Behavioural Profiling of Rural Working Women in an Industrial Estate, Madurai

2026-04-01T11:08:27+00:00

Working women in rural areas are essential to the nation's sustained growth. The Ministry of Labor and Employment reports that while the number of women in the workforce is good at the national level, it is still low globally. Despite these obstacles, rural working women encounter stress, anxiety, and depression due to a lack of employment possibilities, gender inequality, safety concerns, and mental and physical health issues. The methodical process of identifying the reasons behind women's particular reactions is called behavioral profiling. In order to promote teamwork, increase employee performance, and involve people in change management, organizations can use human resource activities such as training, performance reviews, and career development opportunities. Therefore, a study is carried out to investigate the behavior profiling (emotional, cognitive, and motivational components) of rural working women in a South Indian industrial estate. The study follows descriptive research designs and has employed an interview schedule to collect data from 41 employees working in three industries. This study reveals that 68.3% of employees prefer non-monetary benefits. Garrett ranking reveals that problem-solving skills, emotional stability and adaptability are found high among these working women. There is a difference between the rural working women preferring the type of benefit with their emotional state (p = 0.022). There is a moderate positive correlation between emotion and motivation (r = 0.438), cognition and motivation (r = 0.433) at 0.05 level. Researchers had planned a way forward by conducting an intervention through exercises and discussion. Researchers helped employees to set goals and shared strategies to handle stress in day to day life

The Impact of Product Sorting and Cognitive Moderators on Consumer Cart Abandonment in E-commerce: A SEM-Based Analysis

2026-04-01T11:08:27+00:00

The rapid growth of electronic commerce has encouraged online retailers to enhance the functionality and capacity of virtual shopping carts. Despite these advancements, a substantial number of consumers place items in their online carts but fail to finalize their purchases, resulting in what is commonly referred to as online shopping cart abandonment. This study investigates how product sorting influences cart abandonment behaviour and further examines the mediating effects of choice overload and consumers’ tendency to forget items added to their shopping carts. An exploratory and descriptive research design was adopted. Data were collected through an online questionnaire distributed via social media platforms such as Instagram and Facebook Messenger during the period from July to September 2025. Using purposive sampling, 370 completed responses were obtained, of which 20 were excluded after data screening. The final sample exceeded the minimum requirement for large populations. The survey consisted of 22 items measured on a 5-point Likert scale. Structural Equation Modeling (SEM) was employed to test the proposed relationships. The findings reveal a significant positive relationship between product sorting and online shopping cart abandonment. Additionally, choice overload and forgetting were found to mediate this relationship, indicating that extensive sorting options may increase cognitive burden and reduce purchase completion. The study offers practical implications for e-commerce practitioners aiming to optimize sorting mechanisms and enhance user experience. Future research should employ longitudinal or experimental designs, examine additional moderating variables, and conduct cross-cultural studies to improve generalizability and deepen understanding of consumer behaviour in online shopping contexts.

Shanlax International Journal of Management

Behavioural Profiling of Rural Working Women in an Industrial Estate, Madurai

The Impact of Product Sorting and Cognitive Moderators on Consumer Cart Abandonment in E-commerce: A SEM-Based Analysis