Effect of women’s labor market status on online grocery shopping, the case of Turkey

Abstract

The rate of Internet purchasing is increasing day by day. One of the reasons of that increase could be individuals' intention to allocate time effectively in order to maximize their utility function. Within this context, an employed woman would aim to spend less time on household chores such as shopping compared to the ones who are not active in the labor market. When women's age, education and other demographic characteristics are held constant, being employed for a woman is expected to have a positive effect on the increase of Internet purchases. For this purpose, 2004-2013 Household Information Technologies Use Survey, obtained from Turkish Statistical Institute, has been used to estimate the probability of making online grocery shopping with Heckman's two step probit methods. According to the findings, being employed has a positive effect on the probability of using Internet and making online grocery shopping. In addition, as education increases, probability of using Internet and probability of making online grocery shopping increases. The probability of using Internet and making online grocery shopping also increases with age but at a decreasing rate. Moreover, while living in urban areas positively affects online grocery shopping, the number of household size has a negative impact.

Spatial models for online retail churn: Evidence from an online grocery delivery service in Madrid

“…The results, which are in the second column of Table 3, are clear: First, individuals living in economically depressed postal codes tend to be less loyal and stop using the online grocery delivery service more frequently than in higher‐income postal codes. Similar patterns were found in İlhan and İşçioğlu (2015) and Kee and Wan (2004), who both concluded that, as levels of income increase, the probability of grocery shopping online also increases. Our data provides evidence of the varying purchasing power of different areas of Madrid and the differing lifestyle choices in different neighbourhoods.…”

“…This information must be treated carefully to understand the patterns and behaviours behind it. Some papers have used a classic set of variables to predict customer behaviour (De la Llave, López, & Angulo, 2019;_ Ilhan & _ Işçio glu, 2015;Miguéis et al, 2012;Richards, Hamilton, & Allender, 2014;Trivedi, 2011). For example, the specific date when a customer signs up for a service and the specific context of that moment may reveal something interesting about their behaviour.…”

A scoping review of online food retail: Who’s using it, what they are buying, and what tactics are used to promote healthy or unhealthy purchases

“…However, two studies [36,52] reported that greater household size was associated with lower online grocery use. While three studies [52][53][54] found that being employed was associated with using online grocers, one contradictory study [77] found that unemployment was associated with using online grocers.…”

“…However, two studies [36,52] reported that greater household size was associated with lower online grocery use. While three studies [52][53][54] found that being employed was associated with using online grocers, one contradictory study [77] found that unemployment was associated with using online grocers. Female gender identity, higher income, higher education, living in an urban setting, disabilities that make it difficult to leave home, limited free time, living far from a preferred store, familiarity with online shopping, frequent shopping, and large orders were all associated with greater online grocery use.…”

Evaluation of Awareness, Attitudes and Behaviors Related to Covid-19 Pandemic in Society

“…Her 3 kişiden 2'si zorunlu ihtiyaçlar için dışarı çıktığını belirtmiştir. Telefon ya da internet üzerinden market alışverişinin çalışanlarda ve yüksek eğitimlilerde daha sık olduğu bilinmektedir (30). Ülkemizde internet üzerinden alışveriş oldukça yaygınlaşmış olmakla birlikte market alışverişi için kullanım düşüktür (31) (25,26).…”