It is important to ensure that consumers trust the way the company uses their data; when giving consent, they expect that the company will be transparent and fair with their data. If the question arises about exploitation, the users will lose their trust in the company, especially if the data was used without consent and for the wrong purposes, like selling it to other customers. When the company prioritizes consumer trust, it builds long-term loyalty and brand image. An example of the scenario is an e-commerce platform that tracks user behavior and preferences to provide personalized recommendations. However, if the platform secretly sells this data to third parties without informing users, it violates their trust and privacy expectations. Since e-commerce reaches a much larger audience, the company should respect community values and norms, ensuring there is no discrimination against certain users. A global e-commerce platform might use algorithms to suggest products based on trending items. However, if these recommendations inadvertently promote culturally insensitive or offensive products, it can lead to backlash from specific communities and damage the platform’s reputation.
Without robust policies, there is a risk of unethical and biased practices and misuse of consumer data. There should be clear internal policies regarding the data governance framework that clarify who owns what and who is held accountable. Data security protocols should outline the protection against unauthorized access or misuse. Data use sharing policies that establish proper consent to use the data with consumers’ knowledge. With these guidelines, there is less risk that the company will be unethical. An e-commerce platform lacks strict policies on data protection and algorithmic fairness. As a result, its predictive models unintentionally discriminate against certain demographics, leading to unequal treatment of users based on race or gender. Staying ahead of the game in the e-commerce market usually uses predictive models to make better decisions. Still, using the models should be done ethically. For example, a platform uses algorithms to manipulate search rankings, favoring products from sellers who pay higher fees. This undermines fair competition and deceives consumers into believing certain products are more popular or relevant than they are. There should be a clear balance between the desire to compete and the desire to have fair competition. Furthermore, ensure the company is not using data to spy on other companies to steal their strategies, etc.
In summary, ethical considerations should guide the implementation of predictive models in e-commerce to ensure consumer trust is maintained, community values are respected, organizational policies are upheld, and competitive advantage is pursued ethically. E-commerce platforms can foster trust, build stronger customer relationships, and sustain long-term market success by prioritizing these ethical principles.
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Learn more about personas by exploring the following research:
Salminen, J., Sercan, S., Jung, S.G., and Jansen, B. J. (2021) Comparing Persona Analytics and Social Media Analytics for a User-Centric Task Using Eye-Tracking and Think-Aloud. CHItaly2021, 11-13 July 2021, Remote via Internet & Bozen-Bolzano, Italy. Jung, S.G., Salminen, J., and Jansen, B. J. (2021) Persona Analytics: Implementing Mouse-tracking for an Interactive Persona System. ACM CHI Conference on Human Factors in Computing Systems (CHI2021), Yokohama, Japan. 8-13 May. Article No.: 342.