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This study aims to investigate the factors influencing the adoption of online pharmacies in Qatar using the unified theory of acceptance and use of technology-2 (UTAUT-2) framework. Specifically, this study examines the impact of performance expectancy, effort expectancy, social influence, hedonic motivation, habit, technology trust, perceived risk and users’ level of awareness of behavioral intention, which in turn affects the adoption of online pharmacies. Furthermore, this study explores the moderating role of word-of-mouth (WOM) recommendations on the relationship between behavioral intention and online pharmacy adaptation.

This study adopted a descriptive, quantitative approach to investigate the UTAUT-2 model in the context of consumers’ adoption of e-pharmacy in Qatar. Through convenience sampling, 455 responses were collected from regular customers accessing online pharmacy services. The data were analyzed using Smart-PLS 3.2 software to examine the hypothesized relationships.

The results showed that WOM recommendations significantly enhanced the relationship between behavioral intention and adopting online pharmacies in Qatar. This study identified the factors that may hinder or enable the adoption of online pharmacies, including performance expectancy, effort expectancy, social influence, hedonic motivation, habit, technology trust, perceived risk and users’ level of awareness.

This study contributes to the existing literature on technology acceptance by extending the UTAUT-2 model and recognizing three additional variables (perceived risk, technology trust and technology awareness). These need to be investigated against UTAUT-2 variables to detect the significance of their impact on adapting the e-health concept in Qatar. The potential for cultural change to accelerate the adoption of online pharmacies is highlighted. Future research should explore the role of moral and cultural factors in technology adoption.

The results underscore the economic and social significance of e-pharmacy adoption, particularly within the context of a developing country. Considering the positive intentions expressed by individuals toward e-pharmacy, it becomes crucial for managers and decision-makers to make strategic choices to address any challenges that may arise. Policymakers are encouraged to enhance their services and implement various development initiatives to expand e-pharmacy accessibility and availability.

This study builds upon previous research on e-commerce in the pharmaceutical industry and provides a comprehensive understanding of customers in developing countries. Extending the UTAUT-2 model and identifying additional variables contributes to the knowledge of e-health concepts in Qatar. The findings have practical implications for developing strategies to promote online pharmacy adoption in Qatar and other countries.

This chapter of the book seeks to use famous mathematical functions (statistical distribution functions) in evaluating and analyzing supply chain network data related to supply chain management (SCM) elements in organizations. In other words, the main purpose of this chapter is to find the best-fitted statistical distribution functions for SCM data. Explaining how to best fit the statistical distribution function along with the explanation of all possible aspects of a function for selected components of SCM from this chapter will make a significant attraction for production and services experts who will lead their organization to the path of competitive excellence. The main core of the chapter is the reliability values related to the reliability function calculated by the relevant chart and extracting other information based on other aspects of statistical distribution functions such as probability density, cumulative distribution, and failure function. This chapter of the book will turn readers into professional users of statistical distribution functions in mathematics for analyzing supply chain element data.

The tech savvy generation Z consumers constitute a significant market of online shopping. Understanding their shopping behavior is thus a pressing need to expand the e-commerce business. Under this backdrop, the study aims to predict the online shopping behavior of generation Z customers in Bangladesh.

This study used the theory of consumption values (TCV) along with shopping enjoyment to understand the online shopping behavior of generation Z. A structured set of questionnaire was used to gather the responses on a five point Likert scale. A total of 411 valid responses were considered after discarding incomplete responses. The collected data were analyzed using the partial least squares structural equation modeling (PLS-SEM) approach with the help of smart PLS 4.0 software.

The statistical findings reveal that functional value is the most significant determinant of online shopping behavior among generation Z followed by social value, conditional value and epistemic value. The study also reveals that relationship between emotional value and online shopping behavior and relationship between conditional value and online shopping behavior is moderated by shopping enjoyment.

The paper contributes to the consumer behavior literature as the findings provide a comprehensive model from values perspectives to understand online shopping behavior among Gen Z customers in a developing country like Bangladesh. The findings of this study offer important insights to the marketers also since it reveals the values consumers consider while shopping online. The findings might help practitioners develop their online strategies to expand the business.

This study aims to assess the risk profile of millennial investors residing in the Delhi NCR region. In addition, the relationship between the risk profile and demographic traits of millennial investors was also analyzed.

Data was collected using a structured questionnaire segregated into two sections. In the first section, millennials were asked questions on socio-demographic factors, and the second section contained ten Likert-type statements to cover the multidimensionality of financial risk. Factor analysis and one-way ANOVA were used to analyze the primary data collected for this study.

The findings indicate that the risk profile of millennials is mainly affected by three factors: risk-taking capacity, risk attitude and risk propensity. Except for educational qualification and occupation, all other demographic features, such as age, gender, marital status, income and family size, seem to significantly influence the factors defining millennials' risk profile.

Uncertainty is inherent in any financial decision, and an investor’s willingness to deal with these variations determines their investment risk profile. To make sound financial decisions, it is mandatory to understand one’s risk profile. The awareness of millennials' distinctive risk profile will come in handy to financial stakeholders because they account for one-third of India’s population, and their financial decisions will shape the financial world for the decades to come.