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The purpose of this paper is to present and test a model that seeks to answer Robey et al.'s challenge that “ERP's critical success factors offer few insights beyond conventional wisdom.” The model proposes that many so‐called critical success factors (CSFs) affect organizational benefits from enterprise systems use (OBESU) through their impact on three outcomes of an enterprise systems (ES) implementation process, namely functional fit, overcoming organizational inertia, and delivering a working system.

The model is tested using content analysis of 133 customer presentations at SAP's April 2007 Sapphire USA Conference.

The benefit‐driver approach appears to provide at least one good answer to Robey et al.'s challenge.

It seems likely that the model is valid for most large Western organizations implementing ES.

The model provides new insights into how and why the CSFs affect benefits from ES.

No prior study to the best of one's knowledge has sought to explain in such depth how ES project CSFs affect OBESU.

Due to the increasing size and complexity of construction projects, construction engineering and management involves the coordination of many complex and dynamic processes and relies on the analysis of uncertain, imprecise and incomplete information, including subjective and linguistically expressed information. Various modelling and computing techniques have been used by construction researchers and applied to practical construction problems in order to overcome these challenges, including fuzzy hybrid techniques. Fuzzy hybrid techniques combine the human-like reasoning capabilities of fuzzy logic with the capabilities of other techniques, such as optimization, machine learning, multi-criteria decision-making (MCDM) and simulation, to capitalise on their strengths and overcome their limitations. Based on a review of construction literature, this chapter identifies the most common types of fuzzy hybrid techniques applied to construction problems and reviews selected papers in each category of fuzzy hybrid technique to illustrate their capabilities for addressing construction challenges. Finally, this chapter discusses areas for future development of fuzzy hybrid techniques that will increase their capabilities for solving construction-related problems. The contributions of this chapter are threefold: (1) the limitations of some standard techniques for solving construction problems are discussed, as are the ways that fuzzy methods have been hybridized with these techniques in order to address their limitations; (2) a review of existing applications of fuzzy hybrid techniques in construction is provided in order to illustrate the capabilities of these techniques for solving a variety of construction problems and (3) potential improvements in each category of fuzzy hybrid technique in construction are provided, as areas for future research.

This paper gives a review of the finite element techniques (FE) applied in the area of material processing. The latest trends in metal forming, non‐metal forming, powder metallurgy and composite material processing are briefly discussed. The range of applications of finite elements on these subjects is extremely wide and cannot be presented in a single paper; therefore the aim of the paper is to give FE researchers/users only an encyclopaedic view of the different possibilities that exist today in the various fields mentioned above. An appendix included at the end of the paper presents a bibliography on finite element applications in material processing for 1994‐1996, where 1,370 references are listed. This bibliography is an updating of the paper written by Brannberg and Mackerle which has been published in Engineering Computations, Vol. 11 No. 5, 1994, pp. 413‐55.

It evaluated the seismic vulnerability based on fewer factors by presenting the effectiveness of seismic and structural parameters. The proposed method first demonstrated the effect of earthquake ground motion inputs on predicting the slight, moderate, extensive and collapse limit states and confirmed the method’s efficiency. The fragility curves illustrated with the approach of the present study are compared with the traditional techniques, such as analytical methods.

Based on the different macro- and micro-structural characteristics and the earthquake records, achieving a certain relation from regression analysis using artificial neural networks (ANNs) is difficult. With this background in mind, the present study aimed to compare the proposed model of the considered bridge with the analytical and ANN results. After statistical analysis and estimation of the most effective factors in predicting responses from the proposed approach, two-parameter two- and three-dimensional fragility curves are extracted.

Due to the structural differences between horizontally curved bridges, the methodology does not require any classification of bridge classes to predict responses. For a specific L/R of the bridge, the parameters cumulative absolute velocity (CAV) and Sa (T1) can provide a good estimate of the seismic fragility curves, and the proposed approach with less parameter assignment also leads to good results. With less computational effort, fragility curves can be illustrated.

The proposed method demonstrated the ability to accurately estimate the occurrence and non-occurrence limit states while maintaining a low computational cost and the derivation of a curved bridge’s seismic fragility curve.

Supply chains need to be made viable in this volatile and competitive market, which could be possible through digitalization. This study is an attempt to explore the role of Industry 4.0, smart supply chain, supply chain agility and supply chain resilience on sustainable business performance from the lens of natural resource-based view.

The study tests the proposed model using a covariance-based structural equation modelling and further investigates the ranking of each construct using the artificial neural networks approach in AMOS and SPSS respectively. A total of 234 respondents selected using purposive sampling aided in capturing the industry practices across supply chains in the UK. The full collinearity test was carried out to study the common method bias and the content validity was carried out using the item content validity index and scale content validity index. The convergent and discriminant validity of the constructs and mediation study was carried out in SPSS and AMOS V.23.

The results are overtly inferring the significant impact of Industry 4.0 practices on creating smart and ultimately sustainable supply chains. A partial relationship is established between Industry 4.0 and supply chain agility through a smart supply chain. This work empirically reinstates the combined significance of green practices, Industry 4.0, smart supply chain, supply chain agility and supply chain resilience on sustainable business value. The study also uses the ANN approach to determine the relative importance of each significant variable found in SEM analysis. ANN determines the ranking among the significant variables, i.e. supply chain resilience > green practices > Industry 4.0> smart supply chain > supply chain agility presented in descending order.

This study is a novel attempt to establish the role of digitalization in SCs for attaining sustainable business value, providing empirical support to the mediating role of supply chain agility, supply chain resilience and smart supply chain and manifests a significant integrated framework. This work reinforces the integrated model that combines all the constructs dealt with in silos so far in prior literature.

In today’s globalized and heavily industrialized economy, sustainability issues that negatively affect the human population and external environment are on the rise. This study aims to investigate a synergistic combination of supply chain management and quality management practices in strengthening the sustainability performance of Malaysian manufacturing firms.

A total sample of 177 usable surveys was collected. Given the contributions and acceptability of the artificial neural network (ANN) approach in evaluating the findings of this study, this study uses ANN to measure the relationship between each predictor (i.e. supply chain integration [SCI], quality leadership [QL], supplier focus [SF], customer focus (CF) and information sharing [IS]) and the dependent variable (i.e. sustainability performance). Via sensitivity analysis, the relative significance of each predictor variable is ranked based on the normalized importance value.

The sensitivity analysis indicates that CF has the greatest effect on sustainability performance (SP) with 100% normalized relative importance, followed by QL (75%), IS (61.5%), SF (57.3%) and SCI (46.7%).

The findings of this study have the potential to provide valuable guidance and insights that can help all manufacturing firms enhance their SP from the optimum combination of the selected SCQM practices with a focus on sustainability.

Gives a bibliographical review of the finite element methods (FEMs) applied for the linear and nonlinear, static and dynamic analyses of basic structural elements from the theoretical as well as practical points of view. The range of applications of FEMs in this area is wide and cannot be presented in a single paper; therefore aims to give the reader an encyclopaedic view on the subject. The bibliography at the end of the paper contains 2,025 references to papers, conference proceedings and theses/dissertations dealing with the analysis of beams, columns, rods, bars, cables, discs, blades, shafts, membranes, plates and shells that were published in 1992‐1995.

This paper presents a review concerning the analytical and inverse methods of small punch creep test (SPCT) in order to evaluate the mechanical property of component material at elevated temperature.

In this work, the effects of temperature, specimen size and shape on material properties are mainly discussed using the finite element (FE) method. The analytical approaches including membrane stretching, empirical or semi-empirical solutions that are currently used for data interpretation have been presented.

The state-of-the-art research progress on the inverse method, such as non-linear optimization program and neutral network, is critically reviewed. The capabilities of the inverse technique, the uniqueness of the solution and future development are discussed.

The state-of-the-art research progress on the inverse method such as non-linear optimization program and neutral network is critically reviewed. The capabilities of the inverse technique, the uniqueness of the solution and future development are discussed.

Several review articles have been published within the Artificial Intelligence (AI) literature that have explored a range of applications within the tourism and hospitality sectors. However, how efficiently the applied AI methods and algorithms have performed with respect to the type of applications and the multimodal sets of data domains have not yet been reviewed. Therefore, this paper aims to review and analyse the established AI methods in hospitality/tourism, ranging from data modelling for demand forecasting, tourism destination and behaviour pattern to enhanced customer service and experience.

The approach was to systematically review the relationship between AI methods and hospitality/tourism through a comprehensive literature review of papers published between 2010 and 2021. In total, 146 articles were identified and then critically analysed through content analysis into themes, including “AI methods” and “AI applications”.

The review discovered new knowledge in identifying AI methods concerning the settings and available multimodal data sets in hospitality and tourism. Moreover, AI applications fostering the tourism/hospitality industries were identified. It also proposes novel personalised AI modelling development for smart tourism platforms to precisely predict tourism choice behaviour patterns.

This review paper offers researchers and practitioners a broad understanding of the proper selection of AI methods that can potentially improve decision-making and decision-support in the tourism/hospitality industries.

This paper contributes to the tourism/hospitality literature with an interdisciplinary approach that reflects on theoretical/practical developments for data collection, data analysis and data modelling using AI-driven technology.

The paper aims to provide an adaptive neural network controller for permanent magnet synchronous motor (PMSM) under direct torque control (DTC) algorithm to minimize the torque ripple and EMI noise.

The design methodology is based on vector control used for electrical machines. MATLAB simulations supported with experimental study under C++ are used.

The simulated and experimental results show that considerable torque ripple as well as current ripple and EMI noise reduction can be achieved by utilizing adaptive neural switching algorithm to fire the inverter supplying the PMSM.

This research is limited to PMSM, however the research can be extended to include other AC motors as well. In addition, the following points can be studied: the effects of harmonics in control signals on the torque ripple can be analyzed; the actual mathematical relation between the torque and flux ripple can be studied to set the flux and torque bands width in reasonable value; different neural network algorithms can be applied to the system to solve the similar problems.

Based on existing DTC control system, it is only required to change the software switching algorithm, to provide smooth torque, given that the switching frequency of the inverter module is more than or equal to 15 MHz and the system is supplied with timers. In addition a relatively higher DC voltage may be required to achieve higher speed compared with the traditional DTC.

In this paper, the stator flux position, and errors due to deviations from reference values of the torque and stator flux are used to select two active vectors while at the same time the absolute value of the torque error and the stator flux position are used neural network structure to adapt the switching of the inverter in order to control the applied average voltage level in such a way as to minimize the torque ripple, so instead of fixed time table structure, a neural network controller is used to calculate the switching time for the selected vectors and no PI controller is used as the case in the traditional space vector modulation. This work is directed to motor drive system designers who seek highly smooth torque performance with EMI noise reduction.