Influence of Venture Capital and Knowledge Transfer on Innovation Performance in the Big Data Environment
Round 1
Reviewer 1 Report
The manuscript deals with an interesting and novel phenomenon that is worthy of investigation. However, I believe that the draft must be improved by a lot. In this review, I will focus on the most essential points.
The discussion takes place with presentation of the results. There are no limitations, future research and very few implications.
There are no exact boundaries to existing similar studies and the derivation of variables for the model appears partly subjective - here a broader derivation from the literature would make sense. Also, as a reader, I would like to know more about the existing studies and how they compare to this one.
The selection of the model parameters refers, among other things, to existing studies which, however, are only partially referenced. In this respect, some parameters seem to have been chosen arbitrarily, which gives me, as a reader, the impression that results could have been sought here by means of trial-and-error. At best, a better derivation should be made here.
Author Response
The discussion takes place with presentation of the results. There are no limitations, future research and very few implications.
Response:
Thank you for this comment. Based on this comment as well as the suggestions of Reviewer 2, we have made significant changes to the conclusion section. As you will notice in the revised manuscript, we have added one paragraph to explain the limitations of this study and the future research possibilities.
We also updated the manuscript by adding the limitations and future research in the revised paper. We now clearly state:
“Our research carries several important implications. First of all, our model can help venture capitalists to determine the scale and payback period of VC and predict IRR. The model can also help innovative start-ups to predict the performance of new product development, and to illustrate the value and prospects of a project to attract investment from venture capitalists in their business prospectus. Secondly, our research shows that increasing the scale of VC within a certain level of technical knowledge cannot help a venture-backed firm to improve its innovation performance, but increase the burden of cost. From the perspective of venture capitalists, the optimal exit time remains unchanged with the increase of VC investment. Therefore, the scale of VC has little effect on the exit time. Thirdly, the experimental results show that although the private knowledge of investors is beneficial for the innovation of venture-backed firms, relying too much on the knowledge from venture capitalists will make venture-backed firms develop less independent innovation knowledge in new products. Consequently, the payback period of venture capitalists will be extended and the total DEP of venture-backed firms will be greatly reduced. The result implies that venture capitalists should not transfer too much knowledge to innovative start-ups, and they should encourage these firms to carry out independent innovation. Meanwhile, innovative start-ups can use the model in this paper to determine the proper weight of different types of knowledge in new product innovation.
However, due to the complexity of the VC funding process and knowledge transfer, the simplified model in this paper is subjected to a few limitations. First, we assume that the private knowledge from venture capitalists and the big data knowledge are transferred simultaneously to venture-backed firms. However, this assumption is not true in the real world. Future research can relax this assumption to allow different types of knowledge transferring at different time points. Second, the influence of private knowledge from venture capitalists on the innovation performance of venture-backed firms is only considered as one-time knowledge transfer. Actually, knowledge transfer exists in different stages of the new venture funding process. Future research can examine how the roles of private knowledge from venture capitalists play at different stages. In addition, we only consider the benefits of knowledge transfer from venture capitalists, not the reverse effects of knowledge transfer from venture-backed firms to venture capitalists. Future research can consider the reverse effects of the knowledge transfer process and incorporate it as the benefit that venture capitalist can receive from venture-backed firms. For examples, the innovative ideas from venture-based firms could be used by other portfolio companies of a venture capitalist.”
There are no exact boundaries to existing similar studies and the derivation of variables for the model appears partly subjective - here a broader derivation from the literature would make sense. Also, as a reader, I would like to know more about the existing studies and how they compare to this one.
Response:
Thank you for this comment. As a response to this comment as well as the comment of reviewer 2, we have added a comparison with existing similar studies in the introduction section that helps provide boundaries with existing similar studies and highlights the contribution of our work.
Our revision makes the logic clearer. We now clearly state:
“Prior studies have provided evidence of a causal effect of VC and knowledge transfer on patent production at the industry-level (Kortum and Lerner, 1998 Hall et al, 2001; Mollica and Zingales, 2007; Popov and Rosenboom, 2009; Hirukawa and Ueda, 2011). It has been proven that it is difficult to fully demonstrate the innovation performance of VC-backed firms, because innovation performance is not only related to patent knowledge, but also non-patent knowledge (Pahnke et al. 2014; Helmers et al. 2013). Although some scholars have considered the impact of non-patent knowledge from venture capitalists on the innovation of VC-backed firms, they have not considered the influence of the customer demands and user preferences on new product innovation in the big data environment. In our study, we develop a theoretical model to study the influences of VC and knowledge transfer in the big data environment, and to predict the innovative performance of venture-backed firms by using the method of maximizing the present value of the expected profit of new product innovation performance of a venture-backed-firm in the big data environment. This model not only considers the influence of VC and private knowledge from venture capitalists on the innovation performance of venture-backed-firms, but also takes into account the benefits brought by the big data knowledge. The model can help VC investors to determine the scale of investment and the optimal exit time, and predict the internal rate of return. This model can also help innovative start-ups to better illustrate the value and prospects of a project to attract investment in their business prospectus.”
3. The selection of the model parameters refers, among other things, to existing studies which, however, are only partially referenced. In this respect, some parameters seem to have been chosen arbitrarily, which gives me, as a reader, the impression that results could have been sought here by means of trial-and-error. At best, a better derivation should be made here.
Response:
Thank you for this comment. According to your suggestion, we added some references in the third paragraph of section 4.1 to explain the reason for the parameter setting. At the same time, some parameter settings are obtained by adjusting and fitting the existing parameters in the program, not randomly selected. The revised contents are as follows:
“The above parameters are in the context of technological innovation network. In different environment, the parameters should be adjusted accordingly. According to the survey data of market share of high-tech enterprises by DisplaySearch company. The market share of some small firms is less than 0.5%, market share of some large firms is over 38.3%. Meanwhile, the average market share of the household Appliance market of China was 8.54% from January 2000 to March 2002 (Huang et al. 2004). Therefore, the market share is set at the average market level of 8%. The assumption of knowledge absorptive capacity and knowledge update rate of in the network is based on the previous research conclusion of Dai and Xu (2007). In particular, one type of knowledge in this paper is the synthesis of several types of similar knowledge, and the update rates of these types of similar knowledge are different. Conservatively, the reduction of marginal cost caused by that type of knowledge is assumed to be 12%, so the knowledge update rate is set as 88%. According to the neutral risk supposition of Xu and Zhang (2001), the discount rate is set at 10%, and . Therefore, the above parameters are reasonable.
Because the context of knowledge transfer has changed, new values need to be assigned to these new parameters. The values of these new parameters are obtained by adjusting and fitting the existing parameters in the computer program. In the big data environment, the product life cycle becomes shorter and the pace of product renewal accelerates (Wu et al., 2019), and VC firms need to exit portfolio companies within about five years from the investment to generate returns for institutional investors (Da Rin and Penas 2017). Therefore, we suppose the life cycle of the product , and the total market volume of increases in the first period after the entrance of VC and knowledge transfer. Suppose the total investment of VC . The own funds of in independent R&D investment . The reason is that the fund of VC is not fully funded in the first stage, but in installments. VC firms usually exit portfolio companies within about five years, and the VC is assumed to be paid in 5 years (Da Rin and Penas 2017). Then, the average annual VC will be 300. The average annual VC plus the independent R&D investment , and the total R&D investment in the first stage will be 500, which is consistent with the previous research assumption (Wu et al., 2018b).”
Please see the attachment.
Author Response File: 
Author Response.doc
Reviewer 2 Report
The paper is interesting and covers an important topic. Moreover, the introduction provides an adequate motivation for the the study.
My general opinion about the paper is positive. However, I have two main concerns that need to be addressed in a revised version of the paper:
(i) sections 2 and 3 present the approach developed by the authors. However, it lacks some intuition. I suggest to complement the current discussion with some more simpler discussion in order to increase the readability of the paper for a broader audience.
(ii) the conclusion of the paper is poor. It needs to be fully revised. Specificaly, it must adress the main implications of the study and some suggestions for future research on this topic.
Author Response
sections 2 and 3 present the approach developed by the authors. However, it lacks some intuition. I suggest to complement the current discussion with some more simpler discussion in order to increase the readability of the paper for a broader audience.
Response:
Thank you for this comment. To make the modeling method of maximizing the present value of the expected profit of new product innovation performance more intuitive, we add Figure 2 to show our modeling ideas.
Please see the attachment.
the conclusion of the paper is poor. It needs to be fully revised. Specifically, it must address the main implications of the study and some suggestions for future research on this topic.
Response:
Thank you for your comment. Based on this comment as well as the suggestion of Reviewer 1, we revised the conclusion section accordingly. As you will notice in the revised manuscript, we have added one paragraph to explain the implications of this study and the future research possibilities.
The implications and future research in the conclusion section of the revised paper are as follows:
“Our research carries several important implications. First of all, our model can help venture capitalists to determine the scale and payback period of VC and predict IRR. The model can also help innovative start-ups to predict the performance of new product development, and to illustrate the value and prospects of a project to attract investment from venture capitalists in their business prospectus. Secondly, our research shows that increasing the scale of VC within a certain level of technical knowledge cannot help a venture-backed firm to improve its innovation performance, but increase the burden of cost. From the perspective of venture capitalists, the optimal exit time remains unchanged with the increase of VC investment. Therefore, the scale of VC has little effect on the exit time. Thirdly, the experimental results show that although the private knowledge of investors is beneficial for the innovation of venture-backed firms, relying too much on the knowledge from venture capitalists will make venture-backed firms develop less independent innovation knowledge in new products. Consequently, the payback period of venture capitalists will be extended and the total DEP of venture-backed firms will be greatly reduced. The result implies that venture capitalists should not transfer too much knowledge to innovative start-ups, and they should encourage these firms to carry out independent innovation. Meanwhile, innovative start-ups can use the model in this paper to determine the proper weight of different types of knowledge in new product innovation.
However, due to the complexity of the VC funding process and knowledge transfer, the simplified model in this paper is subjected to a few limitations. First, we assume that the private knowledge from venture capitalists and the big data knowledge are transferred simultaneously to venture-backed firms. However, this assumption is not true in the real world. Future research can relax this assumption to allow different types of knowledge transferring at different time points. Second, the influence of private knowledge from venture capitalists on the innovation performance of venture-backed firms is only considered as a one-time knowledge transfer. Actually, knowledge transfer exists in different stages of the new venture funding process. Future research can examine how the roles of private knowledge from venture capitalists play at different stages. In addition, we only consider the benefits of knowledge transfer from venture capitalists, not the reverse effects of the knowledge transfer from venture-backed firms to venture capitalists. Future research can consider the reverse effects of knowledge transfer process and incorporate it as the benefit that venture capitalist can receive from venture-backed firms. For examples, the innovative ideas from venture-based firms could be used by other portfolio companies of a venture capitalist.”
Please see the attachment.
Author Response File: Author Response.doc
Round 2
Reviewer 1 Report
I think that the manuscript has improved considerably and that the comments have been adequately addressed.
Reviewer 2 Report
The new version of this paper provides an adequate answer to my previous concerns. Therefore, I think that the paper can be accepted for publication.
