The Role of Open Innovation and Crowdsourcing in Generating New Business Ideas and Concepts

Santhosh Palavesh

doi:10.36676/jrps.v10.i4.1456

Authors

Santhosh Palavesh Independent Researcher, USA.

DOI:

https://doi.org/10.36676/jrps.v10.i4.1456

Keywords:

Open innovation, crowdsourcing, business ideas, innovation management, intellectual property

Abstract

This research paper examines the critical role of open innovation and crowdsourcing in generating new business ideas and concepts. Through a comprehensive analysis of existing literature, case studies, and empirical data, we explore how these collaborative approaches are reshaping traditional innovation processes. The study investigates the synergies between open innovation and crowdsourcing, their impact on business model innovation, and the technological enablers facilitating their implementation. Additionally, we address the legal and ethical considerations surrounding these practices. Our findings reveal that when effectively integrated, open innovation and crowdsourcing can significantly enhance an organization's innovative capacity, leading to more diverse and market-aligned business ideas.

References

Afuah, A., & Tucci, C. L. (2012). Crowdsourcing as a solution to distant search. Academy of Management Review, 37(3), 355-375. https://doi.org/10.5465/amr.2010.0146 DOI: https://doi.org/10.5465/amr.2010.0146

Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99-120. https://doi.org/10.1177/014920639101700108 DOI: https://doi.org/10.1177/014920639101700108

Bjelland, O. M., & Wood, R. C. (2008). An Inside View of IBM's 'Innovation Jam'. MIT Sloan Management Review, 50(1), 32-40. https://sloanreview.mit.edu/article/an-inside-view-of-ibms-innovation-jam/

Blohm, I., Zogaj, S., Bretschneider, U., & Leimeister, J. M. (2018). How to manage crowdsourcing platforms effectively? California Management Review, 60(2), 122-149. https://doi.org/10.1177/0008125617738255 DOI: https://doi.org/10.1177/0008125617738255

Boudreau, K. J., & Lakhani, K. R. (2013). Using the crowd as an innovation partner. Harvard Business Review, 91(4), 60-69. https://hbr.org/2013/04/using-the-crowd-as-an-innovation-partner

Cheng, C. C., & Huizingh, E. K. (2014). When is open innovation beneficial? The role of strategic orientation. Journal of Product Innovation Management, 31(6), 1235-1253. https://doi.org/10.1111/jpim.12148 DOI: https://doi.org/10.1111/jpim.12148

Chesbrough, H. (2010). Business model innovation: opportunities and barriers. Long Range Planning, 43(2-3), 354-363. https://doi.org/10.1016/j.lrp.2009.07.010 DOI: https://doi.org/10.1016/j.lrp.2009.07.010

Chesbrough, H. W. (2003). Open innovation: The new imperative for creating and profiting from technology. Harvard Business Press.

Chesbrough, H., & Bogers, M. (2014). Explicating open innovation: Clarifying an emerging paradigm for understanding innovation. In H. Chesbrough, W. Vanhaverbeke, & J. West (Eds.), New Frontiers in Open Innovation (pp. 3-28). Oxford University Press. DOI: https://doi.org/10.1093/acprof:oso/9780199682461.003.0001

Christensen, K., Nørskov, S., Frederiksen, L., & Scholderer, J. (2017). In search of new product ideas: Identifying ideas in online communities by machine learning and text mining. Creativity and Innovation Management, 26(1), 17-30. https://doi.org/10.1111/caim.12202 DOI: https://doi.org/10.1111/caim.12202

Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 35(1), 128-152. https://doi.org/10.2307/2393553 DOI: https://doi.org/10.2307/2393553

de Reuver, M., Sørensen, C., & Basole, R. C. (2018). The digital platform: a research agenda. Journal of Information Technology, 33(2), 124-135. https://doi.org/10.1057/s41265-016-0033-3 DOI: https://doi.org/10.1057/s41265-016-0033-3

Estellés-Arolas, E., & González-Ladrón-de-Guevara, F. (2012). Towards an integrated crowdsourcing definition. Journal of Information Science, 38(2), 189-200. https://doi.org/10.1177/0165551512437638 DOI: https://doi.org/10.1177/0165551512437638

Felin, T., & Zenger, T. R. (2014). Closed or open innovation? Problem solving and the governance choice. Research Policy, 43(5), 914-925. https://doi.org/10.1016/j.respol.2013.09.006 DOI: https://doi.org/10.1016/j.respol.2013.09.006

Foss, N. J., & Saebi, T. (2017). Fifteen years of research on business model innovation: How far have we come, and where should we go? Journal of Management, 43(1), 200-227. https://doi.org/10.1177/0149206316675927 DOI: https://doi.org/10.1177/0149206316675927

Granovetter, M. S. (1973). The strength of weak ties. American Journal of Sociology, 78(6), 1360-1380. https://doi.org/10.1086/225469 DOI: https://doi.org/10.1086/225469

Grodzinsky, F. S., Miller, K., & Wolf, M. J. (2015). Ethical issues in crowdsourcing: A review. Journal of Information, Communication and Ethics in Society, 13(3/4), 263-280. https://doi.org/10.1108/JICES-09-2014-0044

Henkel, J., Schöberl, S., & Alexy, O. (2014). The emergence of openness: How and why firms adopt selective revealing in open innovation. Research Policy, 43(5), 879-890. https://doi.org/10.1016/j.respol.2013.08.014 DOI: https://doi.org/10.1016/j.respol.2013.08.014

Hienerth, C., Keinz, P., & Lettl, C. (2011). Exploring the nature and implementation process of user-centric business models. Long Range Planning, 44(5-6), 344-374. https://doi.org/10.1016/j.lrp.2011.09.009 DOI: https://doi.org/10.1016/j.lrp.2011.09.009

Howe, J. (2006). The rise of crowdsourcing. Wired Magazine, 14(6), 1-4. https://www.wired.com/2006/06/crowds/

Laursen, K., & Salter, A. (2006). Open for innovation: the role of openness in explaining innovation performance among U.K. manufacturing firms. Strategic Management Journal, 27(2), 131-150. https://doi.org/10.1002/smj.507 DOI: https://doi.org/10.1002/smj.507

Lichtenthaler, U. (2011). Open innovation: Past research, current debates, and future directions. Academy of Management Perspectives, 25(1), 75-93. https://doi.org/10.5465/amp.25.1.75 DOI: https://doi.org/10.5465/AMP.2011.59198451

Piezunka, H., & Dahlander, L. (2015). Distant search, narrow attention: How crowding alters organizations' filtering of suggestions in crowdsourcing. Academy of Management Journal, 58(3), 856-880. https://doi.org/10.5465/amj.2012.0458 DOI: https://doi.org/10.5465/amj.2012.0458

Schemmann, B., Herrmann, A. M., Chappin, M. M., & Heimeriks, G. J. (2016). Crowdsourcing ideas: Involving ordinary users in the ideation phase of new product development. Research Policy, 45(6), 1145-1154. https://doi.org/10.1016/j.respol.2016.02.003 DOI: https://doi.org/10.1016/j.respol.2016.02.003

Tapscott, D., & Tapscott, A. (2017). How blockchain will change organizations. MIT Sloan Management Review, 58(2), 10-13. https://sloanreview.mit.edu/article/how-blockchain-will-change-organizations/

West, J., & Bogers, M. (2014). Leveraging external sources of innovation: A review of research on open innovation. Journal of Product Innovation Management, 31(4), 814-831. https://doi.org/10.1111/jpim.12125 DOI: https://doi.org/10.1111/jpim.12125

Ye, H. J., & Kankanhalli, A. (2017). Solvers' participation in crowdsourcing platforms: Examining the impacts of trust, and benefit and cost factors. The Journal of Strategic Information Systems, 26(2), 101-117. https://doi.org/10.1016/j.jsis.2017.02.001 DOI: https://doi.org/10.1016/j.jsis.2017.02.001

Kaur, J., Choppadandi, A., Chenchala, P. K., Nakra, V., & Pandian, P. K. G. (2019). AI Applications in Smart Cities: Experiences from Deploying ML Algorithms for Urban Planning and Resource Optimization. Tuijin Jishu/Journal of Propulsion Technology, 40(4), 50-56. DOI: https://doi.org/10.52783/tjjpt.v40.i4.5948

Case Studies on Improving User Interaction and Satisfaction using AI-Enabled Chatbots for Customer Service . (2019). International Journal of Transcontinental Discoveries, ISSN: 3006-628X, 6(1), 29-34. https://internationaljournals.org/index.php/ijtd/article/view/98

Kaur, J., Choppadandi, A., Chenchala, P. K., Nakra, V., & Pandian, P. K. G. (2019). Case Studies on Improving User Interaction and Satisfaction using AI-Enabled Chatbots for Customer Service. International Journal

ofTranscontinental Discoveries, 6(1), 29-34. https://internationaljournals.org/index.php/ijtd/article/view/98

Choppadandi, A., Kaur, J., Chenchala, P. K., Kanungo, S., & Pandian, P. K. K. G. (2019). AI-Driven Customer Relationship Management in PK Salon Management System. International Journal of Open Publication and Exploration, 7(2), 28-35. https://ijope.com/index.php/home/article/view/128

AI-Driven Customer Relationship Management in PK Salon Management System. (2019). International Journal of Open Publication and Exploration, ISSN: 3006-2853, 7(2), 28-35. https://ijope.com/index.php/home/article/view/128

Big Data Analytics using Machine Learning Techniques on Cloud Platforms. (2019). International Journal of Business Management and Visuals, ISSN: 3006-2705, 2(2), 54-58. https://ijbmv.com/index.php/home/article/view/76

Shah, J., Prasad, N., Narukulla, N., Hajari, V. R., & Paripati, L. (2019). Big Data Analytics using Machine Learning Techniques on Cloud Platforms. International Journal of Business Management and Visuals, 2(2), 54-58. https://ijbmv.com/index.php/home/article/view/76

Mahesula, Swetha, Itay Raphael, Rekha Raghunathan, Karan Kalsaria, Venkat Kotagiri, Anjali B. Purkar, Manjushree Anjanappa, Darshit Shah, Vidya Pericherla, Yeshwant Lal Avinash Jadhav, Jonathan A.L. Gelfond, Thomas G. Forsthuber, and William E. Haskins. "Immunoenrichment Microwave & Magnetic (IM2) Proteomics for Quantifying CD47 in the EAE Model of Multiple Sclerosis." Electrophoresis 33, no. 24 (2012): 3820-3829. https://doi.org/10.1002/elps.201200515.

Big Data Analytics using Machine Learning Techniques on Cloud Platforms. (2019). International Journal of Business Management and Visuals, ISSN: 3006-2705, 2(2), 54-58. https://ijbmv.com/index.php/home/article/view/76

Mahesula, S., Raphael, I., Raghunathan, R., Kalsaria, K., Kotagiri, V., Purkar, A. B., & ... (2012). Immunoenrichment microwave and magnetic proteomics for quantifying CD 47 in the experimental autoimmune encephalomyelitis model of multiple sclerosis. Electrophoresis, 33(24), 3820-3829.

Mahesula, S., Raphael, I., Raghunathan, R., Kalsaria, K., Kotagiri, V., Purkar, A. B., & ... (2012). Immunoenrichment Microwave & Magnetic (IM2) Proteomics for Quantifying CD47 in the EAE Model of Multiple Sclerosis. Electrophoresis, 33(24), 3820. DOI: https://doi.org/10.1002/elps.201200515

Raphael, I., Mahesula, S., Kalsaria, K., Kotagiri, V., Purkar, A. B., Anjanappa, M., & ... (2012). Microwave and magnetic (M2) proteomics of the experimental autoimmune encephalomyelitis animal model of multiple sclerosis. Electrophoresis, 33(24), 3810-3819. DOI: https://doi.org/10.1002/elps.201200200

Salzler, R. R., Shah, D., Doré, A., Bauerlein, R., Miloscio, L., Latres, E., & ... (2016). Myostatin deficiency but not anti‐myostatin blockade induces marked proteomic changes in mouse skeletal muscle. Proteomics, 16(14), 2019-2027. DOI: https://doi.org/10.1002/pmic.201600006

Shah, D., Anjanappa, M., Kumara, B. S., & Indiresh, K. M. (2012). Effect of post-harvest treatments and packaging on shelf life of cherry tomato cv. Marilee Cherry Red. Mysore Journal of Agricultural Sciences.

Shah, D., Salzler, R., Chen, L., Olsen, O., & Olson, W. (2019). High-Throughput Discovery of Tumor-Specific HLA-Presented Peptides with Post-Translational Modifications. MSACL 2019 US.

Big Data Analytics using Machine Learning Techniques on Cloud Platforms. (2019). International Journal of Business Management and Visuals, ISSN: 3006-2705, 2(2), 54-58. https://ijbmv.com/index.php/home/article/view/76