Structural equation model for predicting technology commercialization success index (TCSI)

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Abstract

Expecting high return, many firms try to invest on R&D of new technology. However, critical loss of assets would occur, when a firm fails to commercialize the developed technology. It would be of interest to provide the ideal environment for commercialization from the R&D stage. In this study, we use a structural equation model (SEM) to forecast the technology commercialization success index (TCSI) in relation to technology developer, technology receiver, technology transfer center, and environmental factors. The proposed SEM is fitted based on partial least square (PLS) estimation procedure. Individual TCSI is then found following the approach used for American customer satisfaction index (ACSI) for various combinations of characteristics of the type of technology, technology receiver, and technology developer. We expect that the proposed approach for TCSI can be used as guidance for an ideal match of technology with technology developer and technology receiver.

Introduction

Recently, much investment has been committed to R&D in the area of information technology (IT) industry. However, its commercialization rate is reported to be lower than expected. This would imply the waste of huge amount of money. To increase the efficiency of the investment in the IT R&D, it is essential to understand the commercialization process.

Studies on commercialization process and infrastructure mainly have dealt with defining the concept of commercialization or conceptualizing the commercialization process [1]. Some other studies have analyzed the success factors for each stage of technology transfer [2]. What most of these studies missed was a structural relationship among the factors related to the commercialization. Recently, Sohn and So [3] developed a structural equation model (SEM) that captures this kind of structural relationship. Based on the estimated SEM, they suggested strategic policy to improve the commercialization procedure for various combinations of technology types with respect to the type of technology, technology provider (transferor), and technology receiver (transferee). Their model, however, is rather a form of simple structure and did not consider the hierarchical relationship among various factors. Additionally, the main objective of their study was not prediction but diagnosis.

What has not been covered yet in the previous research is a more proactive approach by which one can forecast the success rate of commercialization of the technology in the process of R&D. Such a forecasting model would enable one to figure out the best scenario for a given technology to be matched with a specific transferor and transferee.

In this article, we develop an SEM and apply the ACSI (American customer satisfaction index) concept to predict the technology commercialization success index (TCSI) for a given IT.

The proposed SEM in this article is different from the existing commercialization literature in a way to accommodate hierarchical relationship among the factors and is fitted by partial least square (PLS) method based on the data collected by Korean Information Technology Transfer Center.

ACSI is a representative kind of the CSI, which measures the quality of the goods and services as experienced by the customers. Although the maximum likelihood (ML) approach is a frequently used estimation procedure for SEM, ACSI adopted the PLS approach [4], [5], [12]. The reason is that while ML has the weak points that need the assumptions of multivariate normality, interval scaling, and large sample sizes, PLS does not need such assumptions. Fornell et al. [5] proposed the PLS procedure in view of the fact that customer satisfaction data often do not satisfy the requirements of ML.

The ACSI model is a metric model that reflects the process of the cause and effect relation between customer satisfaction and its leading indicators as well as resulting indicators. SEM was used to accommodate this structure. The leading indicators for the customer satisfaction include expectation level of the customer before purchase (customer expectation), customer-perceived overall quality (satisfaction of the product and services quality), and customer-perceived value (rating of price-given quality). The resulting indicators include customer complaint caused by customer dissatisfaction and customer loyalty that is the ultimate aim of the effort to produce customer satisfaction. These indicator variables can be found as linear combinations of 15 observed variables. Their relation is estimated by PLS method.

For TCSI, we replace the 15 observed variables used in ACSI with the survey variables collected by Korean Information Technology Transfer Center. New indicator (latent) variables are formed in relation to the representative forms of technology transferor, receiver, and environmental factors. Then, expected TCSI is estimated for various combinations of the type of technology, technology transferor, and technology transferee. For a prediction of a specific combination, we suggest a methodology based on a committee of experts.

This article is organized as follows. In Section 2, a new SEM is proposed for TCSI. In Section 3, prediction method for TCSI is explained. In Section 4, we summarize our study results and suggest further study areas.

Section snippets

Research model

First of all, we briefly review the existing studies on the factors affecting commercialization results. With respect to the technology, Lassere [6] found factors affecting commercialization as product and the type of technology, utility of the technology, and competitive edge of the technology. With respect to transferor, Reddy and Zhao [7] suggested that the experience of technology transfer, technological competition, willingness and effort to transfer, organization size of the transferor,

Analysis of SEM

To estimate the proposed SEM, we utilize the survey data conducted by Korean Information Technology Transfer Center in 1998. This includes 489 commercialization projects completed during 1993–1997. The questionnaires were sent to the representatives of technology transferee companies and 284 out of 489 questionnaires received (58%) were valid for our research after eliminating missing values and illogical responses. In the questionnaire, transferees evaluated the research ability of transferor,

Conclusion

In this article, we propose the way to estimate the expected TCSI for IT. This is done by first formulating the SEM and applying the concept used for CSI. Our approach enables to estimate the expected TCSI for a given technology scenario at the planning stage of R&D of IT. Using this approach, one can figure out the better roadmap for the technology commercialization procedure by comparing the expected TCSIs of several alternatives.

For the assessment of TCSI of a new technology, which does not

Acknowledgements

This work was supported by the Korean Ministry of Information and Communication Research Grant 2002.

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