Introduction

Background

Data journals have emerged as a growing form of scholarly publication, enabling researchers to publish data papers alongside their datasets. Unlike traditional articles that emphasize data interpretation, data papers describe the processes of data creation, processing, and analysis, thereby enhancing transparency and facilitating data reuse “beyond its original context” [1]. The peer review of data papers ensures quality and can contribute to academic credit. Depositing datasets in repositories and publishing data papers further enhances discoverability and citation potential through persistent identifiers [2].

Despite these advantages, including reusability, recognition, and quality assurance, there has been limited research on the factors influencing authors’ decisions to publish in data journals, and more importantly, on what motivates them to continue doing so [3]. Since the long-term success of data journals depends on sustained author engagement, this study focuses on continuance intention—that is, the intention to persist in using a system or innovation [4,5].

To explore this topic, the study applies two theoretical models: the post-acceptance model (PAM) and the unified theory of acceptance and use of technology (UTAUT). The PAM explains continuance behavior based on post-adoption beliefs, such as perceived usefulness and satisfaction, while the UTAUT introduces effort expectancy and social influence as additional predictors. These frameworks have previously been applied in contexts such as open access (OA) publishing and data sharing [6,7].

PAM, which is grounded in expectation-confirmation theory, identifies perceived usefulness (a cognitive belief) and satisfaction (an affective response) as key psychological drivers of continued system use [4,8]. Perceived usefulness refers to the belief that using a system enhances job performance, while satisfaction reflects the emotional evaluation of prior use. Given their strong predictive power for continuance intention, this study examines the influence of these variables on authors’ willingness to continue publishing in data journals.

UTAUT identifies four determinants of system usage: performance expectancy, effort expectancy, social influence, and facilitating conditions. In the original model, facilitating conditions were thought to influence actual use of information technology, while the other three variables positively affected behavioral intention [9]. As this study investigates factors associated with the intention to continue publishing in data journals, it focuses on the first three determinants. Notably, performance expectancy in UTAUT closely parallels perceived usefulness in PAM, serving as a shared construct. By contrast, satisfaction (PAM), effort expectancy, and social influence (UTAUT) are distinct to each framework. These four variables form the theoretical basis for this study’s hypotheses.

From the perspective of researchers, perceived usefulness is defined as the belief that publishing in data journals enhances scholarly productivity and recognition. This belief is grounded in the peer-reviewed and citable nature of data papers, which can contribute to academic credit and play a role in tenure and promotion decisions [10]. Peer review in data journals also helps ensure the clarity, completeness, and accuracy of datasets [11], while improving their accessibility and potential for reuse.

Effort expectancy, which is defined as the perceived ease or difficulty of using a system, represents another influential factor. Publishing in data journals requires authors to organize, describe, and share their data, a process that demands both time and effort and may negatively affect their willingness to continue submitting data papers [12]. Therefore, higher perceived effort is expected to reduce continuance intention.

Social influence refers to perceived pressure or encouragement from influential peers and professional communities [9]. Within the context of data sharing, disciplinary norms have been shown to impact behavior directly, or indirectly by shaping attitudes [12,13]. Since publishing in data journals constitutes a form of data sharing, social norms within disciplines likely play a role in shaping authors’ continuance intentions.

To date, no quantitative study has identified the factors that drive authors to continue publishing in data journals, highlighting the need for this research. Drawing from PAM and UTAUT, this study proposes the following hypotheses:

• (1) The perceived usefulness of publishing in data journals is positively associated with continuance intention.

• (2) Satisfaction with prior publishing experiences in data journals is positively associated with continuance intention.

• (3) Effort expectancy (i.e., perceived effort) is negatively associated with continuance intention.

• (4) Social influence, as reflected in disciplinary norms of data sharing, is associated with continuance intention.

Objectives

The aim of this study was to examine whether perceived usefulness, satisfaction, effort expectancy, and social influence are associated with authors’ intention to continue publishing in the same data journal.

Methods

Ethics statement

All participants provided informed consent prior to participation, and their anonymity and confidentiality were strictly protected throughout the study.

Study design and setting

A cross-sectional survey was conducted to examine factors influencing authors’ continuance intention to publish in data journals. The survey was administered using Google Forms (Google) and distributed by email to all sampled authors. To encourage participation, a US $5 gift card was offered to the first 150 respondents. The survey was open from April 29 to May 13, 2020, with two reminder emails sent to nonrespondents. A total of 453 responses were received, resulting in a 6.2% response rate. The distribution of responses by journal closely matched the original sampling frame. All participants answered the same set of questions (Suppl. 1).

Participants

The participants were international, as the eight selected journals have global contributors. Inclusion criteria comprised any author (first author, co-author, or corresponding author) who had published at least one data paper in the selected journals by the end of 2019. There were no exclusion criteria. The survey was anonymous and voluntary, and only the provided response data were analyzed.

Variables

Perceived usefulness was measured as the mean score of four questionnaire items (Cronbach α=0.71). Satisfaction was similarly computed as the mean of four items (Cronbach α=0.73). Higher scores indicated greater perceived usefulness or satisfaction. No additional covariates (such as age, gender, or field) were included, as the analysis focused solely on the theoretical predictors.

Data sources/measurement

The sampling process began by identifying 106 data journals referenced in previous studies [14,15], from which duplicates were removed. To focus on journals with a substantial number of data papers, the Web of Science was used to determine the proportion of publications labeled as “data papers” as of April 4, 2020. Of the 76 journals indexed in the Web of Science, 24 included data papers, and 8 journals with data paper proportions exceeding 20% were selected: Data in Brief, Scientific Data, Earth System Science Data, Geoscience Data Journal, Journal of Open Archaeology Data, Data, Biodiversity Data Journal, and GigaScience.

Authors who had published in these eight journals through 2019 were identified by collecting their names and email addresses from published data papers. This process yielded a sample of 7,339 unique authors, with the majority from Data in Brief (n=5,351, 72.9%) and Scientific Data (n=1,174, 16.0%). Much smaller numbers of authors were identified from the remaining six journals, including 241 (3.3%) from GigaScience, 230 (3.1%) from Earth System Science Data, 142 (1.9%) from Data, 113 (1.5%) from the Biodiversity Data Journal, 61 (0.8%) from the Geoscience Data Journal, and 27 (0.4%) from the Journal of Open Archaeology Data. Including authors from smaller journals increased the diversity of perspectives represented.

A structured online questionnaire was developed, informed by literature on data journals, data sharing, and OA publishing. The survey consisted of 38 five-point Likert scale items, 12 multiple-choice questions, and 5 open-ended questions, divided into four sections: (1) familiarity and perceptions of data journals; (2) experience with publishing in data journals; (3) data sharing experience; and (4) demographics.

The unit of analysis in this study was the individual author, as survey data were collected from 453 data paper authors. Most respondents were male (78.1%), aged 30 to 49 years, and over 80% were affiliated with academic institutions. Professors (full, associate, or assistant) accounted for more than half of respondents (51.7%).

The dependent variable, future publishing intention, was initially measured on a 5-point Likert scale and subsequently recoded into a 3-point scale: “unlikely” (combining “very unlikely” and “unlikely”), “neutral,” and “likely” (combining “likely” and “very likely”) (Table 1).

Four independent variables were identified based on the PAM and UTAUT frameworks: perceived usefulness (common to both models), satisfaction (from PAM), and effort expectancy and social influence (from UTAUT) (Table 1). Perceived usefulness was assessed using four items measuring beliefs about academic credit, data availability, peer review, and data reuse. Satisfaction was measured using four items addressing experiences with templates/guidelines, the submission system, peer review, and post-acceptance processes. Both constructs demonstrated acceptable internal consistency (perceived usefulness, Cronbach α=0.71; satisfaction, Cronbach α=0.73) (Table 2), and factor analysis confirmed their unidimensionality.

Effort expectancy and social influence were each measured by a single item. Effort expectancy was assessed by agreement with the statement that publishing in a data journal takes too much time. Social influence measured the belief that data sharing is normative within the respondent’s field. Both variables were recoded into three categories: “disagree,” “neutral,” and “agree.”

Bias

Given the response rate of only 6.2%, the results may overrepresent authors who are more engaged or who hold stronger opinions about data journals. To assess representativeness, the distribution of respondents by journal was compared with the overall sampling frame and found to be similar; however, differences in seniority or research field remain unknown. All variables were self-reported, introducing the possibility of social desirability bias.

Study size

We aimed to survey the entire population of identified data paper authors, rather than selecting a subset. As a result, the final sample size (n=453) was determined by the response rate (6.2%). No formal power calculation was performed; instead, we sought to maximize inclusion of respondents from the target population to explore the associations of interest.

Statistical methods

All four factors (perceived usefulness, satisfaction, effort expectancy, and social influence) were included in a single multivariable ordered logistic regression model. No additional covariates were adjusted for, as the objective was to assess the direct association of these theoretical factors with continuance intention. Subgroup analyses and interaction terms were not conducted, as the study was exploratory and subgroup sample sizes would have been small. Ordered logistic regression analysis was performed using Stata ver. 18.0 (Stata Corp), which is appropriate for ordinal dependent variables. The proportional odds assumption was tested using the Brant test, which was not significant (P=0.275), confirming the model’s validity.

Results

Out of 7,339 eligible authors who were invited, 453 participated in the survey, resulting in a response rate of 6.2%. All 453 respondents answered the key questions and were included in the analysis.

Ordered logistic regression analysis identified perceived usefulness, satisfaction, and effort expectancy as significant factors associated with future publishing intention. Satisfaction with data journals exerted the strongest positive effect on authors’ continuance intention to publish in these journals (odds ratio [OR], 6.41; 95% confidence interval [CI], 4.08–10.07). Perceived usefulness was also positively related to future publishing intention (OR, 1.89; 95% CI, 1.29–2.77). In contrast, effort expectancy showed a negative association with continuance intention (OR, 0.54; 95% CI, 0.30–0.94). This finding indicates that respondents who strongly agreed or agreed that publishing in a data journal was too time-consuming were significantly less likely to intend to publish in the same journal again, compared with those who strongly disagreed or disagreed. Social influence, reflecting the perceived disciplinary norm of data sharing, was not significantly associated with the intention to continue publishing in data journals (Table 3).

Discussion

Key results

This study found that satisfaction and perceived usefulness were significant predictors of authors’ intention to continue publishing in data journals, consistent with PAM. While satisfaction has been extensively studied in the context of mobile and internet service adoption, its influence on academic publishing behavior has been less explored. The present findings reaffirm satisfaction as a key determinant of continued use in data journal publishing, thereby extending the applicability of PAM to scholarly communication—a domain not previously examined in this model.

Interpretation/comparison with previous studies

Perceived usefulness (also referred to as performance expectancy within the UTAUT framework) was significantly and positively associated with publishing intention. This result is in line with previous PAM-based studies in academic environments, such as those on e-learning systems and collaborative tools, as well as UTAUT-based research on acceptance of OA journals [16]. In the context of data journals, perceived usefulness encompasses benefits such as increased academic productivity, peer-reviewed credibility, enhanced data accessibility and reusability, and recognition in promotion and tenure processes.

Conversely, effort expectancy demonstrated a negative association with continuance intention. This suggests that authors who perceive the data journal publishing process as time-consuming or overly complex may be discouraged from future submissions. A previous study on OA and open government data adoption similarly identified effort expectancy as a substantial barrier to acceptance [17]. Streamlining the submission process may therefore be essential to encourage ongoing participation.

In contrast, social influence, another core construct in UTAUT, did not significantly affect future publishing intention in this study. While earlier research found that social norms could shape OA publishing behavior [18], the present study’s measure of social influence—namely, perceived disciplinary norms around data sharing—may not directly correspond to journal-specific publishing decisions. Given that researchers can share data through various channels (e.g., repositories or as supplements to traditional articles), general norms around data sharing may have less impact on the specific intention to publish in data journals.

Implications

These findings have several practical implications. First, satisfaction emerged as the most influential factor, highlighting the need for data journal publishers to monitor and enhance author experiences throughout the submission process, including templates, submission systems, peer review, and post-acceptance procedures. Previous studies have noted issues such as inconsistent templates, insufficient methodological detail in data papers, and questionable peer review standards [1], all of which can undermine satisfaction and decrease the likelihood of continued submissions.

Second, the significance of perceived usefulness suggests that academic institutions and funding agencies should provide explicit incentives and recognition for data journal publication. While increased citation rates from data papers may promote data reuse [19], perceptions of academic credit for such publications remain mixed [1,3]. To foster continued engagement, stakeholders should establish supportive policies and formally recognize data publication in academic evaluations.

Third, addressing the perceived burden of publishing can facilitate ongoing author engagement [20]. Academic libraries and data services can offer support such as guidance on metadata, formatting, and research data management, reducing the time and effort required to prepare and submit data papers, and thereby supporting sustainable data sharing practices.

Limitations

The generalizability of these findings may be limited to authors experienced with data journals similar to those included in this study, which span several scientific disciplines. Care should be taken in extrapolating results to all researchers or journals. For example, authors in unrepresented fields or those without experience publishing data papers may face different motivators and barriers. Nevertheless, the sample’s inclusion of diverse journal platforms and disciplinary backgrounds (from archaeology to biology to earth science) suggests that the identified factors may be relevant across a range of domains.

Conclusions

This study identified satisfaction, perceived usefulness, and effort expectancy as key factors associated with data paper authors’ continuance intention to publish in data journals. By contrast, social influence was not a significant predictor. Enhancing satisfaction through clearer submission guidelines, consistent templates, and transparent peer review processes may promote ongoing author engagement. Emphasizing the benefits of data journal publication, including increased citation and academic recognition, can further strengthen perceived usefulness. To reduce barriers related to effort, academic libraries and research data services should provide education and support in data management and sharing practices. Future research may usefully investigate broader dimensions of social influence, such as institutional encouragement or peer recommendations, to further clarify their role in scholarly publishing behavior.

Notes

Conflict of interest

No potential conflict of interest relevant to this article was reported.

Funding

The authors received no financial support for this article.

Data availability

Dataset file is available from the corresponding author upon reasonable request.

Supplementary materials

References

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References

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