Neural Networks and Rules-based Systems used to Find Rational and Scientific Correlations between being Here and Now with Afterlife Conditions
February 17, 2026
Neural Networks and Rules-based Systems used to Find Rational and
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ReserarchID
H2E8E
Mathumita Mukherjee Basu
Crossref Journal DOI 10.17406/GJMBR
Print ISSN 0975-5853
e-ISSN 2249-4588
The methods for personal identification and authentication are no exception.
Neural Networks and Rules-based Systems used to Find Rational and
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There are several factors which influence consumers to buy counterfeit products. Today, any product in any nation is vulnerable to this malady. Counterfeits are packaged and labeled to resemble the original brand-name and generic products. Therefore, fake products often illusion the consumers to thinking that they are buying authentic goods. Counterfeits are a real and looming threat to all manufacturers. Counterfeit policing measures are yet to mature and become omnipresent. With this background information, it is noteworthy to observe how the Theory of Reasoned Action (TRA) could help identify the factors responsible for influencing behavioral intentions of a consumer towards purchasing counterfeit products. The present study reviews existing literature on counterfeit products, identifies potential improvements, and provides further insight into consumer motives behind the purchase of counterfeits. Six primary factors that influence counterfeit purchase have been identified and the TRA has been applied to investigate the impact of these factors on consumer behavioral patterns. The factors are (1) social motivation, (2) personal gratification, (3) perception, (4) value, (5) brand loyalty, and (6) ethics. The ‘influence of society’ and ‘value for money’ have been identified as the top two reasons that motivate consumers to buy fake products based on a survey conducted. A mathematical ‘covariate interactions’ analysis as well as a Chi-square regression analysis corroborated the same finding- identifying the top two factors that most strongly influence a customer’s ‘Intent to purchase’. A logistic regression analysis was run on the survey results that yielded a mathematical expression which can predict how likely a customer is to buy a counterfeit [p(Y)]. The proposed correlation matches the obtained survey data very well.
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