International Journal of Information Technology & Decision Making

Supply chain management has evolved from local and regional purchasing and supply activities prior to the industrial revolution to the current form of technology-led, data-driven, collaborative, and global supply network. Data-driven technologies and applications in supply chain management enable supply chain planning, performance, coordination, and decision-making. Although the literature on procurement, production, logistics, distribution, and other areas within the supply chain is rich in their respective areas, systematic analyses of supply chain analytics are relatively few. Our objective is to examine supply chain analytics research to discover its intellectual core through a detailed bibliometric analysis. Specifically, we adopt citation, cocitation, co-occurrence, and centrality analysis using data obtained from the Web of Science to identify key research themes constituting the intellectual core of supply chain analytics. We find that there has been increasing attention in research circles relating to the relevance of analytics in supply chain management and implementation. We attempt to discover the themes and sub-themes in this research area. We find that the intellectual core of SCA can be classified into three main themes: (i) introduction of big data in the supply chain, (ii) adoption of analytics in different functions of operations management like logistics, pricing and location, and (iii) application of analytics for improving performance and business value. The limitations of this study and related future research directions are also presented.

Young people’s skills and attitudes must be observed, studied and evaluated in order to create appropriate models that would serve an educational purpose. In this paper, we propose a decision-making model with the aim to detect certain attitudinal and behavioral patterns of actively engaged young people. The data used in this research resulted from a questionnaire drawn up by a group of researchers from six European countries with the aim to investigate the youth’s awareness about the Sustainable Development Goals and their engagement as active agents of development and change at regional level. For the purpose of this study, we selected the regional results obtained from administering this questionnaire in Dolj County (Romania) and La Rioja (Spain). We developed a neutrosophic model that determines the Mindchanger profile of the respondents based on a minimal set of questions, which is dependent on the inclusion or exclusion of the nationality-specific traits. The resulting decisions were then compared with the respondents’ self-evaluations, yielding high precisions (more than 0.83) for all the investigated evaluation scenarios. Our results were significantly better than the ones provided by several machine learning models applied on the same set of data. A direct impact of our model is that it can be applied to questionnaires which include linguistic responses that express, among others, unclear or vague thoughts. Additionally, it offers the possibility to identify the minimal set of questions that impact the respondent’s answer choice to a target question.

In today’s fiercely competitive business environment, product design and development play critical roles in an enterprise’s success; therefore, consumer demand must be understood. Enterprises used to understand their consumers’ demands through time-consuming questionnaire surveys and statistical analyses. As the Internet and the popularity of virtual communities have grown, more consumers are leaving comments about their perceptions of the appeal of products on online social media platforms, thus enabling enterprises to more objectively understand consumers’ product preferences and demands. Therefore, determining how to effectively assist enterprises in analyzing valuable information beneficial to product design and development that can be gleaned from the large amount of social media data available is critical to promoting an enterprise’s competitive advantage in the product market. However, previous studies have primarily focused on understanding consumer viewpoints of products through review articles or electronic word-of-mouth (eWOM; it is a common channel for spreading product appraisals) from online social media. For these review articles and eWOM that can imply consumer demand for product features, there were no relevant studies focused on analyzing consumer demand by using both online review articles and eWOM for product feature evolution. Therefore, for new product features, this study developed a mechanism for product evolution course mining from product-related review articles (such as product functions or specifications) and eWOM on online social media to realize the prediction of future product features or specifications, and then assist enterprises in rapidly and accurately grasping product development trends to effectively discern key reference information for product design and development. The study was achieved by (1) designing a process for online information-based product evolution course mining and prediction, (ii) developing techniques related to online information-based product evolution course mining and prediction, and (iii) implementing and evaluating an online information-based product evolution course mining and prediction mechanism. The experimental results indicated that approximately 81% of developers were satisfied with the proposed system for predicting smartphone features. The results of the study can facilitate the mining and prediction of online information-based product evolution course to assist enterprises in rapidly and accurately grasping product development trends and effectively provides important reference information for product design and development; therefore, it can enhance competitive advantages and customer satisfaction of enterprise products in the marketplace.

This study explores the strategic issues related to the mobile learning quality in Taiwanese higher education during the COVID-19 pandemics. While much previous study determined the factors affecting mobile learning quality, seldom research works discussed the influence weights of the elements and their interrelations in the higher education through a comprehensive view. Hence, the purpose of this research is to explore the interrelationships and feedback between the factors affecting mobile learning quality via a combined multiple attribute decision-making (MADM) method, which will include decision-making trial and evaluation laboratory (DEMATEL), and DEMATEL-based ANP (DANP) approaches. This recommended approach can offer guidance to prioritize the influence elements in the enhancement plans for mobile learning quality. This research uses the recommended approach in the higher education in Taiwan as an empirical study during COVID-19. These findings results state which service quality needs to be enhanced primarily, and learning ability is the most important factor influencing mobile learning quality during COVID-19. Furthermore, the results show that system quality perspective has the strongest impact on the relations and it also has an important effect on other perspectives. The results can provide guidance on assigning resources of limit to improve this mobile learning quality in Taiwanese higher education in the COVID-19 pandemics.

Digitalization is increasingly embracing nearly every part of our private and professional lives. However, whether digitalization is deemed to be a necessity that can boost the performances of small- and mid-size enterprises (SMEs) or it is a frivolous costly investment that hinders corporate activities is still unclear. This paper examines the impact of digitalization on the financial performance of SMEs in the context of an emerging economy. Using a comprehensive firm-level dataset consisting of over 5,000 SMEs in Vietnam during the period from 2005 to 2015, we find that digitalization plays a vital role in facilitating the financial performance of SMEs. Specifically, we find that the investment in computers, an e-mail system, internet connection and e-trading would pay off as it can lead to a significant increase in firm financial performance. We furtherfind that firms can benefit from digitalization because it can significantly improve productivity and sales, and yetdoes not lead to an acceleration in total cost. The positive effect of digitalization on firm performance is even more pronounced during the 2007–2009 global economic crisis, and thus highlights the pivotal role of digitalization in tackling the economic shock.

Amid the pandemic infection, people are bound to use contactless mobile payment (M-Payment) services. M-Payment is a payment method using an application in a mobile device, such as a mobile phone, and gadget. Owing to the convenience, reliability and contact-free feature of M-Payment, it has been diffusely adopted to reduce the direct and indirect contacts in transactions, allowing social distancing to be maintained and facilitating the stabilization of the social economy. Consequently, it has become one of the day’s most important topics. Therefore, the purpose of this study is to provide a systematic literature review (SLR) on the applications of M-payment services in financial strategies, focusing on the pandemic crisis. 19 papers were collected and divided into three groups for further analysis. The results showed that M-Payments applications in financial strategies during the pandemic crisis could help reduce the spread of infection risks by hastening the transition to touchless habits.

Rapid motorization and uncertainty in urban growth patterns make parking space management a serious task, especially in middle-income developing countries, and this has severe social, economic, and environmental repercussions including increased congestion, crash frequency, fuel and time consumption, and air pollution. Due to the complexity of the urban transportation issue and the wide variety of variables involved, a multicriteria assessment is essential. This study used fuzzy logic and geographical information systems (GIS) to develop a multi-criteria decision making (MCDM) model for managing parking in Shiraz’s central business district (CBD). The literature was mined for information on the variables that affect parking site placement, and a poll of experts ($n=11$) was used to determine their relative importance. The distance to travel attraction centers, distance to roads, land price, population density, and available land for multi-storey parking were among the factors considered. Meanwhile, the parking space shortage for each TAZ is calculated by subtracting the estimated parking space supply from the estimated parking space demand. An overlay of these two layers distinguishes locations that are in parking shortage zones and also meet multiple criteria. The results may aid policymakers in controlling parking demand by pinpointing the most promising places for investment.

Nowadays, many online users find the selection of information and required products challenging due to the growing volume of data on the web. Recommender systems are introduced to deal with information overload. Cold start and data sparsity are the two primary issues in these systems, which lead to a decrease in the efficiency of recommender systems. To solve the problems, this paper proposes a novel method based on social network analysis. Our method leverages a multi-agent system for clustering users and items and predicting relationships between them simultaneously. The information on users and items is extracted from the user-item matrix as distinct graphs. Each of the graphs is then treated as a social network, which is further processed and analyzed by community detection and link prediction procedures. The users are grouped into several clusters by the community detection agent, which results in each cluster as a community. Then link prediction agent identifies the latent relationships between users and items. Simulation results show that the proposed method has significantly improved performance metrics as compared to recent techniques.

Internet of Things (IoT) plays a vital role in the world of Internet and integrates computing devices. The digital machines provide unique identifiers. The capacity to send data through the network using a better path is one of the capabilities of digital machines. IoT generates huge data based on fast aggregation data and processes these data efficiently. In this paper, a multi-objective hybrid woodpecker and flamingo search optimization algorithm is proposed for finding optimum Cluster Head (CH)-based energy-aware routing protocol in IoTs environment (Hyb-WFSOA-CHS-IoT). Here, the proposed Hyb-WFSOA-CHS-IoT model executes routing method through CH. The woodpecker and flamingo search optimization algorithm is used as the intelligent CH selection that consumes same energy as sensors. Hybrid woodpecker and flamingo search optimization algorithm (Hyb-WFSOA) is examined by using fitness functions, like distance, delay, energy consumption, throughput. The proposed Hyb-WFSOA-CHS-IoT method is activated in MATLAB software. Then, the performance of the proposed system is examined with different metrics, like delay, packet delivery ratio, throughput, network lifetime, energy consumption. Therefore, the proposed method attains lower delay 28.38%, 32.34% and 47.45%, higher delivery ratio 19.34%, 23.12% and 18.96% and lower energy consumption 11.25%, 7.90% and 12.88% compared with existing methods, like multi-objective fractional gravitational search algorithm for energy-efficient routing on IoT (FGSA-CHS-IoT), multi-objective sunflower-based gray wolf optimization algorithm for multiple path routing on IoT network (SFG-CHS-IoT) and energy-aware routing in the IoT with improved grasshopper metaheuristic algorithm with chaos theory and fuzzy Logic (FLGOA-CHS-IoT).

This paper focuses on the computation issue of portfolio optimization with scenario-based mean-average value at Risk (AVaR) in uncertain environment. The portfolio optimization problem is designed in two cases: risk-taker and risk-averse models. The main idea is to replace the portfolio selection models with linear programming (LP) problems. Since the computing time required for solving LP greatly depends on the dimension and the structure of the problem, the conventional numerical methods are usually less effective in real-time applications. One promising approach to handle online applications is to employ recurrent neural networks based on circuit implementation. Hence, according to the convex optimization theory and some concepts of ordinary differential equations, a neural network model for solving the LP problems related to portfolio selection problems is presented. The equilibrium point of the proposed model is proved to be equivalent to the optimal solution of the original problem. It is also shown that the proposed neural network model is stable in the sense of Lyapunov and it is globally convergent to an exact optimal solution of the portfolio selection problem with uncertain returns. Some illustrative examples are provided to show the feasibility and the efficiency of the proposed method in this paper.

Research in Customer Relationship Management (CRM) in the healthcare sector is in a developing stage and demands further research to get more in-depth insight. We present a comprehensive model to develop and prioritize CRM readiness factors in hospitals using fuzzy DEMATEL-ANP, which helps top managers allocate their limited resources to enhance the required infrastructure for a CRM system. After extracting proper readiness factors, multiple criteria decision-making (MCDM) techniques are applied to assess CRM readiness. First, using the fuzzy decision-making trial and evaluation laboratory approach (DEMATEL), the interdependent relations amongst criteria are designated. Second, the fuzzy analytic network process (ANP) is applied to weigh the sub-criteria. Top management support and structure are the most critical factors which play an essential role in the CRM readiness concept. The importance of top management’s factor has been investigated in many previous works. The structure has been neglected in the previous studies; however, our results demonstrate that it should be considered a crucial factor. This study’s findings can facilitate the CRM system’s adoption process to be employed by decision-makers within hospitals to mitigate the failure rate of the CRM system’s implementation, leading to providing plenty of advantages to the patient association and hospitals. The results of this paper can also have a contribution to the implementation of CRM and artificial intelligence (AI) as an innovative strategy in organizations, particularly hospitals.

For a company, it is important to know which products to launch to the market that may get the maximal profit. To achieve this goal, companies not only need to consider these products’ features, but also need to analyze how customers make their purchase decisions. For most customers, the price of a product is the most important purchase factor. If the price of a product can be adjusted, the purchase decision of a customer may change. With different price settings, we can speculate on the expected number of customers and the profit of the products. Motivated by this, we want to find the most profitable products among the candidates for the company. A distance-based adoption model can be used to evaluate the expected customers for products at different prices. The computational cost is high in two parts. One is the computational cost of obtaining the most profitable information on each set of candidate products. Another part is that many candidate product combinations need to be calculated. To tackle the computation problem, we propose two strategies. One is to avoid considering all possible price settings. The other is to avoid processing all possible subsets of the candidate products. Experimental results reveal the efficiency of our strategies.

Learning Classifier Systems (LCSs) are a kind of evolutionary machine learning algorithms that provide highly adaptive components to deal with real world problems. They have been widely used in resolving complex problems such as decision making and classification. LCSs are flexible algorithms that are able to construct, incrementally, a set of rules and evolve them through the Evolutionary Algorithm (EA). Despite their efficiency, LCSs are not capable of handling imperfect information, which may lead to reduced performance in terms of classification accuracy. We propose a new accuracy-based Michigan-style LCS that integrates the belief function theory in the supervised classifier system. The belief function or evidence theory represents an efficient framework for treating imperfect information. The new approach shows promising results in real world classification problems.

Credit risk evaluation has been vital for financial institutions to identify default customers and to avoid financial loss. Machine learning and data mining techniques have been adopted to develop scoring models for enhancing the prediction performance of default customers. However, it is difficult for these machine learning models for explaining the rejection or approval decision-making process to customers and other non-technical personnel. This paper presents an evidence reasoning (ER) rule-based ensemble learning approach for credit risk evaluation considering customer heterogeneity. Firstly, customers are segmented into different groups by $k$-means clustering algorithms and a two-stage weighting method is proposed to determine the significances of attributes by their discriminating powers between groups and within groups. Then, the attribute-related evidence is obtained by Bayesian statistics to represent the relationships between the attributes and credit risks, and a two-stage weighting evidential reasoning (TER) is developed as a base learner for credit scoring. Lastly, multiple base learners TERs are aggregated for evaluating customers’ credit risks. An empirical study on three credit datasets demonstrated that the proposed approach can achieve high performance with good explainability. The predicted results of the model can be well comprehended by providing the contribution of attributes and the activated rules in evidential reasoning processes.

The efficiency of a spatiotemporal data analysis algorithm decreases as the amount of data increases. Many clustering techniques have been proposed for data analysis applications. However, applying those techniques to spatiotemporal data clustering is still in its infancy. In this paper, we tackle the issue of clustering spatiotemporal data on public Cloud based on the distance between them. To increase the efficiency of spatiotemporal clustering, we have proposed a MapReduce-based framework for clustering. However, as spatiotemporal dataset contains sensitive information, directly outsourcing spatiotemporal data to Cloud servers will raise privacy concerns. To address the problem of privacy, we have proposed a privacy preserving clustering algorithm based on MapReduce for spatiotemporal data that can be efficiently outsourced for data processing on the Cloud servers. The proposed scheme allows the clustering operation to be performed directly on the encrypted spatiotemporal data by Cloud server. Extensive experimental evaluation with trajectory data shows that our scheme efficiently produces higher quality clustering results.

Information Retrieval is the most predominant topic in the field of Information Systems as the generation of data over various systems and channels is growing every day. The proposed system which works on the offline basis is designed for the purpose of organizing data in a defined manner and for the purpose of increasing relevancy in the retrieval of the required information from this largely generated data. In the proposed system two novel algorithms, Dynamic Path Selection Clustering (DPSC) algorithm for clustering and the Rearward Binary Window Match (RBWM) algorithm for search process are introduced to overcome the difficulty in data organization and search. The evaluation of the entire system is done and the results are compared along with the results of the existing techniques.

This paper presents a new approach based on multi-dimensional evaluation and benchmarking for data hiding techniques, i.e., watermarking and steganography. The novelty claim is the use of evaluation matrix (EM) for performance evaluation of data hiding techniques; however, one major problem with performance evaluation of data hiding techniques is to find reasonable thresholds for performance metrics and the trade-off among them in different data hiding applications. Two experiments are conducted. The first experiment included LSB techniques (eight approaches) based on different payload results and the noise gate approach; a total of nine approaches were used. Five audio samples with different audio styles are tested using each of the nine approaches and considering three evaluation criteria, namely, complexity, payload, and quality, to generate watermarked samples. The second experiment involves the use of various decision-making techniques simple additive weighting (SAW), multiplicative exponential weighting (MEW), hierarchical adaptive weighting (HAW), technique for order of preference by similarity to ideal solution (TOPSIS), weighted sum model (WSM) and weighted product method (WPM) to benchmark the results of the first experiment. Mean, standard deviation (STD), and paired sample t-test are then performed to compare the correlations among different techniques on the basis of ranking results. The findings are as follows: (1) A statistically significant difference is observed among the ranking results of each multi-criteria decision-making (MCDM) technique, (2) TOPSIS-Euclidean is the best technique to solve the benchmarking problem among digital watermarking techniques. (3) Among the decision-making techniques, WSM has the lowest rank in terms of solving the benchmarking problem. (4) Under different circumstances, the noise gate watermarking approach performs better than LSB algorithms.