Advancement of Sustainable Development, Decision Support Systems, and Data Science Based on the Seventeenth ICMSEM Proceedings

. Management science (MS) uses a variety of scientiﬁc research-based principles and analytical methods, such as mathematical modeling and data analysis, to make decisions and solve complex problems, and has strong connections to management, data, economics, engineering, and other ﬁelds. The scientiﬁc MS community has grown signiﬁcantly over the past few decades, particularly in sustainable development, decision support systems, and data science. This paper gives a brief introduction to Volume I of the seventeenth ICMSEM proceedings. First, the key MS research areas are reviewed and the reasons given as to why sustainable development, decision support systems, and data science have been hotspots. Then, the literature in the primary study areas in the seventeenth ICMSEM proceedings Volume I is summarized. Finally, CiteSpace is employed to analyze future MS developments. ICMSEM continues to provide a valuable forum for academic exchanges and communication to promote future innovation in management science and engineering management (MSEM).


Introduction
The seventeenth International Conference on Management Science and Engineering Management (ICMSEM) in Cape Town, South Africa was held so that Management Science and Engineering Management (MSEM) academics could present original research in sustainability, decision-making, data science, management, economics, and risk assessment. The papers in this volume demonstrate the significant developments that have been made in integrative MSEM approaches and applications. The ICMSEM intends to be the principal forum for academics and practitioners to engage in discussions on cutting-edge MSEM research and development. Management science (MS) has had a long connection with global management research fields. Scientific management owes its origins to the innovative ideas of Taylor and Fayol and their associates, who attempted to solve industry problems at the turn of the century through strict time monitoring, technical production methods, incentive wage payments, and rational factory organization based on efficiently structured work assignments [1]. Management science is sometimes used by authors and editors as a synonym for "operations research" (OR), which is a collection of analytical methods, and others use the term to refer to the use of proven tools to handle challenges in areas such as operations research, economics, and behavioral sciences [2]. Due to the recent developments in economics and social science, MS has also adopted research methods from other disciplines, such as systems theory, cybernetics, information theory, mathematics, and economics. MS comprises a wide range of areas, such as assignment, data mining, financial decision-making, forecasting, optimization, project planning and management, simulations, and transportation [3], and uses mathematics, information science, systems science, cybernetics, statistics, and other theories and methods derived from natural science to develop innovative management and control systems [4]. Therefore, the integration of these numerous separate study areas has resulted in the major advancements reported in Volume I of the seventeenth ICMSEM proceedings, which is primarily concerned with sustainable development, decision support systems, and data science. The remainder of this paper is organized as follows. Section 2 reviews the relevant research related to the main topics, Section 3 summarizes the central issues in proceedings Volume I, and Section 4 analyzes the MS development trends.

Literature Review
The current and future directions of given scientific fields and the theoretical and methodological contributions in the three main focus areas; sustainable development, decision support systems, and data science; are reviewed.

Sustainable Development
The United Nations developed "The 2030 Agenda for Sustainable Development", which included 17 Sustainable Development Goals that were developed to be a "blueprint to reach a better and more sustainable future for all" [5]. As an important goal, sustainable development has become a vital central issue for many problems. Sharpley and Richard analyzed the theoretical relationship between tourism and sustainable development and presented sustainable de-growth as an alternative development approach [6]. Ni and Juan analyzed the sustainable development of cities using environmentally sustainable development indicators based on the PSR [7]. Zakari and Khan used Panel Correction Standard Error (PCSE) estimations to reveal the positive relationship between sustainable development and energy efficiency [8]. Tsolakis and Niedenzu investigated blockchain-centric food supply chain designs to advance the Sustainable Development Goals [9]. Therefore, sustainable development is playing an increasingly prominent role in modern society, with many technical growth and resource management scholars placing a high value on this aim.

Decision Support Systems
Decision support systems (DSS), which are information systems that support human decision-making [10], are now a critical part of management science, operations research, cybernetics, and behavioral science as they use computer simulations and information technologies to solve decision problems in highly uncertain and complex environments. For example, Zhai and Fernan Martinez investigated the upcoming challenges in the use of agricultural decision support systems to promote Agriculture 4.0 [11], Parshikova developed DSS automation to assist in making managerial judgments [12], and Attia and Gratia built a residential benchmark by linking sensitivity analysis modeling and energy simulation software to provide an effective decision support tool that allowed designers to rapidly and flexibly assess the thermal comfort and energy performances of early design alternatives [13]. In other studies, Alavi and Tavana proposed a dynamic decision support system (DSS) for sustainable supplier selection in circular supply chains [14] that can be applied to most decision-making processes, Wober designed a marketing decision support system (MDSS) for the successful implementation of a tourism MDSS [15], Peng and Zhang designed two information fusion methods based on the Heronian mean operator and then developed a hotel decision support model [16], and Vasey and Nagendran built multi-stakeholder, consensus-based reporting guidelines for the Developmental and Exploratory Clinical Studies for Artificial Intelligence-Driven Decision Support Systems (DECIDE-AI) [17]. This research reveals that more advanced decision support system approaches are required as situations become more complicated.

Data Science
Data science (DS) is the statistical examination of vast volumes of acquired data to identify relevant information and draw conclusions. Karpatne and Atluri explicitly constructed a theory-guided DS paradigm and proposed a taxonomy of theory-guided DS research themes to improve the usefulness of data science models in generating scientific discoveries from abundant scientific knowledge [18]. Zhang and Porter developed an intelligent bibliometric framework that combined several conventional bibliometric techniques with a cutting-edge technique for mapping developmental scientific discovery paths [19]. Hazen and Boone defined the data quality issues within a supply chain management (SCM) framework and suggested techniques for observing and managing data quality [20]. Overall, as data analysis has become a more important function in contemporary society, data analysis research has generated a wide range of competing theories and made significant theoretical and practical contributions to organizational resource management and technology.

Major issues in Proceedings Volume I
Based on the most popular research topics, papers were called for from around the world, 100 of which were finally accepted. These were then divided into two proceedings volumes, with Volume I comprising 50 of these. The keyword analysis revealed that the first volume reflected the latest theoretical and methodological MS research trends and development frontiers for sustainable development, decision support systems, and data science.
Based on the consensus reached in the Paris Agreement, many countries are now seeking green development to achieve peak and net zero greenhouse gas emissions. Sustainable development requires both academic and societal efforts to reduce carbon emissions and develop viable carbon sinks. Haiyue et al. investigated the relationship between green innovation and supply chain financing using data from 3490 Chinese listed firms from 2012 to 2019, and Weike et al. constructed a two-way fixed-effects model with a sample of Chinese A-share listed companies to investigate the relationship between ESG ratings and share price synchronization. Some scholars have developed green supply chains to activate sustainable development. Decision support systems continue to be a popular MS research topic, with many studies focusing on technological and application breakthroughs, the organization and processing of uncertain information, and the comprehensive, integrated application of decision making data resources. For example, based on Perceived Risk theory, emotion infection theory, and information processing theory, Renshu et al. investigated the influence of herd mentality on panic buying behavior, Junwu et al. constructed and solved a direct sales model and a trybefore-you-buy strategy by online retailers under the premise of whether to allow returns and then explored optimal strategies for e-retailers that had different aims, and Qiyang et al. built a mathematical model to select community stores for the end distribution of fresh food by e-commerce enterprises to minimize input costs and maximize customer satisfaction. Some scholars have also used machine learning to construct decision support systems. For example, Jinjiang et al. constructed a scientific evaluation university patent index system using machine learning algorithms, and Min et al. constructed a multi-player evolutionary game to prove that the external benefits of vocational education developments positively influenced the probability that local governments would choose cooperative strategies.
Data science applies statistical methods and tools to evaluate data, extract information, and draw conclusions in a variety of domains. For example, Mengze et al. used an endogenous switching regression model within a counterfactual inference framework to evaluate the effects of non-agricultural employment on household poverty alleviation, Yanheng et al used an ordinal logistic regression model to analyze the prevention effects of recreational fishing on anxiety disorder and the heterogeneity of this effect, and Yi using a data science analysis method based on a unique geographical distance perspective to analyze the impact of geographical distance between VCs and start-up operating performances. Some scholars have also used data science methods for semantic feature analyses. For example, Zhixuan et al. investigated how content semantic features can affect the effectiveness of rumors using the reputation effectiveness index as the response variable, and based on a person perspective, Xing et al. constructed a structural equation model of place attachment to explore its causes and influences and to identify the possible resident place attachment predictors.

MS and ICMSEM Development Trends
CiteSpace software was used to reflect research data in a scientific knowledge map and forecast future MS research and practice. Because Citespace elucidates the trends within a knowledge domain over a specific period by integrating an information visualization method, a bibliometric method, and a data mining algorithm in a knowledge map visualization, it can identify research evolutions at the research frontier. In this section, CiteSpace is first briefly introduced and the technical research route is described, after which the MS research hotspots and the hotspots in the 17th ICMSEM are analyzed. Finally, the research evolution is described and prospective MS research directions are illuminated.

CiteSpace Analysis
This subsection takes a bibliometric investigation standpoint to discuss comprehensive scientific mapping analyses for image-based visualization studies. Literature mining, which is generally employed to determine the most relevant scientific research in an area of particular interest [21], has been proven to be an effective tool for exposing the important trends over time in published scientific literature and providing the information needed for detailed topic maps. Therefore, CiteSpace, which is a free Java program for visualizing and analyzing citations and the content of scientific literature, was used as the analysis tool to construct an image-based visualization of the research and reveal the emerging trends and knowledge inflection points.
Citespace has commonly been used to slice a time interval into smaller segments and investigate the connections between the co-citation networks within the separate time slices. As Citespace primarily pertains to Metrology Science's macro information visualization technology, it has distinct meanings and measurement indicators. The basic CiteSpace principle involves unit similarity analyses (literature, keyword, author, and so on) and has its own measuring indicators. The software imports data from the Web of Science (WOS), the Chinese Social Science Citation Index (CSSCI), the China National Knowledge Infrastructure (CNKI), NSF, Derwent, Scopus, arXive-Print, Pubmed, and the Sloan Digital Sky Survey (SDSS), and then analyzes the author, institution, country, terms, keywords, categories, cited references, cited authors, and cited journals.
To determine the MS scientific knowledge map, the MS keyword trends were determined based on qualitative scientometrics. Because the ISI Web of Science database (WoS) accesses many databases and allows for in-depth examinations of specific sub-fields within academic or scientific disciplines, it was chosen as the primary resource to search for relevant research. Appropriate search strategies were then specified to ensure adequate correlations for the data cleaning, that is, the search string "management science" was used to search for and retrieve articles from 1990 to 2023, the advanced search from which identified 78,156 articles. "AK = (management science)" was then set to refine the research selection, which reduced the article number to 4,551. After selecting "Article", "Proceedings Paper", and "Review" as the document types, the less formal literature was eliminated. In this way, the literature was scrutinized until only 3,882 MS documents were finally extracted and input to Citespace, after which each data record; author, title, abstract, and research citation; was downloaded and entered into Citespace for further analysis.

MS Developments
The full records and cited references for the 3,882 articles exported from the WOS database were stored as Txt files. First, the text documents were imported into Citespace to "Remove Duplicates", after which the data were converted into a format that the software could recognize for parameter selection. The time span was set from 1990 to 2023, the time slice was set at one year, and to allow for the node selection, the theme selection was based on the titles, abstract subject words, identifiers, and keywords. The networks were then constructed using the "Pathfinder" pruning method.
By setting "Threshold = 30", a total of 912 nodes and 5,645 edges were obtained, with the overall network density being 0.0136, S = 0.7684. The keyword co-occurrences are shown in figure 1. Using the keywords and label title clustering, 43 categories were identified, of which management, data science, sustainable development, decision support systems, and information were the most highly ranked, indicating that these were the most popular current management science research fields and the future MS development trends. After the keyword reference frequencies were organized from high to low, the top ten shown in table 1 were more closely studied, from which it was discovered that keywords such as data science, sustainable development, knowledge management, and decision support systems had relatively high centralities.
The analysis indicated that the theme of our proceedings was very much in line with current MS trends. Therefore, at the 17th ICMSEM, the sustainable development research has focused on many standout strategies and decision problems, and the decision support systems research has elucidated the possible connections with artificial technologies.

MS researcher cooperation network
In this part, the data that was directly transferred to the CiteSpace software and the author and countries/region cooperation between the selected journals are analyzed, the results of which are illustrated in figure 2. Each node in figure 2 represents an author (research institution/country), the node size represents the publication quantities, each line represents the node betweenness cooperation and its thickness represents the cooperative intensity. The lines between the nodes represent the authorial cooperation, the line widths represent the cooperative intensity, and the line colors represent the time at which the authors first cooperated. After the network generation, the author cooperation formed several natural clusters, which indicated that the authors within the clusters cooperated closely, but the authors between the different classes had less cooperation.
The cooperative network analysis revealed that the cooperation was generally within the same country, with significantly few cooperative research papers between authors from different countries. However, because of the international conference platform offered through the ICMSEM, many IDMSEM authors have co-authored with scholars from other countries, which has expanded academic exchange and discipline development. The ICMSEM has authors from over 26 nations.

Future development predictions
To enable the ICMSEM to achieve its goal of growing into a worldwide research forum, this article examined the prominent MS research fields and assessed future ICMSEM initiatives. The layout was therefore modified to a "Timeline View" to illustrate the MS evolution locus, with time being the horizontal axis, as seen in Figure 3. The majority of high-frequency phrases emerged in the early years, which indicated that MS has had a long history, a wide variety of applications, and gradual research differentiation. In the data-led era, data science has stressed the need to fully analyze data, and the importance being given to green development has highlighted sustainable development as an efficient management analysis method.
After the 368 keyword reference frequencies were organized from high to low and the top ten were analyzed (table 2), keywords such as data science, decision support system, sustainable development, and knowledge management were found to have relatively high centralities. Volume I includes the main research areas associated with management, economics, engineering, and data and information, all of which have strong links to MSEM methodologies and models. The latest research areas and research directions are also included, such as design science, information technology, and the associated models, many of which were the basis for many of the innovations in last year's proceedings.

Conclusion
MS employs various scientific research-based principles, strategies, and analytical methods, such as mathematical modeling, statistics, and numerical algorithms to improve an organization's ability to determine rational and accurate optimal or near-optimal management solutions to complex decision problems. This Volume I Proceedings review revealed that MS blends scientific exploration in sustainable development, decision support systems, and data science. To better help the reader comprehend the substance of this year's papers, these three MS sub-disciplines were evaluated to determine the primary research foci in the first volume of the ICMSEM Proceedings, and Citespace was used to sub-iterate these three areas. Then, using the generated scientific knowledge map, the MS and ICMSEM development trends were examined, from which it was discovered that Volume I of the ICMSEM Proceedings had a focus that was similar to yet slightly distinct from mainstream MS research. Researchers interested in MS issues should examine the dominant tendencies in leading MSEM journals as these could assist in identifying future ICMSEM research expectations and opportunities.