Lung Tsai
Sustainability 2021, 13(19), 10687; https://doi.org/10.3390/su131910687 - 26 Sep 2021
Cited by 2 | Viewed by 2171
Abstract
The emergence of digital technology has compelled the retail industry to develop innovative and sustainable business models to predict and respond to consumer behavior. However, most enterprises are crippled with doubt, lacking frameworks and methods for moving forward. This study establishes a five-step [...] Read more.
The emergence of digital technology has compelled the retail industry to develop innovative and sustainable business models to predict and respond to consumer behavior. However, most enterprises are crippled with doubt, lacking frameworks and methods for moving forward. This study establishes a five-step decision-making framework for digital transformation in the retail industry and verifies it using real data from convenience stores in Taiwan. Data from residential type and cultural and educational type convenience stores, which together account for 75% of all stores, underwent a one-year simulation analysis according to the following three decision models for promotions: the shelf-life extended scrap model (SES), the fixed remaining duration model (FRD), and the dynamic promotion decision model (DPD). The results indicated that the DPD model reduced scrap in residential type stores by 12.88% and increased profit by 15.43%. In cultural and educational stores, the DPD model reduced scrap by 10.78% and increased profit by 7.63%. The implementation of the DPD model in convenience stores can bring additional revenue to operators, and at the same time address the problem of food waste. With the full use of resources, sustainable operation can be turned into a concrete and feasible management decision-making plan. Full article
(This article belongs to the Special Issue Frontier Research of Management Sciences: Business Analytics, Prediction Markets and Customer Relationship Management)
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