^{1. INTRODUCTION:}

With the global economy re-accelerating, the advanced countries chased by late-movers exert efforts to secure technological superiority and to sustain the competitive advantages of their national industries by dint of original core technologies, for which they have continuously increased the R&D investment¹.

As the saying goes, the early bird catches the worm, which possibly applies to a business that gains profits by moving and acting faster than others. That is, even top players should pursue constant innovation without settling for the status quo in order to succeed. Investment in R&D is one of the overarching pre-requisites for innovation and success².

Businesses innovate to survive the ever tougher competition. In general, R&D serves as a route to success. The outcomes of R&D become intellectual property (IP) including patents. R&D investment plays a pivotal role in corporate innovation, and thus the success or failure of an innovation depends on the approaches to and investment in R&D.

March (1991) categorized corporate behavior into exploration and exploitation³. As corporate activities relevant to R&D involve both, those activities are costly and may hamper risk-taking. Therefore, businesses choose to exploit the established competencies. Still, it is necessary to brace for the ever-evolving copycats drawing on the dramatically fast-paced advancement of technology, which warrants the persistent R&D in tandem with the exploration and implementation of new IP ⁴.

When a business finds its exploitative R&D hardly serving the purpose of developing new products and technology, it turns to the explorative R&D. Increasing R&D investment cost indicates the focus is put on explorative R&D, whereas decreasing R&D investment cost implies the focus is put on the exploitative R&D.

Technology develops in a continuous and cumulative manner. Scientific technology advances by exploiting previously researched and accumulated technology, or prior technology. Such innovative technologies need be protected by virtue of intellectual property rights (IPR). In particular, technologies developed at an enormous R&D cost have to be protected with patents⁵.

Patented technologies are legally protected for two decades. Yet, the rapid development of technology and globalization shorten the life cycle of products and technologies. In this context, businesses rely on R&D to respond to the ephemeral trend. However, exploiting patents is as important as the R&D investment.

As a rule, patent activities largely involve patent management activities, patent development support activities, patent rights enforcement activities, patent infringement response activities, patent-based technology project development, and patent valuation activities. These patent activities influence intangible corporate performance (e.g. brand images), technological performance (e.g. reducing development lead time and cost), and product performance.

Most research on patent activities focused on the causality between patent activities and management performance, the methodology of patent valuation, response to patent disputes and patent exploitation. In contrast, the present study identified the relationship between exploration and exploitation activities and patent activities and the relationship between such activities and innovation performance so as to present some reference data conducive to viable patent strategies.

2. THEORETICAL BACKGROUND:

2.1. Exploration and Exploitation:

Exploration and exploitation override any other strategic approaches to the rapidly changing market environment. Originally suggested by March (1991), the theory of exploration and exploitation has been applied to a range of strategies including organizational learning, innovation and creation, and sustainable competitive advantages across disciplines^6,7,8,9,10.

According to Baum et al. (2000), exploration refers to learning from a nexus of variants, planned experimentation and play, while exploitation refers to learning from local search, experiential improvement and reuse of selective existing routines¹¹. According to Benner and Tushman (2003), an explorative innovation is followed by a transition to a new technology trajectory, whereas an exploitative innovation reinforces the established technology trajectory by improving the existing components retained⁷.

2.2. Patent Activities:

Berkowiz (1993) advocated the legitimate ways of patent rights enforcement and defined the patent activities as the legitimate use of patents granted and patent rights enforced¹². As the important factors of patent activities, he suggested the awareness of invention, records of invention, decision making on patent application (registration), prior patent search, patent registration strategies, demarcation of scope of claims in patent application, PCT registration, complementary patent, patent education, and confidentiality.

2.2.1. Patent Management Activities:

Patent management is comparable to patent administration. Yet, the patent administration involves macroscopic activities whereas the patent management is related to microscopic ones. Today, patent management is an overarching and indispensable element of corporate activities. Patent management activities refer to a broad range of patent-related activities involving new technology development with R&D followed by patent registration, strategic exploitation of existing in-house patents, proactive detection of patent infringement, and prevention of infringement on patent rights.

2.2.2. Patent Development Support Activities:

Patent development activities are part of important patent-related corporate activities, referring to supporting the patent application and registration on technologies internally researched and developed, in contrast to technology development activities intended to support the new technology development.

Generally, the patent activities relevant to technology development are divided into three phases, i.e. ideation, development and commercialization. First, the ideation involves the patent activities associated with the technology development support activities. The development phase deals with the patent activities related to technology development support, response to infringement, and rights enforcement activities. The commercialization involves the response to infringement, enforcement of rights, commercialization and business project development¹³.

2.2.3. Patent Right Enforcement Activities:

Businesses adroit at patent exploitation can be in a strong position for developing business projects. They select superior patents and formulate the most efficient measures for the enforcement of patent rights as well as viable patent strategies. Unlike in the past, the patent rights enforcement activities are highly important. It is far from guaranteeing preemptive profits to apply for and register patents on superior R&D outcomes, develop and market a product or service based on the patents for profits under the tough competition because technological convergence hinders a single patent from turning into a product.

Thus, it is necessary to double check before manufacturing a product if any relevant prior technologies or patents exist, to what extent the pending product will infringe the existing patent rights, and the likelihood of patent disputes being raised.

2.3. Innovation Performance:

Schumpeter (1942) associated the technological innovation performance with corporate size, market concentration, external network and export activities¹⁴. Previous studies verified the variables suggested by Schumpeter^15-18. Laursen and Salter (2006) measured the radical innovation performance based on the new product sales (% of revenue) in global market¹⁹.

Also, they measured the incremental innovation performance based on the percentage of new product sales versus that of a significantly improved product. Zahra and Nielsen (2002) measured the technology commercialization in light of new product counts, qualitative assessment of radical new products, patent counts, and the rate of technology commercialization²⁰.

2.3.1. Intangible Performance:

Once granted, patents benefit corporate images in terms of brands and technologies. Park et al. (2013) asserted businesses applied for patents on their R&D outcomes with intent to reinforce and improve their existing product lines, technologies and the power of products.

That is, patents are conducive to the betterment of corporate images (brand images, technology images and image improvement), with the official announcement of the enforced patent rights exerting positive effects on corporate value²¹.

2.3.2. Technological Performance:

As a rule, businesses exert substantial efforts to have proprietary rights enforced on their R&D outcomes. In particular, they put greater emphasis on patents than any other measures to become proprietary right holders ²². Still, such activities to get a patent granted and enforced are often faced with the cost barrier.

The costs incurred by the patent application and registration as well as the legal service fees for lawyers in case of patent disputes often discourage quite a few businesses from patent activities. Also, for fear of the leakage of internally developed technology, companies choose not to embark on patent activities and instead apply it to improve and reinforce existing product lines and production process. Yet, the grant of a patent has positive effects on the corporate images associated with brands and technologies.

2.3.3. Product Performance:

Patent activities have a great influence on product performance. In particular, application for patents on R&D outcomes precede the activities for commercialization, which typically involve the diverse application of the patented technology to existing and/or new products and processes^23,24.

Above all, patents often contribute to product improvement. More often than not, patented technologies are applied to products to enhance the competitive advantages in product quality and price, new product and/or technology development, and ultimately corporate performance^25,26.

3. RESEARCH MODELS AND HYPOTHESES:

3.1. Research Models:

Previous studies mostly investigated the effects of explorative and exploitative activities on management or R&D performance. Therefore, the analysis of the causality between explorative and exploitative activities and patent activities is rare.

Based on the foregoing rationale, this study set the explorative and exploitative activities as independent variables; the patent management, patent development support and patent rights enforcement activities as mediators; and the innovation performance (i.e. intangible performance, technological performance and product performance) as dependent variables for statistical analysis and testing. Figure 1 shows the research model designed for the purpose of this study.

H1: Explorative activities will have significant effects on patent activities.

H1-1: Explorative activities will have significant effects on patent management activities.

H1-2: Explorative activities will have significant effects on patent development support activities.

H1-3: Explorative activities will have significant effects on patent rights enforcement activities.

H2: Exploitation activities will have significant effects on patent activities.

H2-1: Exploitation activities will have significant effects on patent management activities.

H2-2: Exploitation activities will have significant effects on patent development support activities.

H2-3: Exploitation activities will have significant effects on patent rights enforcement activities.

H3: Patent activities will have significant effects on intangible performance.

H3-1: Patent management activities will have significant effects on intangible performance.

H3-2: Patent development support activities will have significant effects on intangible performance.

H3-3: Patent rights enforcement activities will have significant effects on intangible performance.

H4: Patent activities will have significant effects on Technological performance.

H4-1: Patent management activities will have significant effects on technological performance.

H4-2: Patent development support activities will have significant effects on technological performance.

H4-3: Patent rights enforcement activities will have significant effects on technological performance.

H5: Intangible performance will have significant effects on product performance.

H6: Technological performance will have significant effects on product performance.

3.2. Operationalization of Variables:

The present study built on previous studies to establish the effects of explorative and exploitive activities on patent activities and corporate performance and to verify the effects with the data collected from the questionnaire survey. The variables used in previous research were adapted for the purpose of this study Table 1.

Table 1: Operationalization of Variables

Factor	Operational Definition
Explorative activities	Activities performed in the early stage of technology and product development
Exploitative activities	Activities of continuously exploiting the existing knowledge and technology
Patent management activities	Development (and patent application) of new technology via R & D and activities related to the existing patents
Patent development support activities	Activities to apply for and register a patent on newly developed technology, to set the direction for technological development and to predict the change in technologies and products
Patent rights enforcement activities	Activities to protect the patents granted on newly developed technologies, and to address patent valuation and other patent-related challenges
Intangible performance	Performance coming from external activities to improve corporate images associated with brands and technologies
Technological performance	Performance coming from corporate technological activities to improve new technological development and new product launching
Product performance	Performance coming from product-related activities intended to shorten lead time in product development and improve product quality and customer satisfaction

4. RESEARCH METHODS:

The present study used the PLS (Partial Least Square) method. The PLS supports the optimal empirical evaluation of measurement and structural models almost at the same time, by estimating the factor loadings of constructs and analyzing the causality between them⁵. The present study used the PLS, instead of the covariance structure analysis tool, e.g. LISREL or AMOS, with intent to analyze the causality between principal factors, rather than the overall goodness-of-fit of the research model.

Current or former R&D staff at Korean companies participated in the online questionnaire survey from August 20 to September 20, 2016. Excluding incomplete response sheets, a total of 142 copies were analyzed. Prior to the survey, the author conducted a preliminary survey from August 1, 2016 to detect and correct any errors in the question items.

4.1. Data Collection and Sample Characteristics:

The sample consisted of current or former R&D staff at small-and-medium-sized manufacturers in Korea. The characteristics of respondents are summarized. 85 respondents (59.9%) were males, 57 respondents were (40.1%) females.

Those who were in their 30s accounted for the highest percentage (43.0%) in age, followed by 40s (25.4%), 50s or older (17.6%) and 20s (14.1%). 36.6% of respondents had 5-10 years of experience, followed by less than 5 years (33.1%), 10～15 years (21.8%), 15～20 years (4.9%), and 20 years or longer (3.5%). As for positions, 35.9% of respondents were department heads, followed by deputy section chiefs or staff (35.2%), section chiefs (26.8%) and directors (2.1%).

As for industries, 35.2% of respondents worked in electronics/electricity, followed by other manufacturing sectors (26.1%), beverage/foods (12.0%), metal/machine (7.0%), auto and wood/furniture (6.3% each) and textile and petrochemistry (3.5% each). The surveyed companies’ revenue averaged KRW6.506 billion (2015), KRW 5.951 billion (2014), and KRW 5.230 billion (2013). On average, their R&D spending accounted for 19.25% (2015), 19.04% (2014), 18.81% (2013) of their yearly revenues.

4.2. Measure Models:

The internal consistency, convergent validity and discriminant validity of the constructs and question items used in the proposed research model were verified. First, to verify the convergent validity of the measures, the factor loadings of constructs and t-values were analysed with the PLS Bootstrap.

The greater the reliability of a measure, the greater the internal consistency. Also, to measure the reliability in the confirmatory factor analysis in the structural equation, the standardized factor loadings and error terms between the measurement variables and the factors were used.

In brief, the composite reliability was above the reference value 0.7 as suggested by Nunnally (1987) and Thompson et al. (1995)^27,28. As for the Chronbach’s alpha widely used in reliability analysis, α≥0.6 is accepted in social science although Nunnally (1987) suggested 0.7 or greater as the reference value.

The Chronbach’s α was above 0.6 for explorative, exploitative and patent rights enforcement activities and above 0.7 for patent management and patent development support activities as well as intangible/technological/product performance Table 2.

Table 2: Discriminant Validity Analysis

	Factor Loading	Composite Reliability	Cronbach’s Alpha	AVE
ERA1	0.7768	0.8465	0.648	0.7288
ERA	0.7853
ERA3	0.8508
EIA1	0.7688	0.8387	0.6344	0.712
EIA2	0.8372
EIA3	0.7819
IP1	0.8943	0.8667	0.686	0.7685
IP2	0.8612
IP3	0.7188
PDSA1	0.8345	0.8937	0.737	0.8214
PDSA2	0.8678
PDSA3	0.8726
PMA1	0.8662	0.8827	0.7151	0.8007
PMA2	0.8352
PMA3	0.8351
PP1	0.7847	0.9	0.6927	0.8516
PP2	0.8237
PP3	0.8638
PP4	0.8547
PRA1	0.8415	0.8681	0.687	0.7724
PRA2	0.8178
PRA3	0.8271
TP1	0.8231	0.8983	0.7467	0.8304
TP2	0.897
TP3	0.8707

The AVE used to verify the convergent validity was above 0.5, the reference value suggested by Fornell and Larcker (1981) and Chin (1998)^29,30. The factor loadings of constructs were above 0.7, the reference value suggested by Fornell and Larcker (1981).

As for the discriminant validity, the smallest square root of AVE (0.764) was greater than the largest correlation coefficient (0.747), which indicated all question items had good discriminant validity.

Hence, the constructs and question items used in the research model met the requirements for internal consistency, convergent validity and discriminant validity, and proved fit for the structural model analysis Table 3.

4.3. Hypothesis Testing Results:

The PLS analysis result of the research model. In the PLS analysis, the explanatory power is represented as R², the explained variance^31,32. The PLS analysis of R²found the explorative and exploitative activities explained 33.9%, 42.8% and 47.7% of the patent management, patent development support and patent rights enforcement activities, respectively.

Also, the patent management, patent development support and patent rights enforcement activities explained 56.6% and 66.1% of the intangible and technological performance, respectively. The intangible and technological performance explained 68.0% of the product performance. These figures were above the acceptable power of 10% suggested by Falk and Miller (1992)³³.

Lately, the Goodness-of-Fit (GoF) analysis of the PLS path modeling is recommended ³⁴. In this study, the power of GoF was 0.486. Here, small, medium and large power of GoF was applied as suggested by Wetzels et al.(2009).

The present GoF proved very high, exceeding the large power defined by Wetzels et al. With the GoF proved very high, to verify the path coefficients and their significance, the path coefficients of the structural model were found in the entire sample, while t-values were calculated with the Bootstrap method of the PLS^35,36.

<Table 4> outlines the analysis results, which indicate all hypotheses are accepted within the significance level.

Table 4: Hypotheses Testing

Path		Coefficient	T-value	Result
H1-1	ERA → PMA	0.300	5.181^***	Accepted
H1-2	ERA → PDSA	0.345	6.315^***	Accepted
H1-3	ERA → PRA	0.289	6.126^***	Accepted
H2-1	EIA → PMA	0.330	5.883^***	Accepted
H2-2	EIA → PDSA	0.363	7.015^***	Accepted
H2-3	EIA → PRA	0.454	10.224^***	Accepted
H3-1	PMA →IP	0.177	3.936^***	Accepted
H3-2	PDSA →IP	0.435	8.096^***	Accepted
H3-3	PRA →IP	0.209	4.309^***	Accepted
H4-1	PMA →TP	0.192	4.054^***	Accepted
H4-2	PDSA →TP	0.337	5.857^***	Accepted
H4-3	PRA →TP	0.364	8.740^***	Accepted
H5	IP→PP	0.254	7.157^***	Accepted
H6	TP→PP	0.645	16.004^***	Accepted
^* p<0.05, ^p<0.01, ^*p<0.001

5. CONCLUSIONS:

The present empirical study investigated the effects of explorative and exploitative activities on patent activities and innovation performance. Businesses are faced with tough competition due to the rapid advancement of ICT and market globalization. Also, the deep recession in the world economy following the global financial crisis has coerced businesses into relentless survival games.

Thus, businesses should develop strategies from different angles. R&D, patenting, innovation, and commercialization activities should be organically linked with one another. If these activities take discrete routes, problems will ensue. The R&D activities are divided into explorative and exploitative activities, both of which substantially influence the patent activities and innovative performance.

The present analysis findings come down to the following.

First, concerning the explorative activities will have significant effects on the patent management; patent development support and patent rights enforcement activities, the hypotheses H1-1, H1-2 and H1-3 were accepted.

Second, concerning the exploitative activities will have significant effects on patent management; patent development support and patent rights enforcement activities, the hypotheses H2-1, H2-2 and H2-3 were accepted.

Third, concerning the patent management, patent development support, and patent rights enforcement activities will have significant effects on intangible performance, the hypotheses H3-1, H3-2 and H3-3 were accepted.

Fourth, concerning the patent management, patent development support and patent rights enforcement activities will have significant effects on technological performance, the hypotheses H4-1, H4-2 and H4-3 were accepted.

Fifth, concerning the intangible performance will have significant effects on the product performance, the hypothesis H5 was accepted.

Sixth, concerning the technological performance will have significant effects on the product performance, the hypothesis H6 was accepted.

The present findings have the following implications.

As for the scholarly implications, first, this study divided R&D activities into explorative and exploitative activities and analyzed their causal effects on patent activities, i.e. patent management, patent development support and patent rights enforcement activities. Previous studies focused on the management performance relative to the exploration and exploitation as well as the patent activities, lacking in empirical aspects, whereas the present study attempted to determine the causality from diverse perspectives through the integrated approach to patent activities.

Second, the present study reflected the trends in R&D, innovation and patents, presented three types of patent activities, i.e. patent management, patent development support and patent rights enforcement activities, and operationalized relevant constructs.

Third, this study empirically analyzed the sub-classified patent activities using the data collected from R&D staff working at Korean companies. With the influence factors controlled for, this study focused on measuring the patent activities, i.e. patent management, patent development support and patent rights enforcement activities, from the perspective of corporate R&D staff.

Finally, this study had limitations, which warrant further studies. The respondents of this study were current and former R&D staff, mostly researchers, at local companies. One should be cautious in generalizing the present findings, given the small sample size. Therefore, further studies need to increase the sample size, and develop variables and measurement items to allow for different aspects. Lastly, in that the present findings are applicable to all the other departments, not just the R&D unit, further studies need to investigate other industries and product categories to make meaningful contribution to the body of knowledge.

6. REFERENCES:

1. Park ST, Park EM, Kim YK, Does the Company Size Affect the Purpose of Patent Application? : Case of the Korean Electronics Industry, International Journal of Applied Engineering Research, 2014, 9(21), pp.8955-8966.

2. Mudambi R, Swift T, Knowing When to Leap: Transitioning between Exploitative and Explorative R&D, Strategic Management Journal, 2014, 35(1), pp.126-145.

3. March JG, Exploration and exploitation in organizational learning. Organization Science, 1991, 2(1), pp.71-87.

4. Mudambi R, Location, control and innovation in knowledge intensive industries. Journal of Economic Geography, 2008, 8(5), pp.699–725.

5. Park ST, Lee SJ, Kim YK, Appropriability of Innovation Results: Case of the Korean Industry, Indian journal of Science and Technology, 8(21), pp.1-9.

6. Levinthal DA, March JG, The Myopia of Learning, Strategic Management Journal, 1993, 14(8), pp.95-112.

7. Benner MJ, Tushman ML, Exploitation, Exploration, And Process Management: The Productivity Dilemma Revisited, Academy of Management Review, 2003, 28(2), pp.238-256.

8. Siggelkow N, Rivkin JW, When Exploration Backfires: Unintended Consequences of Multilevel Organizational Search, Academy of Management Journal, 2006, 49(4), pp.779-795.

9. Sidhu JS, Commandeur HR, Volberda HW, The Multifaceted Nature of Exploration and Exploitation: Value of Supply, Demand, and Spatial Search for Innovation, Organization Science, 2007, 18(1), pp.20-38.

10. Raisch S, Birkinshaw J, Organizational Ambidexterity: Antecedents, Outcomes, and Moderators, Journal of Management, 2008, 34(3), pp.375-409.

11. Baum JAC, Calabrese T, Silverman BS, Don’t go it alone: alliance network composition and startups’ performance in Canadian biotechnology, Strategic Management Journal, 2000, 21(3), pp.267–294.

12. Berkowitz L, Getting the Most from Your Patents, Research Technology Management, 1993, 36(2), pp.26-31.

13. Youn SH, An Empirical Study of the Technology Innovation Capabilities and Patent-related Activities on the Business Performance Perspective, Changwon National University, Doctorate thesis, 2013. http://www.riss.kr/link?id=T13443950

14. Schumpeter JA, Capitalism, Socialism, and Democracy. New York: Harper, 1942.

15. Cohen W, Empirical studies of innovative activity in P. Stoneman(ed.), Handbook of the Economics of Innovation and Technological, Change, 1995, pp.182-264.

16. Scherer FM, Industrial market structure and economic performance, R and McNally, Chicago, 1970.

17. Kelly TM, The Influence of Firm Size and Market Structure on the Research Efforts of Large Multiple Firms, Ph.D. Dissertation, Oklahoma State University, 1970.

18. Brockhoff K, Teichert T, Cooperative R&D & Partners' Measures of Success, International Journal of Technology Management, 1995, 10(1), pp.111-123.

19. Laursen K, Salter A, Open for Innovation: The Role of Openness in Explaining Innovation Performance among U. K. Manufacturing Firms, Strategic Management Journal, 2006, 27(2), pp.131-150.

20. Zahra SA, Nielsen AP, Source of Capabilities, Integration and Technology Commercialization, Strategic Management Journal, 2002, 23(5), pp.377-398.

21. Park ST, Kim YK, Kim TU, A Study on Influencing Factors of Patent Activities on Management Performance, Entrue Journal of Information Technology, 2013, 12(3), pp.121-129. http://www.riss.kr/link?id=A99888326

22. Park ST, Kim YK, Difference Across Indutries of Innovation Appropriability Mechanism's Effectiveness and Classification, Journal of Digital Convergence, 2014, 12(6), pp.135-144. http://www.riss.kr/link?id=A100043954

23. Kim JD, The Effect of New Product Innovativeness on the Relationship between New Development Process and New Product Performance, Korean Management Review, 2002, 31(3), pp.679-702. http://www.riss.kr/link?id=A100856361

24. Wind J, Mahajan V, Issues and Opportunities in New Product Development: An Introduction to the Special Issue, Journal of Marketing Research, 1997, 34(1), pp.1-12.

25. Voss CA, Determinants of success in the development of applications software, Journal of Product Innovation Management, 1985, 2(2), pp.122-129.

26. Johne A, Snelson P, Successful Product Innovation in UK and US Firms, European Journal of Marketing, 1990, 24(12), pp.7-21.

27. Nunnally JC, Psychometric Theory, McGraw-Hill, New York, 1987.

28. Thompson R, Barclay DW, Higgins CA, The Partial Least Squares Approach to Causal Modeling: Personal Computer Adoption and Use as an Illustration, Technology Studies: Special Issue on Research Methodology, 1995, l2(2), pp.284-324.

29. Fornell C, Larcker D, Evaluating structural equation models with unobservable variables and measurement error, Journal of Marketing Research, 1981, 18(1), pp.39-50

30. Chin WW, Issues and opinion on structural equation modeling, MIS Quarterly, 1998, 22(1), pp.7-16.

31. Barclay D, Higgins C, Thomson R, The Partial Least Squares Approach to Causal Modeling, Personal Computer Adoption and Useasan Illustration, Technology studies, 1995, 2(2), pp.285-309.

32. Chin WW, Todd PA, On the Use, Usefulness, and Ease of Use of Structural Equation Modeling in MIS Research: A Note of Caution, MIS Quarterly, 1995, 19(2), pp.237-246.

33. Falk RF, Miller NB, A Primer for Soft Modeling, 1st ed. US: University of Akron Press, 1992, p.103.

34. Wetzels M, Odekerken-Schroder G, Oppen CV, Using PLS path modeling for assessing hierarchical construct models: guidelines and empirical illustration, Management Information Systems Quarterly, 2009, 33(1), pp.177-195.

35. Tenenhaus M, Vinzi VE, Chatelin YM, Lauro C, PLS path modeling, Computational statistics anddata analysis, 2005, 48(1), pp.159–205.

36. Cohen J, Statistical Power Analysis for the Behavioral Sciences, Hillside, NJ: Lawrence Erlbaum, 1988.

Received on 22.06.2017 Modified on 28.07.2017

Research J. Pharm. and Tech. 2017; 10(8): 2735-2742.

DOI: 10.5958/0974-360X.2017.00486.3

	ERA	EIA	IP	PDSA	PMA	PP	PRA	TP
ERA	0.805
EIA	0.710	0.796
IP	0.629	0.643	0.828
PDSA	0.603	0.608	0.720	0.858
PMA	0.534	0.540	0.652	0.744	0.846
PP	0.668	0.632	0.646	0.765	0.697	0.832
PRA	0.613	0.658	0.656	0.738	0.722	0.790	0.829
TP	0.566	0.614	0.607	0.748	0.706	0.789	0.752	0.864

Table 1: Operationalization of Variables

Factor

Operational Definition

Table 3: Correlation between Latent Variable

ERA

EIA

IP

PDSA

PMA

PP

PRA

TP

ERA

EIA

IP

PDSA

PMA

PP

PRA

TP

Table 4: Hypotheses Testing

Path

Coefficient

T-value

Result

ERA → PMA

ERA → PDSA

ERA → PRA

EIA → PMA

EIA → PDSA

EIA → PRA

PMA →IP

PDSA →IP

PRA →IP

PMA →TP

PDSA →TP

PRA →TP

IP→PP

TP→PP