A Review for the Mechanism of Research Productivity Enhancement in the Higher Education Institution

The main purpose of this review is to find out the mechanism of research productivity enhancement proposed by each researcher in the papers they have published. The availability of these various mechanisms raises the desire of the authors to compare each mechanism. The focus of the review lies in the mechanism, characteristics, source of data, and evaluation methods used by each researcher. The review then jumps to the results obtained by each mechanism. The author also compares the types of data used by each researcher and the parties involved in the mechanism. There are some differences in the use of terminology between one to another mechanism, but in essence, it has the same goal, research productivity enhancement.


Introduction
Through this paper, the author would like to explain the results of the review related to the mechanism of research productivity optimization in higher education institutions.The mechanism discussed here focuses on the model, framework, or method.The reason the higher education institution environment was chosen is because increasing research capability in higher education institutions can increase public awareness about the importance of utilizing research results.This is evident from several studies that have been done, among them are the prevention of natural disasters [1] [2] or the discovery of treatment methods for certain types of diseases [3] [4].In Indonesia, there is support from the government for research development in higher education institutions through various schemes.The determination of the scheme and the number of grants managed for research depends on the level of each institution in clusters based on its performance each year [5].Higher education cluster membership is always evaluated and updated regularly by the government through the Ministry of Research and Technology as a Stakeholder.Other alternatives, research at universities can receive funding from institutions outside the government through joint project mechanisms or contractual agreements [6] [7].The industry is one of those that need research output produced by higher education institutions.
The main purpose of this review is to find and explain research or publications related to the mechanism of research productivity optimization in higher education institutions that have been conducted by several researchers [8] [9][10] [11] [12].Through this review, opinions were given about the strengths and limitations of each mechanism.The opinion given is based on the understanding obtained from various papers or journals related to the domain being discussed.Based on the data in Table 1, it explains the gap between the number of lecturers, including the number of full professors, and the amount of research output produced.Although if viewed from 2014 to 2018 the number of publications continues to increase, the increase is still not optimal or has not yet reached the ideal conditions and this will affect the overall research productivity performance.If we look at the number of recipients of research grants and community service, in 2014 there were 3.853 recipients from private universities under the Ministry of Research, Technology, and Higher Education.In 2015 as many as 12,069 recipients for batch 1, and 381 recipients for batch 2 [24][25 ].Research grantees continue to increase in the following years, an increase in the number of grant recipients should be followed by the increase in the number of research productivity.So it is hoped that this review becomes an alternative way for research managers to improve research productivity in the higher education institution environment.

Review Mechanism
Before further discussing the mechanism of research productivity optimization in higher education institutions, we will explain the big picture of mechanisms already published or developed related to research productivity optimization in the higher education institution environment (Table 2).The researcher along with eight faculty staff participated in this 12-month project, starting from June 2010 to the end of May 2011.While working on this project, the author also observed the ideas, actions and research productivity of her colleagues, as well as the perceptions of the MBS Dean, Chair of the University, two senior FTI managers and five NESAC members.
In conducting this review, the systematic mapping study method is used, where the selection of papers is not done subjectively by the author, but instead uses the protocols and filters set at the beginning of the literature review process.The steps in conducting the literature review are shown in Figure 1.The review consists of 3 main stages, pre-review, review, and post-review.Pre-review consists of collecting related publications, filtering, and sorting, review consists of constructing a matrix for literature review, finding the source of data, finding the mechanism and the characteristics, finding the evaluation and the result.The last one is the post-review, which made a summary and conclusion.

Results and Discussion
The review is carried out based on the steps in Figure 1, the first stage id the comparison of mechanisms used by each researcher.Each mechanism has a different construct than the other mechanism.The selection of constructs is based on needs and where the mechanism is implemented.Comparisons are also accompanied by the characteristics of each mechanism (Table 4).

S. Chanthes [12]
Triple-helix model (governmentuniversity-industry) Explain empirical evidence about the important role of the triple-helix in improving faculty research productivity.In its implementation, the authors make triple-helix modeling (governmentuniversity-industry) used in joint projects, research collaboration, and funding.A detailed explanation of the proposed triple-helix model is needed so it can be implemented in other higher education institutions.
The first mechanism proposed by Aithal [8], Sims -Model (Model of improving higher education research productivity).Involving students in research has a positive impact; however, the impact of involving faculty members is unknown.Who are the faculty members here?The model has not yet explained the extent of the involvement of faculty members in research activities.The model already has researcher leveling but has not demonstrated the ability to measure the performance of each individual involved in research (only HEI's overall performance).Next, we will discuss the source data vs the evaluation method used by each researcher.The second mechanism proposed by Morales, et al. [9], The results of the generalized estimating equation (GEE) model analysis using the normal distribution show average duration = Statistically Significant (p <0.001) for duration > one year.For a short duration (one summer or less), p = 0.004 less productive in terms of collaborative faculty-student publications.Faculty members who teach students about research publications have a significance of p = 0.006.Faculty members who guide African-American student research projects have a significance of p = 0.009.Other variables significantly influenced are faculty members with good H-index scores (p <0.001), faculty members with years of research experience (p = 0.025), and faculty who get more research grants (p = 0.001).Gender and race/ethnicity do not have a statistically significant effect on the model.The author needs to clarify the high H-index (which according to researchers has a significant effect) whether pure-citation or self-citation.The experience used as a variable is actually very difficult to measure.It is necessary to explain the values used as indicators to measure the significance of the experience.Furthermore, the third mechanism proposed by Fauzi, et al. [10], the integration of three models showing academics engaging each other in Knowledge Sharing (KS) activities.The selection of PLS-SEM as a method for analyzing and testing hypotheses is very appropriate because (1) it involves a non-normal dataset, which will increase the goodness of fit.(2) PLS-SEM can accept small sample sizes, while SEM-based covariant cannot be implemented for small data sizes.The authors have not shown the testing results of the proposed framework, still unknown how many hypotheses were accepted and how many hypotheses were rejected, so it is unknown whether the proposed framework is valid/acceptable or not.The next papers [11] are still related to the papers discussed earlier, so the data used remains the same but with different contributions: Through the KS framework model proposed by the authors, the role of academics (with 13 constructs) has a positive effect (substantial impact) on research productivity.The 13 constructs used are commitments, social network, management support, social media, attitude toward KS, and subjective norm toward KS, KS intention, KS behavior, perceived behavior control, facilitating conditions, trust, and research productivity.Research productivity is used to determine the position/ranking of an HEI (Higher Education Institution) on a national or international scale.So the authors need a mechanism (a model framework or method) to increase research productivity.The last mechanism proposed by Chanthes [12], these papers discusses increasing faculty research productivity via a Triple-Helix Modeled (government-university-industry).We also display the results obtained by each researcher shown in Table 5.

S. Chanthes [12]
Observation Grounded theory approach The final result of this research is the implementation of triple-helix modeling which was carried out after the proposed strategic plan with the title "The development of strategic approach to the building of Thai-Lao economic partnership" was accepted by the Council of Ministers (council of ministers) in July 2011, which means a collaboration between triple-helix (university-industry-government) established, which is named MBS-FTI-NESAC.
Most of the data collection methods used are through surveys and questionnaires.Aithal [8] took research publication data (from 2013-2016).Morales, et al. [9] through a web survey involving 468 faculty members across 13 research-intensive institutions.Fauzi, et al. [10][11] distributing questionnaires in the form of questions involving sample data taken randomly from instructors (tutors to professors) in Malaysia.Chanthes [12] made observations to obtain research data.The author of this paper along with eight staff faculty participated in a project, starting in June 2010 and ending in May 2011.While working on this project, the author observed the actions, ideas, and research productivity of her colleagues.Comparisons were also made with the types of data used by each researcher, divided into two types, primary data, and secondary data (Table 6).Furthermore, involvement in the mechanism, we divide into three groups namely academics, government, and industries.
So based on the constructs and hypotheses already explained, and then the initial model proposed is shown in Figure 2: This framework model is a combination of the knowledge sharing model proposed by Fauzi, et al. [10] [11], the triple-helix model [12], Sims-Model [8], and some elements we took from gamification [36][37] [38].This conceptual model is also an adaptation of the model that has been proposed previously [39].This framework model consists of 9 independent variables and 4 dependent variables.The independent variables used are social networks, teamwork, competition, points, and bonuses, leveling up, appropriate research funds, research-based on goals, good research facilities, and joint projects (triple-helix).The dependent variable used is knowledge sharing behavior, motivation, capability, and enhancement of research productivity.The following are references that we use in selecting the gamification element used in the framework model (Table 7).In future work, the author will conduct a pilot test of the proposed framework model.For testing and analysis of the proposed model, PLS-SEM (Partial Least Square -Structural Equation Modeling) is used.The reason for choosing the SEM method is because it supports complex modeling constructs with minor correspondents.SEM is powerful in modeling latent variables, measuring error correction, and estimating simultaneous parameters for the whole theories [46].PLS-SEM is an alternative structure equation modeling method used to explain relationships between constructs, emphasizing the theory of the value of these relationships with a small sample data size.Some common reasons for choosing PLS-SEM as a method for testing the proposed method are as follows: 1.The PLS algorithm is not limited only to the relationship between indicators and their latent constructs which are reflective, but the PLS algorithm is also used for formative relationships.
2. PLS can be used to estimate the path model with a small sample size.
3. PLS-SEM can be used for very complex models (consisting of many latent variables and manifests) without experiencing problems in estimating data.
2. PLS can still be used when the data distribution is skewed.

Conclusion
In this article, the authors review the mechanism of research productivity enhancement of publications contained in several reputable journals.Broadly speaking, the discussion on research productivity enhancement mechanisms is very dynamic.The authors focus on the mechanism, characteristics, sources of data, and evaluation methods used by each researcher.The review then jumps to the results obtained by each mechanism.Next, the comparison of the types of data used by each researcher and the parties involved in the mechanism.An indicator of the number of publications in the form of articles and the amount of industry-funded research still dominates the measurement of the success of research productivity enhancement.There are some differences in the use of terminology between one mechanism to another, but in essence, it has the same goal, research productivity enhancement.The evaluation method for each mechanism is adjusted to the level of complexity of the problem and the source of data used.Based on the summarization of the review, the author proposes a conceptual model which is a combination of several mechanisms already discussed.This framework model consists of 12 constructs and 11 hypotheses.Over the initial hypothesis, the model proposed is expected to be better than some of the mechanisms already discussed.The indicators for each construct have not been determined.In future work, the author will conduct a pilot test of the proposed model by involving academics' in the higher education institution.

Fig 1 .
Fig 1.The systematic mapping study

Table 1 .
The publication of scientific articles in Indonesia

Table 2 .
The mechanism of research productivity optimization

Table 4 .
The comparison of the mechanism used by the researcher

Table 5 .
The comparison of the source data vs evaluation method

Table 7 .
The relevant elements from gamification