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A rich stream of research has identified numerous antecedents to employee compliance (and noncompliance) with information security policies. However, the number of competing theoretical perspectives and inconsistencies in the reported findings have hampered efforts to attain a clear understanding of what truly drives this behavior. To address this theoretical stalemate and build toward a consensus on the key antecedents of employees’ security policy compliance in different contexts, we conducted a meta-analysis of the relevant literature. Drawing on 95 empirical papers, we classified 401 independent variables into 17 distinct categories and analyzed each category’s relationship with security policy compliance, including an analysis for possible domain-specific moderators. A meta-analytic relative weight analysis determined the relative importance of each category in predicting security policy compliance, while adding robustness to our findings. At a broad level, our results suggest that much of the security policy compliance literature is plagued by suboptimal theoretical framing. Our findings can facilitate more refined theory-building efforts in this research domain and serve as a guide for practitioners to manage security policy compliance initiatives.

This article advocates for the wider use of relative importance indices as a supplement to multiple regression analyses. The goal of such analyses is to partition explained variance among multiple predictors to better understand the role played by each predictor in a regression equation. Unfortunately, when predictors are correlated, typically relied upon metrics are flawed indicators of variable importance. To that end, we highlight the key benefits of two relative importance analyses, dominance analysis and relative weight analysis, over estimates produced by multiple regression analysis. We also describe numerous situations where relative importance weights should be used, while simultaneously cautioning readers about the limitations and misconceptions regarding the use of these weights. Finally, we present step-by-step recommendations for researchers interested in incorporating these analyses in their own work and point them to available web resources to assist them in producing these weights.

PurposeThis study integrates previous research on the intention to use Airbnb to determine which antecedents provide a parsimonious explanation.Design/methodology/approachMeta-analyses based on 61 samples estimate how 8 antecedents are associated with the intention to use Airbnb. Subsequent analyses utilize meta-analyses to estimate a regression model to simultaneously estimate the relationship between the antecedents and the intention to use Airbnb. Relative weight analysis then determined each antecedent’s utility.FindingsA parsimonious model with only four antecedents (hedonic motivation, price value, effort expectancy and social influence) was nearly as predictive as the full eight-antecedent model. Ten moderating variables were examined, but none were deemed to consistently influence the relationships between the antecedents and the intention to use Airbnb.Practical implicationsRelatively few measures (i.e. four) effectively explain customers’ intentions to use Airbnb. When these measures cannot be readily influenced, alternatives are also presented. Implications for the travel industry are considered and straightforward approaches to increasing users are presented.Originality/valueThis is the first integrative review of customers’ intentions to use Airbnb. We integrate what is currently known about customers’ intentions to use Airbnb and then provide a robust model for Airbnb use intentions that both researchers and practitioners can utilize.

PurposeThis study examines antecedents to fintech use intention to determine which antecedents can provide a parsimonious, yet accurate explanation.Design/methodology/approachMeta-analyses based on 42 samples estimate how seven antecedents are associated with fintech use intentions. Subsequent analyses utilize meta-analyses to estimate a regression analysis to simultaneously estimate the relationship between the antecedents and fintech use intention. Relative weight analysis then determined each antecedent's utility.FindingsHedonic motivation, price value, performance expectations and social influence had the strongest relationships with intention to use fintech. Further analyses found a parsimonious model with only three antecedents was nearly as predictive as the full seven antecedent model. Four moderating variables were examined but played minor roles.Research limitations/implicationsCommon method variance may impact the findings because all primary studies used cross-sectional surveys.Practical implicationsVery few measures (i.e. three) can robustly explain fintech use intention. When these measures cannot be readily influenced, alternatives are also presented.Originality/valueThis is the first integrative review of fintech use intentions. The authors integrate what is currently known about fintech use intentions and then provide a robust model for fintech use intentions that both researchers and practitioners can utilize.

A method for determining the optimal target weight by using a fuzzy membership function is proposed to address the subjectivity of target weight selection. This method first establishes the planning objectives and constraints of the comprehensive energy system and linearizes the nonlinear model based on the Big-M method. Then, multiple objectives are given weights and transformed into a single objective to obtain a target solution set composed of different weights. Based on this, a fuzzy membership function is proposed to determine the optimal weight of the objective and determine the maximum comprehensive satisfaction solution. The example results verify the feasibility of this method.

Resin and interlayer properties play significant roles in the resistance to impact of fibre-reinforced polymer composites (FRPCs). To investigate the contribution of each factor within the coupled variables to the impact resistance ability of FRPCs, in this work, waterborne polyurethane (WPU) with different tensile elastic modulus, tear strength and bonding strength was obtained. To systematically evaluate the impact resistance and failure mechanisms of the composite materials under varying external loads, impact resistance tests, numerical simulations, and relative weight analysis were conducted. The relative weight analysis results quantified the individual contributions of these three factors to the overall energy absorption capacity across diverse loading conditions. The results indicated that with the increasing rate of the external loading, the resin modulus consistently contributed more significantly to energy absorption than tear strength of resin and interlayer strength, reaching up to 44.3%. In ballistic penetration tests, with the increase in resin modulus, the ballistic performance of PE/WPU laminates demonstrated an S-shaped downward trend. Composites prepared with more rigid matrix could lead to unsatisfactory interlayer damage. A more robust structure could result in fibre pull-out and breakage to a greater extent at the point of forced impact while less in the secondary affected area, presenting comparatively lower impact resistant performance.

This paper presents a finite difference-based analysis of the cone penetration test (CPT) in saturated intact clays. The modified Cam clay (MCC) model was used to capture the undrained behaviour of the saturated clays. The cone tip resistance values obtained from the numerical analyses were validated against those obtained from field and calibration chamber tests, as well as numerical analyses reported in the literature. The effect of the MCC parameters on tip resistance was investigated through sensitivity analyses. Based on a large number of numerical analyses of CPT with different MCC parameters performed in this study, a relatively comprehensive database of CPT results was obtained. Relative weight analysis was then used to determine the relative effect of each MCC parameter on the tip resistance. Using nonlinear regression analysis of the obtained database, a relationship between tip resistance and soil properties was developed. Finally, the application of the proposed equation in estimating the overconsolidation ratio and effective friction angle is illustrated with useful charts, numerical examples, and comparisons with different case studies.

The Network Readiness Index (NRI) is one of the most prominent indicators that shows the digital development of countries. In contrast to the International Digital Economy and Social Index (I-DESI) of the European Union (EU), in 2020, it showed the development of 134 countries compared to 45 countries in I-DESI of EU, which measures only the most developed countries. The aim of this paper is to provide a viable alternative framework to the equal weights scheme of the original NRI scoring model using the Data Envelopment Analysis (DEA) Without Explicit Input (WEI) method and Common Weight Analysis (CWA) method. After determining the common weights, we compare the digital development of the countries in the NRI dataset based on the results obtained, focusing on the countries of the Central and Eastern European (CEE) region and the former Soviet Union.

Travel time reliability is the key aspect that indicates the quality of urban public transit service. The studies on travel time reliability of the public transit system in Indian traffic conditions are few. Also, the impact of side friction elements on travel time reliability has not been considered in the previous studies. Hence, the present study aims to quantify the different types of side friction elements and analyse their impact on the travel time reliability of the public bus transit system. The field data consisting of side friction elements, traffic volume, and travel time of public bus transit have been collected and extracted at two different road sections (divided and undivided) in the Mysore city (Karnataka, India) during weekdays and weekends. The data are grouped into static and dynamic side frictions. An approach has been proposed to represent different types of side friction elements with a single index called the Side Friction Index (SFI) using relative weight analysis. Travel time reliability is represented using measures such as Buffer Time Index (BTI), Planning Time Index (PTI), Travel Time Index (TTI) and Reliable Buffer Index (RBI). The impact of side friction on travel time reliability was found to be sensitive to traffic volume, and hence the thresholds for different traffic volume levels have been determined using K-means clustering method. It was observed from relative weight analysis that the static side friction has a higher weightage (0.509 and 0.327 for the undivided road and divided road respectively) than the dynamic side friction elements in describing the variation of travel time. The impact of side friction on reliability measures at different traffic volume levels has been studied and found to have a non-linear (exponential) relationship. The impact of SFI has been observed to be higher on TTI and PTI in comparison with BTI. The study outcomes show that the impact of side friction on TTI and PTI is sensitive to traffic volume, especially at higher traffic volume level and impact of side friction on BTI is less, especially at medium traffic volume level. The inference from the study shows that the impact of side friction elements varies with respect to the type of road (divided and undivided), traffic volume levels, different days of week (weekday and weekend), and different time periods of day.

The main goal of the project is weight analysis of scissor lifting table in the ware house, the scissor lifting table is a common device for all kind of peoples, we go to do the structural analysis of the scissor lifting table with the various human weights scissor lifting table modelled in Solid works software and structural analysis of human weights 50kg, 75kg, 100kg and 125kg done in ANSYS workbench software Scissor-type systems are frequently used as