1 Be transparent
People are skeptical of how their information is used. Be upfront about the information you collect and why and how it is used. Allow them to control what they share and let them remain anonymous if they choose. Provide options to trade functionality for increased security and, as a general rule, always ask permission.
2 Be accurate
The level of trust shoppers have in an application is affected by how well it matches their mental model of the expected experience. If the application doesn’t map to the expectations of the shoppers, they will question its accuracy. While we won’t always have control over how shoppers interpret the interface or features, we can mitigate the risk of losing trust by allowing them to control the experience. Pervasive applications are not intended for the constant attention of the shopper, so when they choose to view and interact with your application, it better accurately meet their expectations.
3 Be responsive
Trust in the application erodes when the system is slow to respond. Any lag in the time it takes for a response from the application chips away a little bit of its perceived value. Slowness and lag are inevitable. If we provide appropriate feedback that the system is still working to catch up, the shopper will be far more forgiving. Remember, these types of applications are used on the go so the system needs to keep up with the pace of the shopper. Just think if in-store navigation was two turns behind. You wouldn’t use a GPS program in your car if that was the case. As a rule, if it can’t keep up, it hinders more than it helps and will be abandoned.
11 references informed this principle
 Angin, Pelin; Bhargava, Bharat & Helal, Sumi, A Mobile-Cloud Collaborative Traffic Lights Detector for Blind Navigation, 2010 Eleventh International Conference on Mobile Data Management, 396--401, 2010.
 Duan, Yitao & Canny, John, Protecting User Data in Ubiquitous Computing: Towards Trustworthy Environments, Privacy Enhancing Technologies, 2005.
 Lim, Brian, Improving Understanding, Trust, and Control with Intelligibility in Context-Aware Applications, Human-Computer Interaction, May 2011.
 Lo Presti, S; Butler, M; Leuschel, M & Booth, C, A Trust Analysis Methodology for Pervasive Computing Systems, 129-143, 2005.
 Octavia, J & Vanacken, Lode, Facilitating adaptation in virtual environments using a context-aware model-based design process for User Interface Design, 2010.
 Ohanian, R & Tashchian, A, Consumers' Shopping Effort and Evaluation of Store Image Attributes: The Roles of Purchasing Involvement and Recreational Shopping Interest, Journal of Applied Business, 2011.
 Rosi, Alberto; Mamei, Marco; Zambonelli, Franco; Dobson, Simon; Stevenson, Graeme & Ye, Juan, Social sensors and pervasive services: Approaches and perspectives, 2011 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), 525-530, March 2011.
 Roussos, G., Enabling RFID in Retail, Computer, Vol. 39, No. 3, 25-30, March 2006.
 Roussos, George & Moussouri, Theano, Consumer perceptions of privacy, security and trust in ubiquitous commerce Personal and Ubiquitous Computing, Vol. 8, No. 6, 416-429, September 2004.
 Personalized In-Store E-Commerce with the PromoPad: an Augmented Reality Shopping Assistant, W. Zhu, C. Owen, L Hairong, J. Lee, 2004.
 Building Trust in Pervasive Retail, G. Roussos, November 2004.