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Abstract

This study aims to investigate key stakeholders’ impact and to assess its effect on Research Data Management (RDM), thereby setting the agenda for sound local government Research Data Management (RDM) policy for maximum profit. This study relies mainly on the stakeholder’ theory. Our target population is stakeholders in RDM. The survey used the Partial Least Squares and Structural Equation Modeling tool with a sample of 295 received from respondents to find the effect among the stakeholders. The Statistical results confirmed there is a positive effect among stakeholders. The Funders agencies criteria influence researchers’ work. The effect of government support policies on librarians RDM support services was positive but not significant. The impact of computer literacy of librarians on the RDM support service delivered by librarians was insignificant. The finding is mainly due to overreliance on manual approach to work in most libraries in Ghana as a result inadequate fund support. Librarians need to take a leading role in RDM to implement RDM policy fully. This study contributes to the discourse by helping to strengthen the existing relationship and also to understand the role of various stakeholders in RDM. Stakeholders in Ghana and developing countries need to take greater responsibility to achieve success. Government can rely on it for policy formulation. The research can be used to expand the existing stakeholders’ effective support and interactions as well as in teaching. The survey results can also help identify best practices, improve current observation, and compare practices across different countries.

Keywords

research data management stakeholders librarians RDM policy stakeholders’ relationship

Introduction

Research Data Management (RDM) is a comprehensive set of activities for the organization, storage, access, and preservation of data (Alexandre Ribas Semeler et al., 2019). Research data includes but is not limited to all data created during the research project undertaken by researchers and third-party data from within or beyond the borders of the institutions. The purpose of RDM is to produce quality research, avoid data loss and enable research to be reproduced. Various activities are then accordingly undertaken to safeguard the data. These activities include services, tools, and infrastructure supporting research data across its lifecycle (Schmidt et al., 2016). The services are gathering, planning, RDM informatics, research data citation, RDM training, storage access, and impact with the research support office (Tang & Hu, 2019). To better manage the services, many policies have been formulated with the underlying guidelines that govern the management of the research data and the roles of various stakeholders for its effective management. These Stakeholders are individuals or groups of people that affect or are affected by the organization’s decisions and practices, namely funders, institutions, researchers, librarians, and government. Funders provide the money to support a research project and require a Data Management Plan (DMP) for research they sponsored as a condition for accessing funds. Its purpose is generally to enable research data to be accessible and shared. The institutions also provide data management resources, infrastructure, training, and enabling environments for others to perform their roles. Researchers, on their part, design the study, specify how data will be collected, analyzed and finally draw conclusions. The government provides policy direction and implement research findings. Academic librarians cannot be left in this task as they have the experience that could be translated into their new role, such as DMP support, archiving, preservation, and other support services. The success of RDM depends on the interaction among key players such as the librarians providing RDM support services, government with support policies, the funders or donors with agency criteria to access funding, institution with support services with the purpose to affect researchers’ work (Whitehead & Bourne-Tyson, 2016). Their decisions can affect positively or negatively the research data management agenda. Following the reluctance of some of these stakeholders, especially the researchers and librarians, to keep to best practices in handling research data, there is a clamoring call for educational institutions, funders, and government to get actively involved in the management of the research data (Stall et al., 2019).

Computer literacy is a survival skill in the information age, and it is finding more mutual ground with other literacies. It has been described as literacy with digital texts. Computer literacy is viewed as a general knowledge of both software and hardware. A broad knowledge may not qualify one to be computer literate but rather the ability to run and navigate through the application. It was discovered by Baro et al. (2019) that, to be computer literate or fluent, there should be grounded contemporary skills, and a foundational concept, and intellectual capabilities. There is a need to have basic knowledge of communication relating to applying computer technology to solve problems. According to Binici (2021) technology significantly affects the librarians ‘skills and competence. Therefore, there is no doubt that all stakeholders especially librarians need these skills to assist researchers in this technology world for better communication and management. Computer literacy should include among others skills that will enable them to store, share, preserve, and archive research data.

Over the years, many researchers have sought to understand the dynamism of the various stakeholders’ support and challenges in RDM to find the best practices that will serve the researchers’ purpose (Flores et al., 2015; Šuman et al., 2020; Tang & Hu, 2019) and (Tang & Hu, 2019). The authors emphasize that despite the challenges that the data in research is relevant and assert that managing research contributes to quality research and the relevance of RDM is not in doubt. Researchers have been encouraged to embrace the practice to derive the full benefit. For example, RDM policies increase work outputs, make research results verifiable, and promote replication of the research findings. Burgeoning literature shows that the stakeholders benefit more if they participate in policy formulations for sustainable competitive advantage in funded research. Despite this, librarians generally were reluctant to engage in RDM (Wilkinson et al., 2018). Besides that, the discussions on data management in developing countries are still at a very early stage and lagged in RDM eﬀorts (Chawinga & Zinn, 2019).

In the context of Ghana, the practice of research data management is still new and underdeveloped compared to other developed countries. Many research data are not available and, therefore, not reusable as in developed countries such as the USA and UK (Arkorful et al., 2021). There is, therefore, no research data management policy. In a study conducted by Benneh et al. (2016) to better understand data management, it was found that some level of research data management practices exist. However, the universities have no RDM support function. The phenomenon is far from overarching. It is also reported that there is no research data management policy at the University of Ghana. The current RDM at the university is not yet fully developed (Avuglah & Underwood, 2019). The situation of Ghana is not different from many other countries especially in Africa. In South Africa for example, despite the directive of the National research foundation directive to all researchers to make data accessible, there is no compliance, as there are still challenges with access, sharing, retention, preservation, and storage of data, Factors impeding the research data management in developing countries, include lack of time and data misuse, absence of proper RDM training, reward and legal framework, cope with weak regulation. It was also reported lack of data infrastructure and interoperability challenge (Anane-Sarpong et al., 2018).

According to Liu et al. (2020), in the USA, UK, and Australia, data sharing, data management plan, access, and retention are critical requirements from funders as conditions to access research grants. The authors found that there is no difference in these countries regarding RDM. In Addition, there are research data management services to help the researchers to comply with the funder’s requirement. Larivière and Sugimoto (2018) found that 90% of research funded in US National Institute of Health are freely available. In addition, there are research data management services to help the researchers to comply with the funder’s requirement (Tang & Hu, 2019). These services are provided mainly by librarians unlike in Africa as such Ghana. It is a phenomenon practiced worldwide, especially in developed countries which is reflected in their development. The aforementioned requirements from the developed countries can be extrapolated and serve as benchmark in the case of Ghana and other country to derive the maximum benefit.

Extant literature did not test the effect of the stakeholders in RDM and their effect on research work. This paper investigates stakeholders’ support effect in Ghana. The absence of RDM may impede the urgency for sound RDM practices, as seen in other countries.

Literature Review and Hypothesis Development

Stakeholders and Their Relationship

RDM involves several stakeholders at all levels, such as journal publishers and funding agencies (Chen & Wu, 2017). Others are institutions, librarians, government, researchers. Previous findings in Hall et al. (2013) show that the stakeholders benefit more if they participate in policy formulations for a sustainable competitive advantage of the institutions in funded research. In Darch et al. (2021), it was found that comparing motivation to satisfaction with the project outcome gives a valuable lesson for the success of RDM through partnership. However, opinions are divided among the management of many institutions. While some see the relevance of participating in RDM, others remain adamant that investments into RDM are not competitive. The standpoint group also thinks that not all institutions need DMP (Vilar & Zabukovec, 2019). While cost could be a challenge, it may not be the only reason that handicaps the implementation of RDM (Tenopir et al., 2017). Previous research found conflict and tension among these stakeholders in various institutions (Verbaan & Cox, 2014). In addition, the level of mutual understanding was found to be low among the stakeholders sampled. Despite the attempt to resolve this by providing division of role, it remains doubtful. If this is not resolved, it can impede the success of RDM. In Whyte (2014) librarians and research offices show commitment by coordinating and promoting the RDM policies. There is also some level of mixed stakeholders supervising the implementation and governance. Close collaboration among all the stakeholders is required to initiate, promote, foster, and strengthen collective learning, and overcome new problems, and research activities. The diversity of stakeholders suggests careful management of these relationships along the data management lifecycle (Sturges et al., 2015). There must exist good communication and cooperation among these groups (Jolak et al., 2018). This is deemed very important for a successful RDM. The lack of proper management may be a factor impeding the urgency for sound RM practices, as seen in other countries such as developed countries.

Librarians and Research Data Management (RDM)

Globally, librarians over the years have not been involved in RDM. They are basically into collection development (Johnson, 2019), but with time there is a call for librarians to participate in RDM. In making a case for librarians to be involved in RDM, some argued that services in the library in higher institutions are strategically positioned to offer a significant role in RDM (Andrikopoulou Rowley & Walton, 2021). The librarians have practical skills that could be used in RDM. These include advisory and training services. The result of the study in Tenopir et al. (2017) revealed that libraries of 20 best universities demonstrated enough commitment to provide services such as how to write DMP, data preservation, publishing, managing personal, and sensitive data. They suggested that libraries need to take a leading role in managing the research data generated in academic institutions.

Another study conducted by Faniel and Connaway (2018) in which thirty-six (36) academic librarians were sampled about the factors that influence RDM support and experience, the result revealed that 25% of the academic librarians state that there is a negative perception about them by researchers. The libraries initiated the support late, compiling researchers to start their initiative to embrace the current trend. Librarians have to initiate support for researchers to change the negative perception by providing and engaging researchers.

Librarians in RDM are key stakeholders. This is because they have relationships with all these stakeholders’ groups, which positions them to be better placed to lead the RDM agenda. A key challenge will be the roles of all the various stakeholders involved in RDM, such as the government, academic institutions, funders, and researchers (Cox & Verbaan, 2016). According to Tenopir et al. (2017), academic librarians are directly collaborating with researchers. There is a need for stakeholders’ engagement for librarians in high levels of interaction with researchers while cooperating with other support service providers such as institutions, funders, and government. There must be a reciprocal relationship between stakeholders for data to be managed cheaply (Malthouse et al., 2019). The support from key players such as the library professionals, government, top management in various institutions, and other funding agencies in Ghana for successful RDM.

Conceptual Framework and Hypothesis Development

The leading theory base of this study is that of stakeholders. Although it has developed significantly it remains a focal point. According to Freeman (1984), organizations’ work affects other parties that are within and external to the institutions. Identifying such individuals or groups of people is vital to their success since they can negatively or positively affect organizational performance. These stakeholders’ requirements must be known and well understood for proper implementation (Wilburn & Swanson, 2016). Key stakeholders are responsible for the adoption of RDMP. With these policies stakeholders have no choice than to comply if they would like to enjoy a good relationship and derive the full benefit.

This theory was used with a proposed model (Cocos & Lepori, 2020). This model is in three level such as policy level (government), program level (funding agency), and project level (performing institution including researchers and librarians). The stakeholders’ demands were incorporated to forecast how they will influence organizational response to such multiple stakeholders. The stakeholder’s theory as applied here only shows role and responsibility and its effect on research project (Cornel et al., 2021). This is because the various group involved in RDM must share information to enable their roles to be performed most efficiently to achieve all goals. It is important to note that stakeholder theory outline ethics in a pragmatic viewpoint. It says ethics are things that are done daily to solve our problems and make things better to meet individual needs. Example of such ethics in RDM are defining ownership of data, agreement, share data, accessibility, protecting the identity, and information of participant as well as the licensing of data. These ethics are key in DM and are the foundation of RDM as such the application of stakeholder’s theory.

In Ghana, most of the funding comes from private individuals, non-profit organizations (NGOs) and international funders instead of the government. The situation is mainly due to economic conditions in most developing countries with scarce resources. As in Rogers (2021), scientific research is not integral to national policy. Most of the research are conducted through research grants sponsored by multilateral organizations such as the World Bank, the InterAmerican Development funders, and the European Regional Development Fund. The funding comes with policy requirements that go beyond the funding role and overlap the country’s governmental support. Therefore, there is little or no connection between funders and the government concerning research funding. Research institutions receive a little support from the government for administrative and labor costs but they are independent.

Library Oriented Model of Institutional RDM

This model by Pinfield et al. (2014) focuses on issues that concern the challenges with RDM by distinguishing the various levels of activities, different stakeholders and drivers, and numerous factors affecting the adoption of any program. This model is adopted because it addressed the relationship’ effect among the stakeholders as key actors in RDM and their roles for a successful program. It states that the implementation of RDM is affected by a set of influencing factors that either impact or have been impacted by the program. These factors will either facilitate or constitute a hindrance by influencing the character and the course of action. Therefore, success will be achieved if various stakeholders perform their roles per policies and strategies with various influencing factors in perspective. This study is in line with Shah et al. (2020), which revealed that “data management, data curation, data mining, data science, data-savvy, and big data purpose is to identify relationships and causal associations, classify and predict events, identify patterns and anomalies, and infer probabilities, interest, and sentiment.”

The model presented here provides a relevant basis for our studies. This model engages various stakeholders. Based on an extant literature review, the research model was designed. Figure 1 shows our research framework.

Figure 1.

Conceptual framework.

Development of Research Hypothesis

Funders and Librarians

Funders’ role in supporting librarians cannot be overemphasized. Funding research is key for the librarians to be proactive. Funding helps academic librarians to research into new knowledge. It is essential to help them be abreast with the current development in information science, manage other researchers that fall on them for academic direction, and, more importantly, manage research data. All these cannot be done without adequate support through funding. The soul of the library is money. Inadequate funds impede the effectiveness of any library activity, which will affect the work of librarians (Gao et al., 2020). The greater part of the sponsorship for librarians comes from state and local sources; federal funding provides critical support, enabling them to fulfill their communities’ obligations. As such, the role of funders is a sine qua non for the success of librarians. Therefore, it is hypothesized that: H1: The funders agency criteria have positive effect on librarians RDM support services.

Institutions and Librarians

Institutions where librarians are working influence positively the work of librarians provided the institutions give the needed support. The quality of service rendered by librarians is dependent on the support provided by the institutions. The Institution is the librarians’ employer and provides guidelines that librarians need to follow. Job satisfaction and leadership style improve the relationship between an employee and employer (Albro & McElfresh, 2021). Institution aims and staff development bring positive perception among the staff. It is hypothesized that: H2 The institutions support services affect positively the librarian RDM support services.

Institutions and Researchers

Institutions play a significant role in the work of researchers. The quality of programs and the services offered will affect the researcher’s efficiency and increase its output of work (Cowen et al., 2019; Ponomariov & Boardman, 2010). There is growing competition for the fund due to the growing number of institutions. Institutions are now forced to compete for scarce resources and funds. Institutions are therefore very keen on the caliber of researchers that are recruited. The institutions would like to influence researchers’ work performance to benefit from funding that only the best researchers can help to access. Therefore, it is hypothesized that: H3 institutions support services positively affect the work of researchers.

Funders and Researchers

In a study conducted by Gök et al. (2016), where researchers in all of the four countries have the chance to access the supra-national research funding from the European Union, it was found that citation impact of the researchers’ work is positively related to funding variety and negatively related with funding intensity. Funders could influence the researchers’ choice of topic (Webster et al., 2021). This burgeoning in terms of researchers’ work raises issues for various funders and the quality of work done. Funders expect to see researchers demonstrating the highest possible profit on investment for their grant. It was also argued that funders affect researchers’ knowledge production; the hypothesis is that H4 Funders agencies criteria affect the researchers’ work.

Government and Librarians

According to a study conducted in Indonesia (Nashihuddin, 2018), it was noted in the conference that the government’ program initiated needs to be followed up by a librarian. Government must include the librarians as key stakeholders in program implementation, such as research data management policy, because librarians’ work is not generally on the government’s top policies. In Osunrinde and Adetunla (2018) the study reveal that the knowledge level of librarians toward the preservation and conservation of information materials was quite high, and the level of training has improved greatly when government sponsored. It is there hypothesized that: H5 Government support policies affect the librarians RDM support services.

Computer Literacy and Librarians

The computer can help things to be done faster and easier. The librarians with computer literacy are likely to be more productive and efficient at work than those who are not. The Librarian’s ability to manage research data depends on the computer literacy level with a set of technical skills such languages (Python, Structured Query Language, Java, and eXtensible Markup Language), design and structures of databases, user-centered design, natural language processing tools, Internet of Things, and large data (Semeler et al., 2019). Computer literacy is viewed today as a fundamental part of librarians’ skills. Librarians require new skills to be relevant and compete with other field peers. Computer skills will impact their performance. It is hypothesized that: H6 computer literacy of librarians affects librarians RDM support services.

Methodology

Population and Data Collection

The research approach used in this research is the deductive research where the study refers on the previous concept approach and theories in formulating the research hypotheses and objectives. The research approach is also explanatory (Casula et al., 2021). This is to enable us to have better understanding of the phenomenon. The study sample were selected by ensuring that the right target fill the questionnaires in order to collect in-depth data from them. The questionnaire seek to understand the effect of the various stakeholders in RDM namely librarians, funders, researchers, government (Table 1). The researchers were guided by the list released by commonwealth network (Nexus, 2020) to select the institutions or organization in order to recruit the respondents. Through the help of research assistants, the various units were contacted first to check their availability. After acceptance the questionnaire were sent to them. So, the analysis is based on the responses received which are fit for our data analysis. Therefore, the study population are all researchers in academic research institutions in Ghana notably, the University of Ghana’s Institute for Medical Research, The Cocoa Research Institute of Ghana (CRIG), African Centre for Conflict Resolution and Reconciliation, Centre for Sustainable Development Initiatives, Council of Scientific & Industrial Research, Geological Survey of Ghana, Ghana Meteorological Services Department, Institute of Local Government. Studies. A purposeful sampling was adopted to ensure the right people fill the questionnaire. For a person to be qualified, the respondent must with the institution for not less than 2 years. In addition, the respondent must be involved in research activities and has individually or as team benefited from research grant. The questionnaires were first piloted with few researchers to check for its feasibility. Upon careful scrutiny some corrections were made and was finally administered which lasted for 2 months (January –February). Each respondent answered all questions. A survey questionnaire was administered to 501 respondents, and a response rate of 59% was successful. So, the analysis was based on 295 respondents. No one was forced to answer any question and confidentiality and anonymity were guaranteed. The questionnaires were coded in IBM-SPSS (version 25).

Table 1.

variables and Descriptions.

Variable	Type	Descriptions
Researchers’ works (RW)	Dependent	1. Academic or scientific research undertaken by a researcher with RDM best practices
Librarians RDM support service (LIB)	Dependent	They are RDM-related service and activities and the service provided by librarians in supporting RMD service
Institutional RDM support service (INST)	Independent	employer of the researchers and librarians providing institutional support
Funders’ agency criteria (FAC)	Independent	They are condition from funders to qualify individual to access research funding
Government support policies (GOV)	Independent	Funding policies and guidelines from the governing body or local authorities of a nation, state, or community in RDM.
Computer literacy of librarians (CL)	Independent	Technology and level of computer literacy of librarians

Instrumentation

The stakeholders in RDM in Sturges et al. (2015) were adapted namely (institutions, funders, researchers, government, and learned society of which librarians are members) with focus on their Support services in RDM from which we derived the variables namely Researchers’ works, Librarians RDM support, Institutional support services, Funders’ agency criteria, Government support policies, and Computer literacy of librarians. Computer literacy was included to check its effect on of librarians since it is key for their new role. Since RDM policy stipulates roles, duties and responsibilities of each stakeholder, it may be logical to believe that if every stakeholder performs their supporting roles there will be some amount of effect. So, the questions are related to their roles and support services in RDM. Previous finding in Liu et al. (2020), Research data management policies in USA, UK and Australia universities has demonstrated the areas of focus of research data management policies and the stakeholders’ roles and its intended benefit. Therefore, the researchers’ work was measured with RDM support received, and their roles in RDM such as data sharing and access and DMP data and data analysis. Librarians RDM support service’ items were related to the support provided to researchers with regard to RDM such as data lifecycle needs, workshop and training, DMP and grant application support. Funders agencies criteria were measured with their requirement such DMP, data sharing, access, retention, preservation, and provision of funding. Government support policies was measured with the provision of financial support. setting of RDM national policies and implementation of research findings. Computer literacy were measured with the skills needed in RDM such as ability to use metrics tools as In-Cites Benchmarking & Analytics, providing advice on informatics and discovery and analytical tools; Data linking and data integration techniques skills. Institutions support services also measured with provision of infrastructure, such as archiving and storage; setting of internal RDM policies and implementation; support from government totaling 37 itemized questions all related to their duties and responsibilities as shown in Appendix 1. A 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree) were used. This was adapted to make the questions clear, which in turns creates an appropriate data sample. The feedback can come in handy, as you can compare against specific outcomes instead of indescribable goals (Joshi et al., 2015).

Data Analysis

Quantitative data were analyzed with the partial least square-structural equation modeling (PLS-SEM) method. IBM-SPSS (version 25) and the SmartPLS software were preferred because they could explore small data sets in quantitative studies. It also enables path analysis that shows the direction and the effect of the various variables (Sarstedt & Cheah, 2019). The Cronbach’s alpha value was used to check data validity in factor analysis. Significant figures were determined at a confidence interval of 0.05%.

Results and Discussion

The data assessment was done to check for internal consistency, reliability, convergent and discriminant validity to check the strength of the measuring items and how they are holding together. The minimum criteria have all been met for various loadings, the average variance of various extracted (AVE) ranged from 0.504 to 0.698. The AVE value of ≥0.5 satisfies the condition for convergent validity. Apart from funders and computer literacy, Cronbach α was all above .7. The composite reliability ranges from 0.774 to 0.898. As shown in Table 2, the result was consistent with the study (Hair et al., 2016).

Table 2.

Data Reliability and Convergence Test.

Constructs	Notation	Convergent validity		Internal consistency reliability
		Outer loading	(AVE)	Cronbach alpha	Composite reliability (CR)
LIBRARIANS’ RDM SUPPORT SERVICE	LIB_3	0.758	0.504	.837	0.877
	LIB_4	0.719
	LIB _5	0.746
	LIB _6	0.708
	LIB _7	0.694
	LIB _8	0.689
	LIB _9	0.651
COMPUTER LITERACY OF LIBRARIANS	CL_2	0.786	0.698	.574	0.822
COMPUTER LITERACY OF LIBRARIANS	CL_3	0.882	0.698	.574	0.822
FUNDERS AGENCY CRITERIA	FAC_X1	0.855	0.632	.425	0.774
FUNDERS AGENCY CRITERIA	FAC_X3	0.730	0.632	.425	0.774
GOVERNMENT SUPPORT POLICIES	GOV_1	0.742	0.686	.788	0.867
	GOV_2	0.885
	GOV_3	0.851
INSTITUTIONS SUPPORT SERVICES	INST_2	0.718	0.689	.847	0.898
	INST_3	0.856
	INST_4	0.870
	INST_5	0.866
RESEARCHER’ WORK	RW_Q2	0.814	0.560	.802	0.864
	RW_Q3	0.765
	RW_Q4	0.791
	RW_Q6	0.667
	RW_Q7	0.696

Evaluation of Measurement Model

Table 3 shows the Heterotrait-Monotrait Ratio of correlation (HTMT). It was also used to assess the discriminant validity. The HTMT is below.

Table 3.

Discriminant Validity of Factors.

Fornell-Larcker criterion
	CL	FAC	GOV	INST	LIB	RW
CL	0.836
FAC	−0.024	0.795
GOV	0.555	−0.060	0.828
INST	0.026	0.391	−0.038	0.830
LIB	0.061	0.455	−0.029	0.596	0.710
RW	0.071	0.363	−0.022	0.515	0.974	0.748
Heterotrait-Monotrait Ratio (HTMT)
	CL	FAC	GOV	INST	LIB	RW
COMPUTER_LIT
FUNDERS	0.070
GOVERNMENT	0.864	0.116
INSTITUTION	0.101	0.634	0.077
LIBRARIANS	0.120	0.787	0.049	0.724
RESEARCHERS	0.110	0.593	0.032	0.610	1.164

Note. The factor loading are all higher than 0.70. This means that causal relationships under study are truly distinct from each other. In other words, they are different and not measuring the same thing. There is no multicollinearity.

Evaluation of Structural Model

Discriminant validity is how a construct is different from other constructs. It infers that a construct is unique. In order to evaluate the discriminant validity of the model, the Fornell & Larcker criterion, Heterotrait-Monotrait Ratio of correlation (HTMT) and Cross Loadings were used. Results of the discriminant validity of all the constructs computer literacy, funders, government, Institution, librarians, and researchers obtained using the square root of the AVE and cross-loading matrix, respectively, are shown in Table 3. According to Hair et al. (2016), the square root of the AVE of a construct should be greater than its correlation with other constructs for satisfactory discriminant validity, and the diagonal elements must be larger than the entries in corresponding columns and rows to satisfy discriminant validity (Henseler et al., 2009). Additionally, to use Fornell and Larcker’s criterion, the squared root of the AVE values was compared with the latent variables (Fornell & Larcker, 1981).

Path Analysis and Hypothesis Testing

The Structural model (Figure 2) shows relationships between the constructs in the research model. The collinearity calculation, path coefficient (β) using t-statistics, coefficients of determination (R² values), effect size, blindfolding and predictive relevance (Q² values), and effect size (q² values) were used to test the strength of the relationship between the dependent and independent variables. Furthermore, the collinearity among latent variables is measured through the variance inflated factor (VIF). There was no collinearity as they were less than 5. According to Hair et al. (2011), the maximum value of VIF greater or equal to 5 (VIF ≥ 5) is an indication that the latent collinearity is problematic. The value of R² in Table 4 is substantial. A range of R² from 0 to 1 is acceptable. Any value higher than this shows some levels of predictive accuracy. In Hair et al. (2011, 2013) and Henseler et al. (2009), R ² values of .75 are substantial, .50 is moderate, and .25 is weak predictive abilities of the variable. Chin (1998) also articulates the values of 0.67, 0.33 and 0.19 as substantial, moderate and weak, respectively.

Figure 2.

PLS-SEM path model showing relationships between study variables.

Table 4.

Hypotheses Testing.

Hypothesis	Path	Path coefficientB	t-Statistics	p Value	Hypothesis supported or not
H1	FAC → LIB	.092	9.973	.000^a	Supported
H2	INST → LIB	.103	9.835	.000^a	Supported
H3	INST→ RW	.515	14.426	.000^a	Supported
H4	FAC → RW	.363	9.554	.000^a	Supported
H5	GOV → LIB	.003	0.206	.837	Not Supported
H6	CL → LIB	.004	0.261	.794^a	Not Supported
Result of the coefficients of determination (R² value)
CONSTRUCTS		R Square	R Square Adjusted	Remarks
RESEARCHERS		0.968	0.968	Substantial
Result of blindfolding and predictive relevance (Q²values) and effect size (q²values)
CONSTRUCTS		Q ²	CONSTRUCTS	q ²
INST		.180	INST → LIBRARIANS	.574
LIB		.479	RESEARCHERS → LIBRARIANS	.800
RW		.062	RESEARCHERS → LIBRARIANS	.800

It is a significant value therefore support the hypothesis.

The Q² is an indicator of the model’s predictive power or relevance. The result is obtained by using blindfolding procedures for a specified omission distance D with values between 5 and 10. Q² values have predictive relevance as they are above zero (0), and q² values of 0.35 indicating an exogenous construct, meaning that there is large predictive relevance for the endogenous constructs.

These effects were tested based on the criteria proposed by Hair et al. (2017) and Zhao et al. (2010) . The indirect and direct effects are also significant.

Discussions

The structural model in Figure 2 shows that funders agency criteria have a significant positive effect on librarians RDM Support service with a path coefficient of 0.092. The amount of funding that a library receives directly influences the quality of its services (Arnott et al., 2020). Their findings buttress our results in that a grant funding a librarian to join a research team to provide direction regarding research data management has significantly impacted the team’s data gathering, storage, and curation methods. It is noted, however, that the path coefficient is below 0.1. This is because funders agency criteria do not directly affect librarians, since the funding is still relatively low. The librarians in Ghana and Africa still need more support.

This study also observed that the institutions support services positively affect the librarians RDM support service in RDM with a path coefficient value of 0.102. This result is clear evidence that the institutions’ support can increase librarians’ output in research data. Regular training to upgrade librarians’ skills and knowledge will go a long way to produce much more positive results to boost the confidence level (Lockhart & Majal, 2012). Many institutions though independent in Ghana and other African countries still need government support and policy direction as a result are not able to provide as much as librarians work demand. The institutions depend on government for infrastructure development. Even though the institution may have the willingness and prepare the librarians until government policy support them, this vision will never be fulfilled (Darch et al., 2021).

The institutions RDM support service has a positive effect on the work of researchers with a path coefficient of 0. 515. The Institution where the researchers work is responsible for policy implementation. It is also responsible for the provision of some infrastructure and training despite the autonomy to researchers to complement that of government. All these contribute to the level of significance reported in this study. This assertion is also in line with Moher et al. (2020) that institutional support positively affect researchers’ work in fulfilling better the RDM. The existing guideline is impactful. The institutions perform a leadership role as well; the leadership style exhibited, therefore, positively influences the researcher’s job satisfaction, which in turn translates to greater efficiency and output of researchers.

The funders agency criteria influence researchers’ work with a path coefficient of 0.363. The impact of research alludes to how research influences a wide variety of phenomena and trends in society, which emerged from the combined effect of research findings (Hove, 2020) that generally manifest over the long term. Funders (research councils, charities, foundations) required researchers to make research data underpinning their work freely available, reusable, accessible, and stored. These funders’ policies go a long way to positively affect researchers’ work by causing them to produce quality research. Our finding is consistent with Benner and Sandström (2000) who argued that funding positively affects the researchers’ output and behavior. Funders agencies criteria have a positive impact on the quality of the university industry of the researcher (Hottenrott & Thorwarth, 2011). In Gök et al. (2016), a citation impact of the researchers’ work positively impacts funding-related variety and negatively impacts funding intensity. This could account for the level of significance reported in this work.

Government support policies influence on Librarians RDM support services has no significant influence (p = .03). This means that hypothesis 5 (“government support policies affect librarian RDM support services ") was rejected as the study results did not confirm it. However, this does not mean that government does not have any influence on librarians. It could mean that its effect is an indirect one, given that government interventions to librarians are not direct, which was found to be the main hindrance to RDMP. This may also be due to the lack of policies since the government relies on such policies as RDMP to release funds. Our result is line with the finding of Huang et al. (2021) that government effect on librarians’ work in China is insignificant. The authors argued that the librarians in China lack professionalization. Furthermore, there is a “wait and see” attitudes because they were not involved in RDM policy formulation and implementation. It is a wake-up call to all governments yet to formulate RDM to fully involve librarians at early stage in order not to have any implementation drawback the case in China. Librarians need to update their knowledge to remain relevant. The positive effect could be demonstrated by getting a national award, successful grant applications, and project work, especially in efficiently and effectively managing research data (DeLuca, 2020).

Computer literacy of librarians and Librarian RDM support service recorded a path coefficient value of 0.004. The result shows an insignificant effect of computer literacy on the work of librarians. According to Julien et al. (2018) and Zhou (1996) the effect of computer-related skills is very substantial, if not critical, for librarians in academic institutions. However, this is not so significant in our findings. Our study found that most of the Librarian sampled have either no computer skills or have only a basic level of computer literacy, so instead of a computer helping them to be faster, it rather slows them. As a result, they rely on manual ways of working instead.

Furthermore, again this may be because the automation of Ghanaian libraries for research data management would not automatically lead to greater efficiency and productivity unless librarians acquire essential skills to use this technology effectively. The most common problems cited were the system’s frequent breakdown, followed by electric power failure, and inadequate computers in the libraries.

Similarly, in Emojorho and Adomi (2006), unreliable telecommunication, infrastructure, and electrical power outages are barriers that negatively influence the use of IT facilities by the majority of staff. These challenges are common in developing countries such as Ghana and many other African countries. Librarians and researchers do not also possess analytical skills and lack the abilities needed for working conﬁdently in this new ﬁeld of the changing information environment (Kirkwood & Price, 2016). In Baro et al. (2019), many librarians rank themselves low in skills related to RDM. As a result of these challenges, more time is spent on the work than expected, leading to the level of insignificant reported in this study, thereby rejecting hypothesis 6. In order to compensate for this weakness, librarians and researchers need to be prepared to adapt to the new circumstances and comply with the emerging requirements of research data management. Unless these issues are resolved, the impact may not be so significant as expected.

Conclusions and Recommendation

This paper sought to address a set of research questions to discover the causal effect among key variables in research data management: researchers’ work, librarians RDM support services, funders agencies criteria, government support policies, and Institutions RDM support service with a focus on librarians’ roles toward research data management. The stakeholder’s theory has been extended through its application to research data management instead of the organization. Six hypotheses were formulated (Table 4) and four were accepted, and two were rejected. Funders ‘agencies criteria and Institutional support service positively affect the librarians’ RDM support service work. Besides that, funders agencies’ criteria also significantly affect the outcome of researchers ‘work in research data management. The study found that the Librarian’s contribution to research data management is impactful and positively affects researchers’ work. Hench, it is concluded that there is a positive relationship on the research variables and as such, stakeholder relationships affect the research data management (Kim et al., 2020). This means that when various stakeholders play their roles and responsibility, it motivates one another and cause the other party to be more efficient and effective in managing research data management resulting in quality research. Concerning the librarians, the more the support, the more the output. Librarians are well-positioned to lead the RDM agenda and therefore should take a leading role. This study is consistent with Andrikopoulou et al. (2021), who found a positive collaboration among RDM stakeholders. It was also found that corporate stakeholders such as institutions can influence the performance directly through other internal staff or indirectly through other external stakeholders such as the government and funders (Li et al., 2020). The study recommends computer adaptation in research data search and management. The researchers should be encouraged to use FAIR data to harness socio-economic development benefits (Stall et al., 2019). In Africa, as elsewhere globally, RDM can advance research and accelerate development through data sharing and reuse. However, this demands that governments, funders, researchers and institutions work as a team to realize its full potential through policies and incentives that promote data sharing. Moreover, because the effect of computer literacy was not significant, we recommend that librarians should be trained in computer literacy to achieve a significant impact. Other related tools to help them operate without interruption must be in place. This includes an uninterrupted power supply, provision of computers and good network infrastructure in order not to fall back on manual ways of performing these tasks. Technical/technological skills, with a focus on metadata, knowledge of data resources is needed; other skills are communication and project management and soft skills, oral communication skills and the ability to cultivate relationships with patrons. It is not easy to practice FAIR data principles without research data management policies. The institutions through the government should also provide an enabling environment and needed infrastructure to do the work smoothly with RDM policies.

There will be a need for data-intensive services such as data sharing, access, preservation, storage, and services to assist researchers in writing DMP. Government and funders should continue to provide the necessary support, and more funds should be released. The higher the funds which funders and government provide, the more significant the impact on Librarian’s work in managing the research data and other services they engage. The researchers should take full advantage of RDM. This will go a long way to increase productivity and help manage better research data, thereby increasing the impact factor for the full realization of the RDM benefit. Such collaborations with librarians can significantly enhance the scientific research process. The movement of librarians into new informationist roles has significant implications for the information professionals and researchers to perform better. This study is useful to all developing countries for education and teaching as it effectively bridges the gap between theory and practices. It could serve as the basis for policy formulation.

Limitation and Future Work

This research is limited to only key stakeholders’ contribution in data management, which is because their role is critical to the success of RDM. However, future research can broaden the scope of stakeholders and other aspects of RDM, such as infrastructure development and training to verify and complement this research’s findings.

Appendix 1.

Questionnaire.

		Please Tick [√ ]
S/N	Support services	1	2	3	4	5
Librarians
1	Support researchers’ work to submit their metadata and to store data.
2	Provide data lifecycle needs of faculty, students, and staff. Assist researchers in accessing external datasets and finding external and local experts
3	Provide publications metrics reports at all levels to increase visibility and determine research impact
4	Provide backup support for the research works and institutional repository
5	Provide workshop, training on topics about citation analysis and research impact and new knowledge to researchers
6	Involve in research collaboration and institutional goals to improve research teamwork
7	Provide data management plan and grant applications assistance
Researchers
8	Receive data management support
9	Design the study
10	Specify what data will be collected
11	Determine how to analyze the data
12	Draw conclusion from the analysis
13	Share data
14	Reuse data
15	Provide DMP
16	Ensure data quality and integrity
Funders
17	Require Data management plan
18	Require Research data to ownership
19	Require Research data to be Shared
20	Retained Research data
21	Provide money to support a research project
22	Preserved and secured the data
23	More research is conducted with the same data.
Government
24	Provide financial support
25	Set RDM policies
26	Implement result of research findings
Computer literacy
27	The level of computer literacy affects my performance and enhances RDM
28	Have computer skills needed for RDM
29	The availability of computers and reliable internet affect performance
30	Able to use a computer to manage research data
31	Utilize metrics tools such as In-Cites Benchmarking & Analytics
32	32 Advice on informatics, discovery, and analytical tools
33	Possess Data linking and data integration techniques skill
Institutions
34	Provide training
35	Receive support from the government
36	The institution provides storage and archiving facilities and other resources.
37	Institution set internal data management policies and implement

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the General Project of National Social Science Foundation of China Research on Design and system construction of scientific data fusion mode Grant /Award Number [21BTQ080].

ORCID iD

Isidore Komla Zotoo

References

Albro

McElfresh

J. M.

(2021). Job engagement and employee-organization relationship among academic librarians in a modified work environment. The Journal of Academic Librarianship, 47(5), 102413.

Anane-Sarpong

Wangmo

Ward

C. L.

Sankoh

Tanner

Elger

B. S.

(2018). “You cannot collect data using your own resources and put it on open access”: Perspectives from Africa about public health data-sharing. Developing World Bioethics, 18(4), 394–405.

Andrikopoulou

Rowley

Walton

(2021). Research data management (RDM) and the evolving identify of academic libraries and librarians: A literature review. New Review of Academic Librarianship, 28(4), 349–365.

Arkorful

Barfi

K. A.

Aboagye

I. K.

(2021). Integration of information and communication technology in teaching: Initial perspectives of senior high school teachers in Ghana (pp. 1–17). Education Information Technologies.

Arnott

J. C.

Kirchhoff

C. J.

Meyer

R. M.

Meadow

A. M.

Bednarek

A. T.

(2020). Sponsoring actionable science: What public science funders can do to advance sustainability and the social contract for science. Current Opinion in Environmental Sustainability, 42, 38–44.

Avuglah

B. K.

Underwood

P. G.

(2019). Research data management (RDM) capabilities at the University of Ghana, Legon. Library Philosophy and Practice (e-journal.

Baro

E. E.

Obaro

O. G.

Aduba

E. D.

(2019). An assessment of digital literacy skills and knowledge-based competencies among librarians working in university libraries in Africa. Digital Library Perspectives, 35, 172–192.

Benneh

E. A.

Ayiah

E. M.

Kantanka

E. S.

(2016). Institutionalizing research data management practices in academic institutions in Ghana: The role of the library. STU International Journal of Technology, 1(2), 1–17.

Benner

Sandström

(2000). Institutionalizing the triple helix: Research funding and norms in the academic system. Research Policy, 29(2), 291–301.

10.

Binici

(2021). What are the information technology skills needed in information institutions? The case of “code4lib” job listings. The Journal of Academic Librarianship, 47(3), 102360.

11.

Casula

Rangarajan

Shields

(2021). The potential of working hypotheses for deductive exploratory research. Quality & Quantity, 55(5), 1703–1725.

12.

Chawinga

W. D.

Zinn

(2019). Global perspectives of research data sharing: A systematic literature review. Library & Information Science Research, 41(2), 109–122.

13.

Chen

(2017). Survey on the needs for chemistry research data management and sharing. The Journal of Academic Librarianship, 43(4), 346–353.

14.

Chin

W. W.

(1998). Commentary: Issues and opinion on structural equation modeling. MIS quarterly, 22, 7–16.

15.

Cocos

Lepori

(2020). What we know about research policy mix. Science and Public Policy, 47(2), 235–245.

16.

Cornel

M. C.

Rigter

van El

C. G

. (2021). Roles and Responsibilities of Stakeholders in Informing Healthy Individuals on Their Genome: A Sociotechnical Analysis. In Personalised Health Care (pp. 77–94). Springer.

17.

Cowen

Gracey

Johnson

D. A

. (2019). Reflection: Putting social responsibility at the heart of the institution: The research experience and career development of early career researchers. In Fenby-Hulse

Heywood

Walker

(Eds.), Research Impact and the Early Career Researcher (pp. 199–207). Routledge.

18.

Cox

A. M.

Verbaan

(2016). How academic librarians, IT staff, and research administrators perceive and relate to research. Library & Information Science Research, 38(4), 319–326.

19.

Darch

P. T.

Sands

A. E.

Borgman

C. L.

Golshan

M. S.

(2021). Do the stars align?: Stakeholders and strategies in libraries’ curation of an astronomy dataset. Journal of the Association for Information Science and Technology, 72, 239–252.

20.

DeLuca

(2020). Searching FOIA Libraries for government information. Government Information Quarterly, 37(1), 101417.

21.

Emojorho

Adomi

(2006). An assessment of the use of information technology facilities for academic pursuit. The Electronic Library, 24(5), 706–713.

22.

Faniel

Connaway

(2018). Librarians’ perspectives on the factors influencing research data management programs. College & Research Libraries, 79(1), 100.

23.

Flores

J. R.

Brodeur

J. J.

Daniels

M. G.

Nicholls

Turnator

(2015). Libraries and the research data management landscape. The process of discovery: The CLIR postdoctoral fellowship program, 2010 (pp. 82–102).

24.

Fornell

Larcker

D. F.

(1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Sage.

25.

Freeman

(1984). Strategic management: A stakeholder approach (p. 46). Pitman.

26.

Gao

Xiao

(2020). Training for librarians: An analysis of the institute of museum and library services laura bush 21st century librarian program. Journal of Library Administration, 60(3), 282–294.

27.

Gök

Rigby

Shapira

(2016). The impact of research funding on scientific outputs: Evidence from six smaller European countries. Journal of the Association for Information Science and Technology, 67(3), 715–730.

28.

Hair

J. F.

Jr Hult

G. T. M.

Ringle

Sarstedt

(2016). A primer on partial least squares structural equation modeling (PLS-SEM). Sage publications.

29.

Hair

J. F.

Jr Matthews

L. M.

Matthews

R. L.

Sarstedt

(2017). PLS-SEM or CB-SEM: Updated guidelines on which method to use. International Journal of Multivariate Data Analysis, 1(2), 107–123.

30.

Hair

J. F.

Ringle

C. M.

Sarstedt

(2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing theory, 19(2), 139–152.

31.

Hair

J. F.

Ringle

C. M.

Sarstedt

(2013). Partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance. Long Range Planning, 46(1-2), 1–12.

32.

Hall

Corey

Mann

Wilson

(2013). Model language for research data management policies. ASERL & SURA. https://www.fosteropenscience.eu/sites/default/files/original/619.pdf

33.

Henseler

Ringle

C. M.

Sinkovics

R. R.

(2009). The use of partial least squares path modeling in international marketing. In Sinkovics

R. R.

Ghauri

P. N.

(Eds.), New challenges to international marketing (Vol. 20, pp. 277–319). Emerald Group Publishing Limited.

34.

Hottenrott

Thorwarth

(2011). Industry funding of university research and scientific productivity. Kyklos: Jahrbuch des Instituts Fur Geschichte Der Medizin An Der Universitat Leipzig, 64(4), 534–555.

35.

Hove

K. H.

(2020). Does the type of funding influence research results – And do researchers influence funders? Prometheus, 36(2), 153–172.

36.

Huang

Cox

A. M.

Sbaffi

(2021). Research data management policy and practice in Chinese university libraries Journal of the Association for Information Science and Technology, 72(4), 493–506.

37.

Johnson

A. M.

(2019). Connections, conversations, and visibility: How the work of academic reference and liaison librarians is evolving. Reference & User Services Quarterly, 58(2), 91–102.

38.

Jolak

Wortmann

Chaudron

Rumpe

(2018). Does distance still matter? revisiting collaborative distributed software design. IEEE Software, 35(6), 40–47.

39.

Joshi

Kale

Chandel

Pal

(2015). Likert scale: Explored and explained. Current Journal of Applied Science Technology, 7, 396–403.

40.

Julien

Gross

Latham

(2018). Survey of information literacy instructional practices in US academic libraries. College & Research Libraries, 79(2), 179–199.

41.

Kim

S. Y.

Nguyen

M. V.

Luu

V. T.

(2020). A performance evaluation framework for construction and demolition waste management: Stakeholder perspectives. Engineering, Construction and Architectural Management, 27(10), 3189–3213.

42.

Kirkwood

Price

(2016). Technology-enabled learning implementation handbook. Commonwealth of Learning (COL).

43.

Larivière

Sugimoto

C. R.

(2018). Do authors comply when funders enforce open access to research? Nature, 562(7728), 483–486.

44.

Lim

M. K.

Wang

(2020). Stakeholders, green manufacturing, and practice performance: Empirical evidence from Chinese fashion businesses. Annals of Operations Research, 290, 961–982.

45.

Liu

Zotoo

I. K.

(2020). Research data management policies in USA, UK and Australia universities: An online survey. Malaysian Journal of Library & Information Science, 25(2), 21–42.

46.

Lockhart

Majal

(2012). The effect of library staff training and development on the user experience: A case study at the Cape Peninsula University of Technology (CPUT). In Proceedings of the IATUL Conference Paper 44. http://does.lib.purdue.edu/iatal/2012/papers/44

47.

Malthouse

E. C.

Buoye

Line

El-Manstrly

Dogru

Kandampully

(2019). Beyond reciprocal: The role of platforms in diffusing data value across multiple stakeholders. Journal of Service Management, 30, 507–518.

48.

Moher

Bouter

Kleinert

Glasziou

Sham

M. H.

Barbour

Coriat

A. M.

Foeger

Dirnagl

(2020). The Hong Kong principles for assessing researchers: Fostering research integrity. PLoS Biology, 18(7), e3000737.

49.

Nashihuddin

(2018). Librarian efforts in supporting movement of the digital literacy and scientific literacy in Indonesia [Conference session]. Paper presented at the Conference: 17th CONSAL “Next Generation Libraries: Collaborate and Connect”, Myanmar.

50.

Nexus

C. N. G.

(2020). Research Institutes in Ghana. https://www.commonwealthofnations.org/sectors-ghana/education/research_institutes/

51.

Osunrinde

Adetunla

(2018). The effect of librarians’ knowledge and funding of preservation and conservation practices in public university libraries in Nigeria. Journal of Library and Information Sciences, 8(3), 338–358.

52.

Pinfield

Cox

A. M.

Smith

(2014). Research data management and libraries: Relationships, activities, drivers and influences. PLoS One, 9(12), e114734.

53.

Ponomariov

B. L.

Boardman

P. C.

(2010). Influencing scientists’ collaboration and productivity patterns through new institutions: University research centers and scientific and technical human capital. Research Policy, 39(5), 613–624.

54.

Rogers

J. D.

(2021). Public funding of research in emerging economies: In search of a rationale amongst. Systems Research, 27(3), 171–183.

55.

Sarstedt

Cheah

J.-H.

(2019). Partial least squares structural equation modeling using SmartPLS: A software review. Springer.

56.

Schmidt

Calarco

P. V.

Kuchma

Shearer

(2016). Time to Adopt: Librarians’ New Skills and Competency Profiles. Paper presented at the ELPUB.

57.

Semeler

A. R.

Pinto

A. L.

Rozados

H. B. F.

(2019). Data science in data librarianship: Core competencies of a data librarian. Journal of Librarianship and Information Science, 51(3), 771–780.

58.

Shah

N. U.

Naeem

S. B.

Bhatti

(2020). Emerging Trends of data management and data analytical practices in academic libraries: A theoretical lens. Journal of Information and Computational Science, 10(4), 545–556.

59.

Stall

Yarmey

Cutcher-Gershenfeld

Hanson

Lehnert

Nosek

Wyborn

(2019). Make scientific data FAIR. Nature Publishing Group.

60.

Sturges

Bamkin

Anders

J. H. S.

Hubbard

Hussain

Heeley

(2015). Research data sharing: Developing a stakeholder-driven model for journal policies. Journal of the Association for Information Science and Technology, 66(12), 2445–2455.

61.

Šuman

Poščić

Gligora Marković

(2020). Big data management challenges. International Journal of Advanced Trends in Computer Science Engineering, 9(1), 717–723.

62.

Tang

(2019). Providing research data management (RDM) services in libraries: Preparedness, roles, challenges, and training for RDM practice. Information Management, 3(2), 84–101.

63.

Tenopir

Talja

Horstmann

Late

Hughes

Pollock

Schmidt

Baird

Sandusky

R. J.

Allard

(2017). Research data services in European academic research libraries. The Library Quarterly, 27(1), 23–44.

64.

Verbaan

Cox

A. M.

(2014). Occupational sub-cultures, jurisdictional struggle and third space: Theorising professional service responses to research data management. The Journal of Academic Librarianship, 40(3–4), 211–219.

65.

Vilar

Zabukovec

(2019). Research data management and research data literacy in Slovenian science. The Journal of Documentation; Devoted to the Recording, Organization and Dissemination of Specialized Knowledge, 75, 24–43.

66.

Webster

E. M.

Jensen

P. H.

Clark

Hirsch

(2021). Research funding mechanisms and biomedical research outputs. SSRN Electronic Journal, 35(2), 142–154. https://doi.org/10.2139/ssrn.3952254

67.

Whitehead

Bourne-Tyson

(2016). Multi-stakeholder engagement in research data management [Conference session]. International Association of University Libraries, 2016 Conference.

68.

Whyte

(2014). A pathway to sustainable research data services from scoping to sustainability. In: Pryor

Jones

Whyte

(Eds.), Delivering research data management services: Fundamentals of good practice (pp. 59–88). Facet Publishing.

69.

Wilburn

D. B.

Swanson

W. J.

(2016). From molecules to mating: Rapid evolution and biochemical studies of reproductive proteins. Journal of Proteomics, 135, 12–25.

70.

Wilkinson

M. D.

Sansone

S. A.

Schultes

Doorn

Bonino da Silva Santos

L. O.

Dumontier

(2018). A design framework and exemplar metrics for FAIRness. Scientific data, 5(1), 1–4.

71.

Zhao

Lynch Jr

J. G.

Chen

(2010). Reconsidering Baron and Kenny: Myths and truths about mediation analysis. Journal of Consumer Research, 37(2), 197–206.

72.

Zhou

(1996). Analysis of trends in demand for computer-related skills for academic librarians from 1974 to 1994. College & Research Libraries, 57(3), 259–272.

The Impact of Key Stakeholders and the Computer Skills of Librarians on Research Data Management Support Services (Id so-21-1893.r2)

Abstract

Keywords

Introduction

Literature Review and Hypothesis Development

Stakeholders and Their Relationship

Librarians and Research Data Management (RDM)

Conceptual Framework and Hypothesis Development

Library Oriented Model of Institutional RDM

Development of Research Hypothesis

Funders and Librarians

Institutions and Librarians

Institutions and Researchers

Funders and Researchers

Government and Librarians

Computer Literacy and Librarians

Methodology

Population and Data Collection

Instrumentation

Data Analysis

Results and Discussion

Evaluation of Measurement Model

Evaluation of Structural Model

Path Analysis and Hypothesis Testing

Discussions

Conclusions and Recommendation

Limitation and Future Work

Footnotes

Declaration of Conflicting Interests

Funding

ORCID iD

References