Organizational Learning Curves for Customer Dissatisfaction: Heterogeneity Across Airlines
AbstractIn the extensive literature on learning curves, scholars have ignored outcome measures of organizational performance evaluated by customers. We explore whether customer dissatisfaction follows a learning-curve pattern. Do organizations learn to reduce customer dissatisfaction? Customer dissatisfaction occurs when customers' ex ante expectations about a product or service exceed ex post perceptions about the product or service. Because customers can increase expectations over time, customer dissatisfaction may not decline even when the product or service improves. Consequently, it is an open question whether customer dissatisfaction follows a learning-curve pattern. Drawing from the literatures on learning curves and organizational learning, we hypothesize that customer dissatisfaction follows a U-shaped function of operating experience (Hypothesis 1), that focused airlines learn faster to reduce customer dissatisfaction than full-service airlines (Hypothesis 2), and that organizational learning curves for customer dissatisfaction are heterogeneous across airlines (Hypothesis 3). We test these hypotheses with quarterly data covering 1987 to 1998 on consumer complaints against the 10 largest U.S. airlines. We find strong support for Hypothesis 1 and Hypothesis 3. Hypothesis 2 is not supported in the sense that the average focused airline did not learn faster than the average full-service airline. However, we do find that the best focused airline learns faster than the best full-service airline. We explore this result by extending a knowledge-based view of managing productivity learning curves in factories to complaint learning curves in airlines.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoArticle provided by INFORMS in its journal Management Science.
Volume (Year): 52 (2006)
Issue (Month): 3 (March)
learning curve; organizational learning; customer complaints; customer dissatisfaction; airlines;
You can help add them by filling out this form.
CitEc Project, subscribe to its RSS feed for this item.
- Xiujian Chen & Shu Lin & W. Robert Reed, 2010.
"A Monte Carlo evaluation of the efficiency of the PCSE estimator,"
Applied Economics Letters,
Taylor and Francis Journals, vol. 17(1), pages 7-10.
- Xiujian Chen & Shu Lin & W. Robert Reed, 2006. "A Monte Carlo Evaluation of the Efficiency of the PCSE Estimator," Working Papers in Economics 06/14, University of Canterbury, Department of Economics and Finance.
- Robert S. Huckman & Bradley R. Staats, 2008. "Variation in Experience and Team Familiarity: Addressing the Knowledge Acquisition-Application Problem," Harvard Business School Working Papers 09-035, Harvard Business School.
- Cheng, Kuangnen & Lee, Zu-Hsu & Shomali, Hamid, 2012. "Airline firm boundary and ticket distribution in electronic markets," International Journal of Production Economics, Elsevier, vol. 137(1), pages 137-144.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Mirko Janc).
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.