Back-propagation neural network based importance–performance analysis for determining critical service attributes

Deng, Wei‐Jaw; Chen, Wen‐Chin; Wen, Peng

doi:10.1016/j.eswa.2006.12.016

Cited by 130 publications

(87 citation statements)

References 64 publications

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“…is an essential two-part question. It can be answered with respect to the economic value of assets [62]. To find the main factors that appeal to tourists is relevant to economic value and a reflection of the cultural value of the sites: Which factors in heritage buildings would be the main factors appealing to tourists?…”

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confidence: 99%

An Empirical Investigation of Architectural Heritage Management Implications for Tourism: The Case of Portugal

2018

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Abstract:The aims of this study are manifold. First, to investigate the potentials of architectural heritage in the context of tourism destination development, as well as examine public sector policies and make plans toward the preservation of these resources. Secondly, to appraise the outcome of preservation and its implications for tourism. The study is an effort to explore and understand the interrelationships between tourism and architectural heritage sites through tourist image and perception. For the purposes of this research, numerous heritage sites were sampled in Portugal. A mixed research method was utilized to gauge tourists' image/perception of heritage resources, and impact (quantitative approach). A qualitative approach was utilized to assess the priority of tourists in their visits and public-sector policies toward heritage resource management and planning. The fuzzy logic method was used to assess the architectural value and the tourist and preservation potential of historical buildings in Porto/Aveiro. The contribution and implications of the study are also explained. The results revealed that architectural heritage resources have the most appeal to tourists. The study to date demonstrates the architectural value and tourist and preservation potential of the buildings observed via evaluation by fuzzy logic methods.

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confidence: 99%

An Empirical Investigation of Architectural Heritage Management Implications for Tourism: The Case of Portugal

2018

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“…Then the performances of several network configurations (with 2, 5, 7, 10, 12-13 hidden neurons) are measured by three indicators: the mean absolute error (MAE), the RMSE and goodness-of-fit (R 2 ). Note that, the MAE and RMSE approach to 0 indicate that BPNN model has precise prediction ability whereas R 2 close to 1 indicates that BPNN model has excellent goodness-of-fit [9]. The best performing network is the network with the two hidden neurons (Training-MAE = 0.588, RMSE = 0.770 and R 2 = 0.442; Testing-MAE = 0.617, RMSE = 0.802) since its RMSE value in training being the lowest one and its R 2 value being the highest one.…”

Section: Methodsologymentioning

confidence: 99%

“…Section 4 concludes the paper. [9] 20-31-1 Hyperbolic tangent/ Hyperbolic tangent Deng and Pei (2009) [11] 20-26-1 Hyperbolic tangent/ Hyperbolic tangent Chen, Lin and Lin (2010) [12] 7-15-1 Sigmoid/ Sigmoid Mikulić and Prebežac (2012) [13] 8-10-1 Hyperbolic tangent/ Hyperbolic tangent Hosseini and Bideh (2013) [14] 7-7-1 Hyperbolic tangent/ Hyperbolic tangent Krešić, Mikulić, and Kožić (2013) [10] 11-11-1 Hyperbolic tangent/ Identity…”

Section: Introductionmentioning

confidence: 99%

“…Recently, back-propagation neural network (BPNN) has become an alternative method for implicitly deriving importance which is called BPNN based IPA. It is reported that the BPNN based IPA approach yields significant improvement in estimating of importance compared to conventional IPA and regression-based IPA approaches [9], [10], thereby several researchers [11]- [14] have applied and extended BPNN based IPA for a customer satisfaction improvement.…”

Section: Introductionmentioning

confidence: 99%

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Attribute Importance Measure Based on Back-Propagation Neural Network: An Empirical Study

Phadermrod¹,

Crowder²,

Wills³

2015

IJCEE

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Over the years, many different importance-performance analysis (IPA) variations have emerged as it is a primary tool for analyzing customer satisfaction. One of the recent IPA variations is back-propagation neural network based importance-performance analysis (BPNN based IPA) that utilizes BPNN to measure Importance. To investigate the performance of the BPNN based IPA, the authors compared two types of BPNN models that have one and multiple output neurons referred as BPNN (regression) and BPNN (classification) respectively, with multiple linear regression (MLR). This comparison demonstrates that the BPNN (regression) does not outperform MLR in term of model accuracy and training time, yet BPNN (classification) is superior to MLR and BPNN (regression) in term of model accuracy and predictive power. This finding leads to a reconsideration of the BPNN model used in the present BPNN based IPA.

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“…Tenfold cross validation process was used for train and test process. For determining the relative importance of each input to the output several studies have performed [50][51][52]. Accuracy of ANN is compared with Multiple Linear Regression (MLR) and Nonlinear Least Square Fitting (NLSF) methods.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Relative Importance of Intellectual Capital Determinants, Using an Artificial Neural Network Approach: Case Study - Mellat Bank of Iran

Shad¹,

Nedjati²,

Shafiei³

2017

J Bus Fin Aff

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it is essential to offer valuations associate with shares of intangible properties, since administrators require to spot their company's most precious resources. Furthermore, focusing on how much important these assets are, it is valuable to determine the intangible assets' status in value creation procedure. When generating value, competitive advantage is accomplished with the ideal use of intangibles [13]. Whenever a company's administration is not conscious of what its intangible properties are, it could miss beneficial opportunities depending on these intangible capitals, since key decisions will made without considering all possible variables [14].The main aim of this paper is identifying and quantifying the relative importance of various IC determinants in Mellat Bank of Iran. The ANN and Hierarchy Process (AHP) methods are used to determine and quantify the three main determinants of the IC. The ANN results are compared with direct evaluating of human by AHP. Many scholars have examined categorization models for projects selection and evaluation by Analytic Hierarchy Process method [15][16][17][18][19][20][21] and Mehralian et al. [22], used fuzzy TOPSIS for Prioritization of IC indicators; But innovation of this research is evaluating the relative importance of IC factors by ANN. Intellectual capital backgroundBy a close review of Intellectual Capital literature, it is observed that all existing models of IC evaluation are identified three similar determinants for IC [23]. Based on some pioneers opinion IC is composed of three main components [11,[24][25][26][27][28] IntroductionBy the growth of the modern economy, that is primarily powered by knowledge and information, is credited towards the improved dominance of intellectual capital (IC) as an investigation subject [1]. Drucker was pioneered in appearance of a novel economy, known as the knowledge contemporary society [2]. He states that in this modern society, knowledge is not only an additional source together with the conventional aspects of production (land, labor, and capital) however it is typically the solely significant resource for manufacturing [3]. In the last decade, there is a more focus on the knowledge participation in global competitiveness in 21st century. Knowledge is known as an indelible strategic resource to gain and sustain reasonably competitive benefits [4,5]. Since IC is based on knowledge, it is the central part of organizational capabilities. The necessity of improvement and upgrading this knowledge has never been focused. Core competencies represent organizational excellence scopes, which are resulted from capabilities combinations [6].In recent century, IC has become the main asset of large international companies. Tangible properties include physical property (machines, land, and buildings) and current property (inventory). The other category comprises intangible properties: individuals as well as their skills, business procedures and market assets including customer loyalty, recurring business reputation, and so on [7]....

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Back-propagation neural network based importance–performance analysis for determining critical service attributes

Cited by 130 publications

References 64 publications

An Empirical Investigation of Architectural Heritage Management Implications for Tourism: The Case of Portugal

An Empirical Investigation of Architectural Heritage Management Implications for Tourism: The Case of Portugal

Attribute Importance Measure Based on Back-Propagation Neural Network: An Empirical Study

Relative Importance of Intellectual Capital Determinants, Using an Artificial Neural Network Approach: Case Study - Mellat Bank of Iran

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