The Effects of Process Orientations on Collaboration Technology Use and Outcomes in Product Development

Abstract

Notwithstanding interest in implementing information technologies (ITs) that facilitate collaboration among employees, there has been limited research that examines how such technologies influence collaboration in strategic business processes, such as new product development (NPD), and how this influence is contingent on the nature of the process. We develop and test a model that posits that employees’ collaboration technology use in NPD will influence collaboration outcomes, such as IT-enabled collaboration capability and collaboration satisfaction. The model postulates that NPD involves multiple process orientations—exploration, exploitation, and ambidexterity—and that these orientations have a moderating effect on the relationship between collaboration technology use and outcomes. We conducted two studies in a major pharmaceutical company (N = 1,749 and 1,454, respectively) and found support for our model. We found that the effects of collaboration technology use on outcomes were stronger for employees involved in the NPD process phase that had an ambidexterity orientation. Our findings contribute to theory and practice related to the use of collaboration technology in the NPD process by offering insights on how process orientations influence the dynamics of the relationship between employees’ use of collaboration technologies and associated outcomes.

Key words and phrases:

• ambidexterity
• collaboration technology
• collaboration capability
• collaboration satisfaction
• innovation
• information technology use
• new product development

Acknowledgments

The authors thank Vladimir Zwass, the Editor-in-Chief, for his guidance, suggestions, and support throughout the review process. They also thank the three anonymous reviewers for their constructive comments and suggestions that significantly helped improve the study.

Supplemental File

Supplemental data for this article can be found on the publisher’s website at 10.1080/07421222.2017.1334494

Notes

1. Cohen f ² is an appropriate effect size for regression-based models. It can be calculated in two ways: (a) R² /(1 – R²), (b) (R² _{full model} – R² _{nested model}) /(1 – R² _{full model}). Given that our analytical models did not include nested models, we calculated Cohen f ² using the first approach.

Additional information

Notes on contributors

Hillol Bala

Hillol Bala ([email protected]) is an associate professor of information systems and the Whirlpool Corporation Faculty Fellow in the Kelley School of Business at Indiana University. He received his Ph.D. from the University of Arkansas.His research interests focus on IT-enabled business process change and management, IT use, adaptation and impacts, and the use of IT in health care. His work has been published or is forthcoming in Information Systems Research, Journal of Management Information Systems, MIS Quarterly, Management Science, and other journals. He has served on the editorial board of Information Systems Research, and other journals, and as a track chair or program committee member of major information systems conferences.

Anne P. Massey

Anne P. Massey ([email protected]; corresponding author) is the Albert O. Nicholas Dean of the Wisconsin School of Business at the University of Wisconsin, Madison. She received her Ph.D. from Rensselaer Polytechnic Institute. Her research focuses on computer-mediated collaboration and the role of IT, including most recently the effects of enterprise social media and 3D virtual environments on team performance. She is also interested in IT-enabled innovation and IT implementation processes. She has received research funding and support from industry as well as National Science Foundation, the Robert Wood Johnson Foundation, and others. Her work has been published in MIS Quarterly, Journal of Management Information Systems, and Academy of Management Journal, among others.

Mitzi M. Montoya

Mitzi M. Montoya ([email protected]) is the Sara Hart Kimball Dean of the College of Business at Oregon State University. She received her Ph.D. from Michigan State University. Her research has focused on innovation processes and strategies and the role of technology as an enabler of distributed team decision making. With a focus on the dynamics and performance of globally distributed work teams, her research has appeared in Management Science, Journal of Marketing Research, Marketing Science, Academy of Management Journal, and MIS Quarterly, among others. She has been the recipient of grants from, among others, the National Science Foundation, U.S. Department of Defense, U.S. Department of Education, and corporate foundations.