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Based on a thorough review and synthesis of the literature in behavioral finance, the purpose of this paper is to develop three measures of heuristics that tend to influence investment decisions of individual investors.

Using perceptual data collected from a sample of 167 individual investors in the USA, the reliability and validity of heuristics measures are assessed by confirmatory factor analysis with structural equation modeling. Then, the second-order model is executed in order to indicate the paths among the study’s constructs. Finally, a multiple-group analysis is conducted to analyze the moderating effects of demographic factors on the relationship between the perceived level of heuristics and their constituent dimensions.

Of the three groups of heuristics, salience is found to be the most important followed by mental accounting, while representativeness features as relatively less important. Regarding the moderating effects, only investment experience is noted to have a significant moderating impact.

The data utilized for testing and validating this instrument was acquired from a relatively small sample of individual investors in the USA, which makes the generalization of findings somewhat limited.

Both researchers and practitioners in behavioral finance can use these measurement scales to better understand the impact of heuristics on individual investment decisions and also to develop models that relate the critical factors of heuristics to the performance of individual investment decisions.

To date, there has been no systematic attempt in the extant behavioral finance literature to develop a valid and reliable instrument on heuristics which would aid to improve the quality of decision making in investment analysis.

The purpose of this study is to show in power-voltage curve, a unique maximum power point (MPP) is existed which has the maximum power.

This paper presents a MPP tracker algorithm for a standalone system includes DC-DC buck converter and battery storage.

By using this algorithm, the maximum available power is achieved and simultaneously, the battery is charged and also protected against overcharge and discharge. The operation of the proposed algorithm is evaluated in with Proteus software to be sure that it can be implemented in microcontroller in reality.

The simulations results show that the proposed algorithm is able to detect the MPP under different irradiations. Moreover, the battery is charged during the day by PV and protected against overcharge and discharge.

This research explores the effect of e-learning Moodle-based system usability on students' learning outcomes with the possible intervening role of teacher's behavior and online engagement.

In this research, the authors followed a quantitative methodology and a deductive research approach. Data were collected from 433 students at different study levels and academic specializations in higher education institutions (HEIs) in Oman. The data have been analyzed using partial least squares structural equation modeling via Smart-PLS.

The findings of this research show that e-learning system usability affects students' learning outcomes. Moreover, the relationship between these two variables is mediated by teacher behavior and students' online engagement.

This study is important as it adds to the understanding of the role of e-learning system usability in predicting student outcomes. From practical perspectives, especially during the spread of the COVID-19 pandemic, this study also helps practitioners at private HEIs use e-learning systems more efficiently and effectively to improve students' engagement and learning outcomes.

This research examined the factors affecting several types of student engagement, namely agentic, behavioral, emotional and cognitive engagement. Specifically, it examined the effect of e-learning system usability on student engagement and explored teacher behavior's possible intervening impact on this relationship.

Data were collected from 418 students studying at different specializations at Omani private academic institutions. This study employed a quantitative methodology and utilized the Smart-PLS for data analyses.

The findings showed that e-learning system usability influenced significantly and positively agentic, behavioral and cognitive engagement. However, the link between e-learning system usability and emotional engagement was not significant. Moreover, teacher behavior mediated the relationship between e-learning system usability and the four types of engagement.

This study improves one’s understanding of how the interaction of e-learning system usability and teacher behavior affects several aspects of student engagement. It also helps higher education administrators and policymakers by exploring the influential effects of e-learning systems usability and teacher behavior on facilitating students' engagement.

Defining and validating a map of related technologies is critical for managers, investors and inventors. Because of the increase in the applications of and demand for semiconductor lasers, analyzing the technological position of developers has become increasingly critical. Therefore, the purpose of this study is to adopt the technological position analysis to identify mainstream technologies and developments relevant to semiconductor lasers.

Correspondence analysis and k-means cluster analysis, which are data mining techniques, are used to reveal strategic groups of major competitors in the semiconductor laser market according to their Patent Cooperation Treaty (PCT) patent applications.

The results of this study reveal that PCT patent applications are generally obtained for masers, optical elements, semiconductor devices and methods for measuring and that technology developers have varying technological positions.

Through position analysis, this study identifies the technological focuses of different manufacturers to obtain information that can guide the allocation of research and development resources.

A large number of organisations are moving towards adopting Industry 4.0 (I4.0), and simultaneously, the emphasis on attaining sustainability development goals is also increasing. Hence, it is imperative to understand the interplay between I4.0 and sustainability. However, the literature addressing the same is still in infancy. Accordingly, the purpose of this study is to fill this gap in the literature by exploring the potential sustainability impacts of I4.0 on the organisations and society in terms of sustainability risks.

To gain an understanding of sustainability aspects in the I4.0 context, relevant literature is gathered using Scopus and Web-of-Science database. An in-depth review of 51 research papers is performed to determine the sustainability risks associated with I4.0.

From the study, a total of 16 sustainability risks are identified, and I4.0 sustainability risk taxonomy is developed. The proposed taxonomy extends the sustainability implications of I4.0 beyond the triple bottom line umbrella and includes the organisational perspective as well. Furthermore, the study provides future research avenues to scholars by positing five potential research questions under different risk management stages.

The study provides an understanding of sustainability risks associated with the adoption of I4.0. The findings will help practitioners streamline their production and operation processes by finding out possible solution to the sustainability risks of their smart factories in advance. The present research will act as a stepping stone towards I4.0 sustainability. The proposed research questions will assist the future researchers in extending the field of I4.0.

To the best of the authors’ knowledge, this is one of the first studies to address the topic of sustainability risks in the context of I4.0.