Can B. Kalayci, Olcay Polat and Surendra M. Gupta
The purpose of this paper is to efficiently solve disassembly line balancing problem (DLBP) and the sequence-dependent disassembly line balancing problem (SDDLBP) which are both…
Abstract
Purpose
The purpose of this paper is to efficiently solve disassembly line balancing problem (DLBP) and the sequence-dependent disassembly line balancing problem (SDDLBP) which are both known to be NP-complete.
Design/methodology/approach
This manuscript utilizes a well-proven metaheuristics solution methodology, namely, variable neighborhood search (VNS), to address the problem.
Findings
DLBPs are analyzed using the numerical instances from the literature to show the efficiency of the proposed approach. The proposed algorithm showed superior performance compared to other techniques provided in the literature in terms of robustness to reach better solutions.
Practical implications
Since disassembly is the most critical step in end-of-life product treatment, every step toward improving disassembly line balancing brings us closer to cost savings and compelling practicality.
Originality/value
This paper is the first adaptation of VNS algorithm for solving DLBP and SDDLBP.
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The purpose of this paper is to analyze the business model of techno parks (TPs) in Turkey and shed light on the value co-creation in TPs in the light of the service perspective…
Abstract
Purpose
The purpose of this paper is to analyze the business model of techno parks (TPs) in Turkey and shed light on the value co-creation in TPs in the light of the service perspective and stakeholder theory.
Design/methodology/approach
In this conceptual paper, a generic business model canvas for Turkish TPs has been elicited based on an in-depth review of the literature. Then, the functioning of the model and the nature of value co-creation have been viewed through the lenses of service perspective and stakeholder theory, and then the relationships and flows between the components of the business model have been visualized with a dynamic model.
Findings
The institutional environment leads Turkish TPs to have similar business models with functional differences. The value is co-created by stakeholders in TPs and value co-creation depends on the skills, competencies and cooperative efforts of all actors involved in the functioning of the business model.
Practical implications
This paper provides insight for TP management companies to improve their business models, for policymakers to refine institutional framework to enable effective functioning of TPs and for stakeholders to understand their role in value co-creation.
Originality/value
This paper provides a dynamic framework and a model for understanding business models of TPs and the value co-creation process, which is an understudied area, especially in a developing country context. It also extends the business model and value co-creation literature in the context of TPs by integrating multiple theoretical perspectives.
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This research aims to reveal the relationship between ingroup favoritism, seen as a theoretical cause of workplace violence experienced by physicians, with pre-violence, the…
Abstract
Purpose
This research aims to reveal the relationship between ingroup favoritism, seen as a theoretical cause of workplace violence experienced by physicians, with pre-violence, the moment of violence and post-violence, and the role of psychological resilience in coping with workplace violence.
Design/methodology/approach
A descriptive, cross-sectional design was applied in the research. First, data was gathered via structured questionnaire surveys to the 169 physicians and 321 patients with appointments using the simple random sampling method in three public hospitals in the province of Sanliurfa-Turkey between June 3, 2020, and January 1, 2021. The data was then examined through variance-based structural equation modeling and regression analysis.
Findings
Results indicate that the psychological resilience of physicians is essential in coping with workplace violence. The causes of favoritism behaviors were stated as a desire to protect the individuals they are with, increase their power, gain interest and cover their incompetence. It was determined that favoritism behaviors increase violence, but psychological resiliency decreases violence.
Originality/value
Some unobservable markers that impose priority for a patient from one's primary group, favoritism, may predict behaviors including violence. Contrary to popular belief, violence against physicians may be prevented by hospital management and social psychology practices rather than taking legal actions or increasing physical safety procedures. Moreover, the simultaneous collection of the data used in the study from physicians and patients with an appointment makes the study more meaningful and unbiased.
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The aim of this study is to measure the impact of the factors affecting construction labor productivity by focusing on different types of construction works during and after the…
Abstract
Purpose
The aim of this study is to measure the impact of the factors affecting construction labor productivity by focusing on different types of construction works during and after the COVID-19 pandemic in Turkey, as well as discuss solutions and immediate actions.
Design/methodology/approach
This research was conducted in two steps. First, a quantitative survey was carried out to determine the dimension of factors negatively affecting construction labor productivity and the loss rate of different construction works from the employee perspective. The factors were identified through a literature review. The crucial relationships were highlighted as a result of a statistical analysis. Second, a survey was performed to determine the loss rate through a comparison of man-hour values before and after the beginning of the pandemic from the employer perspective. After an analysis and comparison of the results, semi-structured interviews were performed to discuss all findings and discover ways to mitigate the impacts of COVID-19 on construction labor productivity.
Findings
The results of the study clearly show that construction labor productivity was deeply affected by the coronavirus disease (COVID-19) pandemic. Legal obligations, such as social distancing, wearing masks, and limitations on the number of workers, have been major drivers for lower labor productivity. Such obligations have a profound impact on interior construction works, especially based on teamwork. Concerning employer and labor-related factors, problems with getting payments on time, loss of income, and financial hardships are the leading factors resulting in decreased worker performance. Excavation, insulation, and plastering works were determined as the most affected construction works under the influence of the COVID-19 pandemic.
Research limitations/implications
The quantitative portion of this study is limited to a sample of respondents in the Turkish construction industry. Further research is necessary to provide an in-depth review into construction labor productivity in other countries with a larger respondent sample. Another limitation is sourced by the dynamics of the COVID-19 pandemic, which may turn out that some findings are outdated. Despite these limitations, the insights from this study may enable employers to understand the major drivers and deep impacts of labor productivity loss by uncovering the main vulnerabilities during the pandemic. Recommended measures may also help policy-makers and stakeholders in the construction industry take necessary and immediate actions to ensure better construction labor productivity.
Originality/value
The study may contribute to a better understanding of a pandemic's impact on labor productivity by focusing on both employee and employer perspectives, especially in developing countries. The paper may help employers decide which priority measures are required for each construction work separately. The study is crucial not only for minimizing the negative effects of the COVID-19 outbreak on labor productivity but also for preparing for the post-pandemic era.
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Kerim Koc, Ömer Ekmekcioğlu and Asli Pelin Gurgun
Central to the entire discipline of construction safety management is the concept of construction accidents. Although distinctive progress has been made in safety management…
Abstract
Purpose
Central to the entire discipline of construction safety management is the concept of construction accidents. Although distinctive progress has been made in safety management applications over the last decades, construction industry still accounts for a considerable percentage of all workplace fatalities across the world. This study aims to predict occupational accident outcomes based on national data using machine learning (ML) methods coupled with several resampling strategies.
Design/methodology/approach
Occupational accident dataset recorded in Turkey was collected. To deal with the class imbalance issue between the number of nonfatal and fatal accidents, the dataset was pre-processed with random under-sampling (RUS), random over-sampling (ROS) and synthetic minority over-sampling technique (SMOTE). In addition, random forest (RF), Naïve Bayes (NB), K-Nearest neighbor (KNN) and artificial neural networks (ANNs) were employed as ML methods to predict accident outcomes.
Findings
The results highlighted that the RF outperformed other methods when the dataset was preprocessed with RUS. The permutation importance results obtained through the RF exhibited that the number of past accidents in the company, worker's age, material used, number of workers in the company, accident year, and time of the accident were the most significant attributes.
Practical implications
The proposed framework can be used in construction sites on a monthly-basis to detect workers who have a high probability to experience fatal accidents, which can be a valuable decision-making input for safety professionals to reduce the number of fatal accidents.
Social implications
Practitioners and occupational health and safety (OHS) departments of construction firms can focus on the most important attributes identified by analysis results to enhance the workers' quality of life and well-being.
Originality/value
The literature on accident outcome predictions is limited in terms of dealing with imbalanced dataset through integrated resampling techniques and ML methods in the construction safety domain. A novel utilization plan was proposed and enhanced by the analysis results.