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This study aims to present a scaffolding framework incorporating sophisticated technology that can inform instructional design to support student inquiry learning in the self-regulated online environment.

The classic predict, observe and explain (POE) framework has been extended through the incorporation of an additional evaluate (E) phase into the model to enhance the self-regulated online learning environment. The extended POEE scaffolding approach, in this study, has been conceptualized as an implicit guide to support the process of guided inquiry for learning particular science concepts. Digital tools were sourced and integrated into this design framework to substitute for the support typically offered by teachers and peers in classrooms.

The findings suggest that the POEE pedagogical design facilitated the inquiry process through promoting self-regulation and engaged exploration. It also promoted positive emotions in students towards the scaffolded learning modules.

Integrating technologies that benefit students differentially in educational settings remains a considerable challenge. More specifically, in science education, an appropriate inquiry learning context that allows access to well informed pedagogical design is imperative. The application of this inquiry-based scaffolding framework can inform educators in the process of creating their own instructional designs and contexts to provide more effective guided learning.

This study aims to examine the relationship between emotional attachment (EA) and intelligent personal assistant (IPA) continuance intention. While existing theories emphasize purely rational and goal-oriented factors in terms of information technology (IT) continuance intention, this research examines how users' EA toward technology impacts their continuance intention in the absence of cognitive and habitual factors.

This study contextualizes attachment theory from the social psychology/consumer psychology literature to an IT application and formulates and tests a new model that is proposed in the context of IPA continuance. Five research hypotheses developed from contextualization and application of the theory were posited in a structural model and empirically validated using survey results from IPA users.

The results show that users' EA to IPA use significantly influences their IPA continuance intention, along with emotional trust and interaction quality with the IPA.

This study contextualizes attachment theory developed in the social psychology/consumer psychology literature to formulate and test a new model in the context of IPA continuance. This work contributes to the theoretical understanding by investigating IPA continuance intention in the absence of cognitive or habitual factors and fills a critical research gap in IT post-adoption literature. IPA is just one example of technologies to which individuals can form attachments and this research provides an important foundation for future research by positing and testing the value of EA in IT post-adoption behavior. This research also contributes to practical knowledge by inferring that IPA manufacturers, managers and vendors could extend their revenue streams by integrating product features that capture emotion.

The purpose of this study is to understand the inhibitor behavior of specific drug against mild steel corrosion and their adsorption mechanism on the surface.

Corrosion rates are influenced by the formation of inhibitor aggregates at the mild steel surface. Detail surface characterizations of mild steel have been studied before and after adsorption of drugs in 1N HCl solution. Scanning electron microscopy, atomic force microscopy and Fourier transform infrared spectroscopy were used to examine the effect of drug adsorption on steel surface.

Scanning electron microscopy analysis suggested that the metal had been protected from aggressive corrosion because of the addition of the inhibitors. Atomic force microscopy visualization confirmed the formation of protective layer on steel surface, resulting in the decrease in surface roughness with corrosion rates. The nature of metal surface has been analyzed by Fourier transform infrared spectroscopy.

The findings of this study will help us to understand the interaction of specific drugs with mild steel surface and their potential inhibition mechanism.

The purpose of the present study is to determine how the cash conversion cycle (CCC) affects the financial performance of manufacturing companies in Bangladesh.

The authors have collected data of 61 Dhaka Stock Exchange (DSE)-listed firms from the 10 distinct manufacturing industries of Bangladesh for 18 years, from 2003 to 2020. The data have been analyzed through the two-steps system generalized method of moment (GMM) regression model, using profitability indicators return on asset (ROA) and earnings per share (EPS) as dependent variables, while CCC has been used as the independent variable, whereas asset turnover (ATO) and financial leverage (LEV) were used as control variables to assess the relationship between the CCC and financial performance.

The findings indicated that CCC has a negative connection with profitability – ROA and EPS, with the connection between CCC and EPS being highly significant. This indicates that reducing the inventory conversion time, reducing the period of receivable collection and making payments to creditors with potential delays might help Bangladeshi manufacturing firms boost their profitability. In addition, the firm-specific characteristics, namely ATO and LEV significantly affect the firm's profitability.

The research was based only on secondary sources and information was scarce. This research was conducted to determine the impact of the CCC on the corporate profitability of the manufacturing sector solely. There might be many other working capital variables that are still unexplored through this study.

The current study's findings are consistent with the traditional rule that minimizing the firm's days of the cash cycle may optimize financial performance. The results of this research have added to the existing body of knowledge on the topic of working capital management (WCM). Future research endeavors can be initiated for assessing the impact of the CCC on the firm's profitability in other industrial sectors or to identify other working capital variables that have much impact on corporate profitability.

This study is an original work of the researchers and adds value to the current literature in the domain of WCM and corporate profitability. The present study is the first one that covers firms in all the manufacturing industries in Bangladesh. The corporate managers, creditors, investors and other concerned stakeholders will be benefited from the findings of the present study.

The application of mobile augmented reality (MAR) for enhancing user experiences and consumer patronizing intention has been the focus of recent MAR literature. Few studies examine the differences between apps. This study fills the research gap by examining how consumers assess their experiences with different MAR applications and how their decision-making process is performed, particularly in the setting of smartphones.

A web-based online survey was administered to collect data on consumers' perceptions of two different MAR apps: utilitarian and hedonic apps. Reliability and validity of the measurement scales, non-response bias and comment method bias were assessed. With the support of measurement model, partial least square (PLS) was employed to test the research hypotheses.

This study reveals that the technological attributes of augmented reality (AR) apps have significant effects on consumer perceptions of their utilitarian and hedonic benefits, including interactivity, visual quality, service quality, technicality and aesthetics. Moreover, this study shows that consumers of hedonic apps place more importance on their enjoyment with the MAR app; consumers of utilitarian apps focus more on the accrued functional values. The findings provide practical insights for retailers in AR marketing and application development in the MAR environment.

This study provides a comprehensive viewpoint for analyzing ongoing use and purchase intentions simultaneously in a unified theoretical framework. In addition, it compares different types of MAR apps: hedonic and utilitarian. Furthermore, it is one of the first few studies attempting to provide a comprehensive understanding of the predictive role of MAR technologies by incorporating privacy concerns into the research model based on user and gratification framework.

This study aims to examine the house price fluctuations in G7 countries by using the multifractal detrended fluctuation analysis (MF-DFA) for the years 1970–2019. The study examined the market efficiency between the short-term and long-term in the full sample period, before and after the global financial crisis period.

This study uses the MF-DFA to analyze house price fluctuations.

The findings confirmed that the housing market series are multifractal. Furthermore, all the markets showed long-term persistence in both the short and long-term. The USA is identified as the most persistent house market in the short run and Japan in the long run. Moreover, in terms of efficiency, Canada is identified as the most efficient house market in the long run and the UK in the short run. Finally, the result of before and after the financial crisis period is consistent with the full sample result.

The contribution of this study in the literature is fourfold. This is the first study that has examined the house prices efficiency by using the MF-DFA technique given by Kantelhardt et al. (2002). Previously, the house market prices and efficiency has been investigated using generalized Hurst exponent (Liu et al., 2019), Quantile Regression Approach (Chae and Bera, 2019; Tiwari et al., 2019) but no study to the best of the knowledge has been done that has used the MF-DFA technique on the housing market. Second, this is the first study that has focused on the house markets of G7 countries. Third, this study explores the house market efficiency by dividing the market into two periods i.e. before and after the financial crisis. The study strives to investigate if the financial crisis determines the change in the degree of market efficiency or not. Finally, the study gives valuable insights to the investors that will help them in their investment decisions.

This paper aims to study the physical and chemical characteristics of inkjet titanium dioxide inks for cotton fabric digital printing.

Different dispersing agents through the reaction of glycerol monooleate and toluene diisocyanate were prepared and then performed by using three different polyols (succinic anhydride-modified polyethylene glycol PEG 600, EO/PO Polyether Monoamine and p-chloro aniline Polyether Monoamine), to obtain three different dispersing agents for water-based titanium dioxide inkjet inks. The prepared dispersants were characterized using FTIR to monitor the reaction progress. Then the prepared dispersants were formulated in titanium dioxide inkjet inks formulation and characterized by particle size, dynamic surface tension, transmission electron microscopy, viscosity and zeta potential against commercial dispersants. Also, the study was extended to evaluate the printed polyester by using the prepared inks according to washing and crock fastness.

The obtained results showed that p-chloro aniline Polyether Monoamine (J) and succinic anhydride modified polyethylene glycol PEG 600 (H) dispersants provided optimum performance as compared to commercial standards especially, particle size distribution data while EO/PO Polyether Monoamine based on dispersant was against and then failed with the wettability and dispersion stability tests.

These ink formulations could be used for printing on cotton fabric by DTG technique of printing and can be used for other types of fabrics.

The newly prepared ink formulation for digital textile printing based on synthesized polyurethane prepolymers has the potential to be promising in this type of printing inks, to prevent clogging of nozzles on the printhead and to improve the print quality on the textile fiber.

The aim of this study was to determine the effectiveness of promoting peer support to reduce depression, anxiety and stress among migrant construction workers in Singapore.

This longitudinal study drew participants from migrant workers of various nationalities in the construction sector in Singapore. Baseline data pertaining to depression, anxiety and stress was established using the DASS-21 questionnaire, and salient covariates such as demographic factors and work environment factors recorded using suitable questionnaires. Intervention was training of participants on peer support techniques, supplemented by episodic support by trained counsellors. At the end of 6 months, DASS-21 was again deployed to obtain the post-results. Comparison of baseline with post-results data was performed to evaluate effectiveness of the peer support intervention.

Statistically significant reduction was observed in measures of all the three parameters studied, namely, depression, anxiety and stress. A decrease of 3.3 (95% CI:2.3 to 4.3) points in mean depression score, a decrease of 2.6 (95% CI: 1.6 to 3.7) points in mean anxiety score and a decrease of 2.7 (with 95% CI: 1.6 to 4.0) points in mean stress scores on the DASS-21 scale were recorded. Conclusions: Peer support is effective in improving mental health of migrant workers in the construction sector in Singapore. This intervention should be considered among other measures to improve their welfare.

This is the first paper that talks about the mental health of migrant workers pre-COVID and hence would be a strong paper for the future comparative studies for pre-and post-COVID periods. This is the first paper that addresses the benefits of peer-support among migrant workers to improve their mental wellbeing.

Blockchain can track the material from the manufacturer to the end customers. Therefore, it can ensure the product's authenticity, transparency and trust in the retail supply chain (SC). There is a need to trace and track the retail products before it reaches the customers to check the quality of the products so that expired products can be recycled and reused, which in turn will help gain customers' trust. This research aims to investigate retail employees' behavioural intention to adopt blockchain in the retail SC.

To examine the behavioural intention of employees in the retail SC, the research uses three theories – the technology acceptance model; the unified theory of acceptance and use of technology; and the theory of planned behaviour. The technology acceptance model measures the employee's acceptance of blockchain in the retail SC. The unified theory of acceptance is used in this research to measure how blockchain adoption will improve the performance of the employees. The theory of planned behaviour is used in this research to measure whether the employees intend to adopt blockchain. A survey was carried out in the retail stores of India. Exploratory factor analysis and structural equation modelling were used for data analysis.

This study found that the employees of the retail stores have a positive intention and attitude to adopt blockchain technology. Further, it was found that perceived behavioural control and effort expectancy was not promoting blockchain adoption in the retail sector.

This study will help the retail stores' employees understand the blockchain in their operations and will motivate the top management of the retail companies to adopt this technology. The study is limited to the retail SC in India only.

This study uses three theories technology acceptance model; the unified theory of acceptance and use of technology; and the theory of planned behaviour, which were not used in earlier studies of blockchain adoption in the retail SC.