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This study aims to propose a computationally efficient framework for multi-objective optimization (MO) of antennas involving nested kriging modeling technology. The technique is demonstrated through a two-objective optimization of a planar Yagi antenna and three-objective design of a compact wideband antenna.

The keystone of the proposed approach is the usage of recently introduced nested kriging modeling for identifying the design space region containing the Pareto front and constructing fast surrogate model for the MO algorithm. Surrogate-assisted design refinement is applied to improve the accuracy of Pareto set determination. Consequently, the Pareto set is obtained cost-efficiently, even though the optimization process uses solely high-fidelity electromagnetic (EM) analysis.

The optimization cost is dramatically reduced for the proposed framework as compared to other state-of-the-art frameworks. The initial Pareto set is identified more precisely (its span is wider and of better quality), which is a result of a considerably smaller domain of the nested kriging model and better predictive power of the surrogate.

The proposed technique can be generalized to accommodate low- and high-fidelity EM simulations in a straightforward manner. The future work will incorporate variable-fidelity simulations to further reduce the cost of the training data acquisition.

The fast MO optimization procedure with the use of the nested kriging modeling technology for approximation of the Pareto set has been proposed and its superiority over state-of-the-art surrogate-assisted procedures has been proved. To the best of the authors’ knowledge, this approach to multi-objective antenna optimization is novel and enables obtaining optimal designs cost-effectively even in relatively high-dimensional spaces (considering typical antenna design setups) within wide parameter ranges.

This study aims to examine the ability of clean energy stocks to provide cover for investors against market risks related to climate change and disturbances in the oil market.

The study adopts the feasible quasi generalized least squares technique to estimate a predictive model based on Westerlund and Narayan’s (2015) approach to evaluating the hedging effectiveness of clean energy stocks. The out-of-sample forecast evaluations of the oil risk-based and climate risk-based clean energy predictive models are explored using Clark and West’s model (2007) and a modified Diebold & Mariano forecast evaluation test for nested and non-nested models, respectively.

The study finds ample evidence that clean energy stocks may hedge against oil market risks. This result is robust to alternative measures of oil risk and holds when applied to data from the COVID-19 pandemic. In contrast, the hedging effectiveness of clean energy against climate risks is limited to 4 of the 6 clean energy indices and restricted to climate risk measured with climate policy uncertainty.

The study contributes to the literature by providing extensive analysis of hedging effectiveness of several clean energy indices (global, the United States (US), Europe and Asia) and sectoral clean energy indices (solar and wind) against oil market and climate risks using various measures of oil risk (WTI (West Texas intermediate) and Brent volatility) and climate risk (climate policy uncertainty and energy and environmental regulation) as predictors. It also conducts forecast evaluations of the clean energy predictive models for nested and non-nested models.

This paper aims to discuss multilevel modeling for longitudinal data, clarifying the circumstances in which they can be used.

The authors estimate three-level models with repeated measures, offering conditions for their correct interpretation.

From the concepts and techniques presented, the authors can propose models, in which it is possible to identify the fixed and random effects on the dependent variable, understand the variance decomposition of multilevel random effects, test alternative covariance structures to account for heteroskedasticity and calculate and interpret the intraclass correlations of each analysis level.

Understanding how nested data structures and data with repeated measures work enables researchers and managers to define several types of constructs from which multilevel models can be used.

This study aims to investigate the response of green investments of emerging countries to own-market uncertainty, oil-market uncertainty and COVID-19 effect/geo-political risks (GPRs), using the tail risks of corresponding markets as measures of uncertainty.

This study employs Westerlund and Narayan (2015) (WN)-type distributed lag model that simultaneously accounts for persistence, endogeneity and conditional heteroscedasticity, within a single model framework. The tail risks are obtained using conditional standard deviation of the residuals from an asymmetric autoregressive moving average – ARMA(1,1) – generalized autoregressive conditional heteroscedasticity – GARCH(1,1) model framework with Gaussian innovation. For out-of-sample forecast evaluation, the study employs root mean square error (RMSE), and Clark and West (2007) (CW) test for pairwise comparison of nested models, under three forecast horizons; providing statistical justification for incorporating oil tail risks and COVID-19 effects or GPRs in the predictive model.

Green returns responds significantly to own-market uncertainty (mostly positively), oil-market uncertainty (mostly positively) as well as the COVID-19 effect (mostly negatively), with some evidence of hedging potential against uncertainties that are external to the green investments market. Also, incorporating external uncertainties improves the in-sample predictability and out-of-sample forecasts, and yields some economic gains.

This study contributes originally to the green market-uncertainty literature in four ways. First, it generates daily tail risks (a more realistic measure of uncertainty) for emerging countries’ green returns and global oil prices. Second, it employs WN-type distributed lag model that is well suited to account for conditional heteroscedasticity, endogeneity and persistence effects; which characterizes financial series. Third, it presents both in-sample predictability and out-of-sample forecast performances. Fourth, it provides the economic gains of incorporating own-market, oil-market and COVID-19 uncertainty.

This work proposes a framework that attempts to explain the connection between the dimensions of consumer perceived corporate social responsibility (social, environmental, economic), firm trustworthiness and firm reputation, using market level of development as a moderating factor.

Mexico and Spain were selected as the emerging and developed markets; a cross-cultural study with 1173 consumers (521 from Mexico and 652 from Spain) was undertaken. In each country, participants evaluated one of two well-known companies (one making consumer products and one providing retail services). The hypotheses were tested through SEM.

The results showed that, in the emerging market, perceived environmental actions did not influence consumers' perceptions and, in the developed market, perceived social actions had no effect.

The study identifies two mechanisms through which consumers' perceptions of a company's CSR influence company reputation, offering evidence that the level of development of a country can have a moderating effect on how the mechanisms operate.

This paper aims to measure and rigorously compare the perceptions of South Korean university social engagement between faculty and students, two definitive stakeholders identified by stakeholder theory – but considerably heterogeneous, to understand how South Korean campus embraces social engagement in practice. To that end, this study delves into the conceptual framework of university social engagement and selects a highly internationalized, research-oriented, four-year comprehensive South Korean university campus that has long sought to become engaged in communities as the research site.

Methodologically, exploratory and confirmatory factor analyses were used to identify the factor model that successfully fit the data of the study. Factorial invariance tests and latent mean analysis were then conducted to measure and strictly compare the between-group mean differences.

According to the findings, neither faculty nor students had positive perceptions of their institution’s social engagement in terms of leadership, participatory decision-making, curriculum and instruction, institutional supports and systemic mechanism. That is, two definitive stakeholders on campus similarly perceived that social engagement has not yet been institutionalized as a core value and therefore embraced in practice. Based on these findings, this study discussed several implications for university decision makers. Specifically, the institutionalization of and the need for authentic leadership in university social engagement were emphasized as a means to encourage and facilitate the delivery of practical, beneficial services to the public.

As with all studies, there are certain limitations that must be noted. The sample for this study represents the experiences and expectations of faculty and students at only one institution. Therefore, the experiences of individuals at this single university are not necessarily representative of all South Korean universities. In addition, given that the public service missions of South Korean universities emanated from Western thoughts (Duke, 2008; Ward, 2003), social engagement in the present study has been discussed and conceptualized according to the dominant Western scholarship.

As both faculty and students similarly perceived, participatory decision-making and systemic mechanism do not work properly, and therefore, social engagement as an institutional value cannot strongly take root on campus. Based on the scale used, this study identified communication and organizational supports as the likely issues that obstruct the institutionalization of social engagement. In relation to communication, Boyte and Hollander (1999) emphasize that it is important that stakeholders are well aware of the engaged effort of the institution. Then, the voices of stakeholders need to be acknowledged as valuable feedback so that university decision makers and stakeholders can discuss mutually important issues and concerns (Minnesota Higher Education Services Office, 2003). Furthermore, the relevant literature consistently contends that engaged effort can only be productive with continuous and systemic organizational supports (Boyte and Hollander, 1999; Holland, 1997; Minnesota Higher Education Services Office, 2003; Weerts and Sandmann, 2008). That is, the engaged work of teaching, research and service should be thoroughly assessed and reported to stakeholders on a regular basis. The implication in this study is that university decision makers should make greater effort to design and implement policies and regulations that enable organizational supports to continue.

For social engagement to be valued in practice, the relevant literature (Kellogg Commission, 1999; Garlick and Langworthy, 2008; Minnesota Higher Education Services Office, 2003; Peterson, 2009) advises that top institutional leaders need to encourage interdisciplinary scholarship that includes research, teaching and learning; develop incentives to encourage faculty involvement in engaged work; support engagement so that it is incorporated into the curriculum and instruction; and secure funding for engagement. The fundamental insight that these suggestions provide to university decision makers is crystal-clear: social engagement must be authentically prioritized in the decision-making process.

The quantitative and descriptive findings of the study seek to provide one further step toward the objective of establishing the groundwork for future research on university social engagement in Asian context. Further, replication studies with various Asian cases and research designs may results in tangible improvements to the theorization of Asian university social engagement.

The purpose of this paper is to investigate the impact of information and communication technology (ICT) development and terrestrial and marine protection on the economic and environmental sustainability of small-island tourism countries. The current study expands the smart specialisation literature in the context of small-island tourism nations. It also proposes sustainable future growth strategies for these countries.

This paper adopted structural equation modelling using the data of 14 island nations between 1995 and 2014. The selection of countries was based on a set of criteria borrowed from literature.

The results indicate that the development of ICT infrastructure was not only associated with a higher inflow of tourists and a higher GDP per capita but also with a higher carbon dioxide (CO₂) emission. Results also suggest that higher protection of terrestrial and marine areas has no impact on tourist inflows in tourism countries. Also, higher protection decreases the dependence of the total output on the tourism sector.

A limitation of this study is the lack of available macro data on some other relevant variables for the countries studied.

Following the findings of this study, the governments of these countries should make stringent environmental regulations and relax the telecom regulations for sustainable smart specialisation.

This study presents a novel insight into the sustainability challenge of island nations through the lens of smart specialisation. It also contributes to the literature on ICT and development.

The study aims to test the applicability of a variant of the model proposed by Hockerts (2017) for assessing the social entrepreneurial intention (SEI) of male and female students. It extends the model by incorporating the university's environment and support system (ESS) as an additional more distal construct. The university's ESS, coupled with the experience with social, cultural and environmental issues can affect SEI by influencing the more proximal precursors of empathy towards others, perceived self-efficacy, perceived community support and social, cultural and environmental responsibility.

A structured non-disguised questionnaire was administered to students at a Canadian university. A sample of 485 usable responses was analysed by means of second-order structural equation modelling.

The results provide confirmation that the proposed model is a multi-group invariant and appropriate for analysing the SEI of male and female students. They also show that the university's ESS helps predict SEI indirectly through the complete mediation of the more proximal antecedents.

The questionnaire is limited to universities with social innovation and entrepreneurship initiatives.

Outcomes of the study can help universities assess the efficacy of their social innovation and entrepreneurship initiatives for instilling a social entrepreneurial mind-set in students. Consequently, universities will be better equipped to raise the perceptions of venture feasibility and desirability, thus increasing students' perceptions of opportunity.

The study advances the social entrepreneurial knowledge of the university's effect on the precursors of SEI.

This study aims to examine the role of an overall model fit assessment in the context of partial least squares path modeling (PLS-PM). In doing so, it will explain when it is important to assess the overall model fit and provides ways of assessing the fit of composite models. Moreover, it will resolve major concerns about model fit assessment that have been raised in the literature on PLS-PM.

This paper explains when and how to assess the fit of PLS path models. Furthermore, it discusses the concerns raised in the PLS-PM literature about the overall model fit assessment and provides concise guidelines on assessing the overall fit of composite models.

This study explains that the model fit assessment is as important for composite models as it is for common factor models. To assess the overall fit of composite models, researchers can use a statistical test and several fit indices known through structural equation modeling (SEM) with latent variables.

Researchers who use PLS-PM to assess composite models that aim to understand the mechanism of an underlying population and draw statistical inferences should take the concept of the overall model fit seriously.

To facilitate the overall fit assessment of composite models, this study presents a two-step procedure adopted from the literature on SEM with latent variables.

This paper clarifies that the necessity to assess model fit is not a question of which estimator will be used (PLS-PM, maximum likelihood, etc). but of the purpose of statistical modeling. Whereas, the model fit assessment is paramount in explanatory modeling, it is not imperative in predictive modeling.

Excessive use of work-related information technology (IT) devices can lead to major performance and well-being concerns for organizations. Extant research has provided evidence of the incidence of such problematic IT use in organizations. We extend the understanding of problematic IT use by examining its individual (proximal) and organizational (distal) antecedents.

Drawing from the self-worth theory and the concept of fear of being left behind, we address proximal antecedents that lead to problematic IT use. Drawing from the concept of autonomy paradox, we address distal antecedents of problematic IT use through a positive association with the two proximal antecedents. We report the results of a field study involving 846 individuals who use IT for work. Structural equation modeling was employed to analyze the data.

The results indicate that the proximal antecedents (IT insecurity and fear of missing out) are positively associated with problematic IT use. The distal antecedents (IT use autonomy and involvement facilitation) are positively associated with the proximal antecedents except for the relationship between IT use autonomy and IT insecurity, which was found statistically non-significant. Furthermore, fear of missing out fully mediates the effect of IT use autonomy on problematic IT use, whereas IT insecurity and fear of missing out fully mediate the effects of involvement facilitation on problematic IT use.

The paper theoretically extends the understanding of problematic IT use and identifies novel its proximal and distal antecedents.