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Axioms
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A Hybrid Analysis of Information Technology and Decision Making
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A Hybrid Analysis of Information Technology and Decision Making

A special issue of Axioms (ISSN 2075-1680).

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 11610

Special Issue Editor

Prof. Dr. Hsin-Pin Fu

E-Mail Website
Guest Editor
Department of Marketing and Distribution Management, National Kaohsiung University of Science and Technology, Kaohsiung 824, Taiwan
Interests: hybrid analysis; information technology and management; decision making

Special Issue Information

Dear Colleagues,

We would like to invite you to submit your research in the area of information technology and decision-making techniques to the Special Issue, “A Hybrid Analysis of Information Technology and Decision Making”, in the journal Axioms. The effects of information technology on decision making and how the development of hybrid decision-making techniques affects information technology are welcome, including new information technology used in decision-making problems and novel hybrid decision-making methods developed in the information technology era. High-quality papers are solicited to address both theoretical and practical issues in the development of efficient information technology methods and a hybrid analysis of decision-making problems. Submissions that present new theoretical results, models, and algorithms, as well as new applications, are welcome. Potential topics include but are not limited to the following: artificial intelligence, multiple criteria decision making, electronic transaction and payment, decision support systems, information technology and information policy, neural networks and performance, optimization and information technology, smart manufacturing and smart retail, and wireless technology and performance.

Prof. Dr. Hsin-Pin Fu
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. Axioms is an international peer-reviewed open access monthly 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

  • artificial intelligence
  • multiple criteria decision making
  • electronic transaction and payment
  • decision support systems
  • information technology and information policy
  • neural networks and performance
  • optimization and information technology
  • smart manufacturing and smart retail
  • wireless technology and performance

Published Papers (6 papers)

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Research

24 pages, 2069 KiB  
Article
MRL-Based Model for Diverse Bidding Decision-Makings of Power Retail Company in the Wholesale Electricity Market of China
by Ying Wang, Chang Liu, Weihong Yuan and Lili Li
Axioms 2023, 12(2), 142; https://doi.org/10.3390/axioms12020142 - 30 Jan 2023
Cited by 1 | Viewed by 1697
Abstract
Power retail companies in the electricity market make profits through buying and selling power energy in the wholesale and retail markets, respectively. Traditionally, they are assumed to bid in the wholesale market with the same objective, i.e., maximize the profit. This paper proposes [...] Read more.
Power retail companies in the electricity market make profits through buying and selling power energy in the wholesale and retail markets, respectively. Traditionally, they are assumed to bid in the wholesale market with the same objective, i.e., maximize the profit. This paper proposes a multiagent reinforcement learning (MRL)-based model to simulate the diverse bidding decision-making concerning various operation objectives and the profit-sharing modes of power retail companies in China’s wholesale electricity market, which contributes to a more realistic modeling and simulation of the retail companies. Specifically, three types of operation objectives and five types of profit-sharing modes are mathematically formulated. After that, a complete electricity market optimization model is established, and a case study with 30 retail companies is carried out. The simulation results show that the proposed method can effectively model the diverse bidding decision-making of the power retail companies, which can further assist their decision-making and further contribute to the analysis and simulations of the electricity market. Full article
(This article belongs to the Special Issue A Hybrid Analysis of Information Technology and Decision Making)
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23 pages, 6368 KiB  
Article
Bayesian Statistical Method Enhance the Decision-Making for Imperfect Preventive Maintenance with a Hybrid Competing Failure Mode
by Chih-Chiang Fang, Chin-Chia Hsu and Je-Hung Liu
Axioms 2022, 11(12), 734; https://doi.org/10.3390/axioms11120734 - 15 Dec 2022
Cited by 1 | Viewed by 1155
Abstract
The study aims to provide a Bayesian statistical method with natural conjugate for facilities’ preventive maintenance scheduling related to the hybrid competing failure mode. An effective preventive maintenance strategy not only can improve a system’s health condition but also can increase a system’s [...] Read more.
The study aims to provide a Bayesian statistical method with natural conjugate for facilities’ preventive maintenance scheduling related to the hybrid competing failure mode. An effective preventive maintenance strategy not only can improve a system’s health condition but also can increase a system’s efficiency, and therefore a firm needs to make an appropriate strategy for increasing the utilization of a system with reasonable costs. In the last decades, preventive maintenance issues of deteriorating systems have been studied in the related literature, and hundreds of maintenance/replacement models have been created. However, few studies focused on the issue of hybrid deteriorating systems which are composed of maintainable and non-maintainable failure modes. Moreover, due to the situations of the scarcity of historical failure data, the related analyses of preventive maintenance would be difficult to perform. Based on the above two reasons, this study proposed a Bayesian statistical method to deal with such preventive maintenance problems. Non-homogeneous Poisson processes (NHPP) with power law failure intensity functions are employed to describe the system’s deterioration behavior. Accordingly, the study can provide useful ways to help managers to make effective decisions for preventive maintenance. To apply the proposed models in actual cases, the study provides solution algorithms and a computerized architecture design for decision-makers to realize the computerization of decision-making. Full article
(This article belongs to the Special Issue A Hybrid Analysis of Information Technology and Decision Making)
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21 pages, 4101 KiB  
Article
Corn Disease Recognition Based on Attention Mechanism Network
by Yingying Wang, Jin Tao and Haitao Gao
Axioms 2022, 11(9), 480; https://doi.org/10.3390/axioms11090480 - 18 Sep 2022
Cited by 6 | Viewed by 1537
Abstract
To extract more accurate and abundant features of corn disease and solve the problems of rough classification and low recognition accuracy, the attention mechanism is introduced into the field of corn disease recognition. The corn disease recognition model (AT-AlexNet) is proposed based on [...] Read more.
To extract more accurate and abundant features of corn disease and solve the problems of rough classification and low recognition accuracy, the attention mechanism is introduced into the field of corn disease recognition. The corn disease recognition model (AT-AlexNet) is proposed based on an attention mechanism. The network was based on AlexNet, and the new down-sampling attention module was constructed to enhance the foreground response of the disease; the Mish activation function was introduced to improve the nonlinear expression of the network; the new module of the full connection layer was designed to reduce the network parameters. In the experiment of the enhanced corn disease datasets, the average recognition accuracy of the attention-based network model AT-AlexNet is 99.35%. The recognition accuracy of using the Mish activation function is 0.65% higher than that of the ReLu activation function. The experiments show that compared with other identification methods, the proposed method has better classification performance for corn diseases. Full article
(This article belongs to the Special Issue A Hybrid Analysis of Information Technology and Decision Making)
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13 pages, 577 KiB  
Article
Critical Factors Considered by Companies to Introduce Business Intelligence Systems
by Hsin-Pin Fu, Tien-Hsiang Chang, Ying-Hua Teng, Chien-Hung Liu and Hsiao-Chi Chuang
Axioms 2022, 11(7), 338; https://doi.org/10.3390/axioms11070338 - 13 Jul 2022
Cited by 3 | Viewed by 1346
Abstract
The advent of intelligent technology has spurred most large companies to introduce business intelligence systems (BIS), but those with low information maturity still have a wait-and-see attitude towards BIS. In order to accelerate the introduction of BIS, this study found and analyzed the [...] Read more.
The advent of intelligent technology has spurred most large companies to introduce business intelligence systems (BIS), but those with low information maturity still have a wait-and-see attitude towards BIS. In order to accelerate the introduction of BIS, this study found and analyzed the critical factors (CFs) considered by companies when introducing BIS. First, the literature on factors considered by companies to introduce BIS was reviewed. The three stages before, during, and after introduction in marketing that organizations undergo during the procurement process were developed into a three-layer hierarchy factor table. An expert questionnaire with pairwise factors was then designed and sent to senior executives in companies that had introduced BIS, and the weights of all factors were calculated by the fuzzy analytic hierarchy process (FAHP) based on the collected questionnaire data. After this, four critical factors—system function integrity, approaching corporate strategy, licensing fee, and information technology maturity—were determined objectively by using the conditions for the acceptable advantage of Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) and further explored in order to help companies input fewer resources, introduce BIS efficiently, and thus increase their decision-making power. Full article
(This article belongs to the Special Issue A Hybrid Analysis of Information Technology and Decision Making)
14 pages, 611 KiB  
Article
Evaluating the Digital Transformation Performance of Retail by the DEA Approach
by Ling-Jing Kao, Chih-Chou Chiu, Hung-Tse Lin, Yun-Wei Hung and Cheng-Chin Lu
Axioms 2022, 11(6), 284; https://doi.org/10.3390/axioms11060284 - 13 Jun 2022
Cited by 2 | Viewed by 2603
Abstract
In recent years, under the impact of digitization, all industries around the world have undergone unprecedented changes. Such changes have not only altered people’s consumption behavior but have also forced enterprises to accelerate the pace of digitization and actively start digital transformation. In [...] Read more.
In recent years, under the impact of digitization, all industries around the world have undergone unprecedented changes. Such changes have not only altered people’s consumption behavior but have also forced enterprises to accelerate the pace of digitization and actively start digital transformation. In this study, a literature review and focus group interview (FGI) were used to develop the dimensions and criteria to assess enterprise digital transformation status. To illustrate the digital transformation criteria proposed in this research, the retail industry was used as an example to measure the overall digital transformation performance by data envelopment analysis (DEA). The results show that the poor technical efficiency demonstrated by a vendor was not only due to the gradually decreasing returns to scale of the market; a decline in pure technical efficiency was also a contributor. In addition to adjusting their production on the basis of market conditions, vendors should properly manage their internal operations and pay attention to their resource and scale allocations to prevent reductions in their pure technical efficiency. Full article
(This article belongs to the Special Issue A Hybrid Analysis of Information Technology and Decision Making)
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11 pages, 541 KiB  
Article
A Recommendation Model for Selling Rules in the Telecom Retail Industry
by Tsung-Ying Ou, Wen-Lung Tsai, Yi-Chen Lee, Tien-Hsiang Chang, Shih-Hsiung Lee and Fen-Fen Huang
Axioms 2022, 11(6), 265; https://doi.org/10.3390/axioms11060265 - 01 Jun 2022
Viewed by 1556
Abstract
The recommendation of the optimal selling rules for any product or service is challenging, owing to the complexity of the customer’s behavior and the competitiveness existing in the telecom retail industry. This study proposes a recommendation model for selling rules that utilizes a [...] Read more.
The recommendation of the optimal selling rules for any product or service is challenging, owing to the complexity of the customer’s behavior and the competitiveness existing in the telecom retail industry. This study proposes a recommendation model for selling rules that utilizes a hybrid decision-making approach based on K-means and the C5.0 decision tree to analyze the historical sales information of telecom retailers. To evaluate the efficacy of the proposed recommendation model, it was used to analyze original data from a case company. The results indicated that the proposed hybrid decision-making approach resulted in sales content with a high gross profit and high agreement rates. The experimental results show each cluster that can be used to identify rules for the combination of good tariff items in different tariff ranges. Rules for the recommendation of special tariffs are also established to assist salespeople. Full article
(This article belongs to the Special Issue A Hybrid Analysis of Information Technology and Decision Making)
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