Search results

The purpose of this study is to propose a research model based on the stimulus-organism-response (S-O-R) model to examine whether media richness (MR), human-system interaction (HSI) and human-human interaction (HHI) as technological feature antecedents to medical professionals’ learning engagement (LE) can affect their learning persistence (LP) in massive open online courses (MOOCs).

Sample data for this study were collected from medical professionals at six university-/medical university-affiliated hospitals in Taiwan. A total of 600 questionnaires were distributed, and 309 (51.5%) usable questionnaires were analyzed using structural equation modeling in this study.

This study certified that medical professionals’ perceived MR, HSI and HHI in MOOCs positively affected their emotional LE, cognitive LE and social LE elicited by MOOCs, which together explained their LP in MOOCs. The results support all proposed hypotheses and the research model accounts for 84.1% of the variance in medical professionals’ LP in MOOCs.

This study uses the S-O-R model as a theoretical base to construct medical professionals’ LP in MOOCs as a series of the psychological process, which is affected by MR and interaction (i.e. HSI and HHI). Noteworthily, three psychological constructs, emotional LE, cognitive LE and social LE, are adopted to represent medical professionals’ organisms of MOOCs adoption. To date, hedonic/utilitarian concepts are more commonly adopted as organisms in prior studies using the S-O-R model and psychological constructs have received lesser attention. Hence, this study enriches the S-O-R model into an invaluable context, and this study’s contribution on the application of capturing psychological constructs for completely explaining three types of technological features as external stimuli to medical professionals’ LP in MOOCs is well-documented.

During the past few years, social media has faced the challenge of maintaining its user base. Reports show that the social media giants such as Facebook and Twitter experienced a decline in their users. Taking WhatsApp's recent change of its terms of use as the case of this study and using the push-pull-mooring model and a configurational perspective, this study aims to identify pathways for switching intentions.

Data were collected from 624 WhatsApp users recruited from Amazon Mechanical Turk and analyzed using fuzzy set qualitative comparative analysis (fsQCA).

The findings identify seven configurations for high switching intentions and four configurations for low intentions to switch. Firm reputation and critical mass increase intention to switch, while low firm reputation and absence of attractive alternatives hinder switching.

This study extends extant literature on social media migration by identifying configurations that result in high and low switching intention among messaging applications.

The study identifies factors the technology service providers should consider to attract new users and retain existing users.

This study complements the extant literature on switching intention that explains the phenomenon based on a net-effect approach by offering an alternative view that focuses on the existence of multiple pathways to social media switching. It further advances the authors’ understanding of the relevant importance of switching factors.

This study illustrates the economic and financial dynamics of the sector, analysing the evolution of the main ratios of profitability and financial structure of 1,559 Italian real estate companies divided into the three macro-regions: North, Centre and South, in the period 2011–2020. In this way, it is also possible to verify the responsiveness to the 2020 pandemic crisis.

The analysis uses descriptive statistics tools and the ANOVA method of analysis of variance, supplemented by the Tukey–Kramer test, to identify significant differences between the three Italian macro-regions.

The study shows the increase in profitability after the 2008 crisis, despite its reverberation in the years 2012–2013. The financial structure of companies improved almost everywhere. The pandemic had modest effects on performance.

In the future, other indices should be considered to gain a more comprehensive view. This is a quantitative study based on financial statements data that neglects other important economic and social factors.

Public policies could use this study for better interventions to support the sector. In addition, internal management can compare their company's performance with the industry average to identify possible improvements.

The research analyses an economic field that employs a large number of people, especially when considering the construction and real estate services covered by this analysis.

The study contributes to the literature by providing a quantitative analysis of industry dynamics, with comparative information that can be deduced from financial statements over the years.

This paper aims to comprehensively analyze the factors influencing the adoption intentions of the digital rupee, a digital currency, among users in India.

Drawing upon the Technology Acceptance Model (TAM), the study examines the relationships between cognitive beliefs (perceived usefulness, perceived ease of use, perceived trust, perceived self-efficacy, perceived cost and awareness), affective belief (attitude) and adoption intention of the digital rupee. The study uses a structured questionnaire to collect primary data from 1,707 respondents, which are then analyzed using structural equation modeling.

The results indicate that perceived usefulness and perceived ease of use significantly impact users' attitudes toward the digital rupee, as well as their adoption intentions. The findings further reveal that perceived trust, perceived self-efficacy, and awareness positively influence attitude and adoption intention. On the other hand, perceived cost exhibits a negative effect on attitude and adoption intention. These results provide empirical evidence on the factors that shape users' attitudes and intentions toward adopting the digital rupee.

The research methodology used in this study ensures rigorous data collection and analysis. The structured questionnaire enabled the collection of detailed information from a large sample of respondents, allowing for robust statistical analysis. The utilization of structural equation modeling facilitated the examination of complex relationships among variables, enhancing the reliability and validity of the findings.

The study's findings offer practical guidance for policymakers, financial institutions and researchers in shaping digital currency regulatory frameworks, tailored financial services and further exploration of adoption dynamics.

The research has social implications by potentially influencing the way individuals and communities in India engage with digital currencies, impacting financial inclusion and digital economic participation.

This research contributes to the understanding of the adoption of digital currencies in India and provides valuable insights for policymakers, financial institutions and researchers in the field of digital finance and technology adoption.

Marine plastic pollution (MPP) is increasing in recent times because of the high usage of plastic products. Green consumption behaviour (GCB) gaining attention as effective approach to achieving sustainable source reduction of plastic pollution, which negatively affects both human pollution and marine biodiversity and ecosystem. Although, Higher Education (HE) students are key stakeholders in addressing environmental issues, including MPP, there is limited empirical research in Ghana on factors influencing HE students’ GCB. This study, in an endeavour to bridge the gap, used the revised theory of planned behaviour (TPB) framework to investigate the factors influencing higher-education students’ green consumption behaviour in the Ghanaian context. Specifically, the purpose of the study is to examine the interplay of consumer novelty seeking (CNS), environmental concern (EC), perceived behavioural control and social influence on green consumption behaviour among higher-education students in Ghana. The study also explored the moderating role of gender in the relationship between CNS and green consumption behaviour.

This study used quantitative approach to obtain data from a sample of 233 students at the University of Cape Coast and used the partial least squares structural equation modelling approach for the data analysis.

The findings provide valuable insights, highlighting the important role of CNS and ECs in driving higher education students’ green consumption behaviour in Ghana. This study also found a revealing role for gender as a moderator in the relationship between CNS and green consumption behaviour, with females exhibiting a more pronounced response to CNS in influencing green consumption behaviour. On the contrary, the authors found a non-significant impact of perceived behavioural control and social influence.

Although this study presents results that provide valuable insights for policy and practical implications, it has some limitations worth mentioning for future research directions. Firstly, the participants sampled for this study comprised only higher education students from the University of Cape Coast in Ghana, which may limit the applicability of the findings to other student populations at various universities in Ghana and beyond. Moreover, the exclusion of non-students who are considered as “Generation Z” (i.e. born within 1995–2010) may narrow the scope of generalisability in the context of young consumers’ green consumption behaviour in Ghana. To enhance the generalisability of future studies, it is recommended that the scope of this study be extended. Furthermore, it should be noted that this study primarily measured higher education students’ green consumption behaviour based on self-reported data. Therefore, future research could adopt alternative approaches, such as non-self-reported measures or experimental data so to reduce the complexities and the gap that may exist between attitudes and behaviour.

These results provide valuable insights for policymakers, educators and environmental advocates to develop targeted initiatives that resonate with Ghanaian higher education students to foster green consumption practices and contribute to global efforts against marine plastic pollution.

The novelty of this study lies in the decision to propose a TPB model by including variables like CNS and EC that are believed to positively shape attitudes towards green consumption behaviour. The rationale for examining these variables is grounded in the belief that they are appropriate factors that may predict students’ green consumer behaviour, which may serve as a potential solution to marine plastic pollution.

Following behavioral finance and monetary wisdom, the authors theorize: Decision-makers (investors) adopt deep-rooted personal values (the love-of-money attitudes/avaricious financial aspirations) as a lens to frame critical concerns (short-term and long-term investment decisions) in the immediate-proximal (current income) and distal-omnibus (future inheritance) contexts to maximize expected utility and ultimate serenity across context, people and time.

The authors collected data from 277 active equity traders (professional money managers and individual investors) in Pakistan’s two most robust investment hubs—Karachi and Lahore. The authors measured their love-of-money attitude (avaricious monetary aspirations), short-term and long-term investment decisions and demographic variables and collected data during Pakistan's bear markets (Pakistan Stock Exchange, PSX-100).

Investors’ love of money relates to short-term and long-term decisions. However, these relationships are significant for money managers but non-significant for individual investors. Further, investors’ current income moderates this relationship for short-term investment decisions but not long-term decisions. The intensity of the aspirations-to-short-term investment relationship is much higher for investors with low-income levels than those with average and high-income levels. Future inheritance moderates the relationships between aspirations and short-term and long-term decisions. Regardless of their love-of-money orientations, investors with future inheritance have higher magnitudes of short-term and long-term investments than those without future inheritance. The intensity of the aspirations-to-investments relationship is more potent for investors without future inheritance than those with inheritance. Investors with low avaricious monetary aspirations and without inheritance expectations show the lowest short-term and long-term investment decisions. Investors' current income and future inheritance moderate the relationships between their love of money attitude and short-term and long-term decisions differently in Pakistan's bear markets.

The authors help investors make financial decisions and help financial institutions, asset management companies, brokerage houses and investment banks identify marketing strategies and investor segmentation and provide individualized services.

Professional money managers have a stronger short-term orientation than individual investors. Lack of wealth (current income and future inheritance) motivates greedy investors to take more risks and become more vulnerable than non-greedy ones—investors’ financial resources and wealth matter. The Matthew Effect in investment decisions exists in Pakistan’s emerging economy.

Surface acoustic wave (SAW) sensors have attracted great attention worldwide for a variety of applications in measuring physical, chemical and biological parameters. However, stability has been one of the key issues which have limited their effective commercial applications. To fully understand this challenge of operation stability, this paper aims to systematically review mechanisms, stability issues and future challenges of SAW sensors for various applications.

This review paper starts with different types of SAWs, advantages and disadvantages of different types of SAW sensors and then the stability issues of SAW sensors. Subsequently, recent efforts made by researchers for improving working stability of SAW sensors are reviewed. Finally, it discusses the existing challenges and future prospects of SAW sensors in the rapidly growing Internet of Things-enabled application market.

A large number of scientific articles related to SAW technologies were found, and a number of opportunities for future researchers were identified. Over the past 20 years, SAW-related research has gained a growing interest of researchers. SAW sensors have attracted more and more researchers worldwide over the years, but the research topics of SAW sensor stability only own an extremely poor percentage in the total researc topics of SAWs or SAW sensors.

Although SAW sensors have been attracting researchers worldwide for decades, researchers mainly focused on the new materials and design strategies for SAW sensors to achieve good sensitivity and selectivity, and little work can be found on the stability issues of SAW sensors, which are so important for SAW sensor industries and one of the key factors to be mature products. Therefore, this paper systematically reviewed the SAW sensors from their fundamental mechanisms to stability issues and indicated their future challenges for various applications.

With the increasing number of Web services, correct and efficient classification of Web services is crucial to improve the efficiency of service discovery. However, existing Web service classification approaches ignore the class overlap in Web services, resulting in poor accuracy of classification in practice. This paper aims to provide an approach to address this issue.

This paper proposes a label confusion and priori correction-based Web service classification approach. First, functional semantic representations of Web services descriptions are obtained based on BERT. Then, the ability of the model is enhanced to recognize and classify overlapping instances by using label confusion learning techniques; Finally, the predictive results are corrected based on the label prior distribution to further improve service classification effectiveness.

Experiments based on the ProgrammableWeb data set show that the proposed model demonstrates 4.3%, 3.2% and 1% improvement in Macro-F1 value compared to the ServeNet-BERT, BERT-DPCNN and CARL-NET, respectively.

This paper proposes a Web service classification approach for the overlapping categories of Web services and improve the accuracy of Web services classification.

The purpose of this paper is to study the high temperature oxidation behavior of Ti and C-added FeCoCrNiMn high entropy alloys (HEAs).

Cyclic oxidation method was used to obtain the oxidation kinetic profile and oxidation rate. The microstructures of the surface and cross section of the samples after oxidation were characterized by X-ray diffraction (XRD) and scanning electron microscope (SEM).

The results show that the microstructure of the alloy mainly consisted of FCC (Face-centered Cubic Structure) main phase and carbides (M₇C₃, M₂₃C₆ and TiC). With the increase of Ti and C content, the microhardness, strength and oxidation resistance of the alloy were effectively improved. After oxidation at a constant temperature of 800 °C for 100 h, the preferential oxidation of chromium in the chromium carbide determined the early formation of dense chromium oxide layers compared to the HEAs substrate, resulting in the optimal oxidation resistance of the TC30 alloy.

More precipitated CrC can preferentially oxidize and rapidly form a dense Cr₂O₃ layer early in the oxidation, which will slow down the further oxidation of the alloy.

The purpose of the study is to analyse the impact of COVID-19 on the hospitality industry during the rise of worldwide pandemic crises using Twitter analysis. The study is based on 57,794 English-language tweets mined from Twitter from 1 April 2020 to 15 October 2020. Based on thematic and sentiment analysis, the study found that overall sentiments expressed on Twitter were negative. This chapter contributes to existing knowledge about the COVID-19 crisis and broadens the respondents’ understanding of the potential impacts of the crisis on the most vulnerable tourism and hospitality industry. This research emphasises the sustainable revival of the hospitality industry.