By: Shanta R. Yapa
Despite its popularity as a way to promote innovations, organizations face challenges in their open innovation (OI) initiatives. As knowledge flows are vital for the success of OI initiatives, organizations should identify right partners in their open innovation implementations. This article suggests TASC as a tool that can be used by practitioners for better results.
It is based on findings of a mixed method research conducted where time orientation, alignment, sequential coherence and coordination have been identified as factors that can influence innovation performance of open innovation initiatives.
Open innovation (OI) is a popular practice among organizations to promote innovations. OI has become a trend in innovation management (Lopez & Carvalho, 2018) and it is a strategy for firms (Chesbrough, 2017). It enables organizations to acquire external knowledge to accelerate internal innovations and also to early commercialization of them by partnering with external parties.
However, OI initiatives do not always bring the expected benefits (Yapa et al., 2018). Selecting the right partners to acquire external knowledge as well as for early commercialization can be a challenging and a frustrating exercise. This article offers a novel tool for practicing managers facing this challenge through the findings of a mixed method research done in Sri Lanka on software firms. One of the key objectives of the research was to find out new factors that can explain why OI initiatives record varying innovation performance. The methodology, techniques, data analysis and results of the research are not discussed in this article as the focus is to offer a useful tool for practitioners.
TASC as a New Tool
Named TASC model, it suggests managers to pay attention to time orientation, alignment, sequential coherence and coordination which have been identified as important factors influencing innovation performance. The TASC model is easy to understand, remember and practice. Let us next examine the four factors one by one, with a look at their relevance and theoretical backgrounds.
1: Time Orientation
Have you ever paid attention to time orientation differences among your organization and your OI partner firms? Through a qualitative inquiry, the writer identified the influence of time orientation differences on innovation performance. Differences on expected payback period (West & Gallagher; 2006; Rolland et al., 2019; Shaikh & Levina, 2019), project completion time (Ettlinger, 2017) and also the futuristic thinking (Keupp & Gassmann, 2009) can be identified as the measurable indicators of time orientation. Paying attention to these differences and minimizing them will lead to improved innovation performance in OI initiatives.
The writer empirically tested the influence of alignment of partner firms in OI initiatives on innovation performance and observed a significant impact. Although alignment is not often tested in open innovation research extensively, it is a widely used factor in studying inter-organizational relationships. Higher goal complementarity between partners leads to higher effectiveness of the relationship (Bucklin & Sengupta, 1993). Clearly understood objectives and strategies among partner firms will ensure the necessary information flow (Pullen et al., 2012) for successful co-development of products (Emden et al., 2006). Cooperation between partners is increasingly based on the alignment of goals and objectives of partners (Spekman & Isabella, 2000; Duysters & Man, 2003).
3: Sequential Coherence
Yapa et al. (2019) define sequential coherence as the reciprocal result of the push and pull effects induced by individuals of a teaching firm and the learning firm respectively that enables knowledge to flow across boundary of firms. It can be measured through the ability and willingness to teach by the teacher firm participants and the ability and readiness to learn by the participants of the student firm. Can an organizational equally learn from any other organization? Isn’t it very unlikely? Assume that your organization plans to acquire new knowledge from a university as a potential OI partner. You have three options as university A, B and C. Can you assume that your organization can equally learn from any of those universities? The best OI partner will be the one which can show highest sequential coherence.
Purposive management of knowledge flows across boundaries is necessary in OI (Chesbrough & Bogers, 2014; Lakemond et al, 2016) and the writer argues that sequential coherence can explain and ensure a smooth knowledge flow.
Coordination is the process of managing dependencies among activities (Malon & Crowston, 1994). Successful OI requires firms to develop routines and practices for coordination and collaboration (Lu et al., 2017). Zobel (2017) describes coordination as an important element in absorptive capacity. The writer empirically tested coordination as a factor that can influence innovation performance of OI initiatives.
How to Use TASC as a Tool?
Table 1 below shows how practitioners of OI can use the TASC model. It elaborates findings of a mixed method research the writer conducted. The researcher has re-arranged the order of the focus areas to make it a tool easy to remember by practitioners as TASC (where C is pronounced as K and the model is pronounced as the TASK model).
Table 1: The TASC Model for Open Innovation Practitioners
|Before implementation of OI projects||During implementation of OI projects|
|Time orientation||Managers must understand the expectations of their OI partners in terms of urgency in implementing/completing projects, payback period and also future orientation.||Failing to understand time orientation differences before formally starting OI projects can lead to undesirable consequences. Yet, managers can expedite implementations, and attempt to improve returns as remedial measures.|
|Alignment||Managers should ensure that the goals of the firm and the prospective inbound OI partner firms are aligned and/or complement each other.||By showing some flexibility, managers may attempt to minimize the issues arising in OI projects due to non-alignment of goals.|
|Sequential coherence||Selecting the right teams from the teacher firm and student firm with participants having the ability and willingness to teach/learn is crucial for smooth knowledge flows.||Managers should be able to scan the interfaces where cross border knowledge transfers take place and take corrective actions if they observe any bottlenecks by close monitoring, facilitation, changing teams, motivating etc.|
|Coordination||Managers must ensure that there is access to relevant persons and smooth communication can happen between focal firm and OI partner firms.||Coordination is vital when the OI project is being implemented. A higher degree of coordination can also facilitate the other variables mentioned.|
The above is not an exhaustive list. Managers can think of multiple ways of using the TASC model in such a way that the identified four factors namely time orientation, alignment, sequential coherence and coordination are properly addressed.
Researchers have come up with considerable empirical evidence to demonstrate that open innovation leads to an increase in innovation performance of organizations (Hewitt-Dundas & Roper, 2018). Similarly, there are researchers who argue that OI does not always result in higher innovation performance. The conflicting demands of openness and controls may result in the failure of collaborative initiatives (Lauritzen & Karafyllia, 2019). Increasing number of successful OI implementations and the fast-growing number of research studies on OI proves its importance today and in the years to come (Rexhepi et al., 2019).
Despite its popularity among practicing managers as a way to promote innovations, researchers have also highlighted the challenge organizations face in identifying the right OI partners. This article attempted to share the TASC model as a tool so that managers can be cautious and take appropriate actions to ensure better innovation performance in OI initiatives. Interestingly, managers can use the TASC model in selecting the most appropriate OI partner/s among many prospective partners using a radar diagram which can give an easy comparison.
About the Author
Shanta R Yapa counts 35 years of experience in diverse industry sectors of engineering, banking, international business and ICT in progressive positions. He has also been teaching business strategy, organizational behavior and research methods in postgraduate programs of 20 local and international universities in Sri Lanka for 20 years. He is a board director of ICT Industry Skills Council, Sri Lanka. He works for Avonet Technologies, the company known for its AFFINITI delinquency management system used by many banks. He authored the book RISING IN CRISIS – Survival & Growth Strategies for Entrepreneurs and Managers.
- Bucklin, L. P., & Sengupta, S. (1993). Organizing successful co-marketing alliances. The Journal of Marketing, 32-46.
- Chesbrough, H. (2017). The Future of Open Innovation: IRI Medal Address The future of open innovation will be more extensive, more collaborative, and more engaged with a wider variety of participants. Research-Technology Management, 60(6), 29-35
- Chesbrough, H., & Bogers, M. (2014). Explicating open innovation: Clarifying an emerging paradigm for understanding innovation. New Frontiers in Open Innovation. Oxford: Oxford University Press, Forthcoming, 3-28.
- Duysters, G., & Man, A. P. (2003). Transitory alliances: an instrument for surviving turbulent industries? R&D Management, 33(1), 49-58.
- Emden, Z., Calantone, R. J., & Droge, C. (2006). Collaborating for new product development: selecting the partner with maximum potential to create value. Journal of product innovation management, 23(4), 330-341.
- Ettlinger, N. (2017). Open innovation and its discontents. Geoforum, 80, 61-71.
- Hewitt-Dundas, N., & Roper, S. (2018). Exploring market failures in open innovation. International Small Business Journal, 36(1), 23-40.
- Keupp, M. M., & Gassmann, O. (2009). Determinants and archetype users of open innovation. R&d Management, 39(4), 331-341.
- Lakemond, N., Bengtsson, L., Laursen, K., & Tell, F. (2016). Match and manage: the use of knowledge matching and project management to integrate knowledge in collaborative inbound open innovation. Industrial and Corporate Change, 25(2), 333-352.
- Lauritzen, G. D., & Karafyllia, M. (2019). Perspective: leveraging open innovation through paradox. Journal of Product Innovation Management, 36(1), 107-121.
- Lopes, A.P.V.B.V. & Carvalho, M.M.D. (2018). Evolution of the open innovation paradigm: Towards a contingency conceptual model. Technological Forecasting & Social Change, 132, 284-298.
- Lu, P., Yuan, S., & Wu, J. (2017). The interaction effect between intra-organizational and inter-organizational control on the project performance of new product development in open innovation. International Journal of Project Management, 35(8), 1627-1638.
- Malone T W & Crowston K (1994), The interdisciplinary study of coordination, ACM Computing Surveys, Vol. 26, No. 1, March 1994
- Pullen, A. J., Weerd‐Nederhof, P. C., Groen, A. J., & Fisscher, O. A. (2012). Open innovation in practice: goal complementarity and closed NPD networks to explain differences in innovation performance for SMEs in the medical devices sector. Journal of product innovation management, 29(6), 917-934.
- Rexhepi, G., Hisrich, R. D., & Ramadani, V. (2019). Open Innovation and Entrepreneurship: An Overview. In Open Innovation and Entrepreneurship (pp. 1-7). Springer, Cham.
- Rolland, K. H., Mathiassen, L., & Rai, A. (2018). Managing digital platforms in user organizations: the interactions between digital options and digital debt. Information Systems Research, 29(2), 419-443.
- Shaikh, M., & Levina, N. (2019). Selecting an open innovation community as an alliance partner: Looking for healthy communities and ecosystems. Research Policy.
- Spekman, R. E., Isabella, L. A., & MacAvoy, T. C. (2000). Alliance Competence: Maximizing the Value of Your Partnerships, John Wiley& Sons. Inc, New York.
- West, J., & Gallagher, S. (2006). Challenges of open innovation: the paradox of firm investment in open‐source software. R&d Management, 36(3), 319-331.
- Yapa, S. R., Senathiraja, R., & Kauranen, I. (2018). Improving Innovation Performance by Convergence in Open Innovation: Evidence from Software Firms in Sri Lanka. Vidyodaya Journal of Management, 4(1).
- Yapa, S. R., Senathiraja, R., Poesche, J., & Kauranen, I. (2019). Sequential Coherence: A Novel Determinant of Open Innovation Performance. American Journal of Industrial and Business Management, 9(09), 1781.
- Yapa, S. R., Senathiraja, R., Poesche, J., & Kauranen, I. (2019). The role of sequential coherence in open innovation: a qualitative inquiry.
- Zobel, A. K. (2017). Benefiting from open innovation: A multidimensional model of absorptive capacity. Journal of Product Innovation Management, 34(3), 269-288.
Featured image via Unsplash.