Consumer Behavior in Online Risky Purchase Decisions: Exploring Trustworthiness Across Culture

Consumer Behavior in Online Risky Purchase Decisions: Exploring Trustworthiness Across Culture

Kenneth David Strang
Copyright: © 2018 |Pages: 26
DOI: 10.4018/IJOM.2018040101
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There is very little research about how consumers of different races trust online marketing information from businesses or government when making expensive purchase decisions such as cancer treatment medicine. In this article, a large cross-cultural sample was surveyed to evaluate trust belief levels for common online information sources when making risky purchase decisions. Trust belief levels of online information sources were significantly different across ethnicity and gender when making risky decision. Females across all ethnicities held higher trust beliefs for online information sources, and Asian females in particular had the highest trust beliefs for online data from library research to health care providers. Trust belief levels were lower for online social media and bank/financial institution online information sources for risky purchase decisions. These findings can be used by leaders, political authorities, and consumer behavior marketing managers to segment consumers by demographic characteristics.
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Most people around the world who access the Internet are influenced by the trustworthiness of online information when making risky marketing decisions such as purchasing expensive cancer treatment medicine. In complex dilemmas it is difficult to find unbiased online data so when forced to make a decision in the context of uncertainty most people are influenced by their trust of the information sources.

High level decision makers such as surgeons, CEO’s, presidents, security officers, emergency responders and others are often pressured to make decisions online without complete data by trusting whatever information is available (e.g., through email, smart phones, e-commerce or Internet-based applications). Ordinary citizens impact the decision making of leaders through shareholder, voting or public opinion mechanisms. Trust bias in high stakes decisions can be costly for stakeholders. The research question arising from the above is how does trust of online information data impact risky marketing decision making?

This is an important to examine from a stakeholder perspective because most professionals in today’s digital world across all cultures and disciplines access the Internet and rely on online data to make high stakes decisions. This is also an important facet to study from a community of practice standpoint because there is very little scholarly literature focused on trust in online data when making risky marketing decisions. Surprisingly a search of peer reviewed academic journals using the EBSCO index returned only 123 manuscripts since 2004 when searching for ‘trust and decision making and (internet or online)’ when allowing search within text as well as equivalent subjects. Most of those 123 papers concentrated on developing causal mathematical models of hypothesized latent predictors that would be difficult to apply in the real world for understanding or managing decision making by the general population using online data. Cognitive trust in the decision-making process is difficult to externalize through structural models so an alternative is to evaluate identifiable priorities and factors across demographic characteristics.

In this study, the literature was reviewed to compare the factors, theories and approaches that researchers have used to understand how trust applies to high stakes decision making in the context of online information and uncertainty. A survey was employed to collect data from a large sample of Americans with different cultural backgrounds to investigate the relationships between trust of online information sources and demographic characteristics in high stakes decision making. A systematic sample frame of the general population across the USA was preferred over interviewing high-level decision makers because the former provided much larger coverage and the latter was difficult to implement as well as potentially limiting where the results could be generalized.

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