The management information systems (MIS) department has played a role that supports information systems development, functionality and business operations in the organization. However, information system development (ISD) projects have a high failure rate because the users¡¦ needs cannot be met. Most of the previous literature indicated that users should be involved and participate in ISD projects to enhance project performance. Nevertheless, the failure rate of ISD projects is still high because of the complex relationship between users and developers. To deal with this problem, previous studies indicated that co-production behavior can facilitate project performance and success. They did not discuss the drivers that influence users to work as co-producers in ISD projects. Different from previous studies, and based on the MARS model concept adopted from the organizational behavior field, this study applies some drivers of co-production and implies that the information system is co-produced by users and developers. Via the MARS model, we attempt to explore the critical role of motivation, IT knowledge, role clarity, and organizational support in influencing user co-production behavior. We also expected that co-production behavior has an influence on system quality and user satisfaction from the perspective of service-dominant logic. Data collected from 178 users confirmed our hypothesis that co-production behavior is positively associated with user satisfaction and system quality, and extrinsic motivation has a positively significant influence on user satisfaction co-production behavior. Finally, the implications for academia and practitioners are also provided.

In high dimensional data modeling, Multivariate Adaptive Regression Splines (MARS) is a well-known nonparametric regression technique to approximate the nonlinear relationship between a response variable and the predictors with the help of splines. MARS uses piecewise linear basis functions which are separated from each other with breaking points (knots) for function estimation. The model estimating function is generated in two stepwise procedures: forward selection and backward elimination. In the first step, a general model including too many basis functions so the knot points are generated / and in the second one, the least contributing basis functions to the overall fit are eliminated. In the conventional adaptive spline procedure, knots are selected from a set of distinct data points that makes the forward selection procedure computationally expensive and leads to high local variance. To avoid these drawbacks, it is possible to select the knot points from a subset of data points, which leads to data reduction. In this study, a new method (called S-FMARS) is proposed to select the knot points by using a self organizing map-based approach which transforms the original data points to a lower dimensional space. Thus, less number of knot points is enabled to be evaluated for model building in the forward selection of MARS algorithm. The results obtained from simulated datasets and of six real-world datasets show that the proposed method is time efficient in model construction without degrading the model accuracy and prediction performance. In this study, the proposed approach is implemented to MARS and CMARS methods as an alternative to their forward step to improve them by decreasing their computing time