published quarterly by the university of borås, sweden

vol. 26 no. 3, September, 2021

Attributes that influence academics' data sharing in Nigeria: the effects of organization culture

K. A. Abdullahi and A. Noorhidawati

DOI: https://doi.org/10.47989/irpaper908

Introduction. This study was conducted to ascertain the attributes that influence data sharing practices among academics in Nigerian universities. Nigeria is known to be a country with limited resources to acquire digital paraphernalia which may restrict data sharing practices. The purpose of this study was to investigate factors that influence research data sharing practices among academics in Nigeria. The scope entails a broad context in the lens of organization cultural theory as efforts in data sharing practice are hypothesized to be influenced by institutional involvement besides individual decision.
Method. A survey questionnaire was used as the instrument for data collection. A random sample consisting of 378 academics represented the academics in Nigeria.
Analysis. Partial least square analysis was employed to evaluate the proposed model.
Results. The findings indicated several factors which had influenced data sharing practices among academics in Nigeria including organizational, personal, and social attributes.
Conclusion. This study is important to extend knowledge on data sharing behaviour particularly for academics in a periphery country like Nigeria and the attributes expected to influence these practices.


Research data sharing can be traced to the open science movement with open data as a subset of research data sharing. Borgman (2012) described data sharing as enabling the availability of research datasets for use by other researchers. Data sets are usually attached to scholarly articles, deposited in repositories, or kept on personal computers or local servers (Wallis, et al., 2013). One of the main contributions to research and development of data sharing is that it could minimize data re-collection, hence optimising the use of resources (Tenopir, et al., 2011). Data sharing practices among academics are essential to provide transparency and credibility of research findings, and evidence to support research frameworks (Kim and Adler, 2015).

There is a high participation in data sharing practices in developed countries such as United Kingdom and United States of America (Byrd, et al., 2020). This has led to reproduction of research outcomes (Agosti, et al., 2018), recovery and retrieval of old data (Moss, et al., 2015), intensification of research efficiency and regenerating of research data among academics (Gregory, et al., 2018). Data sharing in these countries is encouraged and is increasingly being adopted and enabled through advanced open storing and sharing platforms and technologies. Studies have shown a significant relationship between these nations’ attitudes towards advancing research, protecting privacy, and beliefs about risks and benefits of data sharing (Shah, et al., 2019). Overall, findings have revealed that researchers in advanced nations do prioritise privacy and there is a strong need to control data types shared and with whom data will be shared (Shah, et al., 2019).

In the past, researchers shared their research outputs only through journal articles; now, it has become necessary to embark on other sharing exercises such as data sharing (Shen, 2016). Despite the benefits of data sharing, there is not much literature reporting data sharing practices among the academics particularly in Nigeria (Adam and Usman, 2013). This is probably due to a growing concern in less developed nations regarding how data are managed and the problems related to it (Odigwe, et al., 2020). In Nigeria, there have been limited studies reporting data sharing practices among academics.

This paper, therefore, aims to address this gap by investigating what influences (or hinders) data sharing among academics in Nigeria, in a broader perspective than just available information technologies. This study also aims to investigate attributes from the Theory of Organizational Culture which states that data sharing practices are not only influenced by individual characteristics but also by activities in the organization and social environment. The objective of this study, therefore, is to identify those attributes that encourage or obstruct data sharing practices among researchers in context of Nigerian universities. The research question formulated in this research is: What are the attributes that influence data sharing practices among academics in Nigeria?

Literature review

Open science is a process where people collaborate, contribute, and make research data and research processes freely available to be reused, re-distributed and reproduced (Mons, et al., 2017). Open science comprises activities that promote sharing output, resources, methods, or tools at any stage of the research process. Data sharing is one of the components in open science under the umbrella of open data. Open data signifies sharing and reusing research data and relevant materials when conducting research work (Foster and Deardorff, 2017). Research data sharing practice is aligned with the principle of openness through the entire research cycle, encouraging sharing and collaboration as early as possible (McKiernan, et al., 2016; Morey, et al., 2016). One important component of research is the transparency and reproducibility of the research results. Lack of transparency is, therefore, a setback to research development. Consequently, data sharing is the practice of making scholarly research data to be available and possible re-used by other investigators which has recently gather attention among academic communities driven to improve their reputation (Linek, et al., 2017).

The needs for data sharing have been recently more emphasised around the scholarly world (Shen, 2016). The spirit of openness of research data in an academic environment is gaining momentum as more research funders and journals call for more openness of research results (Gewin, 2016; Nosek, et al., 2015; Vicente-Sáez and Martínez-Fuentes, 2018). The absence of research data on a shared platform can be seen as a threat to verifying the results and conclusions of empirical studies. Open data is expected to solve this problem by ensuring the data are freely accessible. Previous studies revealed that academics. particularly medical practitioners, health policy analysts, and other researchers from the sciences become more self-assured when their results and conclusions can be verified (Allen and Mehler, 2019; Hardwicke, et al., 2018; Naudet, et al., 2018). Thus, the value of researchers increases with more openness and transparency and enables the prospective external researchers to reanalyse and build on previous data. Hence data sharing is essential to for public accessibility and reusability.

Those emphasising data sharing in academic environment gain its benefits but not without some problems. Researchers have realized that they must make their data accessible to attain research progression. Their awareness of such initiatives in data sharing was due to the requirements by journals and funding agencies to deposit their data in data repositories for open accessibility. This practice is enforced by journal publishers, research funders and individuals who wish to further cross-examine their scientific conclusions (Boulton, et al., 2011). There are existing studies which have reported developing new competences in data sharing, data quality, standards, and protection, which had increased researchers’ willingness to share their data (Sayogo and Pardo, 2013).

However, opinions and practices regarding data sharing seem to differ among various disciplines (Creaser, et al., 2010) which have embraced data sharing practices differently. In some disciplines, such as cognitive neurosciences, data sharing is still in the early stages. It is a notable challenge for neuroscience laboratory work and collaborative projects to practise data sharing as an effective form of data management (International Brain Laboratory, et al., 2020). Within sciences such as genomics, researchers share data when requested by peers to gain credit for their work and expect their data will not be misused (Tenopir, et al., 2015). In genetics, genomics and structural biology, shared large datasets are common (Karczewski and Francioli, 2017), and many researchers have used and re-used previously published datasets to enable new discovery (Bonàs-Guarch, et al., 2018).

In physical sciences such as astronomy and astrophysics, telescope data is typically made open (Bhatnagar, 2018). It is a normal practice to share data in ecology as the nature of the field requires and benefits from open data (Baker and Millerand, 2016; Reichman, et al., 2011). This is evident from the increased amount of shared ecology data reported (Michener, 2015) as well as publications in databases supported by large networks of researchers (Gilbert, et al., 2014). In social sciences and humanities, there is a need to access data to replicate existing studies. This practice of sharing data, unfortunately, is not encouraged in social sciences (Kim and Adler, 2015).

Thus, research data sharing practices are given more priority in the field of sciences than in the non-science fields (Kim and Stanton, 2016). There is also support from research stakeholders such as United States’ National Science Foundation, which mandates data sharing practices for grant recipients (Alter and Gonzalez, 2018). In the future, data sharing practices should be accepted across all academic fields because transparency and openness are considered by many to be part of scientific method.

Reviewing the literature has shown that researchers are aware of the benefits and willing to share their research data with others. Despite being aware of the benefits, researchers are also influenced by certain issues that make them less willing to share their data. These include: the potential misinterpretation of data, (un)ethical use of data by government and commercial purposes (Howe, et al., 2018), and how their data would be used once it is shared (Kiran et al., 2015). Researchers need to know more about their potential data users before agreeing to share their data (Asai, et al., 2002.). Studies have shown that researchers might be unaware that their data had already been shared by others (Hate, et al., 2015). For all these reasons, they might choose not to share their data.

The importance of data sharing practices among researchers cannot be over emphasised (Kim and Stanton, 2016; Tenopir, et al., 2011) although journal publishers and research funders have found low take-up of data sharing across some disciplines (Houtkoop, et al., 2018). Despite the advantages of open access, only a small number of researchers are willing to share their research data (Dahrendorf, et al., 2019; Hardwicke, et al., 2019). Stakeholders, such as other researchers, journal publishers, funders, and universities, have expressed their concerns about data sharing practices and its transparency and have demanded an improvement in data sharing structures (Munafò, et al., 2017; Nosek, et al., 2015).

Although data sharing is strongly encouraged globally, researchers from Nigeria were observed to be unwilling to share their research data, information, and knowledge (Awodoyin, et al., 2016). Awodoyin et al. reported that although some data or knowledge were shared through face-to-face interactions, emails and mobile phones, it was not through established or standard platforms set up by institutions, journal publishers or funding agencies (Kaye, et al., 2018). Developing infrastructures, policies and incentives to facilitate data sharing practices would reduce false research, intensify research value and encourage research transparency (Chawinga and Zinn, 2019; Johnson, et al., 2019).

Institutions are reviewing their data-handling policies to ensure research data can be validated and preserved to enable discoverability and reuse by other investigators (MacMillan, 2014). Government agencies such as National Institutes of Health (with the National Institutes of Health Public Access Policy), and Research Councils UK (RCUK, now UK Research and Innovation (UKRI)) have adopted open-access mandates for grant recipients. They have also established policies that require authors to deposit into various data repositories. With the increasing number of research data sharing initiatives, publishers of journals with high impact factors have recently enacted policies requiring authors to share their data (Tannenbaum, et al., 2018). Policies and guidelines endorsed by organizations like the Global Alliance for Genomics and Health (GA4GH) and the Organisation for Economic Cooperation and Development (OECD) have promoted and recommended that data sharing initiatives should include governance, privacy and security protections, and data access in a transparent manner and through accessible digital platforms (Villanueva, et al., 2019). The policies established by the research funders and journals have shown a growing awareness of the importance of data sharing practices across academics although they might have implications as they would remain a significant risk to most of the researchers (Avuglah and Underwood, 2019).

Data sharing practices can be influenced by factors such as individual characteristics of the data provider, the organization, and the social environment surrounding the research activities. Several related studies have employed the theory of organizational culture to determine how individuals interrelate with each other and act with individuals outside their organisation (Kenter, et al., 2016; Little, et al., 2018). Idang (2015) provides insight into how different values can be seen as part of the organizational culture and how the shared set of values and beliefs contributes to organizational success. Other studies have reported the impact of organizational culture on knowledge management process and the potential implications of organizational culture towards sharing data (Lee et al., 2016; Tong, et al., 2015).

The research framework: theory of organizational culture

There are a few theories related to data sharing in an organization including the theory of organizational culture, theory of planned behaviour, institutional theory and social exchange theory (Oloruntoba, et al., 2019). Research data sharing practices are influenced not only by individual behaviour but also by the environment (i.e., organization culture) that either facilitates or discourages such practices. This study investigates attributes that influence data sharing practices among Nigerian academics in a broad perspective including both organisational and social interaction levels.

Organizational culture includes a system of shared assumptions, values, and beliefs which demonstrates proper and improper behaviour of people in organization (Chatman and Cha, 2003; Kerr and Slocum, 2005). Schein (1990) explains the theory of organizational culture through three elements:

  1. Artefacts: noticeable expressions of culture such as structures, practices and processes, rituals, technology, manner of dress and language. They are things that can be seen, heard and felt including constructs like organizational structures, infrastructures, data repositories, funding agencies, journal publishers, and policy/guidelines.
  2. Espoused belief and values: described as looking for a motive behind any observed artefact (Sangiovanni-Vincentelli, 2007). Some examples are creativity, problem solving and relating with others. Constructs include conditions for data sharing, perceived effort, anticipated benefits, legitimate concern, and altruism.
  3. Basic underlying assumptions: described as unconscious features of organizational culture which includes elements like perception, thoughts and feelings. These assumptions are very hard to change.

Figure 1 shows the mapping of the theory of organizational culture with related attributes: organizational attributes (Artefacts), personal attributes (Espoused beliefs and values), and social attributes (Basic underlying assumptions).


Figure 1: Mapping theory of organizational culture and data sharing attributes

Hypothesis development

This research is built based on the theory of organizational culture by Schein (1990) who explains the theory through three elements: artefacts (organizational attributes), espoused beliefs and values (personal attributes) and basic underlying assumptions (social attributes). The following sections examine the relationship between these three elements and data sharing practices.

(a) Relationship between organizational attributes and data sharing practices

Organizational attributes are considered to be factors associated with visible expression of the organization that can be easily viewed, heard and felt by individuals. They include internal and external organizational aspects like structures, practices and processes, and technology (Barrios, 2013). These attributes are easily discovered and acknowledged:

I. Internal organizational attributes include:

II. External organizational attributes include:

Based on the discussion above, the following hypotheses were postulated:

H1a Infrastructure positively influenced academics’ data sharing practices.
H1b: Data repository positively influenced academics’ data sharing practices.
H1c: Organizational structure positively influenced academics’ data sharing practices.
H1d: Research funders positively influenced academics’ data sharing practices.
H1e: Perceived pressure by journal positively influenced academics’ data sharing practices.
H1f: Policies and guidelines positively influenced academics’ data sharing practices.

(b) Relationship between personal attributes and data sharing practices

Personal attributes are basic underlying assumptions that are described as unconscious features of the organizational culture which include elements of individual perceptions, thoughts and feelings. These assumptions are very hard to change (Schein, 1990). Personal attributes comprise:

Based on the discussion above, the following hypotheses were postulated:

H3a: Expected rewards positively influenced academics’ data sharing practices.
H3b: Effort expectancy positively influenced academics’ data sharing practices.
H3c: Conditions for data sharing positively influenced academics’ data sharing practices.
H3d: Legitimate concern positively influenced academics’ data sharing practices.
H3e: Beneficence positively influenced academics’ data sharing practices.

(c) Relationship between social attributes and data sharing practices

Social attributes are formed by interaction with other people over one’s lifetime; that is, how and where we relate with one another which includes discipline norms and community culture. Two aspects are relevant:

Based on the discussion above, the following hypotheses were postulated:

H2a: Discipline norms positively influenced academics’ data sharing practices.
H2b: Community culture positively influenced academics’ data sharing practice.

Figure 2: Attributes in data sharing practices


This study employed semi-structured interviews and a survey to investigate factors that influenced research data sharing among academics in Nigeria. Twenty-two academics who were senior researchers were sampled for interviews that lasted 25-30 minutes. The interviews were audio-recorded with the respondents’ permission. The data were coded, and the themes that emerged indicated several aspects that influenced data sharing behaviour. The findings from the interviews were used to develop the survey instrument.

Self–administered survey questionnaires were distributed among the academics in Nigeria. From a population size of 7,561, a sample of 364 was determined based on Krejcie and Morgan (1970). Stratified sampling technique was used to gather the sample from five universities in Nigeria as shown in Table 1. A total of 440 (over sample) survey questionnaires were distributed, resulting in 378 responses which made an 86% response rate. Data from the survey were analysed using Smart-PLS to statistically confirm the influencing factors of data sharing practices in the context of Nigeria.

Table 1: Number of questionnaires distributed for each university
University Population Stratified sampling Survey distributed Useful responses
Abubakar Tafawa Balewa University 2098 2098/7561 x 364 =101 120 101
Moddibbo Adama University of Technology 2231 2231/7561 x 364 = 107 120 107
Federal University Kashere 1023 1023/7561 x 364 = 49 70 60
Federal University Wukari 1106 1106/7561 x 364 = 53 65 56
Federal University Yobe 1003 1003/7561 x 364 = 48 65 54
Total 7561   440 378


The instrument was developed based on the theory of organizational culture and included organizational, personal, and social attributes. There were thirteen constructs categorised under these attributes. The questionnaire items were developed based on the interview findings and adapted from the literature (Table 2). The questionnaires used a 5-point Likert scale which allowed respondents to indicate a neutral response making the scale scores more reliable; this is also preferred by respondents (Adelson and McCoach, 2010).

Table 2: Development of construct and measurement variables
Constructs Measurement variables Sources
Personal attributes Effort expectancy (EE) Vanneste et al. (2013), Kim (2013), Research interviews*
  Legitimate concerns (LC) Taichman et al. (2016), Kim (2013), Research interviews*
  Beneficence (BNC) Wilbanks and Friend (2016), Tenopir, et al. (2011), Research interviews*
  Conditions for data sharing (CFDS) Borgman (2012), Tenopir et al. (2011), Research interviews*
  Expected rewards (ER) Eshel et al. (2016), Kim (2013), Research interviews*
Organizational attributes Organizational structure (OS) Schein (1985)
  Infrastructure (INF) Research interviews *
  Data repository (DR) Horai et al. (2010), Tenopir et al. (2011)
  Research funders (RF) Piwowar (2011), Kim (2013), Research interviews*
  Perceived pressure by journal (PPJ) Tenopir et al. (2015), Kim (2013), Research interviews*
  Policy/guidelines (PG) Tenopir et al. (2011), Research interviews*
Social attributes Community culture (CC) Research interviews*
  Discipline norms (DN) Kim (2013)
Note: * Interviews were conducted to develop specific questions for the attributes

Validity and reliability

Validity and reliability are two significant factors to consider when developing and testing a survey instrument. They help to ensure the quality of the measurement and the data collected. To reach good reliability and validity, content validity and convergent validity were conducted to achieve internal consistency, average variance, and discriminant validity.

Content validity was secured by modifying most of the survey items from preceding studies via exhaustive literature review. The survey items were also presented to a panel of five experts in the area of this study. They were provided with the measurement items and scrutinized the survey items in terms of the appropriateness and completeness of the measurement of each construct. Some survey items were amended in response to the comments, recommendations, and suggestions made by the panel of experts.

Convergent validity is the degree to which the single item correlates positively with the reflective multi-item measure of service value (Mooi, et al., 2018). Convergent validity is evaluated at the construct level via the average variance extracted (AVE). The common method to measure the internal consistency is using Cronbach's alpha, which provides an estimate of the reliability based on the inter-correlations of the observed indicator variables. It is, however, sensitive to the number of items in the scale and may lead to underestimating the internal consistency reliability. Therefore, it is normally recommended to use a different measure of internal consistency reliability, which is referred to as composite reliability. Composite reliability considers the different outer loadings of the indicator variables and is calculated using the following formula: if the composite reliability is larger than 0.7, it is acceptable. For this study, the composite reliability is between 0.88 and 0.95, while the average variance extracted is above 0.5. Therefore, convergent validity is adequate for all the constructs (see Appendix A).

Discriminant validity was used to test whether unrelated concepts or measurements that are not supposed to be related are actually unrelated. Thus, discriminant validity proposes that a construct is unique and captures phenomena not described by other constructs in the model. (Hair, et al., 2014). Discriminant validity is tested by evaluating the squared average variance extracted for each construct against correlations (shared variance) between the construct and all other constructs in the model. A construct has an adequate discriminant validity if the squared average variance extracted exceeds the correlation among the constructs (Fornell and Larcker, 1981). Table 3 shows the correlation of latent variables and discriminant validity based on Fornell and Larcker. Table 3 also shows that the average variance extracted for each construct is more than each of the squared correlation between constructs. Therefore, discriminant validity is adequate for all constructs.

A pilot study was conducted to make sure that the various scales proved an appropriate level of reliability. The result of the pilot study revealed that the construct reliability constructs were satisfactory. It was discovered that 10-20 minutes was taken to complete the pilot survey. No significant changes were made to the survey questions.

Analysis method

Partial least squares was employed using SmartPLS software to effectively show the reliability of this study, by calculating convergent validity, discriminant validity and the path coefficients of all the factors under the three main attributes. The path coefficients which show the hypotheses testing evaluation is shown in Table 5 indicating all the thirteen independent variables were evaluated and found to be significant on data sharing practices.

Table 3: Correlation of latent variables and discriminant validity
ER 0.811  
BNC 0.313 0.890  
CFDS -0.353 -0.201 0.829  
DN 0.248 0.390 -0.168 0.902  
DR 0.253 0.090 -0.139 0.047 0.863  
DSP 0.623 0.519 -0.361 0.525 0.332 0.878  
RF 0.229 0.300 -0.090 0.294 0.163 0.523 0.765  
INF 0.295 0.197 -0.103 0.259 0.264 0.424 0.222 0.866  
PPJ 0.315 0.316 -0.202 0.390 0.017 0.442 0.190 0.169 0.878  
LC -0.334 -0.131 0.082 -0.185 -0.266 -0.454 -0.186 -0.326 -0.131 0.896  
CC -0.312 -0.169 0.205 -0.162 -0.258 -0.428 -0.203 -0.235 -0.131 0.327 0.905  
OS 0.350 0.264 -0.207 0.278 0.136 0.444 0.222 0.113 0.301 -0.092 -0.193 0.852  
EE -0.139 -0.126 0.057 -0.144 -0.144 -0.313 -0.073 -0.138 -0.105 0.310 0.111 -0.151 0.774  
PG 0.260 0.143 -0.086 0.145 0.118 0.317 0.148 0.211 0.164 -0.158 -0.245 0.210 -0.104 0.838
*All correlations are significant at the p< 0.01 level. The square roots of the average variance extracted are shown on the diagonal in bold.


Demographic characteristics of the survey respondents

The results indicated that 79.6% (301) of the respondents were male and 20.4% (77) were female. About half of the respondents (44.7%) were between thirty-one and thirty-five years old. One third of the respondents had 11-15 years of experience in academia (36.5%). Non-science disciplines accounted for 57.1% respondents and the remaining 42.9 % were from sciences (see Table 4).

Table 4: Survey participants demographics
Variable Level Frequency Percentage
Sex Male 301 79.6
Female 77 20.4
Age 21-25 years old 17 4.5
26-30 years old 46 12.2
31-35 years old 169 44.7
≥40 years old 146 38.6
Years in academia ≤5 years 22 5.8
6 -10 years 78 20.6
11-15 years 138 36.5
16-20 years 106 28.0
21 years 34 9.0
Field Sciences 162 42.9
Non-Sciences 216 57.1

Attributes that influenced data sharing practices of academics in the context of Nigerian universities

Three attributes, organizational, personal and social, were significantly correlated with data sharing practices as shown in Table 5. All six organizational attributes (structure, infrastructure, data repositories, research funders, journal publishers and policy/guidelines), two personal attributes (beneficence and expected rewards), and one social attribute (discipline norms) indicated a positive significant relationship with the data sharing practices of the academics. The results revealed that organizational attributes are the most significant attributes that influenced academics’ data sharing practices. However, conditions for data sharing, effort expectancy, legitimate concerns and community culture are negatively correlated with data sharing practices. The results revealed that personal attributes are the most pessimistic elements that influenced academics’ data sharing practices as three out of five attributes were negatively related. Among all the attributes, expected rewards had the most positive significant relationships with β=0.235 and legitimate concern had the most negative significant relationships with β= -0.130.

Table 5: Attributes that influence data sharing practices
Path coefficients SD t-Values P Values Result
Organizational attributes
Organizational structure (OS) -> Data sharing practices 0.094 0.030 3.111 0.001 Supported
Infrastructure (INF) -> Data sharing practices 0.079 0.023 3.360 0.001 Supported
Data repository (DR) -> Data sharing practices 0.082 0.027 3.061 0.001 Supported
Research funders (RF) -> Data sharing practices 0.234 0.026 8.970 0.000 Supported
Perceived pressure by journal (PPJ)-> Data sharing practices 0.096 0.029 3.283 0.001 Supported
Policy/guidelines (PG) -> Data sharing practices 0.049 0.025 1.944 0.026 Supported
Personal attributes
Beneficence (BNC) -> Data sharing practices 0.157 0.030 5.293 0.000 Supported
Conditions for data sharing (CFDS) -> Data sharing practices 0.098 0.023 4.177 0.000 Supported
Effort expectancy (EE)-> Data sharing practices -0.110 0.030 3.708 0.000 Supported
Expected rewards (EE) -> Data sharing practices 0.235 0.030 7.724 0.000 Supported
Legitimate concerns (LC) -> Data sharing practices -0.130 0.027 4.788 0.000 Supported
Social attributes
Community culture (CC) -> Data sharing practices -0.096 0.025 3.889 0.000 Supported
Discipline norms (DN) -> Data sharing practices 0.169 0.028 5.963 0.000 Supported


Identifying attributes of research data sharing among academics has been a prominent topic of interest in universities (Ünal, et al., 2019). The data presented in this study represent the three attributes (organizational, personal and social) that had influenced data sharing among academics in Nigeria.

Organizational attributes are those attributes that show the overall well-being of the organization include organizational structure, infrastructure, data repositories, research funders, perceived pressure by journal publishers, and policy/guidelines.

To summarize, all organizational attributes measured in this study have been shown to influence data sharing practices.

In this study, personal attributes are those characteristics of an individual that encompass effort expectancy, legitimate concerns, beneficence, conditions for data sharing, and expected rewards.

These personal attributes complement findings from other studies into expected rewards (Allen and Mehler, 2019; Bezuidenhout, 2019), legitimate concerns (Ballantyne, 2019; Banks, et al., 2019; Spallek, et al., 2019), conditions for data sharing (Polanin and Terzian, 2019), beneficence (Hummel, et al., 2019; Ross, et al., 2018), and effort expectancy (Campbell, et al., 2019; Spallek, et al., 2019; Tenopir, et al., 2015). All these factors had profound effect on data sharing practices of individuals.

Social attributes in this study refer to characteristics emanated from the social interactions among the academics: community culture and discipline norms.

The use of the theory of organizational culture in this study has enabled the researchers to appropriately analyse the influence of organizational, personal and social attributes on data sharing among academics. It also enabled us to provide insight into how values can be seen as part of a culture and their influence on research data sharing practices.


This study has contributed to the body of knowledge by investigating attributes of data sharing practices among academics in Nigeria. The findings have added to the knowledge gap on research data sharing culture and the factors that influence the practices. This study employed Schein’s (1985) theory of organizational culture to provide a more complete view of research data sharing practices that involved organizational, personal and social attributes as dictates by artefacts, espoused belief and values, and basic underlying assumption (Bailey, et al., 2019). Within this framework, it can be concluded that data sharing practices among academics are influenced by organizational attributes (organizational structure, infrastructure, data repository, research funders, perceived pressure by journal and policy/guidelines), personal attributes (conditions for data sharing, effort expectancy, expected rewards, legitimate concerns and beneficence) and social attributes (community culture and discipline norms).

This paper also discovered new items such as community culture and infrastructure that are important factors in the uptake of data sharing practices in the Nigerian context. Findings presented in this study can show academics, journal publishers and research funders the perceived risks in data sharing, promote the sharing and reuse of research data, and create awareness among academics of data sharing sources. It can be concluded that academics have opportunities to advance in their research while keeping their data freely available to other researchers.

Recommendations and limitations

Some recommendations can be made:

The limitation in this study is that only responses from academics were included. Further studies should be carried out to investigate research data sharing from other perspectives such as local policy makers at the institutional level, funding agencies and the public. The scope of future studies could be broadened to cover research data management closely related to data sharing activities. Although this study employed a survey as the data gathering technique, rigorous mixed methods design that includes interviews might be suitable to further explore the reasons to support or not support data sharing practices. The survey was focused on universities although data sharing practices may display different prevalence and be influenced by different attributes in other high institutions of learning. Despite these limitations, this study clearly has defined some distinctive attributes that influence academics in developing nations and demonstrated some of the policies and practices about data sharing practices among academics. More research in this area beyond universities could elucidate the negative impacts on conditions for data sharing, community culture, legitimate concerns and effort expectancy. Future policies might be able to address areas where the current activities thwart the data sharing practices.

About the authors

Khalid Ayuba Abdullahi was a PhD student of Department of Library & Information Science in the Faculty of Computer Science & Information Technology, Universiti Malaya. His research interests include information management, library automation and library anxiety. He is currently working as a lecturer at the Department of Library and Information Science, Faculty of Technology Education, Abubakar Tafawa Balewa University, Nigeria. He can be contacted at ayubakhalid6@gmail.com.
A. Noorhidawati is an Associate Professor and currently the head of the Department of Library & Information Science in the Faculty of Computer Science & Information Technology, Universiti Malaya. She is also the program co-ordinator, Master of Library and Information Science (MLIS). She received her Ph.D. from the University of Strathclyde, Glasgow, UK. Her research interests include digital library, digital literacy, information management, scholarly communication, and bibliometrics. She can be contacted at noorhidawati@um.edu.my.


Note: A link from the title is to an open access document. A link from the DOI is to the publisher's page for the document.

How to cite this paper

Abdullahi, K. A., & Noorhidawati, A. (2021). Attributes that influence academics' data sharing in Nigeria: the effects of organization culture. Information Research, 26(3), paper 908. Retrieved from http://InformationR.net/ir/26-3/paper908.html (Archived by the Internet Archive at https://bit.ly/2XdKRT2) https://doi.org/10.47989/irpaper908

Appendix A: Convergent validity of the constructs

Items Loadings A CR AVE
Construct: Organizational attributes
Organizational structure (OS) 0.875 0.914 0.727
The organizational structures create barriers in data sharing 0.860  
The organizational structures facilitates data sharing process 0.836
The organization I work with stresses extent of rules / regulations/ standard procedure for data sharing0.842
There is considerable decision by top management regarding data sharing 0.871
Infrastructure (INF) 0.933 0.947 0.750
Has better resources for training researchers on data sharing 0.882  
Provides the necessary platforms to support data sharing 0.872
Has heterogeneous data sharing platforms 0.840
Ensured adequate facilities available (e.g. internet and electricity) 0.893
Provides the necessary technical support for data sharing 0.876
Provides necessary fund to support data sharing 0.833
Data repository (DR) 0.915 0.935 0.744
Researchers can easily access data repositories 0.869  
Data repositories are available for researchers to share data 0.884
Researchers consider data repositories necessary for sharing data 0.771
The researchers can easily access the metadata 0.907
Metadata are available for researchers to share data 0.876
Research funders (RF) 0.765 0.849 0.585
Data sharing is mandated by the policy of research funders 0.770  
Data sharing policy of research funders is enforced 0.795
Research funders require researchers to share data 0.765
Research funders can penalize researchers if they do not share data 0.727
Perceived pressure by journal (PPJ) 0.901 0.931 0.771
Data sharing is mandated by journals’ policy 0.840  
Data sharing policy of journal is enforced 0.880
Journal require researchers to share data 0.908
Journal can penalize researchers if they do not share data 0.881
Policy/guidelines (PG) 0.859 0.904 0.702
My institution has a flexible policy towards data sharing 0.764  
My institution has an established policy/guideline on the data copyright/intellectual property rights 0.883
My institution has an established policy/guideline on data management 0.865
My institution has an established policy on the access control of shared data 0.834
Construct: Personal attributes
Beneficence (BCN) 0.935 0.950 0.793
I would share research data to promote innovation by potential new data users 0.896
I would share research data to encourage scientific enquiry 0.896
I would share research data to encourage the improvement and validation of research methods 0.907
I would share research data to enable scrutiny of research findings 0.918
I would share research data to provide important resources for education and training 0.832
Conditions for data sharing (CFDS) 0.924 0.939 0.688
I would use other researchers’ datasets if their datasets were easily accessible 0.808
I would be willing to place at least some of my data into a central data repository with no restrictions 0.833
I would be willing to place all of my data into central data repository with no restrictions 0.729
I would be more likely to make my data available if I could place conditions on acces 0.877
I would be willing to share data across a broad group of researchers who use data in different ways 0.876
It is important that my data are cited when used by other researchers 0.838
It is appropriate to create new datasets from shared data 0.834
Effort expectancy (EE) 0.829 0.880 0.600
I would find data sharing easy to do 0.535  
Sharing data would involve too much time for me (e.g. to organize/annotate) 0.853
I need to make a significant effort to share data 0.813
I would find data sharing difficult to do 0.813
Overall, data sharing requires a significant amount of time and effort 0.783
Expected rewards (ER) 0.831 0.885 0.658
Data sharing would enhance academic recognition 0.852  
Data sharing would improve my status in a research community 0.868
Sharing research data leads to new collaboration between data users and data creators 0.756
Sharing research data increases the impact and visibility of research 0.763
Legitimate concerns (LC) 0.918 0.942 0.804
There is a high probability of losing publication opportunities if I share data 0.877  
Data sharing may cause my research ideas to be illegally used by other researchers 0.915
The data I shared may be misinterpreted by other researchers 0.923
I believe the overall risk of data sharing is high 0.869
Construct: Social attributes
Community culture (CC) 0.924 0.946 0.814
My community expects researchers to actively contribute in data sharing 0.922  
My community stresses the importance of data sharing to the development of Organization 0.920
The culture of the community encourages data sharing 0.911
The culture of the community provides opportunity for data sharing 0.855
Discipline norms (DN) 0.926 0.947 0.818
Researchers care a great deal about data sharing 0.916  
Researchers share data even if not required by policies 0.900
Many researchers are participating in data sharing 0.917
It is expected that researchers would share data 0.886

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