Search results
1 – 10 of 411Patrik Jonsson, Johan Öhlin, Hafez Shurrab, Johan Bystedt, Azam Sheikh Muhammad and Vilhelm Verendel
This study aims to explore and empirically test variables influencing material delivery schedule inaccuracies?
Abstract
Purpose
This study aims to explore and empirically test variables influencing material delivery schedule inaccuracies?
Design/methodology/approach
A mixed-method case approach is applied. Explanatory variables are identified from the literature and explored in a qualitative analysis at an automotive original equipment manufacturer. Using logistic regression and random forest classification models, quantitative data (historical schedule transactions and internal data) enables the testing of the predictive difference of variables under various planning horizons and inaccuracy levels.
Findings
The effects on delivery schedule inaccuracies are contingent on a decoupling point, and a variable may have a combined amplifying (complexity generating) and stabilizing (complexity absorbing) moderating effect. Product complexity variables are significant regardless of the time horizon, and the item’s order life cycle is a significant variable with predictive differences that vary. Decoupling management is identified as a mechanism for generating complexity absorption capabilities contributing to delivery schedule accuracy.
Practical implications
The findings provide guidelines for exploring and finding patterns in specific variables to improve material delivery schedule inaccuracies and input into predictive forecasting models.
Originality/value
The findings contribute to explaining material delivery schedule variations, identifying potential root causes and moderators, empirically testing and validating effects and conceptualizing features that cause and moderate inaccuracies in relation to decoupling management and complexity theory literature?
Details
Keywords
Farouq Sammour, Heba Alkailani, Ghaleb J. Sweis, Rateb J. Sweis, Wasan Maaitah and Abdulla Alashkar
Demand forecasts are a key component of planning efforts and are crucial for managing core operations. This study aims to evaluate the use of several machine learning (ML…
Abstract
Purpose
Demand forecasts are a key component of planning efforts and are crucial for managing core operations. This study aims to evaluate the use of several machine learning (ML) algorithms to forecast demand for residential construction in Jordan.
Design/methodology/approach
The identification and selection of variables and ML algorithms that are related to the demand for residential construction are indicated using a literature review. Feature selection was done by using a stepwise backward elimination. The developed algorithm’s accuracy has been demonstrated by comparing the ML predictions with real residual values and compared based on the coefficient of determination.
Findings
Nine economic indicators were selected to develop the demand models. Elastic-Net showed the highest accuracy of (0.838) versus artificial neural networkwith an accuracy of (0.727), followed by Eureqa with an accuracy of (0.715) and the Extra Trees with an accuracy of (0.703). According to the results of the best-performing model forecast, Jordan’s 2023 first-quarter demand for residential construction is anticipated to rise by 11.5% from the same quarter of the year 2022.
Originality/value
The results of this study extend to the existing body of knowledge through the identification of the most influential variables in the Jordanian residential construction industry. In addition, the models developed will enable users in the fields of construction engineering to make reliable demand forecasts while also assisting in effective financial decision-making.
Details
Keywords
Anushree Tandon, Samuli Laato, Najmul Islam and Amandeep Dhir
A major portion of our social interaction now occurs online, facilitated by social networking sites (SNSs) that enable people to connect and communicate at will. However, the…
Abstract
Purpose
A major portion of our social interaction now occurs online, facilitated by social networking sites (SNSs) that enable people to connect and communicate at will. However, the characteristics of SNS communication can introduce problematic outcomes on otherwise healthy processes, one of which is social comparison. In this work, we investigate whether compulsive SNS use could be driven by two phenomena related to social comparison: the fear of missing out (FoMO) and envy.
Design/methodology/approach
Using the stimulus-organism-response framework, we developed a model that was tested with data from a sample of SNS users (N = 330) based in the United States. The analysis was done through partial least squares structural equation modeling.
Findings
Our findings show FoMO's association with the two forms of dispositional experienced envy, benign and malicious, as well as expected envy of others (expected envy). Interestingly, benign and expected envy were associated with SNS stalking and self-disclosure, but malicious envy had non-significant associations. Finally, both SNS stalking and self-disclosure were linked to compulsive SNS use.
Originality/value
We study the nuanced ways in which the two forms of experienced envy and expected envy can be triggered by FoMO and result in users' engagement with problematic SNS use. Our research provides evidence that, in addition to benign envy being an antecedent of compulsive SNS use, the wish to invoke envious feelings in others can also significantly drive compulsive use.
Details
Keywords
Xianjin Zha, Zeyu Lu and Yalan Yan
For undergraduate and graduate students in universities, social media are playing an important role in their study/research because a large amount of academic information has been…
Abstract
Purpose
For undergraduate and graduate students in universities, social media are playing an important role in their study/research because a large amount of academic information has been accumulated on social media. Indeed, social media are complementing university libraries. Given that intelligent recommender systems have been widely implemented on social media, this paper aims to examine the adoption mechanism of intelligently recommended information by university students in their study/research.
Design/methodology/approach
Building upon the updated information system success model and herding theory, this study developed a research model to examine the determinants of recommended information adoption in mobile applications for social media. Data were collected through an online questionnaire and analyzed with partial least squares structural equation modelling.
Findings
The results suggest that herding belief is a valid second-order construct, comprising two first-order dimensions of imitating others and discounting their own information. Information quality, system quality and service quality directly impact satisfaction with the intelligent recommender system. Furthermore, satisfaction with the intelligent recommender system and herding belief directly impact recommended information adoption by university students in their study/research.
Originality/value
This study draws on the updated information system success model and incorporates herding belief as an extended component to investigate recommended information adoption, providing a new lens for understanding recommended information adoption by university students in their study/research.
Details
Keywords
Xueyan Dong, Zhenya Tang and Houcai Wang
Unverified information avoidance behavior refers to the conscious effort made by individuals to avoid consuming information that has not been verified by credible sources. This…
Abstract
Purpose
Unverified information avoidance behavior refers to the conscious effort made by individuals to avoid consuming information that has not been verified by credible sources. This behavior is essential in preventing the spread of misinformation that can hinder effective public health responses. While previous studies have examined information avoidance behavior in general, there is a lack of research specifically focusing on the avoidance of unverified information during health crises. This study aims to fill this gap by exploring factors that lead to social media users’ unverified information avoidance behavior during health crises, providing novel insights into the determinants of this protective behavior.
Design/methodology/approach
We based our research model on the health belief model and validated it using data collected from 424 individuals who use social media. The proposed model was tested by using the partial least squares structural equation modeling (PLS-SEM) approach.
Findings
Our results indicate that individuals’ government social media participation (following accounts and joining groups) affects their health beliefs (perceived severity and benefits of information avoidance), which in turn trigger their unverified information avoidance behavior.
Originality/value
Our study contributes to the current literature of social media crisis management and information avoidance behavior. The implications of these findings for policymakers, social media platforms and theory are further discussed.
Details
Keywords
Bo Zhang, Xi Chen, Hanwen You, Hong Jin and Hongxiang Peng
Ultracapacitors find extensive applications in various fields because of their high energy density and long cycling periods. However, due to the movement of ions and the…
Abstract
Purpose
Ultracapacitors find extensive applications in various fields because of their high energy density and long cycling periods. However, due to the movement of ions and the arrangement patterns on rough/irregular electrode surfaces during the charge and discharge process of ultracapacitors, the parameters of ultracapacitors usually change with the variation of operating conditions. The purpose of this study is to accurately and quickly identify the parameters of ultracapacitors.
Design/methodology/approach
A variable forgetting factor recursive least square (VFFRLS) algorithm is proposed in this paper for online identifying the equivalent series resistance and capacitance C of ultracapacitors. In this work, a real-time error-based strategy is developed to adaptively regulate the value of the forgetting factor of traditional forgetting factor recursive least square (FFRLS) algorithm. The strategy uses the square of the average time autocorrelation estimation of the prior error and the posterior error between the predicted output and the actual output as the adjustment basis of forgetting factors.
Findings
Experiments were conducted using the proposed scheme, and the results were compared with the estimation results obtained by the recursive least squares (RLS) algorithm and the traditional FFRLS algorithm. The maximum root mean square error between the estimated values and actual values for VFFRLS is 3.63%, whereas for FFRLS it is 9.61%, and for RLS it is 19.33%.
Originality/value
By using the proposed VFFRLS algorithm, a relatively high precision can be achieved for the online parameter estimation of ultracapacitors. Besides, the dynamic balance between parameter stability and tracking performance can be validated by dynamically adjusting the forgetting factor.
Details
Keywords
Anshuman Kumar, Chandramani Upadhyay, Ram Subbiah and Dusanapudi Siva Nagaraju
This paper aims to investigate the influence of “BroncoCut-X” (copper core-ZnCu50 coating) electrode on the machining of Ti-3Al-2.5V in view of its extensive use in aerospace and…
Abstract
Purpose
This paper aims to investigate the influence of “BroncoCut-X” (copper core-ZnCu50 coating) electrode on the machining of Ti-3Al-2.5V in view of its extensive use in aerospace and medical applications. The machining parameters are selected as Spark-off Time (SToff), Spark-on Time (STon), Wire-speed (Sw), Wire-Tension (WT) and Servo-Voltage (Sv) to explore the machining outcomes. The response characteristics are measured in terms of material removal rate (MRR), average kerf width (KW) and average-surface roughness (SA).
Design/methodology/approach
Taguchi’s approach is used to design the experiment. The “AC Progress V2 high precision CNC-WEDM” is used to conduct the experiments with ϕ 0.25 mm diameter wire electrode. The machining performance characteristics are examined using main effect plots and analysis of variance. The grey-relation analysis and fuzzy interference system techniques have been developed to combine (called grey-fuzzy reasoning grade) the experimental response while Rao-Algorithm is used to calculate the optimal performance.
Findings
The hybrid optimization result is obtained as SToff = 50µs, STon = 105µs, Sw = 7 m/min, WT = 12N and Sv=20V. Additionally, the result is compared with the firefly algorithm and improved gray-wolf optimizer to check the efficacy of the intended approach. The confirmatory test has been further conducted to verify optimization results and recorded 8.14% overall machinability enhancement. Moreover, the scanning electron microscopy analysis further demonstrated effectiveness in the WEDMed surface with a maximum 4.32 µm recast layer.
Originality/value
The adopted methodology helped to attain the highest machinability level. To the best of the authors’ knowledge, this work is the first investigation within the considered parametric range and adopted optimization technique for Ti-3Al-2.5V using the wire-electro discharge machining.
Details
Keywords
Siying Zhu and Cheng-Hsien Hsieh
Maritime transportation plays an important role in facilitating both the global and regional merchandise trade, where accurate trend prediction is crucial in assisting…
Abstract
Purpose
Maritime transportation plays an important role in facilitating both the global and regional merchandise trade, where accurate trend prediction is crucial in assisting decision-making in the industry. This paper aims to conduct a macro-level study to predict world vessel supply and demand.
Design/methodology/approach
The automatic autoregressive integrated moving average (ARIMA) is used for the univariate vessel supply and demand time-series forecasting based on the data records from 1980 to 2021.
Findings
For the future projection of the demand side, the predicted outcomes for total vessel demand and world dry cargo vessel demand until 2030 indicate upward trends. For the supply side, the predominant upward trends for world total vessel supply, oil tanker vessel supply, container vessel supply and other types of vessel supply are captured. The world bulk carrier vessel supply prediction results indicate an initial upward trend, followed by a slight decline, while the forecasted world general cargo vessel supply values remain relatively stable. By comparing the predicted percentage change rates, there is a gradual convergence between demand and supply change rates in the near future. We also find that the impact of the COVID-19 pandemic on the time-series prediction results is not statistically significant.
Originality/value
The results can provide policy implications in strategic planning and operation to various stakeholders in the shipping industry for vessel building, scrapping and deployment.
Details
Keywords
Omprakash Ramalingam Rethnam and Albert Thomas
The building sector contributes one-third of the energy-related carbon dioxide globally. Therefore, framing appropriate energy-related policies for the next decades becomes…
Abstract
Purpose
The building sector contributes one-third of the energy-related carbon dioxide globally. Therefore, framing appropriate energy-related policies for the next decades becomes essential in this scenario to realize the global net-zero goals. The purpose of the proposed study is to evaluate the impact of the widespread adoption of such guidelines in a building community in the context of mixed-mode buildings.
Design/methodology/approach
This study decentralizes the theme of improving the energy efficiency of the national building stock in parcels by proposing a community-based hybrid bottom-up modelling approach using urban building energy modelling (UBEM) techniques to analyze the effectiveness of the community-wide implementation of energy conservation guidelines.
Findings
In this study, the UBEM is developed and validated for the 14-building residential community in Mumbai, India, adopting the framework. Employing Energy Conservation Building Code (ECBC) compliance on the UBEM shows an energy use reduction potential of up to 15%. The results also reveal that ECBC compliance is more advantageous considering the effects of climate change.
Originality/value
In developing countries where the availability of existing building stock information is minimal, the proposed study formulates a holistic framework for developing a detailed UBEM for the residential building stock from scratch. A unique method of assessing the actual cooling load of the developed UBEM is presented. A thorough sensitivity analysis approach to investigate the effect of cooling space fraction on the energy consumption of the building stock is presented, which would assist in choosing the appropriate retrofit strategies. The proposed study's outcomes can significantly transform the formulation and validation of appropriate energy policies.
Details
Keywords
Paul Cropper and Christopher Cowton
The accuracy of budgeting is important to fulfilling its various roles. The aim of this study is to examine perceptions of budgeting accuracy in UK universities and to identify…
Abstract
Purpose
The accuracy of budgeting is important to fulfilling its various roles. The aim of this study is to examine perceptions of budgeting accuracy in UK universities and to identify and understand the factors that influence them.
Design/methodology/approach
A mixed methods research design comprising a questionnaire survey (84 responses, = 51.5%) and 42 semi-structured, qualitative interviews is employed.
Findings
The findings reveal that universities tend to be conservative in their budgeting, although previous financial difficulties, the attitude of the governing body and the need to convince lenders that finances are being managed competently might lead to a greater emphasis on a “realistic” rather than cautious budget. Stepwise multiple regression identified four significantly negative influences on perceived budgeting accuracy: the difficulty of forecasting student numbers; difficulties associated with allowing unspent balances to be carried forward; taking a relatively long time to prepare the budget; and the institution’s level of financial surplus. The interviews are drawn upon to both explain and elaborate on the statistical findings. Forecasting student numbers and associated fee income emerges as a particularly challenging and complex issue.
Research limitations/implications
Our regression analysis is cross-sectional and therefore based on correlations. Furthermore, the research could be developed by investigating the views of other parties as well as repeating the study in both the UK and overseas.
Practical implications
Implications for university management follow from the four factors identified as significant influences upon budget accuracy. These include involving the finance department in estimating student numbers, removing or controlling the carry forward of unspent funds, and reducing the length of the budget cycle.
Originality/value
The first study to examine the factors that influence the perceived accuracy of universities’ budgeting, this paper also advances understanding of budgeting accuracy more generally.
Details