Search results

The purpose of this research is to examine the relationship between employer and employees in a public power sector undertaking through the validation of CODE (compensation, organizational justice, dispute resolution and employee empowerment) and PLE (workforce productivity, employee loyalty and employee engagement) model.

A hypothesized research model was developed and validated by using structural equation modeling (AMOS 20). In total, 303 responses were accumulated by administering a structured questionnaire among the employees of a state-owned power sector.

The results revealed that a harmonious climate of employee relations is prevailing in the public power utility. Additionally, the findings suggest that the CODE and PLE model of employee relations are found to be positive and significant by investigating the impact of compensation, organizational justice, dispute resolution and employee empowerment as the predictors; and workforce productivity, employee loyalty and employee engagement as the critical outcomes of employee relations.

The study recommends some plausible insights for practitioners, decision-makers and policy formulators to develop strategies and policies for nurturing congenial employee relations and also to cultivate a facilitative work environment for generating contented and competent manpower.

Validation of CODE and PLE model of employee relations in the new perspective of power sector undertaking is an epoch-making and novel contribution that offers significant empirical evidence to the extant literature. Moreover, the exploration of employer–employee relations in this context is a unique and innovative effort toward existing research.

From the factors that affect shear strength of reinforced concrete (RC) beams, the study examines the effect of controversial parameters, width-to-depth (b/d) and effective length-to-depth (l_eff/d) ratio on shear strength of RC slender beams.

The researchers utilized a database of 676 experimental test results from ACI-DAfStb database, Conducted regression analysis to examine relationship between b/d and l_eff/d ratios and shear strength, compare and analyze sensitivity to changes in b/d and l_eff/d ratios for the selected 12 shear models for RC beams.

Increasing b/d ratio enhanced shear strength until b/d ˜ 3, but further increases had limited impact and increasing l_eff/d ratio resulted in decreased shear strength. From comparative analysis, the models provided by various design standards were found to be safe, with EC-2 and JSCE models being conservative. From considered research models, Campione and Arslan models were conservative, while Kim and White model were observed to be unsafe. Sensitivity analysis indicated ACI318-19, JSCE, CEB-FIP-90 and Arslan models were sensitive to changes in b/d and l_eff/d ratios. National code models generally captured shear strength characteristics well. Certain models suggested a constant/decreasing b/d effect despite observed shear strength enhancement. Most models indicated improved shear strength with an increasing l_eff/d ratio, contrary to experimental findings while TS500 and Hwang models aligned with experimental results.

The study's limitations include the dependence on the available database, which may not encompass all possible experimental scenarios. Further research should aim to expand the database and investigate additional parameters that may influence shear strength in RC beams.

The findings of this study have practical implications for the design and analysis of RC beams by suggesting that the width-to-depth and length-to-depth ratios should be carefully considered to optimize shear strength. The identified models can assist engineers in selecting appropriate shear strength prediction models based on specific design scenarios.

The study contributes to the advancement of knowledge in the field of reinforced concrete beam design, which has implications for the safety and reliability of structural systems. By understanding the factors influencing shear strength, engineers can design more efficient and robust structures, ensuring the safety of buildings and infrastructure.

This study provides valuable insights into the influence of the width-to-depth and effective length-to-depth ratios on shear strength in reinforced concrete beams. It contributes to the understanding of these factors and their impact on shear strength, addressing the lack of consensus among researchers. The comparative analysis of shear models and the sensitivity analyses add value by identifying the models that align better with experimental observations. The study emphasizes the need for accurate models that account for these factors and highlights the importance of further research to refine and develop improved predictive models.

This study explores the applicability of Anderson's (1999) code of the street framework to an Asian context by examining the relationship between residents' perceptions of police ineffectiveness, nationality, and street codes in foreigner-concentrated areas in South Korea.

The authors used survey data from 1,865 residents of diverse nationalities living in 20 foreigner-concentrated areas in South Korea. Mixed-effects ordinary least squares (OLS) regression and moderation analysis were used to examine (1) the association between residents' perceptions of police ineffectiveness and their street codes and (2) how this association varies across nationality groups.

The study findings overall indicated that higher resident perceptions of police as ineffective tended to correspond with greater levels of street codes. However, the interaction analysis results found that this relationship was conditioned by nationality. Specifically, perceptions of police ineffectiveness were associated with greater levels of street codes among foreign residents, particularly Korean-Chinese residents, whereas this was not the case among Korean residents.

The relationship between perceptions of the police and street codes described by Anderson (1999) has primarily been examined in the context of the U.S. To the best of the authors' knowledge, this study represents the first attempt to explore the link between residents' perceptions of the police and street codes by different nationality groups within an Asian setting.

This study seeks to deepen the understanding of the political process underlying the establishment and evolution of corporate governance (CG) regulations in a developing country.

Drawing on regulatory space concept (Hancher and Moran, 1989) and Oliver's (1991) typology of strategic responses, the authors identify which actor participated in and benefitted from the establishment of a new transnational CG regulation in Pakistan. Data were collected through interviews and from the published secondary sources.

The findings highlighted regulations are being influenced and shaped up by the political process of negotiation, bargaining, manipulation and domination between powerful and resourceful actors in a given regulatory space. National regulators and regulatees can be indeed fervent opponents to the transnational regulations when it comes to protecting their well-rooted national interests.

This study contributes to the accounting literature by illustrating political processes through which internationally recognised CG practices are resisted, negotiated and implemented in the developing countries. The regulator must pay attention that the outcome of the regulatory change process is the result of carefully crafted and conscious strategies of actors in the regulatory space.

Earned value management (EVM)–based models for estimating project actual duration (AD) and cost at completion using various methods are continuously developed to improve the accuracy and actualization of predicted values. This study primarily aimed to examine natural gradient boosting (NGBoost-2020) with the classification and regression trees (CART) base model (base learner). To the best of the authors' knowledge, this concept has never been applied to EVM AD forecasting problem. Consequently, the authors compared this method to the single K-nearest neighbor (KNN) method, the ensemble method of extreme gradient boosting (XGBoost-2016) with the CART base model and the optimal equation of EVM, the earned schedule (ES) equation with the performance factor equal to 1 (ES1). The paper also sought to determine the extent to which the World Bank's two legal factors affect countries and how the two legal causes of delay (related to institutional flaws) influence AD prediction models.

In this paper, data from 30 construction projects of various building types in Iran, Pakistan, India, Turkey, Malaysia and Nigeria (due to the high number of delayed projects and the detrimental effects of these delays in these countries) were used to develop three models. The target variable of the models was a dimensionless output, the ratio of estimated duration to completion (ETC(t)) to planned duration (PD). Furthermore, 426 tracking periods were used to build the three models, with 353 samples and 23 projects in the training set, 73 patterns (17% of the total) and six projects (21% of the total) in the testing set. Furthermore, 17 dimensionless input variables were used, including ten variables based on the main variables and performance indices of EVM and several other variables detailed in the study. The three models were subsequently created using Python and several GitHub-hosted codes.

For the testing set of the optimal model (NGBoost), the better percentage mean (better%) of the prediction error (based on projects with a lower error percentage) of the NGBoost compared to two KNN and ES1 single models, as well as the total mean absolute percentage error (MAPE) and mean lags (MeLa) (indicating model stability) were 100, 83.33, 5.62 and 3.17%, respectively. Notably, the total MAPE and MeLa for the NGBoost model testing set, which had ten EVM-based input variables, were 6.74 and 5.20%, respectively. The ensemble artificial intelligence (AI) models exhibited a much lower MAPE than ES1. Additionally, ES1 was less stable in prediction than NGBoost. The possibility of excessive and unusual MAPE and MeLa values occurred only in the two single models. However, on some data sets, ES1 outperformed AI models. NGBoost also outperformed other models, especially single models for most developing countries, and was more accurate than previously presented optimized models. In addition, sensitivity analysis was conducted on the NGBoost predicted outputs of 30 projects using the SHapley Additive exPlanations (SHAP) method. All variables demonstrated an effect on ETC(t)/PD. The results revealed that the most influential input variables in order of importance were actual time (AT) to PD, regulatory quality (RQ), earned duration (ED) to PD, schedule cost index (SCI), planned complete percentage, rule of law (RL), actual complete percentage (ACP) and ETC(t) of the ES optimal equation to PD. The probabilistic hybrid model was selected based on the outputs predicted by the NGBoost and XGBoost models and the MAPE values from three AI models. The 95% prediction interval of the NGBoost–XGBoost model revealed that 96.10 and 98.60% of the actual output values of the testing and training sets are within this interval, respectively.

Due to the use of projects performed in different countries, it was not possible to distribute the questionnaire to the managers and stakeholders of 30 projects in six developing countries. Due to the low number of EVM-based projects in various references, it was unfeasible to utilize other types of projects. Future prospects include evaluating the accuracy and stability of NGBoost for timely and non-fluctuating projects (mostly in developed countries), considering a greater number of legal/institutional variables as input, using legal/institutional/internal/inflation inputs for complex projects with extremely high uncertainty (such as bridge and road construction) and integrating these inputs and NGBoost with new technologies (such as blockchain, radio frequency identification (RFID) systems, building information modeling (BIM) and Internet of things (IoT)).

The legal/intuitive recommendations made to governments are strict control of prices, adequate supervision, removal of additional rules, removal of unfair regulations, clarification of the future trend of a law change, strict monitoring of property rights, simplification of the processes for obtaining permits and elimination of unnecessary changes particularly in developing countries and at the onset of irregular projects with limited information and numerous uncertainties. Furthermore, the managers and stakeholders of this group of projects were informed of the significance of seven construction variables (institutional/legal external risks, internal factors and inflation) at an early stage, using time series (dynamic) models to predict AD, accurate calculation of progress percentage variables, the effectiveness of building type in non-residential projects, regular updating inflation during implementation, effectiveness of employer type in the early stage of public projects in addition to the late stage of private projects, and allocating reserve duration (buffer) in order to respond to institutional/legal risks.

Ensemble methods were optimized in 70% of references. To the authors' knowledge, NGBoost from the set of ensemble methods was not used to estimate construction project duration and delays. NGBoost is an effective method for considering uncertainties in irregular projects and is often implemented in developing countries. Furthermore, AD estimation models do fail to incorporate RQ and RL from the World Bank's worldwide governance indicators (WGI) as risk-based inputs. In addition, the various WGI, EVM and inflation variables are not combined with substantial degrees of delay institutional risks as inputs. Consequently, due to the existence of critical and complex risks in different countries, it is vital to consider legal and institutional factors. This is especially recommended if an in-depth, accurate and reality-based method like SHAP is used for analysis.

Risk factors related delay hinder the schedule performance of most construction projects in the world. It is a critical challenge to realize the advantages of prefabricated construction projects (PCPs) under the negative effect of schedule delay. This paper aims to propose an exhaustive list of risk factors impeding the progress of PCPs and evaluate the collected risk factors based on the cause–effect relations. The ultimate goal is to improve the understanding of the complex relations among various risk factors related delay in PCPs, and also offer managers a reference on aspect of schedule risk management.

This paper proposes a hybrid method of GT–DEMATEL–ISM, that is combing grounded theory, DEMATEL (decision-making trial and evaluation laboratory) and ISM (Interpretative Structural Modeling), to collect, evaluate and structure risk factors related delay for PCPs. The research procedure of this methodology is divided into three stages systematically involving qualitative and quantitative analysis. In the first stage, GT is utilized to implement qualitative analysis to collect the risk factors leading to schedule delay in PCPs. While, the quantitative analysis is to analyze and evaluate the collected risk factors based on the cause–effect relations in the next two stages evaluation by the DEMATEL focuses on quantifying the priority and intensity of the relations between factors. Additionally, ISM is employed to construct the hierarchical structure and graphically represent the pairwise relations between factors.

The outcome of qualitative investigation by grounded theory proposes a theoretical framework of risk factors related delay for PCPs. The framework contains three levels of category, namely, core category, main category and initial category and provides a list of risk factors related delay. Following this finding, evaluation results by the DEMATEL classify factors into cause and effect groups and determine 11 critical delay risk factors. Meanwhile, the findings show that risks referring to organizational management issue foremost impact the progress of PCPs. Furthermore, a systemic multilevel hierarchical structure model is visually constructed by ISM to present the pairwise linkages of critical factors. The model provides the risk transmission chains to map the spread path of delay impact in the system.

The contribution of the study involves twofold issues. Methodologically, this research proposes a hybrid method GT–DEMATEL–ISM used to identify and analyze factors for a complex system. It is also applicable to other fields facing similar problems that require collecting, evaluating and structuring certain elements as a whole in a comprehensive perspective. The theoretical contribution is to fill the relevant research gap of the existing body of knowledge. To the best knowledge of the authors, this paper is the first attempt to integrate qualitative and quantitative research for risk analysis related delay and take the insight into the whole process of PCPs covering off-site manufacture and on-site construction. Furthermore, the analysis of findings provided both a micro view focusing on individual risk factor and a managerial view from a systematic level. The findings also contribute the effective information to improve the risk management related schedule delay in PCPs.

Safety management plays an important part in coal mine construction. Due to complex data, the implementation of the construction safety knowledge scattered in standards poses a challenge. This paper aims to develop a knowledge extraction model to automatically and efficiently extract domain knowledge from unstructured texts.

Bidirectional encoder representations from transformers (BERT)-bidirectional long short-term memory (BiLSTM)-conditional random field (CRF) method based on a pre-training language model was applied to carry out knowledge entity recognition in the field of coal mine construction safety in this paper. Firstly, 80 safety standards for coal mine construction were collected, sorted out and marked as a descriptive corpus. Then, the BERT pre-training language model was used to obtain dynamic word vectors. Finally, the BiLSTM-CRF model concluded the entity’s optimal tag sequence.

Accordingly, 11,933 entities and 2,051 relationships in the standard specifications texts of this paper were identified and a language model suitable for coal mine construction safety management was proposed. The experiments showed that F1 values were all above 60% in nine types of entities such as security management. F1 value of this model was more than 60% for entity extraction. The model identified and extracted entities more accurately than conventional methods.

This work completed the domain knowledge query and built a Q&A platform via entities and relationships identified by the standard specifications suitable for coal mines. This paper proposed a systematic framework for texts in coal mine construction safety to improve efficiency and accuracy of domain-specific entity extraction. In addition, the pretraining language model was also introduced into the coal mine construction safety to realize dynamic entity recognition, which provides technical support and theoretical reference for the optimization of safety management platforms.

Previous research has identified associations between quality management and employee health. This study's purpose was to (1) examine those associations in a public healthcare organisation and (2) explore and describe the association between digitalisation and employee health.

An online questionnaire including indices to measure quality management values, employee health and digitalisation was answered by 118 managers in Swedish public healthcare. Correlation analysis was used to analyse the data. Based on the survey results, 12 qualitative, in-depth interviews were conducted with healthcare managers.

The findings show that employee health is associated with quality management and digitalisation. Categories were defined to describe the managers' views of the relationship between digitalisation and health.

Causality was not explicitly tested and cannot be assumed. However, the results strengthen the body of research showing that quality management is related to employee health, and associations between health and digitalisation were identified.

The findings and model should be helpful for healthcare managers in a digitalising environment who aim to preserve or enhance employee health whilst ensuring high service quality.

The results were used to create an integrated conceptual model depicting the association between quality, digitalisation and health. This association has not previously been studied.

This study aims to investigate the effects of organizational culture factors on the selection of software process development models and develops a conceptual model for selecting and adopting process development models with an organizational culture approach, using 12 criteria and their sub-criteria defined in Fey and Denison’s model (12 criteria).

The research hypotheses were investigated using statistical analysis, and then the criteria and sub-criteria were selected based on Fey and Denison’s model and the experts’ viewpoints. Afterward, the organizational culture of the selected company was measured using the data from 2016 and 2017, based on Fey and Denison’s questionnaire. Due to the correlation between the criteria, using the decision-making trial and evaluation technique, the correlation between sub-criteria were determined, and by analytical network process method and using Super-Decision software, the process development model was preferred to the 12 common models in information systems development.

Results indicated a significant and positive effect of organizational culture factors (except the core values factor) on the selection of development models. Also, by changing the value of organizational culture, the selected process development model changed either. Sensitivity analysis performed on the sub-criteria implied that by changing and improving some sub-criteria, the organization will be ready and willing to use the agile or risk-based models such as spiral and win-win models. Concerning units where the mentioned indicators were at moderate and low limits, models such as waterfall, V-shaped and incremental worked more appropriately.

While many studies were performed in comparing development models and investigating their strengths and weaknesses, and the impact of organizational culture on the success of information technology projects, literature indicated that the impact of organizational sub-culture prevailing in the selection of development process models has not been investigated. In this study, new factors and indicators were addressed affecting the selection of development models with a focus on organizational culture. Correlation among the factors and indicators was also investigated and, finally, a conceptual model was proposed for proper adoption of the models and methodologies of system development.

Executive isolation, also known as workplace loneliness, its factors and impact are major issues for organizational development, future of work for leadership and learning culture. The purpose of this study is to examine the Executive isolation phenomenon where relationships between power distance, organizational culture and executive isolation of Chief Executive Officers (CEOs) are analysed on how it is considered by their teams. The same is contextualized through the inputs received through interviews conducted with CEOs and employee surveys.

The qualitative in-depth interviews of five CEOs, and survey across 34 of the 50 employees, were undertaken over the course of two phases of this study. The investigation focused on identifying executive isolation of CEOs and perspectives of employees that can impact the leadership and learning progress of organizations based on work culture, power distance and decision-making; awareness and experience of executive isolation; workplace friendliness and rejection; and management development initiatives to minimize the impact of executive isolation. Qualitative data analysis was conducted using MAXQDA 2022 (Verbi Software, Berlin, Germany), which is a qualitative data analysis software.

The findings highlight and expose the significant gap between understanding and analysing of the factors due to which the CEOs undergo executive isolation. It also extends to providing details related to the lack of awareness of the teams’ actions contributing to the CEOs’ isolation. It further highlights the fact that the difference of perspectives between the CEOs and teams leads to the organization slowing in its learning activities due to the leaders’ own challenges of executive isolation The findings also provide immense need of developing knowledge assets and management development initiatives for learning interventions, to help understand, analyse and mitigate executive isolation, in the interest of the organizational learning and development.

Earlier research work have contextualized the executive isolation impact on CEOs ability to be a leader. This study extends it to include the implications of leadership and learning culture on the teams that are affected by organization culture, power distance, decision-making and analysing the gap between the understandings about executive isolation of the CEOs. Eventually, it interprets how CEOs courting the executive isolation impacts the overall developmental culture of the organization. This will help in asserting the serious need of new learning frameworks needed to minimize the impact of CEO-level executive isolation.