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This chapter elaborates the concept of Structural Equation Modelling (SEM) and further validates the model for the sustainability of financial innovation using factors of e-payment system, adoption intention, trust and sustainability, and finally derives a model for the sustainability of the financial innovation using SEM. The chapter also presents Sustainability of E-payment System on Customer Perspective (SEPSCP) through segmentation approach.

Images and caption descriptions serve as important visual stimuli that influence consumer preferences; therefore, the current study focuses on property images and captions illustrated on the home pages of accommodation-sharing platforms. Specifically, this study investigates the relative importance of hue, brightness and saturation of a property image and caption description styles on potential consumers’ preferences.

A mixed-method approach was used, and a total of 293 valid responses were collected through a discrete choice experiment approach. Interviews were conducted for additional analyses to explore the detailed explanations.

The utility model demonstrated that the image’s saturation was the most critical attribute perceived by the respondents, followed by caption description style, hue and brightness.

This is one of the first studies to investigate the display of attributes on a digital accommodation platform by exploring potential customers’ stated preferences. This study focuses explicitly on images and captions illustrated on the home page of an accommodation booking platform. Detailed image investigation is also a new research area in sharing economy-related research.

Artificial intelligence (AI) can enhance human resource resiliency (HRR) by providing the insights and resources needed to adapt to unexpected changes and disruptions. Therefore, the present research attempts to develop a framework for future researchers to gain insights into the actions of AI to enable HRR.

The present study used a systematic literature review, bibliometric analysis, and network analysis followed by content analysis. In doing so, we reviewed the literature to explore the present state of research in AI and HRR. A total of 98 articles were included, extracted from the Scopus database in the selected field of research.

The authors found that AI or AI-associated techniques help deliver various HRR-oriented outcomes, such as enhancing employee competency, performance management and risk management; enhancing leadership competencies and employee well-being measures; and developing effective compensation and reward management.

The present research has certain implications, such as increasing the HR team's proficiency, addressing the problem of job loss and how to fix it, improving working conditions and improving decision-making in HR.

The present research explores the role of AI in HRR following the COVID-19 pandemic, which has not been explored extensively.

This study aims to investigate the relationship between dietary polyamine levels, metabolic risk parameters and anthropometric measurements in postmenopausal women.

This cross-sectional study was conducted with 562 45–64-year-old postmenopausal women who presented to a Family Health Center. To collect the data, the Descriptive Information Form and Food Frequency Questionnaire were used. In the data analysis, numbers, percentages, mean, standard deviation and multiple linear regression analysis were used.

The multiple linear regression analysis demonstrated that dietary putrescine intake was negatively associated with systolic blood pressure (ß = −0.179, p < 0.001), dietary spermidine intake was positively associated with waist circumference (WC) (ß = 0.142, p = 0.013), systolic blood pressure (ß = 0.188, p = 0.001), diastolic blood pressure (ß = 0.218, p < 0.001), body mass index (BMI) (ß = 0.169, p = 0.003) and waist-to-height ratio (WHtR) (ß = 0.156, p = 0.006), and dietary spermine intake was negatively associated with WC (ß = −0.158, p = 0.003), systolic blood pressure (ß = −0.195, p < 0.001), BMI (ß = −0.107, p = 0.042) and WHtR (ß = −0.138, p = 0.009).

Owing to the study’s cross-sectional nature, the lack of succession in the cause–effect relationship, the use of self-report Food Frequency Questionnaire to determine dietary polyamine intake and the inability to analyze seasonal differences are among the limitations of the study.

In this study, an association was determined between dietary polyamines, metabolic risk parameters and anthropometric measurements. The findings suggest that dietary polyamines in human health should be further investigated owing to the increasing metabolic risk parameters.