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Because of growing demand for slim, thin and cheap handheld devices, reduced-volume solder interconnects like land grid array (LGA) are becoming attractive and popular choices over the traditional ball grid array (BGA) packages. This study aims to investigate the mechanical shock and impact reliability of various solder alloys and BGA/LGA interconnect configurations.

Therefore, this paper uses drop testing experiments and numerical finite element simulations to evaluate and compare the reliability performance of both LGA and BGA components when exposed to drop and impact loadings. Additionally, three common solder alloys, including 63Sn37Pb, SAC305 and Innolot, are discussed.

The results of this study showed that electronic packages’ drop and impact reliability is strongly driven by the solder configuration and the alloy type. Particularly, the combination of stiff solder alloy and shorter joint, LGA’s assembled with SAC305, results in highly improved drop reliability. Moreover, the BGA packages’ performance can be considerably enhanced by using ductile and compliant solder alloys, that is, 63Sn37Pb. Finally, this paper discussed the failure mode of the various solder configurations and used simulation results to explain the crack and failure situations.

In literature, there is a lack of published work on the drop and impact reliability evaluation and comparison of LGA and BGA solders. This paper provides quantitative analysis on the reliability of lead-based and lead-free solders when assembled with LGA and BGA interconnects.

This study aims to improve the reliability of emergency safety barriers by using the subjective safety analysis based on evidential reasoning theory in order to develop on a framework for optimizing the reliability of emergency safety barriers.

The emergency event tree analysis is combined with an interval type-2 fuzzy-set and analytic hierarchy process (AHP) method. In order to the quantitative data is not available, this study based on interval type2 fuzzy set theory, trapezoidal fuzzy numbers describe the expert's imprecise uncertainty about the fuzzy failure probability of emergency safety barriers related to the liquefied petroleum gas storage prevent. Fuzzy fault tree analysis and fuzzy ordered weighted average aggregation are used to address uncertainties in emergency safety barrier reliability assessment. In addition, a critical analysis and some corrective actions are suggested to identify weak points in emergency safety barriers. Therefore, a framework decisions are proposed to optimize and improve safety barrier reliability. Decision-making in this framework uses evidential reasoning theory to identify corrective actions that can optimize reliability based on subjective safety analysis.

A real case study of a liquefied petroleum gas storage in Algeria is presented to demonstrate the effectiveness of the proposed methodology. The results show that the proposed methodology provides the possibility to evaluate the values of the fuzzy failure probability of emergency safety barriers. In addition, the fuzzy failure probabilities using the fuzzy type-2 AHP method are the most reliable and accurate. As a result, the improved fault tree analysis can estimate uncertain expert opinion weights, identify and evaluate failure probability values for critical basic event. Therefore, suggestions for corrective measures to reduce the failure probability of the fire-fighting system are provided. The obtained results show that of the ten proposed corrective actions, the corrective action “use of periodic maintenance tests” prioritizes reliability, optimization and improvement of safety procedures.

This study helps to determine the safest and most reliable corrective measures to improve the reliability of safety barriers. In addition, it also helps to protect people inside and outside the company from all kinds of major industrial accidents. Among the limitations of this study is that the cost of corrective actions is not taken into account.

Our contribution is to propose an integrated approach that uses interval type-2 fuzzy sets and AHP method and emergency event tree analysis to handle uncertainty in the failure probability assessment of emergency safety barriers. In addition, the integration of fault tree analysis and fuzzy ordered averaging aggregation helps to improve the reliability of the fire-fighting system and optimize the corrective actions that can improve the safety practices in liquefied petroleum gas storage tanks.

The purpose of this study is to find the reliability of the three-component three-phased mission system, which can be repaired by considering the exponential distribution for repair and failure rates in the transitions between the phases based on states with Markov approach. Also, multilevel-phased mission systems are calculated based on states for partially working states.

The reliabilities of the repairable two-level and three-level three-component three-phased mission systems based on states are calculated with the Markov approach. The structure functions are obtained for each phase of the systems, and differential equations are created by the failure and repair of each working state component. These equations are solved using Laplace method.

Reliability values of two-level and three-level three-component three-phased systems with different failure, repair, and time intervals are calculated and compared. The intermediate states that multilevel systems handle differently from two-level systems provide a better investigation of the systems. So, these repairable systems offer transparent information in complex systems like transportation and energy, ensuring appropriate timing and cost for repair operations.

This study is original in terms of calculating the reliability of the repairable phased mission system based on the states using Markov method. It is also important in calculating the reliability of the repairable multilevel phased mission system based on states and making reliability comparisons according to different repair and failure rates, equal and different time intervals.

The purpose of this study is to develop a decision-support framework that can be used by decision-makers to suspend public infrastructure projects. Additionally, the study also investigates how to select the most convenient infrastructure project for suspension.

The proposed framework includes an extensive set of factors and a novel comparison mechanism that can reveal the most convenient infrastructure project to be suspended. A comprehensible literature review and focus group discussion (FGD) sessions were conducted to identify factors that should be considered for suspension. Then, the neutrosophic analytic hierarchy process (N-AHP) method was used to determine the relative importance of the factors. Finally, the proposed comparison mechanism was demonstrated through a hypothetical case study and Technique for order of preference by similarity to ideal solution (TOPSIS) analysis.

Results showed that suspension decisions cannot be made merely based on “financial” factors. Instead, the other aspects, namely “Technical and managerial” and “Social and Environmental”, should also be taken into consideration. Second, factors related to the initial investment, cost of refinancing, cash flow, permits and approvals, insufficiency of bidders, degradation of the components, reputation, impact on stakeholders and criticality of the infrastructure were particularly elaborated as the most significant, needing the utmost attention of the decision-makers. Lastly, the results demonstrated that the proposed comparison mechanism has considerable potential to identify the most convenient infrastructure project for suspension.

Public infrastructure projects are often under pressure due to the inflationary state and economic stagnation of countries after major crises. The suspension decision for infrastructure projects necessitates comprehensible assessments to consider all consequences. Studies have widely investigated the contractual and legal aspects of project suspension in light of existing literature. However, little effort has been devoted to identifying the factors that decision-makers should consider before suspending a particular infrastructure project. Furthermore, existing literature does not investigate how to select the most convenient infrastructure project for suspension either. Thus, by developing a specific suspension framework for infrastructure projects by considering various factors, this study is the earliest attempt to examine the contract suspension mechanism of public infrastructure projects. In this respect, the study significantly contributes to the theory of contract management domain and has important managerial implications.

Considering the direct impact of retailers' performance on the economy, this paper aimed to propose a comprehensive framework to evaluate the performance of different branches of a retailer.

Through a case study, the weights of indicators were calculated by the best-worst method (BWM) and the branches' performance was appraised using data envelopment analysis (DEA).

The branches were ranked in terms of performance, and sensitivity analysis and statistical tests were conducted to realize the weaknesses and strengths of the branches. Then, some strategies were proposed using strengths, weaknesses, opportunities and threats (SWOT) analysis to improve the performance of the weak branches.

This paper contributes to previous studies on the evaluation of retailers' performance by proposing a triple framework based on resilience, sustainability and sales-marketing indicators. This paper focused on branches' operations and branches' optimization by improving performance in terms of these three indicators. This paper also offers a qualitative and quantitative analysis of retailers' performance, which has received less attention in previous studies.

The purpose of this study is to revisit the measures of internal consistency for multi-item scales in hospitality research and compare the performance of Cronbach’s α, omega total (ω_Total), omega hierarchical (ω_H), Revelle’s omega total (ω_RT), Minimum Rank Factor Analysis (GLB_fa) and GLB algebraic (GLB_a).

A Monte Carlo simulation was conducted to compare the performance of the six reliability estimators under different conditions common in hospitality research. Second, this study analyzed a data set to complement the simulation study.

Overall, ω_Total was the best-performing estimator across all conditions, whereas ω_H performed the poorest. α performed well when factor loadings were high with low variability (high/low) and large sample sizes. Similarly, ω_RT, GLB_fa and GLB_a performed consistently well when loadings were high and less variable as well as the sample size and the number of scale items increased. Of the two GLB estimators, GLB_a consistently outperformed GLB_fa.

This study provides hospitality managers with a better understanding of what reliability is and the various reliability estimators. Using reliable instruments ensures that organizations draw accurate conclusions that help them move closer to realizing their visions.

Though popular in other fields, reliability discussions have not yet received substantial attention in hospitality. This study raises these discussions in the context of hospitality research to promote better practices for assessing the reliability of scales used within the hospitality domain.

Construction progress monitoring (CPM) is considered a difficult and tedious task in construction projects, which focuses on identifying discrepancies between the as-built product and the as-planned design. Computer vision (CV) technology is applied to automate the CPM process. However, the synergy between the CV and CPM in literature and industry practice is lacking. This study aims to fulfil this research gap.

A Delphi qualitative approach was used in this study by conducting two interview rounds. The collected data was analysed using manual content analysis.

This study identified seven stages of CPM; data acquisition, information retrieval, verification, progress estimation and comparison, visualisation of the results and schedule updating. Factors such as higher accuracy in data, less labourious process, efficiency and near real-time access are some of the significant enablers in instigating CV for CPM. Major challenges identified were occlusions and lighting issues in the site images and lack of support from the management. The challenges can be easily overcome by implementing suitable strategies such as familiarisation of the workforce with CV technology and application of CV research for the construction industry to grow with the technology in line with other industries.

This study addresses the gap pertaining to the synergy between the CV in CPM literature and the industry practice. This research contributes by enabling the construction personnel to identify the shortcomings and the opportunities to apply automated technologies concerning each stage in the progress monitoring process.

Over the past 20 years, partial least squares (PLS) has become a popular method in marketing research. At the same time, several methodological studies have demonstrated problems with the technique but have had little impact on its use in marketing research practice. This study aims to present some of these criticisms in a reader-friendly way for non-methodologists.

Key critiques of PLS are summarized and demonstrated using existing data sets in easily replicated ways. Recommendations are made for assessing whether PLS is a useful method for a given research problem.

PLS is fundamentally just a way of constructing scale scores for regression. PLS provides no clear benefits for marketing researchers and has disadvantages that are features of the original design and cannot be solved within the PLS framework itself. Unweighted sums of item scores provide a more robust way of creating scale scores.

The findings strongly suggest that researchers abandon the use of PLS in typical marketing studies.

This paper provides concrete examples and techniques to practicing marketing and social science researchers regarding how to incorporate composites into their work, and how to make decisions regarding such.

This work presents a novel perspective on PLS critiques by showing how researchers can use their own data to assess whether PLS (or another composite method) can provide any advantage over simple sum scores. A composite equivalence index is introduced for this purpose.

This chapter aims to identify, analyse, classify and rank the sustainability indices and internationalisation challenges of the footwear industry in the emerging economy of Iran. This would provide deeper decision-making insights into Iranian footwear businesses. First, a list of sustainability indices and internationalisation challenges was obtained by reviewing the literature. Then, a combination of multi-criteria decision-making (MCDM) approaches was implemented. The initial sustainability indices and internationalisation challenges were screened using the fuzzy Delphi method, keeping a total of 14 criteria. The best–worst method (BWM) was employed to weigh and rank the criteria. The interpretive structural modelling (ISM) technique and cross-impact matrix applied in MICMAC were employed to visualise the conceptual model based on the levels and classification of the important criteria for the internationalisation of the Iranian footwear industry. The 14 criteria were demonstrated to be important in internationalisation. The most critical sustainability indices were reducing hazardous substances in leather tanning and labour education and training. In contrast, exchange rate instability in Iran’s economy and strict chemical regulations for clothing and footwear were found to be the most important internationalisation challenges. Hence, these criteria should be considered in the internationalisation strategies of the Iranian footwear industry. A combined multilayer sustainable decision-making approach was used to analyse the Iranian footwear industry’s essential sustainability indices and internationalisation challenges. Furthermore, implications and insights are offered to footwear businesses for future decision-making.

The research aims to study the regression, cointegration and causality between the construction sector (CS) and the Gross Domestic Product (GDP), considering other variables in the study such as interest rate, taxation, industry sector, investment and Foreign Direct Investment (FDI), which are analyzed through unique panel models. The study was conducted in Turkey and the ten other countries of the European Union (EU) from 1988 to 2019.

Regression, cointegration and causality methods were used to investigate the different types of relationships between variables in the models. Data were obtained from official databases and the study contains four main stages, which are explained in detail in the methodology section.

The study used the analysis methods of regression, cointegration and causality tests and found that the CS and GDP have long-run estimates and the relationship between the two for different countries is negative in a two-way direction. Results are detailed in the analysis section.

No data were available for the variables before 1988 for most countries, which led to a limited number of observations and issues in statistical analysis methods.

Previously, only input and output tables were used in the analysis. The impact of interest rate, taxation, investment and FDI has not been analyzed. Key variables are very relevant for Turkey, which suffers from chronical inflation and taxation regimes. These show variability with the EU countries for comparative analysis and have not been explored to date, remaining as a major gap for the construction industry. No attempts were made to use regression, cointegration and causality methods with variables. These analysis methods enable an understanding of the differences in variance (heteroscedasticity) and the presence of cross-sectional dependence (CSD), both critical for the reliability of the comparison of data sets and analysis.