As a side practice in our scalability and availability engagements we often work with companies on the performance of their SaaS offerings by attempting to speed up their web page load times. Citing a Google white paper, “Speed Matters for Google Web Search” by Jake Brutlag, we point to the fact that even tenths or hundredths of a second matter. Brutlag states that through experiments they have shown that increasing web search latency from 100 to 400 ms reduces the daily number of searches per user by upwards of 0.6%. Given that we are attempting to become practitioner-scholars, in order to bridge the gap between academia and practice, we decided to dive into this subject area a little deeper. Our goal was to understand what other research had been done and if there was anything more practitioners could learn besides “speed up your pages!”
Research in computer system delay has been taking place for decades and has shown that excessive computer system delay results in negative responses such as anxiety (Guynes, 1988) and satisfaction with the system itself (Rushinek & Rushinek, 1986). However, the research does not support the relationships between increases in delay and the attitude toward the company (Rose & Straub, 2001). It was shown that increases in delay treatments from near 0 to 15 seconds did not correlate with a reduction in satisfaction measures such as ease of use or content appeal (Otto et al., 2000). It was also found that increases in delay treatments did not consistently predict likelihood of future patronage (Rajala and Hantula, 2000).
So from all this research we have the notion that delays cause frustration, even anxiety, but yet they don’t appear to cause a decrease in satisfaction or even predict continued usage. Why is this?
Attribution theory, which deals with how individuals infer causality between events (Kelley and Michela, 1980), would explain this phenomenon as the customers assigning blame for the delay to something or someone other than the SaaS provider. This theory has also been used to show that the presence of a self-serving attribution bias and an actor–observer attribution bias in entrepreneurs’ representations of events (Rogoff et al. 2004) but we’ll save that for another post. It turns out that perceived wait time is much more critical than the actual wait time (Baker & Cameron, 1996).
Rose, et al. (2005) content that “it may be less important to reduce objective delay than it is to create a system where users will be less likely to attribute the delay to the retailer.” An example would be to give the user the option of selecting a low or high graphic site in order to provide the users with the control. Users will likely perceive this as an active effort on the part of the SaaS provider to minimize download time and thus attribute delays to themselves, their computer, their ISP, etc but not the site.
Baker, J., & Cameron, M. (1996). “The effects of the service environment on affect and consumer perception of waiting time: An integrative review and research propositions.” Journal of the Academy of Marketing Science, 24, 338–349.
Guynes, J. L. (1988). “Impact of system response time on state anxiety.” Communications of the ACM, 31, 342–347.
Kelley, H. H. and J. L. Michela (1980). “Attribution theory and research.” Annual review of psychology 31(1): 457-501.
Otto, J. R., Najdawi, M. K., & Caron, K. M. (2000). “Web-user satisfaction: An exploratory study.” Journal of End User Computing, 12, 3–10.
Rajala, A. K., & Hantula, D. A. (2000). “Toward a behavioral ecology of consumption: Delay-reduction effects on foraging in a simulated internet mall.” Managerial and Decision Economics, 21, 145–158.
Rogoff, E., Lee, M., and Suh, D. 2004. ““Who Done It?’ Attributions by Entrepreneurs and Experts of the Factors That Cause and Impede Small Business Success,” Journal of Small Business Management (42:4), pp 364-376.
Rose, G.M., & Straub, D. (2001). “The effect of download time on consumer attitude toward the e-service retailer.” e-Service Journal, 1, 55–76.
Rose, G. M., M. L. Meuter, et al. (2005). “On line waiting: The role of download time and other important predictors on attitude toward e retailers.” Psychology and Marketing 22(2): 127-151.
Rushinek, A., & Rushinek, S. (1986). “What makes users happy?” Communications of the ACM, 29, 594–598.