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The purpose of this paper is to investigate the benefit of critical success factors (CSFs) clustering in different phases of make-to-order (MTO) projects and develop standards for management.

This study is based on a questionnaire survey. First of all, collecting data by structured interviews, relying on a questionnaire and second from leader contractors who are active in the engineering and steel industry (in Iran). So, the requirements and objective of the research are presented to the top management of MTO projects to gain their support in data collection. Then 20 CSFs were identified by the literature review so a questionnaire survey was prepared for the CSFs assessment and interview with the experts. Finally analyzing the importance and performance of CSFs in project phases and cluster them in different project phases with self-organizing map as one of the artificial neural network (ANN) approaches due to high predictive accuracy. Review the research result with the top management of MTO project and examine the results obtained from neural networks and validation indices.

Cluster analysis shows that the implementation phase is the most important stage in MTO organizations and the other phases like feasibility and start-up, design and planning, delivery and end-phase should be also considered as effective phases in determining the level of organization performance. Different industries with additional data at different periodic times will verify the result. Furthermore, testing the other ANN model will improve risk analysis and could shift this classification approach to a regression type.

The main limitation of the research is related to the sample. Research findings are limited to the time of data collection so validity is limited to the mentioned time. Different industries with additional data will verify the result. Furthermore, testing different ANN models such as K-MEANS, non-negative matrix factorization (NMF) analyses will improve risk analysis and could meet different classification results to find gaps.

In this paper, CSF and project phase dimensions are viewed together which is necessary to meet better results for simplifying social and economic benefits. Merge the new findings and latest technologies could prepare the best results and enable managers to create a better framework or implement key factors for minimizing waste.

This paper moves the definition of MTO organizations beyond measuring cost, complexity and financial variables by clustering CSFs in different phases of projects. So, the results enable managers to use this concept in their daily production to minimize waste and could be implemented to efficiently choose factors.

The purpose of this paper is to develop a reliable model that would predict the compressive strength of slurry infiltrated fiber concrete (SIFCON) modified with various supplementary cementitious materials (SCMs) using artificial intelligence approach.

This study engaged the artificial intelligence to predict the compressive strength of SIFCON through deep neural networks (DNN), artificial neural networks, linear regression, regression trees, support vector machine, ensemble trees, Gaussian process regression and neural networks (NN). A thorough data set of 387 samples was gathered from relevant studies. Eleven variables (cement, silica fume, fly ash, metakaolin, steel slag, fine aggregates, steel fiber fraction, steel fiber aspect ratio, superplasticizer, water to binder ratio and curing ages) were taken as input to predict the output (compressive strength). The accuracy and reliability of the developed models were assessed using a variety of performance metrics.

The results showed that the DNN (11-20-20-20-1) predicted the compressive strength of SIFCON better than the other algorithms with R² and mean square error yielding 95.89% and 8.07. The sensitivity analysis revealed that steel fiber, cement, silica fume, steel fiber aspect ratio and superplasticizer are the most vital variables in estimating the compressive strength of SIFCON. Steel fiber contributed the highest value to the SIFCON’s compressive strength with 16.90% impact.

This is a novel technique in predicting the compressive strength of SIFCON optimized with different SCMs using supervised learning algorithms, improving its quality and performance.

Religion and spirituality (R/S) are recognized components of recovery-oriented mental health services. This study aims to present a clinically focused tool for assessing R/S interest among veterans with serious mental illness (SMI).

A questionnaire including 39 items was developed by experienced chaplains and mental health clinicians and administered to modest pilot sample of 110 participants in a recovery-oriented program at a medical center of the US Veterans Health Administration (VHA).

Altogether 40 (37%) participants said they would like R/S issues to be a greater part of their treatment (i.e. very or extremely). A screening tool to identify veterans for referral to R/S focused interventions was developed based on the selection of the five items most strongly loading on the strongest factor in a factor analysis.

First, the identification of items for the survey was made on the basis of clinical experience with issues discussed by veterans in a VA recovery-oriented program and thus are based on clinician experience and their association with a stated desire for more R/S in their treatment. Since no gold-standard measure of “religion/spirituality” has been universally established and validated, this method, though imperfect, was accepted as practical and as having face validity. Furthermore, the sample size, while substantial, was limited and was not representative of the general population. Again, this was a pilot study of a unique effort to identify R/S issues of greatest relevance in a recovery program for people with SMI.

In this SMI sample, 36% of the participants said that they would like more R/S to be incorporated into their treatment. Factor analysis showed the desire for uplifting religious/spiritual community to be the predominant factor and formed the basis for a five-item screening tool that can be used to briefly identify services needs in this area of recovery.

This screening tool can help incorporate religious and spiritual issues into mental health treatment, and area of importance that is often neglected. The results could help destigmatize this area of recovery practice for people with SMI.

This R/S survey of SMI adults suggests that over one-third of the participants in a pilot sample in a recovery-oriented program would like more R/S emphasis in their treatment. Factor analysis showed the desire for uplifting religious/spiritual community to be the predominant factor.

The study aims to examine the impact of key variables on the success of solicited and unsolicited private participation in infrastructure (PPI) projects using machine learning techniques.

The data has information on 8,674 PPI projects primarily derived from the World Bank database. In the study, a machine learning framework has been used to highlight the variables important for solicited and unsolicited projects. The framework addresses the data-related challenges using imputation, oversampling and standardization techniques. Further, it uses Random forest, Artificial neural network and Logistics regression for classification and a group of diverse metrics for assessing the performances of these classifiers.

The results show that around half of the variables similarly impact both solicited and unsolicited projects. However, some other important variables, particularly, institutional factors, have different levels of impact on both projects, which have been previously ignored. This may explain the reason for higher failure rates of unsolicited projects.

This study provides specific inputs to investors, policymakers and practitioners related to the impacts of several variables on solicited and unsolicited projects separately, which will help them in project planning and implementation.

The study highlights the differential impact of variables for solicited and unsolicited projects, challenging the previously assumed uniformity of impact of the given set of variables including institutional factors.

The Metaverse holds vast amounts of user data, making it essential to address threats to its confidentiality, integrity and availability. These threats are not purely technological, as user actions and perceptions, shaped by psychological factors, can influence cybersecurity challenges. Thus, a holistic approach incorporating technological and psychological dimensions is crucial for safeguarding data security and privacy. This research explores users’ cybersecurity behavior in the Metaverse by integrating the technology threat avoidance theory (TTAT) and the theory of planned behavior (TPB).

The model was assessed using data collected from 746 Metaverse users. The empirical data were analyzed using a dual structural equation modeling-artificial neural network (SEM-ANN) approach.

The main PLS-SEM findings indicated that cybersecurity behavior is significantly affected by attitude, perceived behavioral control, subjective norms, perceived threat and avoidance motivation. The ANN results showed that perceived threat with a normalized importance of 100% is the most significant factor influencing cybersecurity behavior. The ANN results also showed that perceived severity with a normalized importance of 98.79% significantly impacts perceived threat.

The novelty of this research stems from developing a unified model grounded in TTAT and TPB to understand cybersecurity behaviors in the Metaverse. Unlike previous Metaverse studies that solely focused on measuring behavioral intentions or user behaviors, this study takes a step further by evaluating users’ cybersecurity behaviors. Alongside its theoretical insights, the study offers practical recommendations for software developers, decision-makers and service providers.

The article serves to both introduce the special edition of Qualitative Research Journal (QRJ) and explain the purpose of the Foundation of Learning and Literacy (FFLL) Touchstones as principles that should inform language and literacy policy development, leadership in the field and classroom literacy practices. It particularly focuses on Touchstone 1 and the importance of fairness and equity for all literacy learners. It draws on a range of research that articulates ways that inequality of opportunity can be addressed.

This article both introduces the FFLL for the Special Edition of QRJ and examines the first Touchstone or guiding, overarching principle that led to the establishment of the FFLL and the 11 Touchstones that are discussed in subsequent articles. In essence, this article addresses the importance of fairness and equity for all children and young people as they develop deep literacy. The article begins with a brief contextual background explaining how and why FFLL was formed. It then highlights the first Touchstone.

The article demonstrates the need to support all learners as they strive to be deeply literate so they can become active and compassionate members of their communities. Based on a range of research evidence, it suggests ways to make sure that all children and young people can become successful literacy learners. These include homes with books that learners can self-select, sharing stories, substantive conversations, the provision of quality literary texts and rich pre-school experiences.

Practical classroom implications arising from the research are discussed.

The article is a brief introduction to the FFLL and a synthesis of some of the research that underpins the first Touchstone.

The purpose of the paper is to predict mapping of areas vulnerable to flooding in the Ourika watershed in the High Atlas of Morocco with the aim of providing a useful tool capable of helping in the mitigation and management of floods in the associated region, as well as Morocco as a whole.

Four machine learning (ML) algorithms including k-nearest neighbors (KNN), artificial neural network, random forest (RF) and x-gradient boost (XGB) are adopted for modeling. Additionally, 16 predictors divided into categorical and numerical variables are used as inputs for modeling.

The results showed that RF and XGB were the best performing algorithms, with AUC scores of 99.1 and 99.2%, respectively. Conversely, KNN had the lowest predictive power, scoring 94.4%. Overall, the algorithms predicted that over 60% of the watershed was in the very low flood risk class, while the high flood risk class accounted for less than 15% of the area.

There are limited, if not non-existent studies on modeling using AI tools including ML in the region in predictive modeling of flooding, making this study intriguing.

This study aims to assess the impact of personality traits, risk perception and perceived coronavirus disease 2019 (COVID-19) disruption on the investment behavior of individual investors in the Indian stock market.

This study adopts a survey approach. The sample comprises 315 active retail investors investing in the Indian stock exchange. Two-stage analysis technique regression and Artificial Neural Network (ANN) were used for data analysis. Study hypotheses were tested through regression and ANN was adopted to validate the regression results.

Two regression models were modeled to test the research hypotheses. Findings showed that risk perception and COVID-19 disruption have a significant positive and neuroticism has a significant negative impact on short-term investment decisions, while the role of conscientiousness in determining short-term investment decisions was not found significant. Results also showed a positive impact of neuroticism and conscientiousness and a negative impact of risk perception on long-term investment decisions. The role of COVID-19 disruption was found negative but insignificant in predicting long-term investment decisions.

This study has practical implications for many parties like retail investors, financial advisors and policymakers. This study will assist the investors to realize that they do not always take rational financial decisions. This study will suggest the financial advisors to use the knowledge of behavioral finance in making the advisors' advisory and wealth management decisions. This study will also assist the policymakers to outline behaviorally well-informed policy decisions to protect the interests of investors.

India is one of the fast-growing economies in the world. India has a vast population of active investors and determining investors' investment behavior adds novelty to this study as developed economies have remained the main focus of previous studies. The other novel feature of this study is that this study tries to assess the impact of COVID-19 disruption along with personality traits and risk perception on investment behavior. The other valuable factor of this study is the use of ANN to predict the relative importance of the exogenous variables.

In this study, students’ perceptions of the effective practices of their research mentors were examined. The research mentors implemented the practices informed by the Center for the Improvement of Mentored Experiences in Research (CIMER) mentorship competencies to mentor underrepresented students in engineering education research in a virtual environment.

This research experience for undergraduates (REU) program, situated in the United States of America, consisted of undergraduate students (i.e. mentees), graduate students and faculty mentors who all had at least one underrepresented identity in engineering (i.e. Black, Latiné/x, and/or women). Using qualitative methods, we used data from reflection surveys and follow-up interviews with REU mentees to understand the outcomes of the mentorship strategies employed by the mentors in the program. The data were analyzed thematically using CIMER model constructs and social capital theory as guiding frameworks.

The results indicated the identified strategies students perceived as the most impactful for mentorship throughout the program. Students in the REU gained knowledge on how to activate social capital in mentorship relationships and how to better mentor others.

The findings provide insight on how to operationalize the CIMER mentorship competencies to skillfully mentor underrepresented students in engineering. Given the size of the REU and the nature of qualitative research, the sample size was limited.

The results help inform mentorship practices for underrepresented individuals in engineering education and the workforce. Further, they add to the practical knowledge of implementing CIMER best practices virtually, at a time when the world has transitioned to more hybrid and virtual working and learning environments.

This study identifies impactful strategies for operationalizing mentorship strategies informed by theory- and evidence-based CIMER mentorship competencies. In addition, this study extends knowledge about how to implement mentoring best practices and engage mentorship in a virtual environment.

This study aims to objectively synthesize the volume of accounting literature on financial statement fraud (FSF) using a systematic literature review research method (SLRRM). This paper analyzes the vast FSF literature based on inclusion and exclusion criteria. These criteria filter articles that are present in the accounting fraud domain and are published in peer-reviewed quality journals based on Australian Business Deans Council (ABDC) journal ranking. Lastly, a reverse search, analyzing the articles' abstracts, further narrows the search to 88 peer-reviewed articles. After examining these 88 articles, the results imply that the current literature is shifting from traditional statistical approaches towards computational methods, specifically machine learning (ML), for predicting and detecting FSF. This evolution of the literature is influenced by the impact of micro and macro variables on FSF and the inadequacy of audit procedures to detect red flags of fraud. The findings also concluded that A* peer-reviewed journals accepted articles that showed a complete picture of performance measures of computational techniques in their results. Therefore, this paper contributes to the literature by providing insights to researchers about why ML articles on fraud do not make it to top accounting journals and which computational techniques are the best algorithms for predicting and detecting FSF.

This paper chronicles the cluster of narratives surrounding the inadequacy of current accounting and auditing practices in preventing and detecting Financial Statement Fraud. The primary objective of this study is to objectively synthesize the volume of accounting literature on financial statement fraud. More specifically, this study will conduct a systematic literature review (SLR) to examine the evolution of financial statement fraud research and the emergence of new computational techniques to detect fraud in the accounting and finance literature.

The storyline of this study illustrates how the literature has evolved from conventional fraud detection mechanisms to computational techniques such as artificial intelligence (AI) and machine learning (ML). The findings also concluded that A* peer-reviewed journals accepted articles that showed a complete picture of performance measures of computational techniques in their results. Therefore, this paper contributes to the literature by providing insights to researchers about why ML articles on fraud do not make it to top accounting journals and which computational techniques are the best algorithms for predicting and detecting FSF.

This paper contributes to the literature by providing insights to researchers about why the evolution of accounting fraud literature from traditional statistical methods to machine learning algorithms in fraud detection and prediction.