Abstract
Online retailing has been continuously increasing in India, and easy access to the internet has completely overhauled the status of online shopping in the country. Based on the technology acceptance model, this chapter attempts to outline the factors influencing the buying decisions of women consumers in the Tirupathi district in Andhra Pradesh. The study throws light on various factors, namely technology, security, social media, convenience, buying intentions, which influence women purchasing decisions. The chapter uses convenience sampling to select 390 sample units and uses Karl Pearson correlation, linear regression, multiple regression, and SEM for the analysis. The results showed that technological factors, convenience, and security significantly affected buying intentions of online women consumers. In turn, buying choices significantly positively impacted buying decisions. However, social media has a poor effect on their buying intentions. The study has several theoretical and practical implications for future marketing researchers.
TopIntroduction
The Internet has changed customers' shopping habits worldwide (Heinonen, 2011). It is the best retail and business platform available (Rowley, 1998). The millennial generation is more obsessed with the Internet than any other generation in history (Hsieh et al., 2013). Consumers are becoming more comfortable with virtual experiences, which leads to online purchases (Assocham, 2014). According to Lee and Zhang (2002), Web commerce has engulfed the entire online center. Online retailing, often known as online shopping or e-commerce, is a system that allows customers to buy products from shops over the Internet and mobile applications (Dissanayake & Singh, 2007; Park et al., 2007). In the year 1979, Michael Aldrich was the one who invented online selling.
Computer literacy and internet knowledge have become a necessity in today's world, and their importance has been steadily expanding in India. Buying products or services online is similar to buying them in a brick-and-mortar store or shopping center; it is known as business-to-consumer (B2C) online shopping (Sharman et al., 2019). Flipkart, Amazon, Snapdeal, Myntra, and other major online retailers are among the most popular. Retailing is no longer restricted to physical stores; it has expanded to include the sale of goods and services through the Internet, including online payment options (Tandon & Kiran, 2018). As consumers grow every day, online retailers find it increasingly difficult to attract and retain them. India has wholly embraced online retailing because of massive internet penetration growth. According to the reports, India's retail sector is expected to hit between 1.1 and 1.3tn dollars by 2025, up from 0.7tn dollars in 2019, with a CAGR of 9% to 11% (BCG, 2020; 2021). In the country, online retailing is rapidly growing. At this point, the authors decided to explore women online customers who contribute significantly to online sales for any retailer. The study looked at the numerous aspects that influence their attitudes on online shopping. The task at hand is to figure out what motivates online shopping to help retailers increase sales. What variables influence people's purchasing decisions when shopping online (Zhang et al.,2014)? This study investigates the factors influencing female online shopping behavior because women customers play a significant role in online shopping. Their purchasing behavior is critical to increasing sales for any retailer (Tandon & Kiran, 2019).
Many studies have spent a long time attempting to comprehend consumers' attitudes regarding purchasing intent (Jansen, 2006; Tauber, 1972). Consumer online behavior has been linked to various models in the literature, such as the Technology Acceptance Model (Koufaris, 2002), the Extended Technology Acceptance Model (Pavlou, 2003), Theories of Planned Behaviour (Ajzen, 1991; Pavlou & Fygenson, 2006), Perceived Behavioral Control (Pavlou & Fygenson, 2006), and Perceived Behavioral Control (George, 2004; Yang et al., 2007). Customers' purchasing intent on social media sites has also been studied using Gratification Theory (Lim & Ting, 2012) and behavioral decision-theoretic viewpoints (Khan et al., 2005).
According to Javadi et al. (2012), online customer behavior to increase sales is complex, including social and technological elements. The writers attempted to investigate the aspects that influence online shopping behavior to increase sales in this chapter. In addition, the chapter discusses female internet shopping habits. This chapter's conclusions will be helpful to internet retailers in increasing their sales. The following is a breakdown of the chapter's structure: The study's literature review is discussed in the first section. The methodology, findings, and analyses are described in the following section. The results and their theoretical and practical consequences, limitations, and future research directions are discussed in the last part.
Key Terms in this Chapter
Buying Intentions: The likelihood of a consumer purchasing a product or service is referred to as buying intent. Marketers use predictive modelling to determine the likelihood of future events based on historical data to analyses buy intent.
B2C: Business-to-consumer (B2C) refers to the process of selling items and services directly between a business and the end user’s consumers.
Online Retailing: The sale of goods and services over the internet is known as online retailing. Business-to-business (B2B) and business-to-consumer (B2C) sales of items and services are examples of e-tailing.
Online Buying Behavior: During online shopping, online shopping behavior refers to an individual's overall view and evaluation of a product or service.
Technology: Electronic commerce (ecommerce) is a business strategy that allows consumers and businesses to purchase goods and services via the internet.
Security: Secure online transactions may validate their clients' card information without actually seeing it, protecting them from account theft, hacking, and other illicit activities.