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The study explored various dimensions of overconfidence bias (OB) among retail investors in Indian financial markets. Further, these dimensions were validated through formative assessments for OB.

The study applied exploratory factor analysis (EFA) to 764 respondents to explore dimensions of OB. These were validated with formative assessments on 489 respondents by the partial least square path modeling (PLS-PM) approach in SmartPLS 4.0 software.

The major findings of EFA explored four dimensions for OB, i.e. accuracy, perceived control, positive illusions and past investment success. The formative assessments revealed that positive illusions followed by past investment success among retail investors played an instrumental role in orchestrating the OBs that affect investment decisions in financial markets.

The formative index of OB has several practical implications for registered financial and investment advisors, bank advisors, business media companies and portfolio managers, besides individual investors in the domain of behavioral finance.

This research provides a novel approach to provide a formative index of OB with four dimensions. This formative index can acts as an overview for upcoming researchers to investigate the OB of retail individual investors.

1. Overconfidence bias is an important predictor of retail investors' behavior

2. Formative dimensions of the overconfidence bias index.

3. Accuracy, perceived control, positive illusions and past investment success are important dimensions of overconfidence bias.

4. Modern portfolio theory and illusion of control theory support this study.

Overconfidence bias is an important predictor of retail investors' behavior

Formative dimensions of the overconfidence bias index.

Accuracy, perceived control, positive illusions and past investment success are important dimensions of overconfidence bias.

Modern portfolio theory and illusion of control theory support this study.

The purpose of this paper is to assess the appropriateness of equal weights estimation (sumscores) and the application of the composite equivalence index (CEI) vis-à-vis differentiated indicator weights produced by partial least squares structural equation modeling (PLS-SEM).

The authors rely on prior literature as well as empirical illustrations and a simulation study to assess the efficacy of equal weights estimation and the CEI.

The results show that the CEI lacks discriminatory power, and its use can lead to major differences in structural model estimates, conceals measurement model issues and almost always leads to inferior out-of-sample predictive accuracy compared to differentiated weights produced by PLS-SEM.

In light of its manifold conceptual and empirical limitations, the authors advise against the use of the CEI. Its adoption and the routine use of equal weights estimation could adversely affect the validity of measurement and structural model results and understate structural model predictive accuracy. Although this study shows that the CEI is an unsuitable metric to decide between equal weights and differentiated weights, it does not propose another means for such a comparison.

The results suggest that researchers and practitioners should prefer differentiated indicator weights such as those produced by PLS-SEM over equal weights.

To the best of the authors’ knowledge, this study is the first to provide a comprehensive assessment of the CEI’s usefulness. The results provide guidance for researchers considering using equal indicator weights instead of PLS-SEM-based weighted indicators.

This paper aims to help understand how adopting risk allocation criteria impacts the delivery of public–private partnership (PPP) mass housing in Nigeria with the view of promoting the adoption of PPP housing scheme in Nigeria.

The research design adopts the census sampling approach by using well-structured questionnaires distributed to stakeholders involved in PPP-procured mass housing projects, i.e. consultants, in-house professionals, contractors and the organized private sector, registered with PPP departments in the Federal Capital Territory Development Authority, Abuja, Nigeria. Sixty-three risk factors, nine risk allocation criteria and nine project delivery indices were submitted for the respondents to rank on a Likert scale of 7. Two hypotheses were formulated to test whether the risk allocation criteria impacted PPP mass housing delivery or otherwise. The study adopts partial least square-structural equation modeling to model the effect of risk on risk allocation criteria on project delivery indices and risk severity.

The finding shows that project risk allocation criteria have less effect on project delivery indices than on risk severity. The study concludes that risk allocation principles do not directly affect the delivery of PPP-procured mass housing projects. This is evident by the path coefficient of 0.724 values, which is not statistically significant at a 5% alpha protection value. The study concludes that allocating critical risk factors influences the performance of PPP-procured mass housing projects, as the path coefficient of 0.360 is also not significantly far from 0 and at a 5% alpha protection value.

The study is one of the recent studies conducted in PPP-procured mass housing projects in Nigeria owing to the novelty of procurement option in the sector. It highlights the risk factors that can jeopardize the PPP-procured mass housing project objectives. The study is of immense value to PPP actors in the sector by providing the necessary information required to formulate risk response methods to minimize the impact of the risk factors in PPP mass housing projects.

This study aims to investigate the asymmetric impact of daily announcements regarding COVID-19 on investor sentiment in the stock market.

This study uses a Non-Linear Autoregressive Distribution Lag (NARDL) model that relies on positive and negative partial sum decompositions of the Coronavirus indicators. Five investor sentiments had been used and the analysis is conducted on the full sample period from 24th February 2020 to 25th March 2021.

The results show that new cases have a greater impact on investor sentiment compared to daily announcements of new deaths related to COVID-19. In addition to revealing a significant impact of new COVID-19 new cases and new death announcements on a daily basis on investor sentiment over the short- and long-term, this paper also highlights the nonlinearity and asymmetry of this relationship in the short and long run. Investors' sentiments are more affected by negative news regarding Covid 19 than positive news.

Financial markets have been severely affected by COVID-19 pandemic. This study is the first to measure the extent of reaction of investors to positive and negative announcements of COVID-19. Interestingly, this study examines the asymmetric effect of daily announcements on new cases and new deaths by COVID-19 on investor sentiments and derive many implications for portfolio managers.

In this study, the service quality of an automobile authorized service center was investigated based on the European Customer Satisfaction Index (ECSI) model. The ECSI model includes image, customer expectations, perceived quality, perceived value, customer satisfaction, customer complaints and customer loyalty.

In the study, an attempt was made to improve the ESCI model by adding the trust factor as a moderating variable. After an extensive literature review, measurement questions were developed to best represent the factors in the research model. Partial least squares structural equation modeling (PLS-SEM) was used to test the fit of the research model and test the hypotheses.

As a result of the analysis, only one of the 13 hypotheses tested was not supported. According to the results of hypothesis testing, the highest effect was found in the relationship between customer satisfaction customer complaints, customer expectations and perceived quality. In addition, customer expectations affect customer satisfaction indirectly rather than directly. In this case, customer expectations, perceived value and perceived quality influence customer satisfaction.

The customer satisfaction quality index score of the authorized automobile service whose service quality was measured was calculated as 72.75. Although customers were generally satisfied with the authorized service, their expectations were not fully met.

In the study, an attempt was made to improve the ECSI model by adding a trust factor. Trust, which was added to the model as a moderator variable, fit the model. As a result, it was revealed that trust has an increasing regulatory effect on the relationship between perceived quality and customer satisfaction.

The identification of network user relationship in Fancircle contributes to quantifying the violence index of user text, mining the internal correlation of network behaviors among users, which provides necessary data support for the construction of knowledge graph.

A correlation identification method based on sentiment analysis (CRDM-SA) is put forward by extracting user semantic information, as well as introducing violent sentiment membership. To be specific, the topic of the implementation of topology mapping in the community can be obtained based on self-built field of violent sentiment dictionary (VSD) by extracting user text information. Afterward, the violence index of the user text is calculated to quantify the fuzzy sentiment representation between the user and the topic. Finally, the multi-granularity violence association rules mining of user text is realized by constructing violence fuzzy concept lattice.

It is helpful to reveal the internal relationship of online violence under complex network environment. In that case, the sentiment dependence of users can be characterized from a granular perspective.

The membership degree of violent sentiment into user relationship recognition in Fancircle community is introduced, and a text sentiment association recognition method based on VSD is proposed. By calculating the value of violent sentiment in the user text, the annotation of violent sentiment in the topic dimension of the text is achieved, and the partial order relation between fuzzy concepts of violence under the effective confidence threshold is utilized to obtain the association relation.

A semiparametric stochastic frontier model is proposed for panel data, incorporating several flexible features. First, a constant elasticity of substitution (CES) production frontier is considered without log-transformation to prevent induced non-negligible estimation bias. Second, the model flexibility is improved via semiparameterization, where the technology is an unknown function of a set of environment variables. The technology function accounts for latent heterogeneity across individual units, which can be freely correlated with inputs, environment variables, and/or inefficiency determinants. Furthermore, the technology function incorporates a single-index structure to circumvent the curse of dimensionality. Third, distributional assumptions are eschewed on both stochastic noise and inefficiency for model identification. Instead, only the conditional mean of the inefficiency is assumed, which depends on related determinants with a wide range of choice, via a positive parametric function. As a result, technical efficiency is constructed without relying on an assumed distribution on composite error. The model provides flexible structures on both the production frontier and inefficiency, thereby alleviating the risk of model misspecification in production and efficiency analysis. The estimator involves a series based nonlinear least squares estimation for the unknown parameters and a kernel based local estimation for the technology function. Promising finite-sample performance is demonstrated through simulations, and the model is applied to investigate productive efficiency among OECD countries from 1970–2019.

The purpose of this paper is to propose a system dynamic simulated process model for maintenance work management incorporating the Fourth Industrial Revolution (4IR) technologies.

The extant literature in physical assets maintenance depicts that poor maintenance management is predominantly because of a lack of a clearly defined maintenance work management process model, resulting in poor management of maintenance work. This paper solves this complex phenomenon using a combination of conceptual process modeling and system dynamics simulation incorporating 4IR technologies. A process for maintenance work management and its control actions on scheduled maintenance tasks versus unscheduled maintenance tasks is modeled, replicating real-world scenarios with a digital lens (4IR technologies) for predictive maintenance strategy.

A process for maintenance work management is thus modeled and simulated as a dynamic system. Post-model validation, this study reveals that the real-world maintenance work management process can be replicated using system dynamics modeling. The impact analysis of 4IR technologies on maintenance work management systems reveals that the implementation of 4IR technologies intensifies asset performance with an overall gain of 27.46%, yielding the best maintenance index. This study further reveals that the benefits of 4IR technologies positively impact equipment defect predictability before failure, thereby yielding a predictive maintenance strategy.

The study focused on maintenance work management system without the consideration of other subsystems such as cost of maintenance, production dynamics, and supply chain management.

The maintenance real-world quantitative data is retrieved from two maintenance departments from company A, for a period of 24 months, representing years 2017 and 2018. The maintenance quantitative data retrieved represent six various types of equipment used at underground Mines. The maintenance management qualitative data (Organizational documents) in maintenance management are retrieved from company A and company B. Company A is a global mining industry, and company B is a global manufacturing industry. The reliability of the data used in the model validation have practical implications on how maintenance work management system behaves with the benefit of 4IR technologies' implementation.

This research study yields an overall benefit in asset management, thereby intensifying asset performance. The expected learnings are intended to benefit future research in the physical asset management field of study and most important to the industry practitioners in physical asset management.

This paper provides for a model in which maintenance work and its dynamics is systematically managed. Uncontrollable corrective maintenance work increases the complexity of the overall maintenance work management. The use of a system dynamic model and simulation incorporating 4IR technologies adds value on the maintenance work management effectiveness.

The primary objective of this research is to provide evidence that there are two distinct layers of investor sentiments that can affect asset valuation models. The first is general market-wide sentiments, while the second is biased approaches toward specific assets.

To achieve the goal, the authors conducted a multi-step analysis of stock returns and constructed complex sentiment indices that reflect the optimism or pessimism of stock market participants. The authors used panel regression with fixed effects and a sample of the US stock market to improve the explanatory power of the three-factor models.

The analysis showed that both market-level and stock-level sentiments have significant contributions, although they are not equal. The impact of stock-level sentiments is more profound than market-level sentiments, suggesting that neglecting the stock-level sentiment proxies in asset valuation models may lead to severe deficiencies.

In contrast to previous studies, the authors propose that investor sentiments should be measured using a multi-level factor approach rather than a single-factor approach. The authors identified two distinct levels of investor sentiment: general market-wide sentiments and individual stock-specific sentiments.