The Impact of Openness on Innovation Efficiency: Manufacturing and Service Industry

Transcription

1 The Impact of Openness on Innovation Efficiency: Manufacturing and Service Industry Choi JoungIn 1, Byun Jung Wook 3, Lee Byoung Chul 2, Kim Yun Bae 3 1 Korea Israel Industrial R&D Foundation, Seoul, Korea 2 Foundation of Agri. Tech. Commercialization & Transfer, Suwon, Korea 3 Syungkyunkwan University, Suwon, Gyeonggi-do, Korea Abstract--As innovation activity within cooperation, network, and openness is paid attention, open innovation that reinforces competitiveness of innovation activity using various inside and outside sources, was introduced. In this study, the innovation efficiency of Korean manufacturing and service industry is measured and relationship with openness is analyzed. In comparative analysis among observed firms with relative efficiency measurement, it should be evaluated under identical conditions by ruling out external environmental influences. Therefore a three-stage approach is applied to estimate net innovation efficiency that excludes advantage or disadvantage from environmental factors. This study estimates net innovation efficiency that terminates effects of external factors placed in out of operational control of firm using the three-stage approach. It aims to improve reliability of research because observed firms are evaluated under identical conditions. Furthermore, the results show the degree of openness affects innovation efficiency positively in manufacturing sector. It implicates the needs of policy that encourages manufacturing firms to cooperate with external facilities actively. I. INTRODUCTION Technology innovation activity and intellectual property management with cooperation, network, openness, are paid attention where innovation based on distributed network is widespread and approach to knowledge is easily possible due to dramatic development of information and communication in these days. In this circumstance, open innovation that reinforces competitiveness of innovation activity using various internal and external sources was introduced, while closed innovation that production unit addresses innovation inwardly had friction with industrial environment of the twenty-first century. Open innovation system is being a matter of discussion about its validity through successful cases of some advanced corporations including C&D (connect & development) of P&G, open innovation ecosystem of Phillips, and Identify & Accelerate of Air Product & Chemicals[21]. As increased attention is being paid to cooperation with external partner for innovation, many studies were accomplished, however, there are few studies dealing with impact of open innovation to efficient creation of innovation performance. Furthermore, recent researches of open innovation are mostly focused on case studies of famous advanced corporations. However open innovation paradigm should propose expected outcome to not only large enterprises but also small and medium ones using innovation network [21]. The ability that transfer input to output could be determined by the technical efficiency and influence of external condition surrounding production unit both. Therefore separating the management component of inefficiency from the external variables to clarify the nature of efficiency or inefficiency of firms is essential for developing policies for improving public sector resource allocation [18]. In comparative analysis among firms with relative efficiency measurement, it should be evaluated under identical conditions by ruling out disadvantage or advantage from external environment. In this study, efficiency evaluation reflecting pure components of innovation is performed to improve objectivity. The net innovation efficiency in this study means the measured efficiency score controlling components of inefficiency caused by external environment. To obtain net innovation efficiency, a three-stage approach of Wang & Huang based on a four-stage procedure of Fried is used [32]. The paper is organized as follows. Chapter 2 relates conception of efficiency and openness. Chapter 3 explains DEA and the three-stage approach. Chapter 4 deals with data and the procedure. Chapter 5 is a description of results, and Chapter 6 is a conclusion. II. EFFICIENCY AND OPENNESS A. Needs of measuring efficiency Farrell insisted on necessary of measuring efficiency as follows. The problem of measuring the productive efficiency of an industry is important to both economic theorist and economic policy makers [13]. If the theoretical arguments as to the relative efficiency of different economic systems are to be subjected to empirical testing, it is essential to be able to make some actual measurements of efficiency. Equally, if economic planning is to concern itself with particular industries, it is important to know how far a given industry can be expected to increase its output by simply increasing its efficiency, without absorbing further resources. Lovell described about the conception of efficiency as follows [24]. Efficiency of a production means a comparison between observed and optimal values of its output and input. The comparison can take the form of the ratio of observed to maximum potential output obtainable from the given input, or the ratio of minimum potential to observed input required to produce the given output, or some combination of the two. In these two comparisons the optimum is defined in terms of production possibilities, and efficiency is technical. It is also possible to define the optimum in terms of the behavioral goal of the production unit. In this event efficiency is 997

2 economic and is measured by comparing observed and optimum cost, revenue, profit, or whatever the production unit is assumed to pursue, subject, of course, to the appropriate constraints on quantities and prices. According to Lovell, some problems arise on measuring efficiency: how many, and which, outputs and inputs should be included in the analysis, how should they be weighted in the aggregation process? [24] Knight addressed the questions by noting that if all outputs and inputs are included, then since neither matter nor energy can be created or destroyed, all units would achieve the same unitary efficiency score [22]. In this circumstance Knight proposed to redefine efficiency as the ratio of useful output to input. Extending Knight's redefinition to the ratio of useful output to useful input, and representing usefulness with weights incorporating market prices, generates a modern economic efficiency measure. B. Openness Industrial innovation is sought by companies continuously and is becoming more open. Ideas that once germinated only in large companies now may be growing in a variety of settings from the individual inventor or high-tech start-ups in Silicon Valley, to the research facilities of academic institutions, to spin-offs from large, established firms [10]. Therefore R&D activity that corporation or research institute performed itself before, now is accomplished surpassing physical and time boundaries with various channels including inflow from outside, outflow to outside, and cooperation. Chesbrough said the closed innovation paradigm which is fundamentally inwardly focused and its associated mind-set toward organizing industrial R&D has led to many important achievements and many commercial successes on the twentieth century [8]. However, the paradigm is increasingly at odds with the knowledge landscape at the beginning of the twenty-first century. He defined open innovation that it is emerging in place of closed innovation, as a paradigm that assumes that firms can and should use external ideas as well as internal ideas, and internal and external paths to market, as the firms look to advance their technology in his book Open Innovation: The New Imperative for Creating and Profiting from Technology. In other words open innovation means accomplishing technology acquisition and technology development along the innovation process that manage principal technology externally and internally. In open innovation paradigm, the management of intellectual property and acceleration of technology development based on cooperation, network, and openness are emphasized [20]. Thus, open innovation processes involve a wide range of internal and external technology sources, and a wide range of internal and external technology commercialization channels [23]. The knowledge landscape in open innovation has big difference with that in closed innovation as you can see figure next. Figure 1.a shows closed innovation paradigm for managing R&D [8]. The solid lines show the boundary of each firm, A and B. Ideas flow into each firm, on the left, and flow out to the market on the right. They are screened and filtered during the research process, and the surviving ones are transferred into development and then taken to market. Figure 1.b depicts the knowledge landscape that results from the flow of internal and external ideas into and out of firms A and B [8]. Ideas abound in this environment, not only within each firm, but also outside the firms. These ideas are available to be used, and often the people who created them are similarly available for hire. Open innovation could be an explanation of some similar ideas including Open Source, User-Driven Innovation, Distributed Innovation Process, Innovation Networks, Collaboration, and Innovation Services, rather than wholly new conception. a. closed innovation b. open innovation Source: Retouch of Chesbrough Fig. 1 The knowledge landscape in the closed and open innovation paradigm 998

3 III. DEA AND THREE-STAGE APPROACH A. Conception of DEA Farrell divided productive efficiency into allocative efficiency and technical efficiency, and suggested method for measuring efficiency using non-parametric approach. The efficiency measure he developed was then specified as a linear programming (LP) problem and used to measure efficiency of decision making units (DMU) by Charnes, Cooper, and Rhodes (CCR model) in Data Envelopment Analysis (DEA) [3][5][13]. DEA truly does envelop a data set; it makes no accommodation for noise, and so does not "nearly" envelop a data set the way most econometric models do [24]. Banker, Charnes, and Cooper advanced CCR model which assumed constant returns to scale (CRS) to BCC model which makes it possible to determine with variable returns to scale (VRS), i.e., whether operations were conducted in regions of increasing, constant or decreasing returns to scale (in multiple input and multiple output situations)[2][5]. CRS refers to increases in output subsequent to a same proportional change in all inputs. Otherwise it is VRS if it does not. DEA has been used widely to measure frontier production function to calculate of maximum output which is achievable with given input combination. DMUs that are technically efficient are located at the frontier, while those that are not technically efficient appear below the frontier, since their output falls below the technically possible maximum [18]. Guan attempted to find a systematic quantitative methodology to study the relationship between competitiveness and technological innovation capability [15]. They employed DEA model to analyze the data collected covering 182 industrial innovative firms in China. The research results show that only 16% of the enterprises operate on the best-practice frontier and there are some inconsistencies between organizational innovation capability and competitiveness in many enterprises. Decreasing returns to scale were found among about 70% of the inefficient enterprises and increasing returns to scale were found among the remaining 30% of the inefficient enterprises. Thus the internal innovation harmonizing process in these enterprises is considerably inefficient. Based on the restricted ranges of the input/output factors, a multi-objective DEA projection model has also been developed in this study to provide a benchmark for auditing competitiveness. Diaz-Balteiro intended to analyze the relationship between productive efficiency and innovation activity in Spain's wood-based industry using DEA and logistic regression [11]. Results do not show the existence of significant links between firm's efficiency and innovation activities. This outcome is consistent with a low firm priority toward R&D as a means to achieve competitiveness and an innovation strategy followed by many Spanish firms based on the acquisition of embodied technology available in international markets. B. Three-stage approach Fried suggested a four-stage procedure that separates the management component of inefficiency from the influences of the external operating environments such as ownership structure, location, or regulatory regime [14]. They classified previous works on the external operating environment and measures of technical efficiency into three categories: the frontier separation approach; all-in-one approach; the twostage approach. They insisted previous works can not eliminate affection of external conditions for efficient application of inputs or outputs, and introduced a four-stage procedure. The four-stage procedure can be viewed as an extension of the two-stage and frontier separation approaches, where the essential contribution is to generate a pure measure of managerial inefficiency [14]. Using this procedure, we can re-calculate the technical efficiency controlling effects of various environments that surrounds DMU using input slack or output surplus. Wang & Huang proposed the three-stage approach to analyze the relative efficiency of R&D production inspired by the four-stage procedure [32]. The three-stage approach that has great similarity compared to the four-stage procedure, is a method deduced by putting together stage and stage of a four-stage procedure. In the first stage, the input-oriented DEA model is applied to estimate the inter-country efficiency frontier of R&D activities. In the second stage, Tobit estimation equations are specified in which the dependent variable for each equation is the sum of the radial and nonradial input slacks. Finally, in the third stage, parameter estimates from the second stage are used to predict the total input slacks. These input slack are used to calculate the adjusted values for the primary R&D inputs. These adjusted input data are used to re-run the DEA model under the initial output specification. Since the new efficiency scores are obtained from the above processes that control for external influences, they represent the net component of R&D efficiency. The three-stage approach has been applied to evaluate relative efficiency in numerous areas, including manufacturing, banks, and the public sector [4][12][18][32]. Hsu & Hsueh applied the three-stage approach to 110 R&D projects sponsored by government over 9 years (1997~2005) in Taiwan [18]. They found that firm size, industry, and ratio of public subsidy on R&D budget of recipient firm significantly influence the technical efficiency of government-sponsored project (GSP) in Taiwan. After controlling these external variables, the mean value of technical efficiency in the third stage increases and becomes significantly different to that of the first stage. In this study, the three-stage approach is performed to measure the net innovation efficiency of Korean manufacturing and service firms. The net innovation efficiency is a relative efficiency score considering external environmental influences due to different operating environments. Measuring method based on the three-stage approach is described in chapter 4 deeply. 999

4 IV. RESEARCH DESIGN A. Data In this study, results from Report on the Korean Innovation Survey 2008: Manufacturing Sector (KIS2008) and Report on the Korean Innovation Survey 2006: Service Sector (KIS2006), both performed by STEPI(Science and Technology Policy Institute), are used as data. The purpose of the Korean Innovation Survey are providing basic statistic data and building data for national innovative policy establishment and innovation study grasping the present situation and characteristics of Korean corporations on overall innovative activities [27][28]. The survey of KIS2008 had performed from April, 2008 to September, The manufacturing firm established before 2005 with more than 10 full-time employees are used as samples. Firms in the sample of the survey are belonged to 22 industries referred to KSIC (Korea Standard Industry Code) 15~37. The percentage of firms with 10~49 employees is 83.8%, and ones with more than 300 employees are only 727 which takes 1.6% [28]. The survey of KIS2006 had performed from May, 2006 to September, The service firm with more than 10 full-time employees was used as sample. The industries in the sample of the survey are classified as KSIC: I. Transportation; J. Telecommunication; K. Financial and Insurance; M. Business Service; Q. Entertainment, Culture and Sports [27]. Korean Innovation Survey was done through questionnaires. The size of sample of KIS2008 is 3,081 and KIS2006 is 2,498. Among these data, firms recorded 0 or no response on all of output variables that are the number of granted patents, profit-creation or costreduction, and frequency of innovation, are excluded on analysis. Ultimately 1,135 manufacturing firms and 512 service firms are selected for this study and each firm is considered as DMU. Innovation activities are classified into 4 types: Product (service) innovation; Process innovation; Organizational innovation; Marketing innovation. STEPI defined these activities as follow. Product (service) innovation means promoting goods or services that is either new or significantly improved in their fundamental characteristics or their technical specifications and which lead to an increase in the firm's turnover [27][28]. Process innovation implies operating a new or significantly improved production technology and new or significantly improved methods of supplying services, and delivering products which importantly contribute to an increase in productivity. Organizational innovation indicates introducing new methods or improving existing methods significantly, in terms of methods of working, organizing, and creating external cooperation networks that contribute to the increase in the effectiveness and efficiency of firm's internal capabilities. Marketing innovation is, finally, making great difference on product design, packing, arrangement, price and suchlike to increase product attractiveness and recognition. In Korean Innovation Survey, technical innovation includes product (service) and process innovation, and non-technical innovation includes organizational and marketing innovation [27]. B. Variables 1) Inputs The number of R&D employee (R&DE), non-r&d employee (NR&DE), quality of labor (QLAB), and innovation cost (INNCOS) are considered as input variables. Quality of labor means the ratio of employees received higher than Master degree. Innovation cost means expenses relate to innovation activity. The number of employees represents the quantity side of labor. According to literatures, innovation activities are more widespread in the firms having large sizes of employment [31], and higher R&D manpower are found to be predictors of improved firm performance [29]. The ratio of qualified labor is selected to assess the quality of labor. According to Hoffman, most important determinant of innovative activity and economic success is a high incidence of qualified scientists and engineers [16]. Innovation cost includes internal and external R&D expense and relate expense for innovation activities. Raggi studied on the technical indicator of innovation and argued R&D expenditure is one of the most important indicators [25]. 2) Outputs There is a little difference between manufacturing and service sector in selection of output variables. In manufacturing sector, total six variables including the number of granted patents (PAT) and profit-creation (PRO) (or costreduction (RDU)) in three types of innovation activities (product; process; organizational/marketing) are selected as proxy for output. The measurement of profit-creation (or cost-reduction) from innovation activity is calculated by multiply contribution degree of relevant innovation activity by sales (or production cost) on In service sector, innovation activities of firms are classified into service innovation and process innovation. Then frequency of innovation (INN) and the number of intellectual property (IP) in each activity, so total four variables are selected to represent output. The frequency of innovation means how many times innovation was performed on 2003~2005. The number of intellectual property is the sum of numbers of granted patents, patent applications, and other intellectual property. The number of patents is used as output variable in both sectors. Patent statistics are considered by most authors an interesting and sufficiently reliable technological indicator, and the number of patents is considered as one important indicator of technology innovation capability [6][25]. 3) Environments To achieve net innovation efficiency, external environmental variables that are outside of operating control should be selected, and then effect of them is needed to be quantified. Corporation type (CORP), legal type (LEGAL), listing type (LIST), and location of firm (LOC) are selected 1000

5 as environmental factors. Corporation types are consist of independent, domestic affiliated, and international affiliated company. Legal types are divided into large, medium, and small company. Listing types include KOSDAQ (Korean Securities Dealers Automated Quotation), stock market, and unlisted company, and location of firm is consisting of the National Capital region, Deajeon, and the others. The influence of firm size on performance is still being debated [18]. Generally small firms are more flexible and not bureaucratic, so capability of adaptation on innovation and change is great. It means there is negative relation between the size of firm and innovation [26]. However large firm has competency that can increase the investment on new technology, and more possibility of possession of certain complementary to success commercialization of innovation [30]. Despite these opposed arguments, it is certain that firm size should be included into environmental variables. In case of Korea, many kinds of infrastructure and advanced technical labor are concentrated in National Capital region [19]. Therefore advantage or disadvantage may exist in terms of location of firm. 4) Openness Openness score (OPN) is inferred from contribution of external partner on each innovation activities in five points scale. The score 5 means external partner gave great contribution. In this study, openness score is considered as proxy measure of open innovation. Openness scores in product, process, organizational, and marketing innovation are provided in manufacturing, but in the service sector, because of limitation of survey report, openness score in only service/process innovation is gained. Explanation of all selected variables and statistics are described in Table 1 and Table 2. TABLE 1: DESCRIPTIVE STATISTICS OF VARIABLES IN MANUFACTURING SECTOR Dimension Variables Mean Standard deviation Maximum Minimum Input Output Environment Openness R&DE NR&DE , ,950 1 QLAB INNCOS # 12, , ,148, PDTPAT ,500 0 PRCPAT ,500 0 OR/MAPAT PDTPRO # 88, , , PRCRDU # 13, , , OR/MAPRO # 19, , , CORP LEGAL LIST LOC PDTOPN * PRCOPN * OROPN * MAOPN * Unit of variables: percentage (+); millions of Korean Won (#); score in 5 points scale (*); number (others). Note 1: Openness variable is only measured where firms experience cooperate activity with external partner. Note 2: Environmental variables are dummy variables. TABLE 2: DESCRIPTIVE STATISTICS OF VARIABLES IN SERVICE SECTOR Dimension Variables Mean Standard deviation Maximum Minimum Input Output Environment R&DE , NR&DE , QLAB INNCOS # , , SVCINN SVCIP , PRCINN PRCIP CORP LEGAL LIST LOC Openness SVC/PRCOPN * Unit of variables: percentage (+); millions of Korean Won (#); score in 5 points scale (*); number (others). Note 1: Openness variable is only measured where firms experience cooperate activity with external partner. Note 2: Environmental variables are dummy variables. 1001

6 C. Procedure 1) Stage I: Measuring technical efficiency using DEA The first stage begins with set of production model. The aggregate innovation production function of a firm with inputs and outputs has the common properties of a wellbehaved production function. It is assumed that all inputs and outputs are homogeneous and that there is no budget constraint in any forms. It also allows for the possibility of VRS [32]. The piecewise linear input requirement ( ( )) set under VRS for all firms combined can be expressed as the following equation [1]. ( ) = {, R } (1) is an ( 1) vector of outputs, is an ( 1) vector of inputs, is an ( ) matrix of outputs, is an ( ) matrix of inputs, is a ( 1) vector of weights, is a (1 ) vector of ones, is the number of DMUs, is the number of outputs and is the number of inputs. Given output vector, all input combinations that are feasible for producing output vector are in the input requirement set. All convex combinations of input vectors which are less than or equal to the input bundle and are feasible to produce at least output vector establish the reference frontier for output and is the basis for calculating Farrell technical efficiency [14]. Given the piecewise linear input requirement set in (1), the DEA model as defined by Banker, Charnes, and Cooper, that is the BCC model, based on an input-oriented model is performed. In the DEA approach, a best-practice function is built empirically from observed inputs and outputs [2][5]. The efficiency measure of a firm's innovation activity is defined by its position relative to the frontier of best performance [5]. The strength of the DEA approach lies not only in its lack of parameterization, but also in that no assumptions are made about the form of the production function (Fu, 2008). By contrast, it also has weakness that there is no solution for statistic noise or measurement error. Farrell technical efficiency for DMU, ( =1 ), is formulated as the following LP problem [32]. =min, λ λ.. (2) Subject to, λ, = 1, R and are output and input vectors for DMU, respectively, λ is a scalar value representing a proportional contraction of all inputs, holding input rations and output level constant [14]. is a vector of weights. The LP problem is solved once for each DMU, i.e., times. The minimum value of which satisfies all conditions is the Farrell radial technical efficiency measure. It is the first score for each DMU, that ignores affects of the non-radial input slacks, which denotes the possibility of maintaining output while contracting the volume of at least one input while holding the others constant [18]. Fried explained the conception of input slack using Figure 2. Suppose that there are four DMUs:,,, and [14]. Each uses two inputs: and to produce the same quantity of output. is an efficient frontier which is created by a linear combination of input vector and (i.e., in Eq. (2)). It represents the tradeoff between inputs and that is feasible to produce output. The vertical extension is the result of free disposability of input (inequality on the constraint of ); holding input constant at, any amount of input which is at least as large as is feasible. There is no trade off between and along this range. Similarly, the horizontal extension reflects free disposability of input, holding input constant at. is the isoquant used as the reference frontier to measure Farrell radial technical efficiency. Units and are technically efficient, and are not. The radial technical efficiencies for and ( and ) are / and /, respectively. <1 indicates that unit could use the fraction of its current level of and to produce output were it to operate efficiently. The amount ( ) is radial input slack, which is the same proportion for all inputs by definition. Once unit proportionally reduces its inputs to, no further reduction is possible without sacrificing output. Unit, on the other hand, can further reduce input from to after proportionally reducing its current inputs by ( ) to to become radially efficient. The potential additional reduction in input equal to is referred to as nonradial slack in input. Source: Fried Fig. 2 Illustration of radial and non-radial input slack 2) Stage II: Analyzing external variables affecting input slacks The purpose of this stage is quantification of environmental effects belong to input slacks. The initial DEA model does not provide a good measure of managerial performance. It may penalize 'good' performers who operate in an unfavorable external environment and reward 'poor' performers who operate in a favorable external environment [14]. In the next stage, the influences of exogenous conditions are quantified using input slacks. The amount of 1002

7 input slacks represents the level of innovation inefficiency which is postulated as being composed of net technical inefficiency and inefficiency due to external environmental factors. The second stage is to analyze environmental variables which affect input using Tobit regression. The dependent variables are input slack (, ), and the independent variables are measures of external conditions applicable to the particular input. The objective is to quantify the effect of external conditions on the excessive use of inputs [14]. The coefficients from regression are used to measure predicted slacks in next stage. 3) Stage III: Re-measuring efficiency after adjusting input data In the third stage, predicted input slack considering effect of external conditions is measured using the estimated coefficients from the regression. Predicted values,,, are used to adjust the primary input data for each unit according to the difference between maximum predicted slack and predicted slack [32]., =, + Max {, =1. (3) },, =1,,,, denotes the adjusted value of input of DMU. Eq. (3) means making the DMU with the least input slack takes the most unfavorable environment. In consequence, slack of firms with lower inputs is adjusted highly. As increasing input with constant output, external environmental advantage of firms is removed. The new pseudo data set from Eq. (3) is used to re-run the DEA model and generate new measures of efficiency which represent net part of innovative efficiency. The purpose of using maximum predicted slack is to establish a base equal to the least favorable set of external conditions [14]. A firm with external variables of the least favorable level would not have its input vector adjusted at all. On the other hand, input vector of a firm with external variables of lower level than predicted slack, is adjusted highly to make conditions equal to DMU with the least favorable environment. Adjusting data in this way, inputs of firms in more favorable situation are adjusted upward. However if the most favorable is chosen instead in adjusting process, the data are adjusted by reducing the input levels of firms in less favorable situation. In empirical applications, this introduces the possibility of a negative value for an adjusted input, rendering the DEA problem for that unit without a solution [14]. Therefore using the least favorable environment as the base of adjusting input vector is reasonable. V. EMPIRICAL RESULTS A. Stage I: Applying DEA to initial data The initial deterministic DEA model includes four input variables and six output variables. Efficiency scores for all manufacturing firms related to a best practice frontier are calculated using the input-oriented BCC model. EMS (Efficiency Measurement System) 1.3 software is used to evaluate. Table 3 shows the result of efficiency measure of stage I. The mean efficiency score of manufacturing is On average, Korean manufacturing firm could deliver the same level of innovation performance with 35 percent of the current inputs. In other words, it could reduce current inputs by 65 percent. More than half of efficiency scores of manufacturing firms are biased low. But, the mean efficiency score of Korean service is Namely, Korean service firms could deliver the same level of innovation performance with 41 percent of the current inputs during 2003~2005. Otherwise, it could reduce current inputs by 59 percent. More than half of efficiency scores of service firms are biased low. Degree TABLE 3: EFFICIENCY SCORE IN STAGE I USING THE BCC MODEL manufacturing service Mean Standard deviation Maximum Minimum Total 1,

8 B. Stage II: Using a Tobit model to quantify environmental effects Tobit model which is a special regression model dealing with a censored dependent variable is applied. The variable is referred to as being censored when the response cannot take values below (left censored) or above (right censored) a certain threshold value [32]. Both sides of censor are used in this study, and STATA (Statistics/Data Analysis) 10.0 software is used. Each dependent variable is total slacks (the sum of radial slack and non-radial slack) of each input variable. The independent variables are corporation type, legal type, listing type, and location of firm. Table 4, 5 summarizes the result of Tobit regression. The legal type variable has significant coefficients in every equation. The corporation type and listing type show weighty results in three equations, and the location of firm variable has significant coefficient in only one equation. The overall results of stage II say all of environmental variables affect input value significantly. In the case of service industry, the independent and dependent variables for regression is same as manufacturing. According to result of stage II, the legal type and listing type affect slacks of R&DE and NR&DE negatively, and location has negative influence to one of QLAB. The corporation type has no effect to any equations anyhow, so it is excluded in adjustment. Corporation type Legal type Listing type Location TABLE 4: TOBIT REGRESSION RESULTS IN STAGE II (MANUFACTURING) Dependent Variable R&DE NR&DE QLAB INNCOS Constant 9.508*** *** *** *** Independent Domestic affiliated 4.346*** 4.451*** *** International affiliated 3.216* 4.846*** * Large ** - Medium *** *** * Small *** *** * ** Stock market KOSDAG *** *** None * *** *** National Capital *** - Deajeon Others *** Sigma Log likelihood -4, , , , No. of obs. 1,135 1,135 1,135 1,135 Left censored obs Uncensored obs. 1,051 1,054 1,046 1,058 Right censored obs Note: (***), (**), and (*) indicate the significance of the estimates at the 1%, 5%, and 10% levels, respectively. Corporation type Legal type Listing type Location TABLE 5: TOBIT REGRESSION RESULTS IN STAGE II (SERVICE) Dependent Variable R&DE NR&DE QLAB INNCOS Constant *** *** Independent Domestic affiliated International affiliated Large Medium * *** Small *** *** - - Stock market KOSDAG *** None * *** National Capital Deajeon Others * Sigma Log likelihood -2, , , , No. of obs Left censored obs Uncensored obs Right censored obs Note: (***), (**), and (*) indicate the significance of the estimates at the 1%, 5%, and 10% levels, respectively. 1004

9 C. Stage III: Adjustment of input data and re-running DEA After predicted slacks are estimated, a new pseudo data set of adjusted inputs is created using remainder of predicted slack of observed firm and maximum predicted slack. The estimated coefficients in Table 4, 5 are employed to adjust initial input data according to Eq. (3). In the adjusted input data, effect of external operating environmental factors is controlled. Other things being equal, the firm with maximum input slack is the one facing the least favorable environmental conditions. The maximum predicted input slack thus serves as a benchmark of the least favorable set of external conditions [32]. Where firm is located this standard level, the input vector does not change at all. On the other hand, in case of the firm that has lower slack, input vector is adjusted upward compared to firm with the least favorable situation. This prevents the possibility of a negative value for an adjusted input, rendering the DEA problem for that unit without a solution [14]. Table 6 and 7 shows predicted input slacks and adjusted inputs of manufacturing and service industry. The adjustment essentially amounts to penalizing the firm for being able to use fewer inputs under favorable external conditions [32]. Increasing input while output remains same, the advantage possessed due to external environment is removed. The new pseudo input data is reevaluated by DEA. Table 8 lists the descriptive statistics of innovation efficiency scores from stage III. The efficiency scores from stage III refer separating net efficiency from influence of exterior conditions. The average efficiency score of manufacturing is The maximum value is and minimum value is Only 2.91 percent (33 of 1,135 firms) of Korean manufacturing firms is efficient as regards net innovation efficiency. The new radial estimates of inefficiency from re-running DEA with adjusted input represent net innovation inefficiency. These values mean pure technical inefficiency of firms that exclude effect of external operating environment. The average efficiency score of Korean service is The maximum value is and minimum value is Among Korean service firms, 5.66 percent (29 of 512 firms) is efficient as regards net innovation efficiency. More than half of observed firms have high efficiency score, while more than half of scores are biased lowly in stage I. It means the capability of innovation of firm is interrupted by external environments in an amount of Korean service firms. TABLE 6: PREDICTED INPUT SLACKS AND ADJUSTED INPUTS (MANUFACTURING) R&DE NR&DE QLAB INNCOS Mean Predicted input slacks Standard deviation Maximum Minimum Mean Adjusted inputs Standard deviation Maximum 1, , , , Minimum TABLE 7: PREDICTED INPUT SLACKS AND ADJUSTED INPUTS (SERVICE) R&DE NR&DE QLAB INNCOS Mean Predicted input slacks Standard deviation Maximum Minimum Mean Adjusted inputs Standard deviation Maximum 1, , , , Minimum

10 Degree TABLE 8: EFFICIENCY SCORE IN STAGE III USING THE BCC MODEL manufacturing service Mean Standard deviation Maximum Minimum Total 1, D. Comparison of results between stage I and stage III Table 9 juxtaposes the descriptive statistics of innovation efficiency scores from stage I and stage III. Controlling external effects, the average efficiency score is increased slightly from to and the standard deviation of scores is decreased from to The number of efficient firms is decreased from 88 to 33. According to Fried, the increase in average efficiency suggests that without controlling for the operating environment, the penalty to firms operating under unfavorable circumstances was greater than the benefit to firms operating under favorable circumstances [14]. The decrease in the number of efficient firms suggests that without controlling for the operating environment, firms that operate in favorable circumstances are judged efficient as a result of being compared to firms that operate in unfavorable circumstances. The decrease in the standard deviation of the efficiency scores may reflect the fact that without controlling for the external environment, the efficiency scores of firms that operate in favorable circumstances are biased up, and the efficiency scores of firms that operate in unfavorable circumstances are biased down. By adjustment of input data, the bias of efficiency score is eliminated and scope of efficiency is narrowed, and observed manufacturing firms are put on identical evaluating condition. The Pearson correlation coefficient and Spearman rank correlation coefficient are estimated to compare efficiency scores from stage I and stage III. The Pearson correlation coefficient is which is very low value, and significant in the one percent level. This coefficient rejects the null hypothesis that there is a very high positive correlation in efficiency scores between stage I and stage III. It means thus the adjusting input data that excludes advantage from external environmental factors of firms brings mutation to innovation efficiency scores. The Spearman rank correlation coefficient for the efficiency scores between stage I and stage III is This coefficient is significantly different from zero at the five percent level and also rejects the null hypothesis that efficiency scores of stage I and stage III have positive correlation strongly. It indicates that controlling for the external environment does reshuffle the efficiency ranking among firms in terms of net innovation efficiency. It also confirms the importance of exclusion of environmental influences in measurement of efficiency. Table 10 juxtaposes the descriptive statistics of innovation efficiency scores of service from stage I and stage III. The average efficiency score is increased from to and the standard deviation of scores is decreased from to The number of efficient service firms is decreased from 85 to 29. It means adjustment of input data helps bias of efficiency score to be removed and contributes scope to be narrowed. This tendency is same as result of manufacturing. The Pearson correlation coefficient is and Spearman rank correlation coefficient is in level of 1 percent and 10 percent in each. These indicate that there is fluctuation in scores and ranking between innovation efficiency scores of stage I and stage III. TABLE 9: COMPARISON OF STAGE I AND STAGE III RESULTS (SERVICE) Stage I Stage III Mean Standard deviation The number of efficient firms Pearson correlation 0.135*** Spearman rank correlation 0.062** Note: (***), (**), and (*) indicate the significance of the estimates at the 1%, 5%, and 10% levels, respectively. 1006

11 TABLE 10: COMPARISON OF STAGE I AND STAGE III RESULTS (SERVICE) Stage I Stage III Mean Standard deviation The number of efficient firms Pearson correlation 0.114*** Spearman rank correlation 0.079* Note: (***), (**), and (*) indicate the significance of the estimates at the 1%, 5%, and 10% levels, respectively. E. Impact of openness on innovation efficiency In this section, an analysis for appreciation on impact of openness using a degree of contribution of cooperate partners and net innovation efficiency score is performed. The degree of contribution of external partners is considered as the degree of openness of observed firms. Before perform the regression analysis, openness variables in five points scale are transferred into one point scale for reasonable comparison with net innovation efficiency whose scale is one point. The data for analyze are 536 of 1,135 firms that cooperated innovation activity with external partners at least once. The result of regression of openness and net innovation efficiency is presented in Table 11. The openness affects innovation efficiency positively in significant level of five percent in product and process innovation activity. In organizational and marketing innovation, however, no relationship is found. These provide momentous implications to Korean manufacturing firms with regard to open innovation. That is, the innovation efficiency of manufacturing firm may be uplifted where collaboration with external facilities working actively. In other words, the firms employed open innovation system adeptly could yield more outputs with same inputs where firms pursue profit creation and cost reduction as goals of innovation activity. In the firms received budget support from government, significantly positive relationship is found in process and organization innovation activity, while no such result is found in the firms without government support. These days, successful manufacturing firms have come to recognize the importance of non-technological innovations if they are to remain competitive, while successful services firms have come to recognize the importance of more direct technological innovation for serving their customers adequately [17]. The result indicates that the recipient firms from government should employ active strategic cooperation with external partners to maximize performance of nontechnical innovation especially organizational one. In case of support type (2), no relationship is found. Same as previous analysis, openness variable in five points scale transferred into one point scale to compare to net innovation efficiency whose scope is zero to one. The data for analyze are 164 of 512 firms that cooperated innovation activity with other firms or organizations at least once. The results are summarized in Table 12. In contrast of manufacturing, there is no impact of openness on innovation efficiency in service sector. It indicates even though open innovation strategy is employed in service firms, it may do not abet efficient innovation activity. Thus when policy or strategy is planned and conducted for promotion of innovation efficiency in service sector, further research is required more preciously for service sector. TABLE 11: REGRESSION RESULT OF OPENNESS AND EFFICIENCY (MANUFACTURING) Innovation efficiency Support type (1) Support type (2) Y N Y N Constant 0.407*** 0.382*** 0.425*** 0.417*** 0.408*** PDTOPN 0.075** ** ** PRCOPN 0.081** 0.099** ** OROPN *** ** MAOPN Sigma Log likelihood No. of obs Left censored obs Uncensored obs Right censored obs Note 1: Type (1) and (2) means government and financial agency as sponsor respectively. Note 2: (***), (**), and (*) indicate the significance of the estimates at the 1%, 5%, and 10% levels, respectively. 1007

12 TABLE 12: REGRESSION RESULT OF OPENNESS AND EFFICIENCY (SERVICE) Innovation Support type (1) Support type (2) efficiency Y N Y N Constant 0.660*** 0.641*** 0.660*** 0.642*** 0.663*** SVC/PRCOPN Sigma Log likelihood No. of obs Left censored obs Uncensored obs Right censored obs Note 1: Type (1) and (2) means government and financial agency as sponsor respectively. Note 2: (***), (**), and (*) indicate the significance of the estimates at the 1%, 5%, and 10% levels, respectively. VI. CONCLUSION Paying attention to technology innovation activity and intellectual property management using cooperation, network, and openness, open innovation that reinforces competitiveness of innovation activity using various internal and external sources, was introduced. In this study, relationship between innovation efficiency and openness in innovation activity is analyzed. The three-stage approach is applied to demonstrate net innovation efficiency that excludes influence of external environmental factors, and then the impact of openness in innovation on result from the three-stage approach is investigated. The DEA of inputoriented BCC model in stage I and stage III and Tobit regression model in stage II are applied for measurement of efficiency and adjustment of input variables respectively. To analyze relationship between the openness and net innovation efficiency, Tobit regression analysis is used again. The number of R&D employee, non-r&d employee, quality of labor, and innovation cost are considered as input variables. In manufacturing sector, the number of granted patents and profit-creation (or cost-reduction) on innovation activities categorized into three types (product, process, and organizational/marketing), so total 6 variables are selected as proxy for output. In service sector, innovation activities of firms are classified into service innovation and process innovation. Then frequency of innovation and the number of intellectual property, so 4 variables are selected to represent output. Corporation type, legal type, listing type, and location of firm are selected as environmental factors in both industries. Openness score is inferred from contribution of external partner on each innovation activities. This score is assessed by observed firms themselves, and the relationship analyze with efficiency score is applied to the firms have experience of collaboration only. This study finds that there is an increasing on the mean of technical efficiency and a decrease on standard deviation in stage III. The number of efficient firms is also decreased. These results imply that DMUs biased lower due to unfavorable conditions and ones biased higher due to favorable conditions are fallen into an identical operating environment though the three-stage approach. The result of relationship analysis between innovation efficiency and openness in manufacturing sector shows that the degree of openness in product and process innovation activity positively influences upon the improvement of innovation efficiency significantly. In the case of the recipient firms of government support, the positive relations between openness and efficiency in process and organizational activity are found. It indicates that to promote non-technical innovation efficiency which is emphasized recently, governmental supports stimulating open innovation system are needed in manufacturing sector. In the case of financial agencies as sponsors, no relationship between openness and efficiency is found. It could mean the supports of financial agencies are not friendly to the firms operating open innovation system actively. In contrast of manufacturing sector, there is no impact of openness on innovation efficiency in service. Thus when policy or strategy is planned and conducted for promotion of innovation efficiency in service sector, further research is required more preciously for service sector. This study estimates net innovation efficiency that considers effects of external factors placed in out of operational control of firm. It aims to improve reliability of research because observed firms are evaluated under identical conditions. Furthermore, the results show the degree of openness affects innovation efficiency positively in manufacturing. It implicates the needs of policy that encourages manufacturing firms to cooperate with external facilities actively. Based on the reports of Korean Innovation Survey of manufacturing and service sector, the impact of openness on innovation efficiency is analyzed in this study. In the measurement of net innovation efficiency, effects of external environmental components are excluded. It promotes reliability by virtue of equivalent state of evaluation. The results of this study are expected to provide a direction and valuable reference to further evaluation of innovation performance and policy making in manufacturing and service sector. The empirical study has some limitations. First, objective selection for input index and output index is needed. The validity of index selection should be ensured with the choice method considering index correlation and relationship with efficiency. Second, proper reasons should be deduced to 1008