Shakira Irfana
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Online fashion consumerism among women: The interplay of digital experiences and decision-making – a mediated moderated analysis
Madhura K., Niyaz Panakaje
, S. M. Riha Parvin
, Shakira Irfana
, Mural Henrita Cutinha
, Yatheen A
, Rovina Sharon Soans
doi: http://dx.doi.org/10.21511/im.20(4).2024.21
This research examines how digital experiences on social media influence women’s buying behavior towards online fashion. Furthermore, it examines how challenges moderate these purchase decisions, and how attitudes mediate these purchase decisions. The research executed in Southern Karnataka state of India obtained the responses from 800 women employed through a formal questionnaire and a systematic method of selection. The measurement models and structural models were examined using AMOS 23. The study indicates that when women engage with social media and gain easy access to information and feedback through reviews, they develop a stronger positive outlook on buying fashion. These favourable opinions lead directly to consumer buying choices and additionally shape the relationship between the digital experience and their purchasing patterns on online sites. The issues regarding online shopping discourage the transformation of positive views into real transactions. The results reveal that ease of use, exemplified by intuitive navigation (β = 0.188) and easy access to product information, positively affect consumer attitudes. Electronic word-of-mouth (E-WOM), including reviews and recommendations (β = 0.456), also significantly boosts trust, leading to higher purchase intentions. Attitude acts as a strong mediator, with its effect on buying behavior showing a β coefficient of 0.609. However, challenges like concerns over data privacy and difficulties in verifying review authenticity negatively affect the attitude-behavior relationship, with a moderating effect of β = -0.092. The findings show the necessity for more robust data protection laws and better accessibility of online reviews to minimize seen threats and enhance shopper trust in digital clothing shopping.
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Robo-advisors and investment decisions: Assessing the impact of the “snakebite” effect and social-emotional well-being & resilience
Niyaz Panakaje, S. M. Riha Parvin
, Niha Sheikh
, Shakira Irfana
, Madhura K.
, Jeevan Raj
, Tushar Soubhari
doi: http://dx.doi.org/10.21511/imfi.22(1).2025.34
Investment Management and Financial Innovations Volume 22, 2025 Issue #1 pp. 453-468
Views: 70 Downloads: 16 TO CITE АНОТАЦІЯConsidering the snakebite effect experience of investors and their decision-making in the era of robo-advisors, this study focuses on examining the mediating role of the snakebite effect between the value of robo-advisors and investment decisions and assessing the moderation of social-emotional well-being and resilience among active investors. The research process began with an exhaustive review of existing literature and the development of a structured questionnaire. A further survey was undertaken by collecting 361 responses from active investors residing in the region of South India using robo-advisors, and finally, the mediation and moderation were analyzed utilizing confirmatory factor analysis (CFA) to check the model fit and Structural Equation Modelling (SEM) to test hypothetical relationships. The results validate the intervening role of the Snakebite Effect in the relationship between the value of Robo-Advisors and investment decision-making. Further, social emotional well-being and resilience of investors significantly lessen the negative impact of the snakebite effect on investment decision-making. The role of social-emotional well-being and resilience is vital as high tendency leads to a low snakebite effect, better effectiveness of robo-advisors, and investment decision-making. This study provides various theoretical, practical, and managerial implications for improved robo-advisory services and increased adoption among diverse investor segments. In particular, the study emphasizes that financial institutions should focus on hybrid advisory models that combine the analytical capabilities of robo-advisors with the empathetic, personal touch of human advisors.
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