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International Journal of Information and Decision Sciences

International Journal of Information and Decision Sciences (IJIDS)

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International Journal of Information and Decision Sciences (56 papers in press)

Regular Issues

  • A multi-criteria location selection model based on Fuzzy ANP and Z-number VIKOR methods: a case study   Order a copy of this article
    by Pedram Memari, Seyedeh Samira Mohammadi 
    Abstract: Initial investment for construction of a power plant is an important issue which technologies and methods are moving forward to minimize the total costs. Therefore, a power plant should be established in a region which will increase the reliability and efficiency with minimum total cost. In this study, location of a solar power plant is optimized with considering a set of criteria including environmental, economic, social and strategic aspects in which energy resource usage will be maximized along with the least cost and the economic growth of country. For this purpose, Fuzzy ANP and Z-number VIKOR approaches are used to rank the 20 candidate cities in Iran. The obtained results indicate the efficiency and reliability of this study.
    Keywords: location optimization; solar energy; Fuzzy ANP; Z-number VIKOR; reliability.

  • Technical Efficiencies and Technical Change Gaps in Africa: Application of DEA on African Sectors Input-Output (IO) Frameworks   Order a copy of this article
    by Samson Gebrerufael 
    Abstract: This paper presents the African sectors technical efficiencies and the technical change gaps. The non-parametric data envelopment analysis (DEA) is employed using the standard input-oriented BCC model. To present the state of the technical efficiencies of African sectors, the technical coefficients of 25 sectors are examined using the IO tables of 2005 and 2013 taken from the Eora MRIO database (2013). In 2005 and 2013, the only strongly efficient sector (the benchmark sector) is found to be the financial intermediation and business activities sector. Even if the technical inefficiency prevails in Africa, the actual technical efficiency changes are observed between 2005 and 2013 as the technical coefficients of 2013 are found to be relatively smaller than that were in 2005. However, the changes in the actual coefficients are found to be lower (in absolute value) than the potential technical coefficients and therefore lost input savings and lost outputs. Simply, the average potential input savings in 2005 had the sectors had employed the 2013 lowest technical coefficients is found to be 93.9% (in absolute value). Hence, the African sectors have been performing weak in avoiding the wastage of inputs.
    Keywords: technical efficiency; mix-inefficiency; actual technical change; potential technical change; technical Change gap; BCC Model.

  • Three-Level Multi-criteria Analysis for Measuring the Efficiency of Grant Awarded Research Projects   Order a copy of this article
    by Noor Saifurina Nana Khurizan, Adli Mustafa, Hamidah Abd. Hamid 
    Abstract: This paper is written for the purpose of demonstrating a new way of applying Analytic Hierarchy Process (AHP) and Data Envelopment Analysis (DEA) in examining the performance of university funded academic research projects. Three stages of multi-criteria analysis were employed in this study involving AHP, DEA and preferential voting. AHP methodology was employed to construct a four-level hierarchy system to group the evaluation criteria and generate the related priority score. Based on the priority score generated from AHP, a score for the output data was then calculated and standardized. The original DEA model was then used to find the efficiency score for each research grant by solving its multiple objective problems. Later, DEA analysis was conducted using different combinations of output to distinguish the research grant projects that fully utilized its input to consistently produce the output. Further, an alternative way of producing an overall ranking was explored by employing the methodology of preferential voting using DEA. A case study of Universiti Sains Malaysia (USM) funded research projects is presented to demonstrate the application with real life data.
    Keywords: Data Envelopment Analysis; DEA; Analytic Hierarchy Process; AHP; Preferential Voting; R&D; Sponsored Research; Higher Education; Efficiency Anlaysis.

  • Does Doctor's Skills Influence Patient Satisfaction Loyalty and Compliance in Low-Medium Income Countries   Order a copy of this article
    by Firas AlOmari 
    Abstract: In order to achieve quality assurance in healthcare organization, there is a need for unceasing assessment of clinical practice of doctors performance. The purpose of this research paper is to investigate the impact of doctors skills on patients satisfaction, loyalty and behavioural compliance in Syrian healthcare setting. Convenience sampling method was used to select 301 patients from six hospitals, in the Syrian capital Damascus, to complete the questionnaire. The proposed model was created to examine the doctors impact on patient satisfaction, loyalty and compliance. Both exploratory and confirmatory factor analyses are used to verify the scales. The validity and reliability of the proposed model had been confirmed. The results revealed that doctors skills are a multidimensional construct comprised of three dimensions: doctors competence, listening skill and explanation skill. The analysis of the proposed model indicated that doctors skills construct has a significant positive direct effect on patient satisfaction, loyalty and compliance. In addition, the statistical results showed that patient satisfaction has a partial mediating effect between doctor skills and patient loyalty. Besides, results of this study found that satisfied patient with the perceived clinical service does not secure medication compliance; in other words, patient satisfaction does not mediate the relationship between doctor skills and compliance. To the authors knowledge, this is one of very few studies conducted to understand and assess the doctors role on patient satisfaction, patient loyalty and medication compliance in Syrian healthcare setting. The current research introduced several noticeable contributions to the healthcare marketing and management in Syria.
    Keywords: doctor's skill; patient satisfaction; patient loyalty; medication compliance; Syrian Healthcare.

  • Multi-criteria model for selecting project managers in the public sector   Order a copy of this article
    by Fernando Escobar, João Varajão, Nilton Takagi, Ulysses Almeida Neto 
    Abstract: To assure effectiveness in the management of projects is required that project managers have the right competencies, according to the context and characteristics of each project they are involved. Based on several competencies frameworks (including PMIs PMCDF, IPMAs ICB, APMs CF, and AIPMs PCSPM), this paper proposes a new and unified multi-criteria model to be used as a decision-making tool for selecting the most suitable managers and defining competencies pathways for public sector projects. Expanding the current literature, it is proposed a hierarchical structure comprising weighted elements related to behavioural, management, and contextual/organizational competencies. For researchers, the presented unified model of competences enables a better understanding of the phenomena and can be used to structure further research in other contexts than the public sector. It is also a valuable tool for practitioners and project management offices since it allows comparing the candidates for managing a project using an organized and rigorous process anchored on empirically well-grounded criteria.
    Keywords: Project manager; project management; selection; decision; AHP; competencies; frameworks; public sector; Brazil.

  • Development of optimal Ordering strategy, with power demand and changeable deterioration under allowable delay in payments   Order a copy of this article
    by R.P. Tripathi 
    Abstract: In the present competitive trade world, each business would like to compose extra income by means of less investment. In view of an economic ordering quantity (EOQ) system by variable deterioration our objective is to learn the effect of fixed lifetime items under trade credits. At present scenario in any selling operation broker regularly offers the retailer a permissible delay phase .Some commodities like vegetables, fruits, liquids, pharmaceuticals, volatile liquids deteriorate continuously up to termination dates. This study considers an inventory model for power demand with deterioration has their highest life time. Two different cases are discussed, including a sub case. Mathematical formulation is given for two unlike circumstances to demonstrate the proposed model condensed Taylors series is applied in favor of exponential terms for judging blocked form explanation. Numerical designs and sensitivity investigation are made available to reveal the model.
    Keywords: Inventory; expiration; spoilage; trade credit; power demand.

  • Investigate the causal effect of diversification strategy on risk-adjusted performance using Bayesian additive regression trees   Order a copy of this article
    by Zahra Faraji 
    Abstract: This paper aims to develop a hybrid causal model based on the inverse probability weighting (IPW) and Bayesian Additive Regression Trees (BART) as an advanced machine learning technique. IPW relies on parametric logistic regression model to estimate the propensity scores, however, the required assumptions and pre-specified relationships degrade its application for many real-world problems. We use BART to model the propensity scores to mitigates the limitations of standard IPW model. In addition, we apply Bayesian model to estimate the average treatment effect (ATE) in the pseudo-population instead of simple regression to provide posterior predictive distribution of ATE. Using a simulation study, we show our model corrects the bias and RMSE introduced by the original IPW model and can recover the true ATE. Lastly, we apply the new IPW model to investigate the causal effect of diversification strategy on risk-adjusted performance for U.S. public firms. The results show diversification can help firms to improve their performance even after considering the associated risk.
    Keywords: Causal Inference; Diversification strategy; Risk-adjusted performance; Inverse probability weighting; Machine learning; BART.

    by Chekhaprabha Waduge, Naleen Ganegoda, Darshana Wickramarachchi, Ravindra Lokupitiya, Ganhewalage Lanel 
    Abstract: Partitioning a longitudinal context of a time series into a set of subseries in a meaningful way is the first temporal concern here. Thereafter, a cross-sectional approach is implemented to design representative series of that set of time series. The series containing point-wise arithmetic means (mean series) is somewhat the simplest choice, when such a representative is required. However, it may not acquire important temporal variations. In this paper, several alternatives are proposed based on a class of measures called Ultimate Tamed Series (UTS). Here, an operation called taming is implemented upon time series and it is non-commutative and non-associative. The taming is carried out with the aid of discrete Haar wavelet, which is subscribed by both point-wise concern and local trend given by an adjacent point. Catering this adjacency is the key temporal manipulation. UTS measures depend on the order of taming a set of time series adhering series-wise heterogeneity. We trial this with a data set of disease incidence by two predetermined orders as order of occurrence in the timeline (forward) and its reverse order (backward). Order of occurrence yields a UTS that is biased on the pattern and position of data points of earlier series than recent series. The opposite can be observed in its reverse order. We claim this biased nature as a favorable outcome in acquiring long-term trends into a representative series. Two other UTS measures are also presented containing the highest and the least point-wise deviations with the data series. In addition, two supportive indexes are designed in order to understand the variability of concerned UTS measures. Better-informed decisions are possible via proposed architecture of processing time series data.
    Keywords: Time series; Haar wavelet; taming; representative series; decision support system.

  • Proposing a Model for Accepting Core Banking System in Iran Using Fuzzy DEMATEL Technique: A Case Study   Order a copy of this article
    by Ameneh Khadivar, Hamideh Nazarian, Sanaz Bodaghi 
    Abstract: Ever-increasing advancement and expansion of the information and communication technology in the recent decade has led to increased competition in organizations, especially financial organizations. Core banking system implementation project is a time consuming, cost-intensive, and complex task like other investments in information technology. As a result, plenty of core banking system projects have not been successful. This research proposes a model for accepting the core banking system at Parsian Bank operating in Iran using the fuzzy DEMATEL technique. Using the fuzzy DEMATEL procedures, the involved criteria of a system are separated into the cause and effect groups for helping decision-makers focus on those criteria that provide great influence. Identified from the users' standpoint, factors affecting the acceptance of this system have been prioritized through the questionnaire. The identified factors for accepting the core banking system in this research are: output quality of core banking system, experience of using the core banking system, voluntariness and motivation in employees to use the core banking system, perceived usefulness of core banking system by employees, perceived ease of use of the core banking system for employees, quality of core banking system, satisfaction with the core banking system, employee resistance to change and use of the new core banking system, and adequate training to employees to use core banking system. The findings indicate that, three of the influencing factors are identified as critical ones; within the cause group, the criterion of the output quality of the core banking system is the most important factor for accepting the core banking system, whereas the quality of the core banking system has the best effect on the other criteria. By contrast, adequate training for employees to use the core banking system is the most easily improved of the effect group criteria.
    Keywords: Core Banking System; Technology Acceptance; Accepting the Core Banking System; Fuzzy Dematel Technique.
    DOI: 10.1504/IJIDS.2023.10041306
  • A Synergy of Spatial Perspective based Non-Numeric ME-MCDM and Modified Dijkstra Algorithm for Optimal Distribution Route Selection   Order a copy of this article
    by Hartrisari Hardjomidjojo, Marimin Marimin, Suprihatin Suprihatin, Rindra Yusianto 
    Abstract: The optimal distribution routes selection is not only determined based on the shortest distance but needs to consider various alternatives and criteria that are complex and uncertain. Standard mathematical calculations cannot solve complex problems including geographic coordinates (X, Y) and spatial approaches. The contribution of this paper was a new method synergizing advanced non-numeric Multi-Expert Multi-Criteria Decision Making (ME-MCDM) and modified Dijkstra algorithm with spatial perspectives. The selected route was determined by multiplying Distance (D) in the classical Dijkstra algorithm with Alternative Values (AV) from non-numeric ME-MCDM using spatial perspective (S). We argued that spatial perspectives namely topography, road segments, multi-hazard zone indexes, and spatial-temporal congestion need to be considered. In this new method, we provided the ratio values (R) for each spatial variable. The most optimal route (Rs) was determined by calculating the Total Alternative Value (TAV) in each path that was considered conflicting multi-criteria. The smallest TAV value is selected as the most optimal route. The results showed the new method provides a more reasonable and meaningful solution compared with the classical Dijkstra algorithm results. Based on the verification and validation, this new method showed that the optimal route was not always the shortest. So, this new method can be used to determine the optimal distribution route selection which is more suitable for the agro-industrial sector. For further research, this method can be applied to optimize the supply and demand balance.
    Keywords: distribution route selection; non-numeric ME-MCDM; modified Dijkstra algorithm; spatial perspectives.

  • Data Mining based analysis of the Human Activity in healthy subjects using smart phones   Order a copy of this article
    by Ankitha S, Sanjay H S 
    Abstract: Human-Activity-Recognition(HAR) approach provides a valuable insight about their interaction with the surrounding environment. The present work highlights an assessment of HAR using accelerometer and gyroscope sensors embedded in Samsung Galaxy-S2 smartphone to acquire 6 activities namely WAlking(WA), Walking-Upstairs(WU), Walking-Downstairs(WD), SItting(SI), STanding(ST) and Laying(LA) with data mining approaches. HAR data was pertaining to 30 healthy subjects of age 19-48 years, both males and females acquired with an informed consent. Linear Discriminant Analysis (LDA) was used for dimension reduction. Classification was performed with and without LDA using Support Vector Machine (SVM), Multiple Layer Perceptron (MLP), Decision Tree (DT), Extra Tree (ET), K Nearest Neighbor (KNN), Random Forest (RF) and Gradient Boosting Machine (GBM) approaches in python platform. The results showed an increase in accuracy with LDA based dimension reduction. SVM (with C=10, Gamma = 0.001 with RBF kernel) provided the highest accuracy for both the cases (SVM without LDA = SVM with LDA = 96%). However, the highest variation based on LDA was seen in case of DT approach (DT without LDA = 85% and DT-with LDA = 95%). Such predictions can help the subjects with limited locomotion in the field of rehabilitative engineering using Augmented as well as Virtual Reality.
    Keywords: Human Activity recognition; Accelerometer; Gyroscope; Linear discriminant analysis; Rehabilitative engineering.

  • Multiple Criteria ABC Classification: An Accelerated Hybrid ELECTRE-PSO Method   Order a copy of this article
    by Ezzatollah Asgharizadeh, Amir Daneshvar, Ehsan Yadegari 
    Abstract: The objective of inventory management is to make decisions regarding the appropriate level of inventory. ABC classification analysis as the widely used inventory management approach, categorizes inventory items into predefined classes namely A, B and C. The limitation of the ABC management system is that only one criterion is considered, however, as generally emphasized in the literature, the inventory classification is a multi-criteria problem. So, this paper proposed a Multiple Criteria ABC Inventory Classification (MCIC) for the ABC inventory classification. Since, these models cannot handle the qualitative criteria and in many cases the classification problem isnt fully compensatory, this study integrates a non-compensative multi-criteria decision making technique (ELECTRE TRI) with a machine learning algorithm (PSO) to effectively conduct multi-criteria inventory analysis. Since, the application of ELECTRE TRI method requires to determine the preferences of decision makers (DMs) on some parameter values (e.g. prototypes pessimistic intervals and discrimination thresholds), the under consideration criteria and their related parameters are numerous and their interpretation is confusing, especially in large scale problems, the solution process is very complex and time-consuming. Tackling these difficulties, this paper proposed a method to infer all ELECTRE TRI parameters through a procedure using the previously classified inventory items determined by DMs. Then, a hybrid Particle Swarm Optimization (PSO) algorithm applied to induce parameters of ELECTRE TRI. Finally, for accelerating the PSO procedure to find the local and global optima and balance these points, the Variable Position (VP) model is proposed as an exploitation and variable exploration model with new velocity components. The findings indicate that the proposed model maximizes classification accuracy on each inventory dataset. In order to present the validity of the proposed method, it is applied to 6 inventory datasets and the results is also compared with some of the commonly used classification methods from the literature. The results also revealed high applicability of the proposed model to inventory classification problems.
    Keywords: Inventory Classification; Outranking relations; PSO; ELECTRE TRI.

  • Inventory Policies under Fuzzy and Cloud Fuzzy Environment   Order a copy of this article
    by Nita Shah, Milan Patel, Pratik Shah 
    Abstract: This article is an attempt to extend the classical economic order quantity (EOQ) model for deteriorating items under fuzzy and cloud fuzzy environment. Inventory parameters such as holding cost, purchase cost, ordering cost, demand rate and deterioration rate are considered as triangular fuzzy numbers as well as cloud triangular fuzzy numbers to develop fuzzy and cloud fuzzy models respectively. Yagers ranking method and De and Begs ranking methods are used for defuzzification. A comparative study reveals the superiority of cloud fuzzy model over crisp and fuzzy model. Further, numerical example, graphical illustrations and sensitivity analysis are carried out for better understanding of the application of cloud fuzzy approach to inventory optimization problem.
    Keywords: Inventory; EOQ; deterioration; cloud triangular fuzzy number.

  • E-commerce research in developing countries: A systematic review of research themes, frameworks, methods and future lines of research   Order a copy of this article
    by Frederick Pobee, Thuso Mphela 
    Abstract: AbstractrnThis paper presents a systematic review of e-commerce adoption research on developing countries with focus on the classification of literature and their associated themes, frameworks, research methodology over the period of ten years. A total of 151 articles from 35 peer reviewed journals from 2010-2019 were retrieved and used in the analysis. The findings reveal that majority of e-commerce adoption studies on developing countries tend to skew towards trust and satisfaction issues to the detriment of other under researched issue like attitude towards e-commerce adoption. Though there hasnt been a constant increase in e-commerce research on developing countries over the past 10 years, a significant number of published studies used qualitative approach as method of enquiry as compared to quantitative and mixed methodologies. Also, majority of e-commerce studies on developing countries have not been supported with theoretical frameworks and models. As contribution, this paper provides an in-depth analysis of e-commerce adoption in developing countries showing the trends of research themes, methodologies and frameworks. Implications for future research was discussed.rn
    Keywords: Keywords: E-commerce; developing countries; research frameworks; methodologies; adoption.

  • Cybersecurity Antecedents of Trust: Toward OPS adoption in Jordan   Order a copy of this article
    by Yazan Alshboul, Nareman Al.Hamouri 
    Abstract: Online services such as online banking, particularly, the online payment system (OPS), plays an important role in modern life. In developing countries, there is a kind of resistance to adopting OPSs. Therefore, more focus is needed to understand the behavior toward OPS, especially in developing countries. This paper integrates the trust model and the theory of planned behavior and addresses the antecedents of the trust factor in the context of OPSs. Particularly, it focuses on the cybersecurity factors as antecedents to the trust model. We tested our model empirically using data gathered from 200 participants who use eFawateercom system, an online payment system used in Jordan. The results showed that cybersecurity factors like systems security, privacy, and reliability play an essential role in affecting users trust, which has a crucial impact on the attitude toward OPS adoption. This article concluded with implications for academia and practitioners.
    Keywords: Online payment system; cybersecurity factors; trust; security; privacy; reliability.

  • A multi-view approach to multi-criteria decision making   Order a copy of this article
    by Francisco Santos, André Coelho 
    Abstract: Multi-view learning is a field of machine learning that deals specifically with data represented by distinct feature sets (known as views), possibly coming from multiple information sources. In this context, canonical correlation analysis (CCA) stands out as a representative technique, since it allows the automatic extraction of linear correlations among groups of data features in the form of canonical variables. In this paper, inspired by the great success of multi-view learning, we bring about a new perspective to multi-criteria decision making (MCDM), referred to as multi-view multi-criteria decision making (MV-MCDM), which is centered upon the application of CCA to distinct groups of judgement criteria (referred to as criteria views). By resorting to MV-MCDM, one can deal more naturally with multi-view multi-criteria problems than standard MCDM methods. Moreover, our approach enables the estimation of very reliable values for criteria weights via CCA. Another interesting advantage is that MV-MCDM entails the reduction of the dimensionality of the decision matrix by considering only one of the available views. In addition, we show that the MV-MCDM methodology permits the easy multi-view extension of well-known MCDM methods, such as SAW (simple additive weighting) and TOPSIS (technique for order of preferences by similarity to ideal solution). MV-MCDM is also generic enough to allow the adoption of different aggregation methods to generate the overall scores of the alternatives. In this regard, we show that the use of canonical variate correlation coefficients as fuzzy density measurements can make it possible the application of the Choquet integral. Besides, a new heuristic aggregation method based on radar charts is also considered. Finally, a numerical example focusing on the multi-view versions of SAW and TOPSIS demonstrates the applicability of the proposed approach.
    Keywords: MCDM; Multi-view Canonical Correlation Analysis; TOPSIS; SAW; Choquet integral.

  • Discerning the traffic in Anonymous Communication Networks using Machine Learning: Concepts, Techniques and Future Trends   Order a copy of this article
    by Annapurna P. Patil, Lalitha Chinmayee MaheshKumar Hurali 
    Abstract: With the growing need for anonymity and privacy on the Internet, Anonymous Communication Networks (ACNs) such as Tor, I2P, JonDonym, and Freenet have risen to fame. Such anonymous networks aim to provide freedom of expression and protection against tracking to its users. Simultaneously, there is also a class of users involved in the illegal usage of these ACNs. An emerging research topic in the field of ACNs is network traffic classification, as it can improve the network security against illegal users as well as improve the Quality of Service for its legal users. In this study, we review the research works available in the literature relevant to traffic classification in ACNs based on Machine Learning and also present to the researchers the general concepts and techniques in this area. A discussion on future trends in this area is also provided to bring out the future enhancements required in ML-based network traffic classification in ACNs.
    Keywords: Anonymous communication networks; Machine Learning; Traffic classification; Tor; Network security;.
    DOI: 10.1504/IJIDS.2023.10041362
  • Modelling Big Data Analysis Approach with Multi-agent System for Crop-yield Prediction   Order a copy of this article
    by Jaya Sinha, Shri Kant, Megha Saini 
    Abstract: Big data environment in current scenario is dealing with challenges in handling inherent complexity residing in the massive heterogeneous, multivariate and continuously evolving real time data along with offline statistics. The role of big data analytics to analyze such a highly diverse data also plays a significant role in estimating predictive performance of a system. This paper thus aims at proposing an intelligent agent based architecture that coordinates with big data analytics framework to model a system with an objective to improve the predictive performance of system by handling such diverse data. The paper also includes implementing predictive algorithm to predict crop yield in the agricultural domain. Various machine learning analytical tools have been used for data analysis to produce comprehensive and more accurate prediction using the proposed architecture.
    Keywords: Multi-agent System (MAS); Big data; Data acquisition; Data analysis; Data storage; Machine learning; Intelligent agents.

  • The Art of Context Classification & Recognition of Text Conversation using CNN   Order a copy of this article
    by Sandeep Rathor, Sanket Agrawal 
    Abstract: This paper proposes a robust model for recognizing the context of a conversation by using CNN. Initially, preprocessing is performed on the input text conversation. It includes lowercase conversion followed by tokenization, padding, and word embedding. The embedding layer gives out a feature matrix. This feature matrix is passed to a multi-level Convolution Neural Network. The proposed model is designed in such a manner that each CNN reduces the input matrix to half of the input size. Thus, the output of the next CNN layer and the pass of the current CNN layer followed by average pooling can be added. This output is named a global pass and passed to another block of the same architecture. The output from the last CNN layer and global outputs are concatenated and finally, passed into two fully connected layers FC(512) & FC(8). The output from FC(8) gives the probability of conversation to belong to eight contexts. Finally, the context with the highest probability is taken.
    Keywords: Context Recognition; Text Conversation; Text Mining; CNN; Machine learning.

  • In Search of Sustainable Electronic Human Resource Management in Public Organizations   Order a copy of this article
    by Reza Sepahvand, Khaled Nawaser, Mohammad Hossein Azadi, Ali Vafaei-Zadeh, Haniruzila Hanifah, Razieh Bagherzadeh Khodashahri 
    Abstract: Over the past two decades, issues such as environmental degradation, continued marginalization of large groups of people, growth of anti-globalization sentiments, and demand for innovation and creativity in public and private sectors have emerged as prominent global organizational problems. These developments have led to a growing interest in the concept of sustainability, which many Human Resource Management (HRM) researchers believe can enhance HRM capabilities and activities, leading to better organizational performance and competitive advantage. Given the significant impact of Information Technology (IT) on HRM, identification of factors affecting the implementation of sustainable Electronic Human Resource Management (SEHRM) in organizations can result in significant cost reduction and more principled organizational decision making. The present study aimed to identify and evaluate the factors affecting the implementation of sustainable EHRM in public organizations using type-2 fuzzy FMEA. After reviewing the research literature and surveying experts, 29 factors in three dimensions of social, environmental and economic were identified. After designing and distributing the questionnaire among experts, type-2 fuzzy AHP was used to determine the weight of risk factors of FMEA (occurrence probability, severity, and detectability). The identified factors were then ranked using type-2 fuzzy TOPSIS. The results showed that the social dimension is the most important dimension for the implementation of sustainable EHRM in the study area. The critical individual factors in order of significance for this implementation were found to be EHRM Reengineering, Green Performance Evaluation, IT Infrastructure, ECRM, Electronic Customer Satisfaction, Employee Safety, and Electronic Services.
    Keywords: Sustainable Electronic Human Resources Management; FMEA; Type 2 Fuzzy TOPSIS; Type 2 Fuzzy AHP.

  • A Multi-Criteria Decision Support System for the Assessment of Cities based on Air Quality Indicators   Order a copy of this article
    by Supriya Raheja, Rakesh Garg, Aakash Gupta, Geetika Munjal 
    Abstract: A deterministic decision support system is developed for the assessment and ranking of various cities based on the air quality indicators in this research. The present study models such assessment of cities as a multi-criteria decision making (MCDM) problem due to the involvement of multiple air quality indicators. Further, to solve the present assessment problem, a hybrid MCDM approach, namely, Entropy- Evaluation based on distance from average solution (EEDAS) is implemented by integrating two well-known approaches such as Shannon entropy and evaluation based on distance from average solution (EDAS). Shannon entropy approach is used to calculate the priority weights of the air quality indicators whereas the EDAS method is used to get the comprehensive ranking of the cities based on the identified air quality indicators.
    Keywords: Air Pollution; Air quality indicators; Multi-Criteria decision making (MCDM); Entropy; EDAS; Decision support system.

  • Analyzing Impact of IT Investments on Banks Performance using Multi-Stage DEA   Order a copy of this article
    by Ankit Mehrotra, Reeti Agarwal 
    Abstract: The paper studies the impact of investments made in information technology (IT) on a firms performance. The paper makes use of multi-stage Data Envelopment Analysis (DEA) to find out the marginal impact of IT investment on firms performance. The paper uses a three-stage DEA approach by introducing a new variable i.e. communication expenses to study what bearing IT has on a firms efficiency attainment. The paper also extends existing literature related to the indirect influence of IT on a firms performance by making use of a non-parametric approach, DEA. To attain the aims of the study, data of a number of banks was considered for a period of five years. The study introduced IT variables at two stages of the analysis to analyze its marginal effect on efficiency outcome.
    Keywords: Data Envelopment Analysis; DEA; performance; efficiency; information technology; bank; marginal effect.

  • Compromise ranking based on superiority, inferiority and Euclidean normalized similarity metrics: The ESIASP method   Order a copy of this article
    by Moufida Hidouri 
    Abstract: Xiaozhan Xu introduced in 2001 the superiority and inferiority ranking (SIR) methods called SIR.TOPSIS and SIR-SAW. The SIR.TOPSIS method has two quite different variants, which we call here SR.TOPSIS and IR.TOPSIS. What is noteworthy is that each variant gives attention only to one type of indexes rather than both types, which may result in questionable ranking results because both variants ignore available relevant indexes. In addition, the SIR.TOPSIS variants (ranking) indexes have been based on the TOPSIS relative closeness coefficient, which is inflexible in the sense of not being affected by the relative importance of separations of each alternative from positive ideal solution and negative ideal solution. The SIR.SAW method ignores the relative significances of superiority flows (overall degrees of support for alternatives) and inferiority flows (overall degrees of support against alternatives). It is therefore worthwhile to introduce a new ranking method to overcome the flaws seen in Xus SIR methods. The crisp method proposed in this paper, called the Evaluation based on Similarity to Ideal Augmented Superiority Profile (ESIASP) method, fully exploits all the available relevant indexes and takes account of their relative importance. Finally, a supplier selection problem is given to demonstrate the proposed method. A comparison of rankings produced by the ESIASP method, SIR.TOPSIS and SIR.SAW variants shows that the suggested method is a relevant and implementable alternative to Xus SIR methods.
    Keywords: crisp method; inferiority; SIR.SAW; SIR.TOPSIS; superiority; supplier selection.

  • Eliciting individual risk attitudes different procedures, different findings   Order a copy of this article
    by Sven Grüner, Norbert Hirschauer, Felix Krüger 
    Abstract: We compare three procedures for eliciting individual risk attitudes: Holt-and-Laury (2002), Eck-el-and-Grossman (2002), and the general willingness-to-take-risks question of the German socio-economic panel. Using a within-subject design, we carry out a classroom experiment with stu-dents who are enrolled in the degree programs Physics, Computer Sciences, Agricultural Scienc-es, Law, and History. We find that the risk attitudes as measured by the three procedures diverge substantially. This poses a serious challenge to the validity of these measurement instruments.
    Keywords: Risk-attitude; Eckel-and-Grossman procedure; Holt-and-Laury procedure; self-reported willingness-to-take-risks; bounded rationality.

  • Evaluation and selection of a casting process using interval type-2 fuzzy analytical hierarchy process   Order a copy of this article
    by Devesh Sahu, Atul Chakrawarti 
    Abstract: Selection of a casting process is a kind of multi criteria decision making process, in which several factors like Cost, material utilization and flexibility, geometrical complexity and flexibility and dimensional accuracy and surface finish. As cost is considered as one of the prime criteria in decision making, so it is further sub categorised into three sub criteria i.e. tooling cost, equipment cost and labour cost. Past researchers have used multi criteria decision making technique like analytical hierarchy process along with type-1 fuzzy in the evaluation and selection of casting process, which is successful in handling the fuzziness of the system, but are unable to address the issue of uncertainty associated with decision making. So, in this paper interval type-2 fuzzy analytical hierarchy process is used to evaluate and select best casting process, which is capable of handling uncertainty associated with decision making. A numerical illustration is solved using the interval type-2 fuzzy analytical hierarchy process and it is found that investment casting is evaluated as best casting process for cam carrier and is ranked number one.
    Keywords: Casting; MCDM; decision support system; type-2 fuzzy; AHP.

  • Combined Application of Condition Based Maintenance and Reliability Centred Maintenance using PFMEA and Lean Concepts A Case Study   Order a copy of this article
    by Claudia Carvalho De Oliveira, José Cristiano Pereira, Nélio Pizzolato 
    Abstract: Productivity is an essential element for competitiveness, helping companies to be better prepared for the future, given all of today's challenges. Such productivity needs to be clearly confirmed by operational results. This paper covers assets maintenance effectiveness, one of the productivity evaluation components increasingly gaining business attention. The proposed methodology helps the achievement of the required effectiveness on asset maintenance, by combining the concepts of Reliability Centered Maintenance and Condition Based Maintenance, also supported by PFMEA, a successful risk management strategy, and Lean Manufacturing practices. A case study was conducted in a high technology enterprise, combining those referred concepts. The implementation process used Minimum Viable Product strategy. The main concept is based on suitable equipment inspection and diagnosis practices which guides interventions and cleverer behaviour towards asset management. As a result, in the first year of implementation, 5 to 10% cost reduction was obtained together with a significant increase in equipment availability which has reached a level of 95%+.
    Keywords: Reliability Centered Maintenance; RCM; Condition Based Maintenance; CBM; PFMEA; Lean Maintenance; asset Management; MVP.
    DOI: 10.1504/IJIDS.2023.10041745
  • Towards Building a Comprehensive Big Data Management Maturity Framework   Order a copy of this article
    by Mervat Helmy, Sherif Mazen, Amal Elgammal, M.Wagdy Youssef 
    Abstract: Profound insights are generated today from exploiting big data. However, organisations are still not recognising how mature their big data management capabilities are, and what improvements are needed. There is still no structured approach to assess the maturity of big data management capabilities. Existing solutions lack a consistent perception of big data management capabilities, a reliable assessment, and a rigid improvement scheme. So, the main contribution of this article is conducting an analytical study on existing key works in assessing and building big data management capabilities, and upon, the main requirements for building a comprehensive big data management maturity framework are proposed. Results of validating the developed framework proved that it enabled organisations to assess, build, and improve their current big data management capabilities.
    Keywords: big data management maturity; big data management; big data management capabilities; big data capabilities; big data analytics capabilities; big data analytics capabilities construct; big data maturity; big data maturity model; big data maturity assessment; big data capabilities assessment.

  • Determining a Model for Eliminating Organizational Lying: A Grounded Theory Approach   Order a copy of this article
    by Mohammad Hakkak, Khaled Nawaser, Mohammad Jalali, Samaneh Ghahremani, Ali Vafaei-Zadeh, Haniruzila Hanifah 
    Abstract: The present study has been implemented to generate a theory in the field of organizational lying for better understanding and explaining the phenomenon in organizations. The research method is qualitative and based on the Grounded Theory. Semi-structured interviews were used for data collection and Strauss and Corbin Method and paradigmatic model were adopted for data analysis. Sampling was done using a theoretical sampling method and targeted (judgmental) and snowball (chain) techniques based on which 21 interviews were conducted with managers, employees and experts of the Imam Khomeini Relief Foundation (a charitable organization founded in 1979 to provide support for poor families and also known as IKRF) of Kurdistan Province in Iran who were familiar with the issue and context. The results of the analysis of the data obtained from the interviews during the open, axial and selective coding processes led to the formation of a grounded theory concerning the organizational lying based on which the purposeful lying, blind lying, and silent lying were introduced as the basic concepts of the Organizational Lying Model.
    Keywords: Organizational Lying; Antecedents of Lying; Consequences of Lying; Grounded Theory.

  • An Ergonomic Intervention in Engineering Work-shop   Order a copy of this article
    by Debesh Mishra, Suchismita Satapathy 
    Abstract: The present investigation was made for the ergonomic-analysis of workers activities in the engineering work-shops located in Bhubaneswar-city of Odisha (India). In this study, the ergonomic tolls such as Moore and Gargs Strain-Index (SI), Rapid Entire Body Assessment (REBA) and Quick Exposure Check (QEC) were used to distinguish the levels of task-performances and to analyze the working-postures along with the discomfort-levels of workers, respectively. Then, the Strength, Weakness, Opportunities, and Threat (SWOT) analysis was done for the participatory ergonomic-interventions to reduce the performance related risks in the working places of engineering work-shops. Further, on the basis of literature and experts opinions, a questionnaire was designed and responses were obtained from 129 workers and through factor-analysis followed by correlation-matrix, the significant ergonomic-approaches (variables) from the designed questionnaire were obtained and recommended for engineering work-shops.
    Keywords: Engineering; work-shop; Ergonomics; Strain-Index; REBA; QEC; SWOT; Questionnaire; India.

  • EOQ model for time dependent demand with deterioration, inflation, shortages and trade credits   Order a copy of this article
    by R.P. Tripathi 
    Abstract: The Inflation acts an important role for each area of life in the world. Inflation varies rapidly for high tech commodities with passing over time. This study develops an EOQ model with time sensitive demand rate for deteriorating products and shortages with inflation over a predetermined planning horizon. Mathematical formulations are prepared under two cases (i) time for positive inventory (T1) is greater than credit period M and (ii) T1 is less than or equal to credit period M, to gain optimal number of replenishment and cycle time. An algorithm is presented to find most favorable cycle time so that total annual relevant profit is maximized. We then demonstrate the total profit is concave with respect to number of replenishments. Numerical examples are offered to display the model. Sensitivity investigation for variation of a number of key parameters is also discussed. Mathematica 7.0 software is used to calculate numerical results and optimality conditions.
    Keywords: Cash flow; inflation; non- increasing demand; credit period; shortages.

  • Investigation of the rank reversal problem in some novel objective weight based MADM methods   Order a copy of this article
    by Ravindra Singh Saluja, Varinder Singh 
    Abstract: Objective weight based multi-attribute decision making (OWMADM) methods are applied in decision situations where the weight values are not sought from decision makers and are rather obtained based on the range or spread of attribute data. The present study develops three novel OWMADM methods namely Preference Selection Index Proximity indexed value Method (PSIPIVM), Standard Deviation Proximity indexed value Method (SDPIVM) and Entropy Proximity indexed value Method (EPIVM), by combining the objective weights obtained through three different methods with recently developed proximity indexed value method (PIVM), which is known to promote minimization of the rank reversal problem. Two established criteria are adopted to evaluate the occurrence of rank reversal, one by removing the least preferred alternative from consideration while another involved splitting the considered alternatives into two sets and testing for the transitive property. The paper also attempts to identify suitable normalization techniques for the proposed OWMADM methods to yield robust ranking orders, which may enhance the reliability of decision outcome.
    Keywords: Multi-attribute decision making; Objective weight; PSI; SD; Entropy; PIVM; Rank reversal; Decision analysis.

  • A Bi Objective Optimization Technique for Scheduling Repetitive Projects   Order a copy of this article
    Abstract: Repetitive projects are projects in which the same type of works/activities get repeated in different locations or sites. For each of this type of projects, different crew options are available for each activity and selecting the best option corresponding to each activity is a difficult task. Project managers in these projects are often faced with the task of finding out the best schedule corresponding to the optimum project duration and expenditure which will satisfy different constraints like due time in which each activity to be completed, movement of crew from one location/unit to other, precedence relationship among different activities etc. In this case, the decision maker wants to get a solution that simultaneously optimizes the two conflicting and diverse objectives of duration and expenditure while obtaining an acceptable trade off amongst the objectives. Therefore, development of a suitable solution method, which gives near optimal solutions while considering single objectives like minimization of project duration or project expenditure and also for bi objective optimization considering minimization of the combined effect of duration and cost, is very important. Since the computational complexity is very high in these type of projects, an ABC algorithm based heuristic methodology is developed in this study which can give good solutions for satisfying the above mentioned objectives with respect to different constraints. The proposed methodologys performance is analyzed with exact solutions and the results show that an ABC algorithm based methodology gives significantly good quality solutions.
    Keywords: ABC Algorithm; Repetitive projects; Optimization.

  • Availability and Mean Time to Failure for Repairable k-out-N: G Systems with Identical Components   Order a copy of this article
    by Mohammed Hajeeh 
    Abstract: This study presents Markov models for assessing the availability and the mean time to failure (MTTF) of a k-out-of n: G system with exponentially distributed time between failures and repair times. Generalized analytical expressions for the steady-state availability and MTTF are derived. Furthermore, numerical results are provided to illustrate the performance of several models along with the cost of adopting different configurations.
    Keywords: k-out-of n: G system; Availability; Mean time to failure; Cost.

  • NoSQL based approach in data warehousing and OLAP cube computation   Order a copy of this article
    by Abdelhak Khalil, Mustapha Belaissaoui 
    Abstract: Over the last few years, Not only SQL (NoSQL) databases are gaining increasingly significant ground and are considered as the future of data storage. In this paper we are interested in implementing NoSQL-OLAP systems by defining mapping rules from the multidimensional conceptual level to logical key-value model, and providing a set of online analysis operators. We consider two different approaches in order to implement a big data warehouse within key value stores. The first one uses SQL-like table structure layered on top of the key-value schema, the second one uses a simple key-value pair structure. Then we provide aggregation operators (Map-Reduce Cube and Bit-Encoded Cube) for key value models in order to perform OLAP cube computation. We implemented OLAP operator using Oracle NoSQL database and LevelDB, and we conducted experiments on a fictional data warehouse produced by an existing benchmark that considers NoSQL models. Thus, results showed clearly the performance of OLAP implementations under NoSQL key value stores in terms of efficiency and scalability.
    Keywords: OLAP; Data Warehouse; NoSQL; Big Data; Cube Model.

    by Luciano Ferreira Da Silva, Paulo Oliveira, Gustavo Grander, Renato Penha, Flavio Bizarrias 
    Abstract: This study aims to use fuzzy logic to select a project manager based on soft skills. In the first phase, a focus group interview was applied to establish the weights according to the soft skills list selected. In the second phase, the Fuzzy Topsis logic was applied. According to the concept of the Fuzzy Topsis, a closeness coefficient is defined to determine the ranking order of all alternatives. The results allowed the construction of the framework here called Fuzzy Topsis Ranked Multicriteria for selecting the best candidate according to the profile and criteria adopted. The contribution of this study is to allow the attribution of values to soft skills that, in essence, are subjectivity. This framework is friendly, the investment required is low, and it is adaptable to different contexts.
    Keywords: Fuzzy Topsis; Multicriteria Decision; Project Manager Selection; Soft Skill.

  • A GIS-Based Framework For Flood Hazard Vulnerability Evaluation In Thudawa Area, Sri Lanka   Order a copy of this article
    Abstract: At present flash floods became as one of the most devastating natural disasters all over the world especially in tropical areas including Sri Lanka. Considering the increase of flood events in recent years, accurate flood risk assessment is an essential component of flood mitigation in highly populated urban areas. The objectives of our research were to identifying and classifying flood risk areas into different classes in Thudawa area, Sri Lanka, and developing a Geographical Information System (GIS) model to identify flood vulnerability areas accurately in Thudawa area. Through this, it was expected to proposing preventive guidelines of flood hazard vulnerability using geo-informatics. As located near the Nilvala River mouth Thudawa is having a high risk for flash flood events over the recent period. Therefore, it was very important to conducting a Geo informatics-based risk assessment in the area. The methodological procedure is extremely important in this type of research thus, the spatial Multi-Criteria Decision Analysis (MCDA) procedure was used. Multi-criteria analysis has been widely applied to solve decision-making problems related to the environment, and natural resource management. For this research Analytical Hierarchy Process (AHP) was used for the criterion weighting. To run the GIS model in ARC GIS environment AHP calculations run upon the results of experts' judgment as proposed by the Satty incorporating pair-wise comparison method. The results of this study attempt to analyze the existing flash flood risk levels using the GIS-based multi-criteria analysis technique which allowed ranking of risk areas since it is important in the decision-making process to mitigate the flood risk in the study area. And also, the results of the research will be more useful to identify future risk areas for flood hazards in disaster management and land use planning as well as, on the other hand, it can provide valuable support for a range of decisions such as land use master planning, design of infrastructure, and emergency response preparation in the area. This study can be taken as a reference for researchers who engage with similar studies in the same or other areas using Geo-informatics tools after integrating other methods and tools such as fuzzy AHP method, fuzzy multiple-attribute decision-making method, fuzzy TOPSIS, and Pythagorean fuzzy AHP.
    Keywords: AHP; Flood Hazard; GIS; MCDA; Modelling.

  • D4SP Decision Support System based on the use of the AHP method for Science Park Selection   Order a copy of this article
    by Bruno Moura, Ivo Santos, Nelson Barros, Fernando Luis Almeida 
    Abstract: The literature reveals that science parks offer numerous benefits and support services to the activity of a technological startup. However, the decision of choosing the best science park for the startup tends to be an informal process, technically not very rigorous and planning, arising essentially by affinities with the research center and university. In this study, a decision support system is presented to support entrepreneurs in the process of selecting a science park for the implementation of their startup. The AHP method is used to compare the importance of the criteria for selecting a science park, which includes factors such as location, activity sector, infrastructure, cost, and size. The findings reveal that the use of this decision support system helps entrepreneurs to find a science park that is suitable for the needs of their startup and allows them to comparatively identify the most relevant criteria when choosing a science park.
    Keywords: entrepreneurship; decision science; AHP; startups; new venture; science park.

  • Artificial Neural Networks in the Development of Business Analytics Projects   Order a copy of this article
    by Juan Bernardo Quintero, David Villanueva-Valdés, Bell Manrique-Losada 
    Abstract: The accelerated evolution of information and communication technologies, with an ever-growing increase in their access and availability, has become the foundation for the current big data age. Business Analytics (BA) has helped different organizations leverage the large volumes of information available today. In fact, Artificial Neural Networks (ANNs) provide deep data-mining facilities to organizations for identifying patterns, predict future states, and fully benefit from predictions/forecasts. This article describes three ANNs application scenarios for developing BA projects, by using network learning: i) for executing accounting processes, ii) for time series forecasts, and iii) for regression-based predictions. We validate scenarios by implementing an application-case using actual data, thus demonstrating the full extent of the capabilities of this technique. The main findings exhibit the expressive power of the programming languages used in data analytics, the wide range of tools/techniques available, and the impact these factors may have on the BA development projects.
    Keywords: Artificial Neural Networks; Business Analytics; Data Analytics; Big Data; Deep data mining; Network learning process; Time series forecast; Regression-based prediction; Activity Based Costing; Supervised learning; Decision making.

  • Designing a decision support system for integrated production and distribution planning in shrimp agro-industry   Order a copy of this article
    by Lely Herlina, Machfud Machfud, Elisa Anggraeni, Sukardi Sukardi 
    Abstract: The integration of production and distribution planning is essential for the efficiency and responsiveness of the shrimp agro-industry. Due to the large number of actors involved in the supply chain and intense competition from similar industries, the integration needed. Besides, uncertainties in the supply of perishable raw materials, annual growth and harvest, various sizes and yields, and voluminous are challenges for the shrimp agro-industry. To overcome this, the integration production and distribution planning needed a decision support system (DSS). This study aims to develop a prototype of a decision support system that integrates production and distribution planning in shrimp agro-industry. To test the designed system, a multi-objective Evolutionary Algorithm (MOEA) framework used resulting in that DSS-shrimp can provide a validated mechanism for decision making in an integrated production and distribution planning in shrimp agro-industry.
    Keywords: decision support system; production planning; distribution; supply chain; agro-industry; multi-objective Evolutionary Algorithm;.
    DOI: 10.1504/IJIDS.2024.10042651
  • How young consumers are influenced by the valence of positive and negative frames: a cross-cultural perspective   Order a copy of this article
    by Theodore Tarnanidis, Kofi Osei-Frimpong, Jason Papathanasiou, Nana Owusu-Frimpong 
    Abstract: The specific study examines the use and the impact of three types of framing effects in different mental activities and everyday situations in consumers lives, namely: attribute, goal, and risky choice framing. Although, many studies across the globe have proposed empirical framing models that differ each other due to context discrepancies and other implicitly information. Yet, framing effects in the international cross-cultural perspective remain a pervasive issue. Having that purpose in mind this study aims to contribute to the examination of the literatures on framing effects between two diverse contexts with dissimilar environments, i.e. Greece and Ghana. The first one has a strong democratic culture over the last 2.500 years, whereas the other is characterized by long periods of military rule. Thus, cultural variations make people to make decisions differently. To that extent, data was collected in two stages from 590 young consumers (i.e. students) from Greece and Ghana. The different framing types were examined by using a within subjects design that provided participants the positive and negative conditions of each framing task. The results suggest a partial inter-correlation between the three categories of framing effects. In the attribute framing examinations, gain-framed messages make people to focus on the positive outcomes, whereas loss-framed messages have negative evaluations. Likewise, in the goal framing case, the majority of the subjects from both countries preferred the positive condition. And when decisions involve a risky option, Greeks in the positively framed condition were split between risk avoid and risk seeking behaviour, whereas Ghanaians have only a risk-seeking behaviour. In contrast, in the negatively framed condition all study subjects showed a risk seeking attitude. The findings provide unique technical insights into the consumer framing arena for future evaluations.
    Keywords: decision framing; framing effects; cognition; prospect theory; Greece; Ghana.

  • An Intelligent Decision Support System Modelling for Improving Agroindustrys Supply Chain Performance: A Case Study   Order a copy of this article
    by Muhammad Asrol, Marimin Marimin, Machfud Machfud, Moh. Yani 
    Abstract: Decision-making has an important role to improve agroindustrys business process performance. This research comprises 4 important aspects to determine agroindustrys performance, namely supply chain performance, risk management, green productivity performance and agroindustrys business promising. This paper proposed an Intelligent Decision Support System (IDSS) which was organized of main performance models to improve agroindustrys competitiveness. Supply chain performance modeling was organized by Supply Chain Operation Reference (SCOR) framework, agroindustrys risks assessment using Fuzzy House of Risk, green productivity evaluation using Green Productivity Index (GPI) and Fuzzy Inference System (FIS) while agroindustrys business promising and feasibility assessment which was modelled with FIS. Overall supply chain performance was developed by Single Input Single Output Fuzzy model to realize the final agroindustrys supply chain performance. An IDSS was comprised database, model-base and knowledge base to be periodically simulated the supply chain performance measurement of agroindustry. The proposed IDSS was validated under real condition of a sugarcane agroindustry and found that the supply chain business performance was moderate, it had high risk threat, normal green productivity performance and moderate feasible of business promising performance. The overall supply chain performance validations showed that sugarcane agroindustry -as a case study- performance was moderate. For further research, this paper requires experienced expert verification to formulate the supply chain performance improvement strategy and verify the IDSS model to be implemented for the real world.
    Keywords: Agro-industry; Fuzzy system; Green productivity; Intelligent decision support system; Risk management; Supply chain.

    by Rohit Kenge 
    Abstract: Since December 2019, the world is facing COVID 19 pandemic and its impact on the economy. As the product demand is shrinking, the product supply with the pre-installed capacities is facing major issues like job cuts, high unsold material inventory, and the running of companies at lower capacities. To answer these operational issues, we prepared the research hypothesis framework integrating the twelve operational excellence factors into an operational excellence model consisting of people, process, and flow approach. We evaluated this hypothesis through a set of 36 questionnaires for a survey based on the Likert scale and received 402 complete responses. We performed the analysis of survey response data by testing the reliability, correlation, validity, and structural equation modelling and found out that organizational performance has a significant positive impact on our proposed operational excellence model. Also, organizational performance has a significant positive impact on our proposed operational excellence model.
    Keywords: Operational Excellence; Recession; Organizational Performance; COVID-19; Demand-Supply Cycle.

  • Designing a method to model the socio-technical systems   Order a copy of this article
    by Mohammad Mirkazemi Mood, Ali Mohaghar, Yaser Nesari 
    Abstract: To capture the complexity and diversity of systems with both technical and social features, modeling methods are needed that similarly provide various tools and concepts. Study of developed methods shows that despite all of their advantages and strengths, there is a need for a method that with a holistic approach integrates perspectives, strengths and tools of the developed methods and models with different aspects of socio-technical systems. The main aim of the current study is to design a method for modeling complex socio-technical systems. To achieve this goal, it is necessary to design a method that is based on creativity and existing knowledge base. Therefore, Design science research is used as a research strategy to design proposed method. For the first time, design science research in the field of operations research has been used to design a modeling method. This study also presents new tools and concepts for modeling socio-technical systems.
    Keywords: Design science research; Soft operations research; Problem structuring methods; System thinking; Meta-synthesis; Modeling methods.

  • A data mining model to predict the debts with risk of non-payment in tax administration.   Order a copy of this article
    by Maria Hallo, José Ordoñez Placencia, Sergio Luján-Mora 
    Abstract: One of the main tasks in tax administration is debt management. The main goal of this function is tax due collection. Statements are processed in order to select strategies to use in the debt management process to optimize the debt collection process. This work proposes to carry out a data mining process to predict debts of taxpayers with high probability of non-payment. The data mining process identifies high-risk debts using a survival analysis on a dataset from a tax administration. Three groups of tax debtors with similar payment behavior were identified and a success rate of up to 90% was reached in estimating the payment time of taxpayers. The concordance index (C-index) was used to determine the performance of the constructed model. The highest prediction rate reached was 90.37% corresponding to the third group.
    Keywords: Data mining; debt management analysis; machine learning; taxpayer behaviour patterns; survival analysis.

  • Dimensions of anti-citizenship behaviors incidence in organizations: A Meta-analysis   Order a copy of this article
    by Fatemeh Gheitarani, Khaled Nawaser, Haniruzila Hanifah, Ali Vafaei-Zadeh 
    Abstract: Research growth in organizational behavior research, has increased the importance of paying attention to anti-citizenship behaviors. The current research with the aim of quantitative combination, has examined the results of research in effect of underlying factors of organizational anti-citizenship behaviors using meta-analysis method and CMA2 software and 55 articles during the time period of 2000-2020. The results showed a positive significant link between underlying factors of organizational anti-citizenship behaviors and occurrence of these behaviors and this influence was 0.389, 0.338, 0514 and 0.498 (structural, organizational, managerial, employment and professional and socio-economic and cultural factors). The level of connection found relating to each 4 occurrences are 68 links, 49 links, 93 links and 71 links. Findings indicate that minute attention has been paid to organizational anti-citizenship behaviors, especially to job and professional factors in research works. Research should be conducted to control and manage these behaviors more purposefully in organizations.
    Keywords: Organizational Behaviors; Anti-citizenship behaviors; Meta-Analysis; Organizational Factor; Personal Factor.

  • Hybrid of Machine Learning-Based Multiple Criteria Decision Making and Mass Balance Analysis in The New Coconut Agro-industry Product Development   Order a copy of this article
    by Siti Wardah, Moh Yani, Taufik Djatna, Marimin Marimin 
    Abstract: Product innovation has become a crucial part of the sustainability of the coconut agro-industry in Indonesia, covering upstream and downstream sides. To overcome this challenge, it is necessary to create several model stages using a hybrid method that combines machine learning based on multiple criteria decision making and mass balance analysis. The research case study was conducted in Tembilahan District, Riau Province, Indonesia, one of the primary coconut producers in Indonesia. The analysis results showed that potential products for domestic customers included coconut milk, coconut cooking oil, coconut chips, coconut jelly, coconut sugar, and virgin coconut oil. Furthermore, considering the experts, the most potential product to be developed was coconut sugar with a weight of 0.26. Prediction of coconut sugar demand reached 13,996,607 tons/year, requiring coconut sap as a raw material up to 97,976,249.
    Keywords: Coconut Agro-industry; hybrid of machine learning; mass balance analysis; multiple criteria.

  • A Novel approach of psychometric interaction and principal component for analyzing factors affecting e-wallet usage   Order a copy of this article
    by Gurpreet Singh Matharou, Simran Kaur 
    Abstract: The Republic of India has witnessed an enormous leap in financial transactions after a sudden demonetization in 2016. The study represents an in-depth analysis of the factors influencing e-wallets usage post-COVID situation covering the National Capital Region. The scientifically collected data were subjected to Pearsons correlation to recognize the correlation amongst the selected e-wallets. The usage of e-wallets is observed mainly during recharge, UPI payments, and utility payments. Through psychometric response and interaction analysis, six factors were selected and examined for data distribution and stable observation using standard deviation and variance coefficient. The coefficient of variance for six factors was observed ? 1. The weight of the factors noted to be secured way (0.184), to take advantage of cashback (0.182), low risk of theft (0.169), fast service (0.1689), ease to use (0.156), and saves time (0.139) using principal component eigenvectors analysis. Freecharge and Tez wallets reveal a maximum 99.2% correlation.
    Keywords: Wallets; Correlation; Payment; P-value; Technology.

  • Discovering Interesting Relationships in the Factors Relating to the Elderly Living Alone in Thailand Using Association Analysis   Order a copy of this article
    by Nichnan Kittiphattanabawon 
    Abstract: Many countries are facing an aging society situation. The problem of the elderly living alone happens often, even in Thailand. This research aimed to discover the factors that reflect the elderly living alone. Association analysis techniques were employed to unearth all possible factor combinations. The measures, called support and confidence, were utilized to determine the strength of the discovered factors. Association rules have also been mined to reveal the relationship between the factors affecting the elderly living alone. The study sample consisted of 36,574 elderly living alone and was received from the National Statistical Office of Thailand. The study showed that the elderly living alone often do not need a caregiver if they have supportive family members, have public healthcare benefits, earn a living from a social security fund, and have amenities in the house. The primary discovery was that they can carry out a routine without assistance. Furthermore, most importantly, they are in good health.
    Keywords: elderly; aloneness; Thailand; association analysis; association rules; interesting relationships.

  • A Novel SMS Spam Dataset and Bi-directional Transformer based Short-Text Representations for SMS Spam Detection   Order a copy of this article
    by Srishti Maheshwari, Shubhangi Aggarwal, Rishabh Kaushal 
    Abstract: Short Message Service (SMS) is a form of exchanging short messages over mobile phones without the Internet. Unfortunately, the SMS services popularity is exploited to send irrelevant and malicious messages to entrap users into scams and frauds. In this work, we investigate the performance of state-of-the-art Bi-directional encoder representations from transformers for short-text messages in SMS data. For evaluation, we curate a novel augmented SMS spam dataset by extending a classical SMS spam dataset to further categorize spam SMS messages into four fine-grained categories, namely, indecent, malicious, promotional, and updates. We perform experiments on the standard benchmark SMS dataset of spam & non-spam and on our curated multi-class SMS spam dataset. We find that BERT based short-text representations outperform the baseline traditional approach of using handcrafted text-based features by 15-30% for different machine learning algorithms in terms of accuracy on multi-class SMS spam dataset.
    Keywords: Spam Classification;Machine Learning;Word Embedding;Representation Learning;.

  • Multi-Attribute Decision-Making Application Based on Pythagorean Fuzzy Soft Expert Set   Order a copy of this article
    by Muhammad Ihsan, Muhammad Saeed, Atiqe Ur Rahman 
    Abstract: The Pythagorean fuzzy soft expert set (PFSE-set) is a parameterized family and one of the appropriate extensions of the Pythagorean fuzzy sets. It is also a generalization of intuitionistic fuzzy soft expert set, used to accurately assess deficiencies, uncertainties, and anxiety in evaluation. The most important advantage of over existing sets is that the Pythagorean fuzzy soft expert set is considered a parametric tool. The PFSE-set can accommodate more uncertainty comparative to the intuitionistic fuzzy soft expert set, this is the most important strategy to explain fuzzy information in the decision-making process. The main objective of the present research is to establish the new structure of PFSE-set along with its corresponding fundamental properties in a Pythagorean fuzzy soft expert environment. In this article, we introduce Pythagorean fuzzy soft expert set and discuss their desirable characteristics (i.e. subset, not set and equal set), results (i.e. commutative, associative, distributive and De Morgans Laws) and set-theoretic operations (i.e. complement, union intersection AND, and OR ) are explained. An algorithm is proposed to solve decision-making problem. A comparative analysis with the advantages,effectiveness,flexibility,and numerous existing studies demonstrates the effectiveness of this method.
    Keywords: Soft Expert Set;Pythagorean Fuzzy Soft Set;Pythagorean Fuzzy Soft Expert Set.

  • A game theoretic approach on the investment in economic sectors by multiplier analysis: case study of Irans economy   Order a copy of this article
    by Atieh Namazi, Mohammad Khodabakhshi, Vahid Reza Salamat 
    Abstract: There is a debate on how the amount of capital should be invested in economic sectors to achieve the most prosperity in the economy. According to the balanced growth theory, some economists believe that large investment in different economic sectors increases productivity and the production size. However, other economists cling to the belief that limiting investment in key economic sectors results in increasing production. In this article, the game theory approach is utilized by using multiplier analysis and the matrix derived from the input-output table. This method is the middle ground between the balanced and unbalanced growth theories and benefits from them. The results obtained from applying the new approach in the economy of Iran indicate that it is more profitable to invest in different economic sectors; however, the investment should be in accordance with the contribution of the economic sectors in the production process.
    Keywords: Game theory; Multi-criteria analysis; Data envelopment analysis; Input-output analysis.

Special Issue on: ICALT 2020 Decision Analytics for Logistics and Supply Chain Management New Perspectives

  • An Improved Hybrid Genetic Algorithm to solve the multi-vehicle covering tour problem with restriction on the number of vertices   Order a copy of this article
    by Manel Kammoun 
    Abstract: In this paper, we address the multi-vehicle covering tour problem (m- CTP) that is a generalization of the well know vehicle routing problem (VRP) in which we dont need to visit all customers. The objective of the m-CTP is to minimize the total routing cost and fulfill the demand of all customers such that each customer which is not included in any route must be covered. In this work, we deal with a particular case of the m-CTP where we consider only the restriction on the number of vertices in each route and the constraint on the length of the route was relaxed. This special case of the problem called m- CTP-p where each covered vertex must be within a given distance of at least a visited vertex and the number of vertices on a route does not exceed a predefined number p. We propose two approaches to solve this variant. First, we develop a Genetic Algorithm (GA) using an iterative improvement mechanism. Then, an effective Hybrid Genetic Algorithm (HGA) is developed in addition to a local search heuristic based on Variable Neighborhood Descent method to improve the solution. Extensive computational results based on benchmark instances on the m-CTP-p problem show the performance of our methods.
    Keywords: Covering; Genetic Algorithm; Variable Neighborhood Descent ;Hybrid approach.

  • Hybrid Multi Agent Framework for Green Supply Chain Management   Order a copy of this article
    by Mohamed Dif El Idrissi, Abdelkabir Charkaoui, Abdelouahed Echchatbi 
    Abstract: Environmental customer collaboration has recently attracted a big attention from researchers and industrial professionals. Many studies show that companies may reach high performance level by considering customer collaboration and environmental regulations. However, literature in the Green Supply Chain Management (GSCM) suggests having more structured collaboration and information exchange processes between Supply Chain partners based on new technologies. For this reason, this work proposes a hybrid solution based on Multi agent systems (MAS) and Mixed integer linear programming (MILP) to automate and facilitate the environmental customer collaboration process. The study demonstrates how MAS can be used in the GSCM context to improve communication and reduce complexity. An industrial study case in the automotive spare parts sector is used to demonstrate the applicability of the established MAS model.
    Keywords: green supply chain management; multi agent systems; supply chain management; customer collaboration; environmental regulation.

Special Issue on: ETMS2018 and ETMS2019 Performance Analysis and Evaluation in the Business Environment

  • Analysis of the relationship between sustainability and software performance   Order a copy of this article
    by Bersam Bolat 
    Abstract: Sustainability problems are getting more and more critical and increasingly threatening human life day by day. Software, which is developing rapidly and entering into every aspect of our lives, is one of the most fundamental components of the technological society. The widespread use of software applications and limited natural resources have led researchers to focus on research that will ensure sustainability in the software development process. In this study, we conducted a questionnaire study concerning the sustainability factors that affect the software development process. Then the effect of these factors and the level of education, age, and experience of the people involved in the software development process on the software performance was investigated. As a result, it has been determined that the factors affecting the software development process in terms of sustainability and the descriptive attributes of the individual have an effect on software performance.
    Keywords: Sustainability; software development process; software performance; path analysis.

  • An Unsupervised Learning Approach to Basket Type Definition in FMCG Sector Based on Household Panel Data   Order a copy of this article
    by Ahmet Talha Yigit, Tolga Kaya, Utku Dogruak 
    Abstract: The purpose of this study is to propose a clustering based modeling approach to define the main groups of baskets in Turkish fast-moving consumer goods (FMCG) industry regarding the sectoral decomposition, the total value and the size of the baskets. To do this, based on the information regarding nearly three million basket purchases made in 2018 by more than 14 thousand households, alternative unsupervised learning methods such as K-means, and Gaussian mixtures are implemented to obtain and define the basket patterns in Turkey. Additionally, a supervised ensemble learning approach based on XGBoost method is also selected among fully connected neural networks and random forest models to assign the new baskets into the existing clusters. Results show that, SaveTheDay, CareTrip, Breakfast, SuperMain and MeatWalk are among the most important basket types in Turkish FMCG sector.
    Keywords: Basket Analysis; Cluster Analysis; K-Means; FMCG; Supervised Learning; Consumer Panel; Ensemble Learning; Deep Learning.

  • Examination Gym Centers Design Criteria using multicriteria decision analysis methodologies   Order a copy of this article
    by Cansu Ergun, Sumeyra Elif Erdogan, Gokhan Aldemir, Ferhan Cebi 
    Abstract: Designing a gym and selecting its atmospheric elements are time-consuming and difficult to change. Therefore, the aim of our study is to provide a beneficial and different perspective for operators who are considering designing a gym. Our study starts with a participatory observation in order to examine consumer behavior in the natural state after the studies in the literature are examined. An interview and a survey study are conducted to define the crucial criteria for the consumer in the gym. A hierarchy is created, and a paired comparison is made to illustrate the importance levels. Accordingly, analytical hierarchy process (AHP) is applied and the criteria are sorted according to their importance. Different concepts are formed by giving different values to the criteria. The concepts with the highest score are determined by the concept selection matrix and their architectural designs are made by programs. The most optimal design is determined by the concept test.
    Keywords: multicriteria decision making; gym design; AHP; consumer behavior.