Search results

This study aims to examine the potential of two artificial intelligence (AI)-based algorithms, namely, adaptive neuro-fuzzy inference system (ANFIS) and gene expression programming (GEP), for indirect estimation of the ultimate bearing capacity (q_ult) of rock foundations, which is a considerable civil and geotechnical engineering problem.

The input-processing-output procedures taking place in ANFIS and GEP are represented for developing predictive models. The great importance of simultaneously considering both qualitative and quantitative parameters for indirect estimation of q_ult is taken into account and explained. This issue can be considered as a remarkable merit of using AI-based approaches. Furthermore, the evaluation procedure of various models from both engineering and accuracy viewpoints is also demonstrated in this study.

A new and explicit formula generated by GEP is proposed for the estimation of the q_ult of rock foundations, which can be used for further engineering aims. It is also presented that although the ANFIS approach can predict the output with a high degree of accuracy, the obtained model might be a black-box. The results of model performance analyses confirm that ANFIS and GEP can be used as alternative and useful approaches over previous methods for modeling and prediction problems.

The superiorities and weaknesses of GEP and ANFIS techniques for the numerical analysis of engineering problems are expressed and the performance of their obtained models is compared to those provided by other approaches in the literature. The findings of this research provide the researchers with a better insight to using AI techniques for resolving complicated problems.

The purpose of this qualitative study is to investigate how young female customers establish psychological relationships with small- to medium-scale retail stores over time forming purchase intentions, actual purchase patterns and repurchase behaviour. Role of various customer typologies was also considered.

A case study approach was implemented to collect and analyse data, where data was collected from 20 young female customers and ten clothing retailers using purposive sampling via semi-structured interviews. Interviews with customers were conducted in a place of their choice such as in a coffee shop, whereas data from retailers were collected in the retail stores. Both online and offline retail patronage was considered to incorporate the growing tendency towards online shopping. Results were analysed using thematic analysis.

This study managed to reveal a number of interesting findings on how female customers form and develop psychological relationships with clothing retailers over time that ultimately builds customer loyalty. Customer behaviour in pre-purchase, purchase and re-purchase stages can significantly vary according to their individual perceptions, whereas they have a few favourite clothing brands that they frequently shop for. Preference for online shopping was found to be minimal, most of them enjoying in store experiences. Further, word of mouth and unique designs emerged as key contributors in establishing retail brand loyalty.

This paper provides better insights for clothing retailers and industry practitioners in understanding how customer perceptions affect clothing purchase decisions.

This paper contributes to the retail literature by emphasizing on various elements that should be amalgamated through proper synthesis to serve customers. The research is unique as it analyses customer behaviour using a recreational activity model as opposed to marketing models to demonstrate how customers develop relationships with retail brands overtime.

This paper aims to identify the unidimensionality and reliability of 84 factors that influence the performance of construction projects and develop a confirmatory factor analysis (CFA) model.

The study adopted a deductive research approach and started by identifying the positive factors that influence construction project performance. This was followed by the modification of the identified factors. After that, a questionnaire was developed out of the factors for data collection. Exploratory factor analysis was used to establish the factor structure of the positive factors, and this was verified using CFA afterwards. A model fit analysis was performed to determine the goodness of fit of the hypothesised model, followed by the development of the confirmatory model.

The study demonstrated substantial correlation in the data, sufficient unidimensionality and internal reliability. In addition, the estimated fit indices suggested that the postulated model adequately described the sample data.

The paper revealed that performance can be enhanced if stakeholders identify and leverage the positive factors influencing performance. The paper suggests that project stakeholders, particularly government, project owners, consultants and construction firms, can improve project performance by critically examining economic and financial systems (EFS), regulation and policy-making systems (RPS), effective management practices (EMP) and project implementation strategies (PIS).

The contribution of this paper to the present literature is identifying the positive factors and developing the confirmatory factor model. The model comprised 42 positive variables under four indicators: EMP, RPS, PIS and EFS.