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The consumption of supplementary cementitious materials (SCMs) has increased enormously in the construction industry. These SCMs are often waste materials or industrial by-products. This study aims to investigate the bond strength using reinforcing bars in Normal Strength Concrete (M20 grade) and High Strength Concrete (M40 grade), developed using SCMs and data was compared with concrete prepared with ordinary portland cement (OPC). The findings of the study will help in reducing the dependency on OPC and promote the utilization of waste materials in Construction.

In the present study, the bond behavior between the steel bars and the concrete was investigated in controlled, binary and quaternary concretes of M20 and M40 grades. Following the conventional procedures, samples were prepared and mechanical tests conducted (as per IS:2770–1 code for M20 and M40 grade concrete structures), which showed an improvement in the bond strength depending on the extent of overall calcium and silica content in these composite mixtures, and thus reflected the importance of vigilant utilization of used industrial waste in the OPC as a replacement without exceeding silica content beyond certain percentages for enhanced structural properties.

Experimental evaluation of bond behavior results showed a brittle nature for the controlled (OPC) concrete mixtures. While binary and quaternary concrete was able to resist the load-carrying capacity under large deformations and prevented the split cracking and disintegration of the concretes. Among different variations in the chemistry, for both M20 and M40 grades, the maximum bond strengths were observed for 10% Metakaolin + 10% Silica Fume + 30% Fly Ash + 50% OPC composition and this could be attributed to the fineness of the additives, better packing and enhanced calcium silicate hydrate (C-S-H).

Quaternary concrete may be a future option in place of OPC concrete. Very limited data is available related to the bond strength of quaternary concrete. Experimental analysis on quaternary concrete shows that its use in construction can reduce both construction cost and a burden on natural raw materials used to make OPC.

This study aims to investigate the determinants of the dividend policy of public listed companies in Malaysia. The factors examined in this study include earnings, cash flows, free cash flows, debt level, growth, investment, size, largest shareholders, risk and lagged dividend.

Data were obtained from the relevant databases and annual reports of the sampled companies. The study examines a total of 147 listed companies. In analyzing the data, the study used fixed and random effects, pooled least squares model, robust standard errors on fixed effects and random-effects models.

The results revealed the five factors that are earnings, debt, size, investment and largest shareholder have a significant influence on dividend policy, with earnings, firm size and investment revealed to have a positive significant effect, while debt and large shareholders have a negative significant effect.

The findings from this study are useful to the board of directors and management team of companies in deciding an appropriate dividend policy for the company. The results are also useful to shareholders in making investment decisions.

The study extends empirical evidence on dividend policy determinants which are currently reported to be inconclusive. In addition, the study fills the lacuna in the existing literature by focusing on the issue of dividend policy determinants in the context of an emerging economy, namely, Malaysia.

The purpose of this paper is to propose an interpretive structural modeling (ISM) model which highlights the relationships between the identified just-in-time (JIT) elements useful for the implementation of JIT in maintenance and understand mutual influences of these identified JIT elements on JIT implementation in maintenance. Further, this paper seeks to identify dependence power and driving power of identified JIT elements using an ISM and Matrice d’Impacts Croisés Multiplication Appliquée á un Classement (MICMAC) analysis.

The methodology used in the paper is ISM with a view to evolving mutual relationships among JIT elements. The identified JIT elements have been further classified, based on their dependence power and driving power using MICMAC analysis.

This paper has developed the relationships among 16 identified JIT elements using the ISM methodology. Further, this paper analyses the driving power and dependence power of identified JIT elements with the help of MICMAC analysis. The incorporated approach is developed here, as the ISM provides only binary correlation among identified JIT elements. The MICMAC analysis is adopted here as it is useful in specific examination related to driving and the dependence power of identified JIT elements. The ISM developed model and MICMAC analysis finding are validated with the help of industrial experts.

The weightage and validation for the ISM and MICMAC analysis are obtained throughout the opinion of academics and industry experts. Further hypothesis may be conducted to examine the validity of the planned model, and structural model may also be validated statistically with the help of structural equation modeling.

The ISM model development and MICMAC analysis of identified JIT elements provide academics and maintenance managers a macro picture of the profits gained by the organizations by the implementation of JIT in maintenance of an organization.

The results will be useful for maintenance managers to understand the process of implementation of JIT in maintenance and to gain benefits after the implementation of JIT in maintenance of an organization.

The purpose of this paper is to provide a framework for measuring the imprecise and subjective “effectiveness” of a major maintenance activity. Such a measure will not only bring objectivity in gauging the effectiveness of maintenance task carried out by the workforce without any intervention from an expert but also help in measuring the slow degradation of the performance of the concerned major equipment/system.

The paper follows a three-step approach. First, identify a set of parameters considered important for estimating the maintenance activity effectiveness. Second, generate a set of data using expert opinions on a fuzzy performance measure of maintenance activity effectiveness (output). Also, find an aggregated estimate of the effectiveness by analysing the consensus among experts. This requires using a part of the “fuzzy multiple attribute decision making” process. Finally, train a neuro-fuzzy inference system based on input parameters and generated output data.

The paper analysed major maintenance activity carried out on diesel engines of a power plant company. Expert opinions were used in selection of key parameters and generation of output (effectiveness measure). The result of a trained adaptive neuro-fuzzy inference system (ANFIS) matched acceptably well with that aggregated through the expert opinions.

In view of unavailability of data, the method relies on training a neuro-fuzzy system on data generated through expert opinion. The data as such are vague and imprecise leading to lack of consensus between experts. This can lead to some amount of error in the output generated through ANFIS.

The originality of the paper lies in presentation of a method to estimate the effectiveness of a maintenance activity.

Numerous researchers have developed theories and studies to uncover the issues pertinent to dividend policy dynamics, but it is still one of the unresolved problems of finance. The purpose of this paper is to focus on a new dimension, i.e., financial expertise on the corporate board for explaining the dividend policy dynamics in the emerging equity markets of China and Pakistan.

The study employs static (fixed effect (FE) and random effect (RE)) and dynamic models – two-step generalized method of moments (GMM) estimation techniques by Arellano and Bond (1991) and Arellano and Bover (1995) – during the timespan from 2009 to 2014. Further, this study re-estimated FE, RE and GMM two-step estimation techniques by excluding the non-dividend-paying companies, and also employed instrumental variable regressing by using two instrumental variables – industry average financial expertise of the board and board size – as proxies for board financial expertise to control the possible endogeneity.

The study reveals that Chinese firms having more financial expertise on the board do not take dividends as a control mechanism (substitution hypothesis), while Pakistani firms support the compliment hypothesis and use dividends as a control mechanism to mitigate agency conflict to protect shareholders’ interests and keep additional funds from the manager’s opportunism. Further robustness models also confirm the presence of a significant association between dividend policy and board financial expertise in both equity markets.

This study introduces the financial expertise on a board as a determinant of dividend policy. To the best of the authors’ knowledge, no previous studies have focused on board-level financial expertise as a contributing factor toward dividend policy.

This paper explores and interprets the linkage between total quality management (TQM) practices and organizational performance measures for improving the healthcare firms’ performance. Indian healthcare firms are aware of TQM practices and their benefits, but the awareness level varies among the firms and staff. The study looks into the effectiveness of quality awareness to meet quality performance in Indian hospitals.

A questionnaire based on previous research was circulated among the managers and medical staff. The model linking TQM and organizational performance is analyzed with structural equation modelling and confirmed the hypotheses stated. Interpretations to improve hospital performance are made.

The study identified ten relevant TQM factors and confirmed their importance towards the improved organizational performance of Indian hospitals. Top management initiative, continuous process improvement and team work are the most contributing TQM factors. Differences in the awareness levels by the management staff and medical staff are attributed. The managers and medical staff are aware of the benefits of TQM towards firm performance, but it is to be improved further.

Cross-validation and interpretation are affected due to the limited sample size. Longitudinal study is recommended to explore the individual hospital as specific cases. Larger sample size is suggested as an extended work to overcome the demographic and infrastructural limitations of the firms included.

The management is more interested in TQM, but there is lack of awareness among the staff. The quality awareness and customer focus by medical staff are the most weakly loaded factors, and the weaknesses can be remedied by the lead role by the hospital management in providing proper training and thereby improving the attitude of the medical staff.

Effectiveness of hospital operations is highly dependent on customer focus. Properly communicated, committed and trained staff with good-quality awareness can better implement TQM and thereby improve hospital performance. Lead role by the management is very important, and the paper lists ways to attain these outcomes.

Very little is reported from the Indian healthcare sector linking TQM and outcome performance. The quality awareness, customer focus, communication and learning by the medical staff are to be improved, and the paper suggests ways to link TQM more effectively to improve the performance in hospitals. These findings may be useful to the managers, medical staff and researchers in healthcare to bring better results.

The purpose of this paper is to study the performance measures of world class manufacturing (WCM) and to establish relationship among them using interpretive structural modeling (ISM).

The research paper presents a blend of theoretical framework and practical applications. In the paper, 16 performance measures are identified from literature survey and experts’ opinion, and then these are validated by questionnaire survey in India. Finally, ISM is used to obtain structural relationship among these performance measures of WCM.

The results of the survey and the ISM methodology have been used to evolve the mutual relationships among these performance measures.

The adoption of such an ISM-based model on WCM performance measures in manufacturing organizations would help managers, decision-makers and practitioners of WCM in better understanding of these performance measures and to focus on appropriate performance measures while implementing WCM in their organizations.

Performance measures are of paramount importance for the implementation of WCM practices. Knowing the key performance measures and relationship among them can help many organizations to implement WCM practices. It is one of the foremost attempts to model performance measures of WCM. The paper provides useful insights into the WCM practitioners, consultants and researchers.

Affective states in learning have gained immense attention in education. The precise affective-states prediction can increase the learning gain by adapting targeted interventions that can adjust the changes in individual affective states of students. Several techniques are devised for predicting the affective states considering audio, video and biosensors. Still, the system that relies on analyzing audio and video cannot certify anonymity and is subjected to privacy problems.

A new strategy, termed rider squirrel search algorithm-based deep long short-term memory (RiderSSA-based deep LSTM) is devised for affective-state prediction. The deep LSTM training is done by the proposed RiderSSA. Here, RiderSSA-based deep LSTM effectively predicts the affective states like confusion, engagement, frustration, anger, happiness, disgust, boredom, surprise and so on. In addition, the learning styles are predicted based on the extracted features using rider neural network (RideNN), for which the Felder–Silverman learning-style model (FSLSM) is considered. Here, the RideNN classifies the learners. Finally, the course ID, student ID, affective state, learning style, exam score and course completion are taken as output data to determine the correlative study.

The proposed RiderSSA-based deep LSTM provided enhanced efficiency with elevated accuracy of 0.962 and the highest correlation of 0.406.

The proposed method based on affective prediction obtained maximal accuracy and the highest correlation. Thus, the method can be applied to the course recommendation system based on affect prediction.

Technology has revolutionized the delivery of health-care services, with e-consultations becoming popular mode of service delivery, especially during the pandemic. Extant research has examined the adoption of e-health consultation services, with little attention paid to examine the switching behavior. This study aims to identify factors affecting patients’ intentions to switch from conventional mode i.e. visiting hospitals/clinics to e-health consultations.

To understand this we use the push–pull–mooring (PPM) framework and integrate variables from status quo bias framework to the model. A cross-section research design was used, which rendered 413 valid responses which were obtained from the patients visiting a traditional hospital setup. The data was analyzed using partial least square – structural equation modeling using SmartPLS 3.0.

Findings suggest that push effects (inconvenience and perceived risk), pull effects (opportunity for alternatives and ubiquitous care), mooring effects (trust) and inertia significantly influence patients’ switching intentions from visiting hospitals/clinics to e-health consultations. Further, habit and switching cost positively influence inertia.

This study shall enable online health-care service providers and practitioners to understand patients’ intentions to switch to online health platforms and accordingly develop related marketing strategies, services and policies to encourage them to switch to the new offerings.

The current study enriches the previous research on e-health services by applying and extending PPM framework as the base model and showing its efficiency in predicting individuals switching intentions in the context of emerging economies. This study bridges the gap by focusing on switching behavior in context of health services.