scholarly journals NEWS-BASED SOFT INFORMATION AS A CORPORATE COMPETITIVE ADVANTAGE

2019 ◽  
Vol 26 (1) ◽  
pp. 48-70 ◽  
Author(s):  
Ming-Fu Hsu ◽  
Te-Min Chang ◽  
Sin-Jin Lin
Keyword(s):  
Decision Making ◽  
Soft Information ◽  
Forecasting Model ◽  
Data Envelopment ◽  
Financial Indicators ◽  
Promising Alternative ◽  
Hard Information ◽  
Different Types ◽  
Performance Forecasting ◽  
Information Supply Chain

This study establishes a decision-making conceptual architecture that evaluates decision making units (DMUs) from numerous aspects. The architecture combines financial indicators together with a variety of data envelopment analysis (DEA) specifications to encapsulate more information to give a complete picture of a corporate’s operation. To make outcomes more accessible to non-specialists, multidimensional scaling (MDS) was performed to visualize the data. Most previous studies on forecasting model construction have relied heavily on hard information, with quite a few works taking into consideration soft information, which contains much denser and more diverse messages than hard information. To overcome this challenge, we consider two different types of soft information: supply chain influential indicator (SCI) and sentimental indicator (STI). SCI is computed by joint utilization of text mining (TM) and social network analysis (SNA), with TM identifying the corporate’s SC relationships from news articles and SNA to determining their impact on the network. STI is extracted from an accounting narrative so as to comprehensively illustrate the relationships between pervious and future performances. The analyzed outcomes are then fed into an artificial intelligence (AI)-based technique to construct the forecasting model. The introduced model, examined by real cases, is a promising alternative for performance forecasting.

scholarly journals Synergetic Evaluation of Project Portfolio Configuration Based on Data Envelopment Analysis

2017 ◽  
Author(s):  
Libiao Bai ◽  
Sijun Bai ◽  
Tiantian Zhai
Keyword(s):  
Decision Making ◽  
Sustainable Development ◽  
Data Envelopment Analysis ◽  
Computational Experiment ◽  
Evaluation Model ◽  
Evaluation Index System ◽  
Data Envelopment ◽  
Project Portfolio ◽  
Financial Indicators ◽  
Decision Making Unit

Project portfolio configuration (PPC) is an important approach to maintain the sustainable development of enterprises and achieve organizations’ strategy. However, the synergetic efficacy of PPC which determines the degree of the project's strategic objectives achieved is a fuzzy problem and hard to be measured. To solve this problem, this paper takes the data envelopment analysis (DEA) as the tool to measure the efficacy of PPC under deterministic conditions. First, a portfolio evaluation index system which takes financial indicators and non-financial indicators into consideration is developed based on the review of the literature; Second, an evaluation model based on DEA is built to reduce the number of decision making-unit with the perspective of synergetic theory; Then, a computational experiment is studied to verify the feasibility of this proposed model. The results of this computational experiment show that this model can effectively narrow scope of decision-making, improve the decision-making level and provide a reference to decide the DEA effective project portfolio decision-making unit. To our knowledge, this study is the first time to apply the notion of synergetic efficacy and DEA to the PPC domain. It is hoped that this paper may shed lights on any further study about PPC and enterprise competitiveness of sustainable development.

A dynamic network-based decision architecture for performance evaluation and improvement

2020 ◽  
Vol 39 (3) ◽  
pp. 4299-4311
Author(s):  
Kuang-Hua Hu ◽  
Sin-Jin Lin ◽  
Ming-Fu Hsu ◽  
Fu-Hsiang Chen
Keyword(s):  
Performance Evaluation ◽  
Dynamic Network ◽  
Knowledge Generation ◽  
Forecasting Model ◽  
Integration Step ◽  
Stage Model ◽  
Functional Link ◽  
Promising Alternative ◽  
Performance Forecasting ◽  
Set Up

This study introduces a dynamic decision architecture that involves three steps for corporate performance forecasting as such bad performance has been widely recognized as the main trigger for a financial crisis. Step-1: performance evaluation and integration; Step-2: forecasting model construction; and Step-3: knowledge generation. First, the decision making trial and evaluation laboratory (DEMATEL) is incorporated with balanced scorecards (BSC) to discover the complicated/intertwined relationships among BSC’s four perspectives. To overcome the problem of BSC that cannot yield a specific direction, the study then employs data envelopment analysis (DEA). Apart from previous studies that utilize an all embracing one-stage model, this set-up extends it to a two-stage model that calculates the performance scores for each BSC perspective. By doing so, users can realize a company’s weaknesses and strengths and identify possible paths toward efficiency. VIKOR is subsequently used to summarize all scores into a synthesized one. Second, the analyzed outcomes are then fed into random vector functional-link (RVFL) networks to establish the forecasting model. To handle the opaque nature of RVFL, the instance learning method is conducted to extract the implicit decision logics. Finally, the introduced architecture, tested by real cases, offers a promising alternative for performance evaluation and forecasting.

scholarly journals A Hybrid Model for Addressing the Relationship between Financial Performance and Sustainable Development

2019 ◽  
Vol 11 (10) ◽  
pp. 2899 ◽  
Author(s):  
Yanfang Zhang ◽  
Mushang Lee
Keyword(s):  
Financial Performance ◽  
Operating Performance ◽  
Principal Component ◽  
Data Envelopment ◽  
Visualization Technique ◽  
Robust Principal Component Analysis ◽  
Promising Alternative ◽  
Market Participants ◽  
Performance Forecasting ◽  
The Relationship

Measuring financial performance has become an essential topic due to the potential decimating impacts on the corporation itself as well as to whole societies during financial turmoil. In order to provide an overarching description of the multidimensional nature for measuring a corporation’s operations, it is preferable to employ data envelopment analysis (DEA). Different from prior research that merely focuses on a singular DEA performance rank, this study extends it to multiple DEA specifications (i.e., it combines inputs and outputs in several different ways) so as to make judgments more complete and robust. We also execute fuzzy visualization technique (i.e., nonlinear fuzzy robust principal component analysis, NFRPCA) to represent the main characteristics of data so that non-specialists can have better access to the results. The analyzed result is then fed into the restricted Boltzmann machine (RBM) to establish a model to forecast a firm’s operating performance. Even a fraction of accuracy improvement can result in considerable future savings to a firm and investors. When examined using real cases, the model is a promising alternative for operating performance forecasting and can assist both internal and external market participants.

scholarly journals An Advanced Decision Making Framework via Joint Utilization of Context-Dependent Data Envelopment Analysis and Sentimental Messages

Axioms ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 179
Author(s):  
Hsueh-Li Huang ◽  
Sin-Jin Lin ◽  
Ming-Fu Hsu
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Corporate Performance ◽  
Dependent Data ◽  
Twin Support Vector Machine ◽  
Support Vector ◽  
Data Envelopment ◽  
Context Dependent ◽  
Decision Making Framework ◽  
Performance Forecasting

Compared to widely examined topics in the related literature, such as financial crises/difficulties in accurate prediction, studies on corporate performance forecasting are quite scarce. To fill the research gap, this study introduces an advanced decision making framework that incorporates context-dependent data envelopment analysis (CD-DEA), fuzzy robust principal component analysis (FRPCA), latent Dirichlet allocation (LDA), and stochastic gradient twin support vector machine (SGTSVM) for corporate performance forecasting. Ratio analysis with the merits of easy-to-use and intuitiveness plays an essential role in performance analysis, but it typically has one input variable and one output variable, which is unable to appropriately depict the inherent status of a corporate’s operations. To combat this, we consider CD-DEA as it can handle multiple input and multiple output variables simultaneously and yields an attainable target to analyze decision making units (DMUs) when the data present great variations. To strengthen the discriminant ability of CD-DEA, we also conduct FRPCA, and because numerical messages based on historical principles normally cannot transmit future corporate messages, we execute LDA to decompose the accounting narratives into many topics and preserve those topics that are relevant to corporate operations. Sequentially, the process matches the preserved topics with a sentimental dictionary to exploit the hidden sentiments in each topic. The analyzed data are then fed into SGTSVM to construct the forecasting model. The result herein reveals that the introduced decision making framework is a promising alternative for performance forecasting.

Quota Women Are Threatening to Men

2017 ◽  
Vol 76 (3) ◽  
pp. 107-116 ◽  
Author(s):  
Klea Faniko ◽  
Till Burckhardt ◽  
Oriane Sarrasin ◽  
Fabio Lorenzi-Cioldi ◽  
Siri Øyslebø Sørensen ◽  
...  
Keyword(s):  
Decision Making ◽  
Organizational Performance ◽  
Gender Stereotypes ◽  
Perceived Threat ◽  
Preferential Treatment ◽  
Public Sector Employees ◽  
Different Types ◽  
Low Levels ◽  
Women In Decision Making ◽  
The Impact

Abstract. Two studies carried out among Albanian public-sector employees examined the impact of different types of affirmative action policies (AAPs) on (counter)stereotypical perceptions of women in decision-making positions. Study 1 (N = 178) revealed that participants – especially women – perceived women in decision-making positions as more masculine (i.e., agentic) than feminine (i.e., communal). Study 2 (N = 239) showed that different types of AA had different effects on the attribution of gender stereotypes to AAP beneficiaries: Women benefiting from a quota policy were perceived as being more communal than agentic, while those benefiting from weak preferential treatment were perceived as being more agentic than communal. Furthermore, we examined how the belief that AAPs threaten men’s access to decision-making positions influenced the attribution of these traits to AAP beneficiaries. The results showed that men who reported high levels of perceived threat, as compared to men who reported low levels of perceived threat, attributed more communal than agentic traits to the beneficiaries of quotas. These findings suggest that AAPs may have created a backlash against its beneficiaries by emphasizing gender-stereotypical or counterstereotypical traits. Thus, the framing of AAPs, for instance, as a matter of enhancing organizational performance, in the process of policy making and implementation, may be a crucial tool to countering potential backlash.

Relative Efficiency of Decision Making Units Producing Both Desirable and Undesirable Outputs: A Case of Textile Processing Units in Pakistan.

2011 ◽  
Vol 50 (4II) ◽  
pp. 685-698
Author(s):  
Samina Khalil
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Relative Efficiency ◽  
Value Added ◽  
Data Envelopment ◽  
Undesirable Outputs ◽  
Textile Processing ◽  
Decision Making Unit ◽  
Desirable And Undesirable Outputs ◽  
Decision Making Units

This paper aims at measuring the relative efficiency of the most polluting industry in terms of water pollution in Pakistan. The textile processing is country‘s leading sub sector in textile manufacturing with regard to value added production, export, employment, and foreign exchange earnings. The data envelopment analysis technique is employed to estimate the relative efficiency of decision making units that uses several inputs to produce desirable and undesirable outputs. The efficiency scores of all manufacturing units exhibit the environmental consciousness of few producers is which may be due to state regulations to control pollution but overall the situation is far from satisfactory. Effective measures and instruments are still needed to check the rising pollution levels in water resources discharged by textile processing industry of the country. JEL classification: L67, Q53 Keywords: Data Envelopment Analysis (DEA), Decision Making Unit (DMU), Relative Efficiency, Undesirable Output

Design, Development and Comparison of Heuristic Driven Algorithms Based on the Crossed Domain Products’ Reviews for User’s Summarization

2020 ◽  
Vol 13 (5) ◽  
pp. 884-892
Author(s):  
Sartaj Ahmad ◽  
Ashutosh Gupta ◽  
Neeraj Kumar Gupta
Keyword(s):  
Decision Making ◽  
Online Shopping ◽  
Recent Time ◽  
Design Development ◽  
Different Types ◽  
Feature Based ◽  
Use Of Internet ◽  
Day By Day ◽  
Better Than

Background: In recent time, people love online shopping but before any shopping feedbacks or reviews always required. These feedbacks help customers in decision making for buying any product or availing any service. In the country like India this trend of online shopping is increasing very rapidly because awareness and the use of internet which is increasing day by day. As result numbers of customers and their feedbacks are also increasing. It is creating a problem that how to read all reviews manually. So there should be some computerized mechanism that provides customers a summary without spending time in reading feedbacks. Besides big number of reviews another problem is that reviews are not structured. Objective: In this paper, we try to design, implement and compare two algorithms with manual approach for the crossed domain Product’s reviews. Methods: Lexicon based model is used and different types of reviews are tested and analyzed to check the performance of these algorithms. Results: Algorithm based on opinions and feature based opinions are designed, implemented, applied and compared with the manual results and it is found that algorithm # 2 is performing better than algorithm # 1 and near to manual results. Conclusion: Algorithm # 2 is found better on the different product’s reviews and still to be applied on other product’s reviews to enhance its scope. Finally, it will be helpful to automate existing manual process.

An algebraic approach to N-soft sets with application in decision-making using TOPSIS

2021 ◽  
pp. 1-21
Author(s):  
Muhammad Shabir ◽  
Rimsha Mushtaq ◽  
Munazza Naz
Keyword(s):  
Decision Making ◽  
Mathematical Models ◽  
Real World ◽  
Algebraic Approach ◽  
Multi Criteria Decision Making ◽  
Commutative Monoids ◽  
Soft Sets ◽  
Algebraic Properties ◽  
Different Types ◽  
Selection Of

In this paper, we focus on two main objectives. Firstly, we define some binary and unary operations on N-soft sets and study their algebraic properties. In unary operations, three different types of complements are studied. We prove De Morgan’s laws concerning top complements and for bottom complements for N-soft sets where N is fixed and provide a counterexample to show that De Morgan’s laws do not hold if we take different N. Then, we study different collections of N-soft sets which become idempotent commutative monoids and consequently show, that, these monoids give rise to hemirings of N-soft sets. Some of these hemirings are turned out as lattices. Finally, we show that the collection of all N-soft sets with full parameter set E and collection of all N-soft sets with parameter subset A are Stone Algebras. The second objective is to integrate the well-known technique of TOPSIS and N-soft set-based mathematical models from the real world. We discuss a hybrid model of multi-criteria decision-making combining the TOPSIS and N-soft sets and present an algorithm with implementation on the selection of the best model of laptop.

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