Allocation of attention to metadata and retrieval functions: Implications for perceived value and open data discovery and reuse

Abstract

Metadata and retrieval functions play a vital role in aiding researchers in the discovery and reuse of open data. However, the diversity of metadata elements and retrieval functions poses a challenge to data searchers’ limited attentional resources. This study aims to examine the allocation of attention to metadata elements and retrieval functions and its implications for perceived value and intentions to discover and reuse open data by drawing upon the attentional drift-diffusion model, flow theory, and perceived value literature. An experiment with 48 participants was conducted to explore the proposed relationships. Multiple linear regression analysis was performed to analyze the data. The results suggest that researchers’ attention to high-value functions amplifies the perceived value and motivates data discovery intention. Attention to high-value metadata elements motivates data discovery and reuse intention. In contrast, attention to low-value metadata elements hampers the perceived value and inhibits data discovery and reuse intention. These findings put forward a new lens for exploring the attention mechanisms underlying perceived value, data discovery and reuse intention and highlight the important role of the value of metadata and retrieval functions in attention mechanisms. Additionally, this paper identifies the positive effect of perceived ease of use on users’ intentions to find, evaluate, and access open data. Perceived usefulness positively affects users’ intentions to evaluate open data. However, in contrast to perceived intentions to reuse open data assessed by self-reported measures, perceived value is not a salient motivator of open data reuse intention measured by behavioral indicators. These findings reveal the distinct effects of perceived value on perceived intention and intentional action in data reuse. With these insights, this study develops practical strategies to optimize the design of metadata and retrieval functions in data retrieval systems.

Keywords

attention allocation data discovery data retrieval systems data reuse metadata retrieval functions

Introduction

Open data are basic resources that can play an important role in science and technology innovations (European Commission, 2011) and sustainable development (United Nations Educational Scientific and Cultural Organization, 2015) only when they are reused (Janssen et al., 2012). Open data discovery involves data search, evaluation, and access and is a prerequisite to reusing open data (Gregory et al., 2020b; Liu et al., 2021). Data retrieval systems are critical technical tools that support researchers in discovering and reusing open data. It retrieves data according to query and returns a list of relevant datasets sorted by certain rules, resource links, and data descriptions such as metadata and data documents (Kacprzak et al., 2017). Metadata and retrieval functions are key to data retrieval systems. Specifically, metadata describe the key characteristics of data (e.g. title, abstract, source, and citation frequency). Additionally, data retrieval systems provide users with functions such as keyword retrieval, sorting, filtering, and search result analysis (Khalsa et al., 2018; Neves et al., 2018). These can support researchers in searching (Wu et al., 2019), evaluating (Krämer et al., 2021), accessing (Koesten and Singh, 2017), and understanding (Pasquetto et al., 2019) data and then reusing it. However, the diversity of metadata and retrieval functions may lead to the attention-allocation problem. Since information consumes recipients’ attention, a wealth of information may result in a poverty of attention, resulting in a need to efficiently allocate attention (Simon, 1971).

Problem statement

Information search has been regarded as an interactive process in which human attention engages (Belkin and Croft, 1992; Marchionini, 2004). Various metadata elements and retrieval function pose a challenge to data searchers’ limited attentional resources. In the field of cognitive psychology, the attentional drift-diffusion model points out that attention affects individual value perceptions (Orquin and Mueller Loose, 2013). However, to the best of our knowledge, no previous study has investigated data searchers’ attention allocation among metadata and retrieval functions and its impact on their perceived value. Perceived value studies note that value is formed in interaction and reflects users’ evaluations of products or services (Sánchez-Fernández and Iniesta-Bonillo, 2007). Perceived value is found to affect open data discovery and reuse intentions (Talukder et al., 2019). Moreover, flow theory suggests that focused attention affects intention (Liu and Zhou, 2017). However, little is known about the relationships between attention allocation and researchers’ intentions to discover and reuse open data. It is necessary to examine the relationships between researchers’ allocation of attention to diverse metadata and retrieval functions, perceived value, and data discovery and reuse intention. Therefore, this study uncovers the implications of the diversity of metadata and retrieval functions for researchers’ attention allocations, perceived value, data discovery and reuse intention by drawing on perceived value studies, the attentional drift-diffusion model, and flow theory. This study aims to answer the following research questions:

During the interaction with the data retrieval system,

RQ1. What is the relationship between the allocation of attention to metadata and retrieval functions and perceived value?

RQ2. What is the relationship between the allocation of attention to metadata and retrieval functions and open data discovery and reuse intention?

RQ3. What is the relationship between perceived value and open data discovery and reuse intention?

To answer these questions, 48 participants were recruited to participate in a user experiment. We asked the participants to complete four search tasks in the data retrieval system. We then collected their search logs, screen recordings, questionnaires, and eye movement data and used stepwise regression analysis to analyze the data.

The main findings and their theoretical contributions can be summarized as follows: First, it provides new insights into researchers’ attention allocations among diverse metadata elements and retrieval functions and its implications for the perceived value of data retrieval systems. The findings suggest that researchers’ attention to high-value metadata elements and functions amplifies the value they perceive, while attention to low-value metadata elements hampers the perceived value. By highlighting the importance of attention allocation in perceived value during interactive data retrieval, these findings advance the current understanding of perceived value and the role of attention in interactive retrieval. Second, this study sheds light on the motivating and inhibiting effects of attention allocation on data discovery and reuse intention. Specifically, researchers’ attention to high-value functions motivates their data discovery intentions. Attention to high-value metadata elements motivates data discovery and reuse intention, while attention to low-value metadata elements is an inhibitor. It advances the understanding of the cognitive processes underlying data discovery and reuse intention from a novel perspective of attention mechanisms. It also expands the knowledge of the effect of attention on behavioral intention by highlighting the importance of the actual value of gazed objects. Third, this study extends the existing knowledge of the motivating mechanisms underlying perceived intention and intentional action in open data reuse by uncovering the distinct effects of perceived value on perceived intention and intentional action. Previous studies have used self-reported measures to assess users’ intentions to reuse open data and identified the positive effects of perceived value on perceived data reuse intention. This study uses behavioral indicators to investigate open data reuse intention. It identifies that perceived value is not a salient motivator of data reuse intention, as revealed by behavior. Drawing upon intentional action theory, it uncovers the differences between perceived intention and intentional action in open data reuse and the distinct motivations underlying them.

Literature review

Open data discovery and reuse involve open data needs, search, evaluation, access, sensemaking, and reuse, among which open data needs, search, evaluation, and access are the keys to open data discovery, and open data sensemaking and reuse are the keys to open data reuse (Gregory et al., 2019; Kim and Yoon, 2017; Koesten et al., 2017). Previous studies suggested that data retrieval system metadata and retrieval functions (Koesten and Singh, 2017; Wu et al., 2019) are important in open data discovery and reuse. For instance, keyword-based retrieval and browsing functions (Kern and Mathiak, 2015) and metadata have been found to be critical factors for researchers to successfully search data (Löffler et al., 2021). Researchers can save data through data links and download functions (Bugaje and Chowdhury, 2017).

Cognitive processes involved in the use of data retrieval systems for data discovery and reuse are complex (Koesten et al., 2017; Pasquetto et al., 2019). For instance, based on metadata and data documents, researchers evaluate data relevance (Kern and Mathiak, 2015), fitness of data for use (Bishop et al., 2019), availability and quality (Koesten et al., 2017; Krämer et al., 2021), and credibility (Gregory, 2020) and obtain knowledge about data and data creation process to make sense of it (Pasquetto et al., 2019). Several data retrieval systems support interactive data exploration (Devarakonda et al., 2021), which can support researchers in understanding and reusing data (Gregory et al., 2019; Koesten et al., 2021). During these processes, it’s challenging for researchers to allocate limited attention resources to various metadata elements and retrieval functions effectively. However, few studies have revealed the relationships between metadata and retrieval functions, system evaluation, and open data discovery and reuse from a cognitive perspective, especially from an attention perspective.

Additionally, self-reported measures such as questionnaires (Talukder et al., 2019; Weerakkody et al., 2017) and interviews (Kaasenbrood et al., 2015) are used by researchers to investigate open data reuse intention. Rzepka and Berger (2018) noted that there are differences between self-reported perception and pattern or tendency revealed by behavior in the process of user interaction with the system. Thus, this study develops behavioral indicators to investigate open data reuse intention.

Theoretical background

Perceived value

Value is an overall assessment of the utility of a product or service given by consumers according to the perceptions of receiving and giving (Zeithaml, 1988). Perceived value is an interactive relativistic preference generated during a subject’s interaction with a product or service (Holbrook, 1999), which is individualistic and relativistic (Gallarza et al., 2011; Yu et al., 2017). Experience is the essence and basis of perceived value (Hsiao et al., 2016; Turel et al., 2007). Together, value is the subjective evaluations of products or services by subjects based on their interaction with the products or services. In this study, perceived value is regarded as users’ subjective evaluations through their interaction with the data retrieval system.

Perceived value is considered a multidimensional construct that includes functional value, emotional value, social value, epistemic value, and conditional value (Sheth et al., 1991). Functional value and emotional value are the most widely studied. Specifically, functional value comes from a product’s attributes or characteristics (Hou et al., 2020). Emotional value depends on the feelings generated by products or services (Sánchez et al., 2006). In information retrieval systems, usefulness is the perception that the system can support users in completing tasks and improving search efficiency. Ease of use is the perception that systems can help users reduce mental effort to support interaction with the system (Tella et al., 2017). These two factors are closely related to functional value. Therefore, this study explores data searchers’ evaluations of functional value from perceived usefulness and perceived ease of use. Research on emotional value mainly explores users’ positive emotions during interactions with products or services (Sweeney and Soutar, 2001; Zhou, 2008). However, there are few empirical studies on user emotion in the process of data retrieval, and most of them focus on negative emotions (Megler and Maier, 2012; Wiegand and García, 2007). This study investigates users’ positive emotions (i.e. perceived enjoyment) during their interactions with a data retrieval system to understand researchers’ evaluations of emotional value.

Perceived value has been found to affect behavioral intention (Osakwe et al., 2021; Wang et al., 2013). Perceived usefulness and perceived ease of use have been found to affect open data discovery (Kim and Yoon, 2017; Zuiderwijk et al., 2015) and reuse (Jurisch et al., 2015; Weerakkody et al., 2017) intentions. Perceived enjoyment has been found to affect open data reuse intention (Khurshid et al., 2022). However, few studies have investigated the role of perceived value in interactive data retrieval. Driven by this gap, this study explores users’ perceived value and its impacts on open data discovery and reuse intention in the context of researchers’ interaction with data retrieval systems.

Attentional drift-diffusion model and the flow theory

Attention is a critical cognitive process (Léger et al., 2014; Posner, 2012), indicating the selection and further processing of external stimuli (Eysenck and Keane, 2020; Hilberink-Schulpen et al., 2016), which is regarded as an important and limited resource for humans (Cheung et al., 2017). In the process of attention resource allocation, individuals will have an attention preference for some cues, which affects users’ perceived value and choice (Smith and Krajbich, 2019). The attentional drift-diffusion model reveals that attention increases evidence accumulation in decision-making and then increases the decision maker’s perceived value for the option (Mormann and Russo, 2021). The additive model and multiplicative model uncover the distinct mechanisms underlying this effect. In the additive model, attention will enhance the evidence accumulated for the gazed-at option, the magnitude of which is irrelevant to the value of the option itself (Cavanagh et al., 2014). There is an interactive effect of attention and the value of the option itself on evidence accumulation in the multiplication model. The amplified effect of gaze on evidence accumulation is affected by the value of the option itself (Krajbich et al., 2010). Attention positively affects perceived usefulness (Cho et al., 2020), perceived ease of use (Tzafilkou and Protogeros, 2017), and perceived enjoyment (Maughan et al., 2007).

Regarding the effect of attention on behavioral intention, flow theory holds that in the flow state, individuals can focus their attention on the current activity and exclude and filter other irrelevant perceived content (Csikszentmihalyi, 1988). Users enjoy the flow state, which has a positive effect on subsequent behavioral intentions (Kazancoglu and Demir, 2021; Kim and Hall, 2019). Focused attention and concentration are important dimensions and characteristics of flow (Pelet et al., 2017). In addition, focused immersion reflects the extent of involvement of individual attention resources in specific activities (Agarwal and Karahanna, 2000), which has been found to be a significant predictor of user behavioral intentions (Venter and Swart, 2018). Attention is considered an important factor affecting information search (Belkin and Croft, 1992; Marchionini, 2004). However, to the best of our knowledge, no study has analyzed the relationships among users’ attention allocations and perceived value and open data discovery and reuse behavior in a data retrieval system. Therefore, this study investigates user attention allocations to metadata and retrieval functions in data retrieval systems and their relationships with perceived value and open data discovery and reuse intention.

Hypothesis development

To address our research problems, we formulate our research hypotheses accordingly. Figure 1 shows the theoretical model.

Figure 1.

Theoretical model.

Attention is positively related to perceived value (Krajbich et al., 2010; Mormann and Russo, 2021). Specifically, when users focus on information and communication technology tools, they may have higher immersion when they use these tools and perceive the tools to have higher usefulness (Gloria and Achyar, 2021; Zhou and Lu, 2011). In addition, individuals focus their attention on tasks that allow for acquisition of more cognitive resources, leading to a reduction in cognitive load associated with technology use and thus improving the perceived ease of use of information technology (Agarwal and Karahanna, 2000; Saadé and Bahli, 2005). Moreover, concentration reflects the extent to which an individual’s attention is immersed in activities (Pelet et al., 2017), and it is found to be positively related to perceived enjoyment (Liu and Li, 2011; Wang and Lee, 2020). In data retrieval systems, metadata and retrieval functions are key to supporting data searchers’ search tasks (Koesten et al., 2017). When researchers focus on metadata and the functions of data retrieval systems, they are more likely to realize the support provided by the data retrieval systems for the data search task. The more cognitive resources that are allocated to the task, the higher the perceived usefulness and ease of use. In addition, when researchers allocate more attention to metadata and retrieval functions, they experience less interference from task-irrelevant or low-related stimuli, arousing emotional reactions. Therefore, we propose the following hypothesis:

H1 Attention to metadata and retrieval functions positively affects perceived value.

H1(a) Attention to metadata and retrieval functions positively affects perceived usefulness.

H1(b) Attention to metadata and retrieval functions positively affects perceived ease of use.

H1(c) Attention to metadata and retrieval functions positively affects perceived enjoyment.

Perceived value is found to positively affect behavioral intention (Turel et al., 2007). Several empirical studies have found that perceived usefulness and perceived ease of use are positively related to open data reuse intention (Husin et al., 2019; Weerakkody et al., 2017). In addition, perceived enjoyment is positively related to open data use intention (Khurshid et al., 2022). Therefore, we propose the following hypothesis:

H2 Perceived value positively affects open data reuse intention.

H2(a) Perceived usefulness positively affects open data reuse intention.

H2(b) Perceived ease of use positively affects open data reuse intention.

H2(c) Perceived enjoyment positively affects open data reuse intention.

Perceived usefulness and perceived ease of use were found to be critical predictors of the use of open data technology (Saxena and Janssen, 2017; Zuiderwijk et al., 2015). Moreover, perceived enjoyment positively affects the intention to use open-access data repositories (Wen et al., 2021). Therefore, perceived value may positively affect the intention to use data retrieval systems. Data searchers use data retrieval systems to discover data. It implies that perceived value promotes the intention to use data retrieval systems, which in turn stimulates the intention to discover data. Therefore, we propose the following hypothesis:

H3 Perceived value positively affects open data discovery intention.

H3(a) Perceived usefulness positively affects open data discovery intention.

H3(b) Perceived ease of use positively affects open data discovery intention.

H3(c) Perceived enjoyment positively affects open data discovery intention.

Attention is found to positively affect behavioral intention (Fei et al., 2021). Concentration positively affects the intention to use information systems (Liu et al., 2009). Furthermore, as an important dimension of cognitive absorption, attention was found to positively affect the intention to use innovative technology (Chandra et al., 2009). Therefore, attention may positively affect the intention to use data retrieval systems. Data retrieval systems support users in discovering (Koesten and Singh, 2017) and reusing data (Bishop et al., 2019). Therefore, we propose the following hypothesis:

H4 Attention to metadata and retrieval functions positively affects open data reuse intention.

H5 Attention to metadata and retrieval functions positively affects open data discovery intention.

Method

To test research hypotheses, this study used a controlled experiment that examined an interactive data search. In this section, we present the experimental participants, platform, apparatus, tasks, measures, and procedure.

Participants

As early-stage researchers, graduate students have been found to be important data searchers (Gregory et al., 2020a). Therefore, we recruited 48 doctoral students to participate in the experiment. Among the 48 participants, 45.8% were female and 54.2% were male. A total of 12.5% of the participants were 20–24 years old, 75% were 25–30 years old, and 12.5% were over 30 years old. The participants included science (31.3%), engineering (29.2%), social science (25%), medicine (6.2%), and humanities students (8.3%). All participants had research experience; half of the participants had 4–6 years of research experience, 39.6% had 1–3 years of research experience, and 10.4% had 7–9 years of experience. The experiment participants were asked to complete four tasks and fill out questionnaires during the experiment.

Platform and apparatus

Platform

The Data Citation Index (DCI) of the Web of Science platform was selected as the experimental system. As a data citation database, it can provide professional data retrieval services and a real and stable system environment.

Apparatus

In this experiment, a laptop equipped with a Tobii Pro X3-120 eye tracker was used to capture the screen and record users’ interaction with DCI. The device is at a 120 Hz sampling rate, with 5-point calibration, mounted at the bottom of the screen. Before the experiments started, the participants were asked to adjust themselves to a comfortable sitting position and stay 60–80 cm away from the device during the experiment.

Task

COVID-19 is a common and pressing issue facing human beings. To address this challenge, researchers have openly published research data (Shuja et al., 2021). At the same time, there is a need to discover and reuse open data for COVID-19 research (Ulahannan et al., 2020). It provides a suitable context for exploring researchers’ data discovery and reuse. Therefore, this study designs four data search tasks related to COVID-19 research. The topics of the four tasks are the major topics of COVID-19 studies: clinical characteristics of COVID-19, population mobility, genome sequencing, and psychological and economic issues.

Measures

Attention

This study used eye-tracking data to measure the participants’ attention. Fixation can be used to measure users’ attention (Rayner, 1998), and fixation duration is one of the most commonly used measures of attention (Lee and Ahn, 2012; Pieters and Wedel, 2004). This study used a Tobii Pro X3-120 Eye Tracker to record users’ fixation duration on the following areas of interest (AOIs) to measure the participants’ attention to metadata and retrieval functions: eight metadata AOIs (i.e. title, source, DOI, type, abstract, subject category, publication date, and citation frequency and usage frequency) and five retrieval function AOIs (i.e. adding to marked list and exporting records, filtering and sorting search results, search results analysis, viewing data, and query information) (Figure 2).

Figure 2.

An example of 13 AOIs (SERPs and description pages of search results).

Perceived value

Questionnaire data were used to measure perceived value. Perceived usefulness was measured using a three-item scale adapted from Venkatesh et al. (2003) and Chiu and Wang (2008). Perceived ease of use was measured using a four-item scale adapted from Davis (1989). Perceived enjoyment was measured using a three-item scale adapted from Khatri et al. (2018) and Davis et al. (1992). All measures used 7-point Likert scales from 1 (strongly disagree) to 7 (strongly agree).

Open data discovery and reuse intention

The number of documents saved (Belkin et al., 2001; Liu et al., 2012; Zhang et al., 2015) indicates searchers’ usefulness judgments (Liu, 2021). In a data search, the number of documents (i.e. open data) saved by users indicates their usefulness judgments regarding open data. In addition, saving data is the first step in data reuse (Bugaje and Chowdhury, 2017). This study collected log files and screen recording data to obtain this indicator to measure open data reuse intention. In addition, we developed three items in the questionnaire, including open data finding intention, open data evaluating intention, and open data accessing intention, to measure open data discovery intention.

Procedure

Prior to the experiment, we introduced our experiment and required all participants to give informed written consent. Each participant was asked to complete a questionnaire with basic information. Each participant was asked to complete four simulated tasks using DCI. Then, the eye tracker was calibrated using a 5-point standard calibration procedure for each subject. For each task, the participants read the task description and completed a presearch questionnaire. Then, the participants performed the task, each lasting up to 10 minutes. During the task, the participants were able to save data in any way preferred. After completing the task, the participants were asked to complete a postsearch questionnaire. The participants completed four simulated tasks and completed an overall assessment questionnaire. The Tobii Pro X3-120 eye tracker recorded each participant and the entire process. To eliminate the learning effect and fatigue effect, the task order was rotated based on the basic 4 × 4 Latin square (Li and Liu, 2019). The experimental procedure is displayed in Figure 3.

Figure 3.

Experimental procedure.

Data analysis

Stepwise regression is a set of iterative search and model comparison processes during which the independent variables that have the strongest associations with the dependent variable can be identified from a large number of alternative variables (Henderson and Denison, 1989; Ma et al., 2016; Vlachopoulou et al., 2013). To support open data discovery and reuse, data retrieval systems make efforts to develop diverse metadata and retrieval functions. Stepwise regression analysis is suitable for identifying the most influential metadata elements and retrieval functions on researchers’ attention allocation and intentions to discover and reuse open data. Perceived value is a multidimensional construct. Stepwise regression analysis is also an appropriate approach to reveal the most influential perceived value on researchers’ intentions to discover and reuse open data. Therefore, to test these proposed relationships, this study performed stepwise regression analysis in SPSS version 26.

Result

The Cronbach’s alpha coefficients for perceived usefulness, perceived ease of use, and perceived enjoyment were 0.959, 0.906, and 0.986, respectively, which exceeded the 0.7 benchmark (Hinkin, 1998), indicating internal consistency. Descriptive statistics of all variables are shown in Table 1.

Table 1.

Descriptive statistics of variables.

	Min	Max	Mean	Std. Dev
Attention to metadata
Title	5.26	89.57	38.24	21.66
Source	4.61	48.56	16.51	9.21
Type	0.58	8.39	2.39	1.51
DOI	1.23	10.38	3.84	1.78
Abstract	0.00	129.63	30.31	29.50
Publication date	0.00	4.74	1.22	1.04
Subject category	0.00	5.71	1.43	1.33
Citation frequency and usage frequency	0.10	10.78	2.70	2.39
Attention to retrieval functions
Viewing data	0.00	3.08	0.31	0.65
Adding to marked list and exporting records	2.26	58.93	19.21	13.64
Filtering and sorting search results	0.82	109.27	29.14	27.77
Search results analysis	0.00	3.46	0.68	0.81
Query information	0.38	72.70	13.26	14.83
Perceived usefulness	3.00	7.00	5.28	0.87
Perceived ease of use	3.50	7.00	5.29	0.89
Perceived enjoyment	2.75	7.00	4.95	0.83
Intention to reuse open data
Number of documents saved	0.00	49.00	11.27	12.07
Intention to discover open data
Intention to find open data	3.00	7.00	5.38	0.89
Intention to evaluate open data	2.00	7.00	5.06	1.02
Intention to access open data	3.00	7.00	5.29	0.89

As shown in Table 1, researchers allocate most attention resources to data title (M = 38.24, SD = 21.66), followed by data abstract (M = 30.31, SD = 29.50), filtering and sorting search results (M = 29.14, SD = 27.77), adding to marked list and exporting records (M = 19.21, SD = 13.64), data source (M = 16.51, SD = 9.21), query information (M = 13.26, SD = 14.83), DOI (M = 3.84, SD = 1.78), citation frequency and usage frequency (M = 2.70, SD = 2.39), data type (M = 2.39, SD = 1.51), subject category (M = 1.43, SD = 1.33), publication date (M = 1.22, SD = 1.04), and search results analysis (M = 0.68 SD = 0.81). The least attention resources are allocated to the function of viewing data (M = 0.31, SD = 0.65).

The effect of attention to metadata and retrieval functions on perceived value

Stepwise regression analysis, in which the dependent variables were perceived value and the independent variables were participants’ attention to metadata and retrieval functions, was performed (Table 2). The variance inflating factors of all variables in all models (min = 1.000, max = 1.113) were less than 2, indicating the absence of multicollinearity. The Durbin–Watson statistic for all models (min = 1.955, max = 1.995) was between 1.5 and 2.5, indicating the independence of observations. According to the model F test, all models were statistically significant.

Table 2.

Multiple linear regression: perceived value (independent variable: attention measured by fixation duration).

	B	SE	β	p
Perceived usefulness
R² = 0.317, adjusted R² = 0.271 F = 6.822, p = 0.001
Query information	0.014	0.007	0.275	0.041
Subject category	−0.201	0.073	−0.349	0.008
Search results analysis	0.284	0.124	0.301	0.027
Perceived ease of use
R² = 0.097, adjusted R² = 0.077 F = 4.936, p = 0.031
Data abstract	−0.009	0.004	−0.311	0.031
Perceived enjoyment
R² = 0.116, adjusted R² = 0.097 F = 6.032, p = 0.018
Query information	0.018	0.007	0.340	0.018

B: coefficient of nonstandard regression; SE: B standard errors; β: coefficient of standard regression.

According to Table 2, attention to the subject category of data has a negative effect on perceived usefulness. However, attention to the query information and the search results analysis function in SERPs are positively related to perceived usefulness. Thus, H1(a) is partially supported. Attention to the data abstract negatively impacts perceived ease of use. Thus, H1(b) is rejected. Attention to the query information has a positive effect on perceived enjoyment. Thus, H1(c) is supported.

The effects of attention to metadata and retrieval function on the intentions to discover and reuse open data

Stepwise regression analysis, in which the dependent variables were the intention to reuse open data and the intention to discover open data and the independent variables were the participants’ attention to metadata and retrieval functions, was performed (Table 3). Regarding the stepwise models in which dependent variables were the intention to reuse open data, the variance inflating factors of all variables in all stepwise models (min = 1.085, max = 1.256) were <2, indicating the absence of multicollinearity. The Durbin-Watson statistic was 2.179, indicating the independence of observations. According to the F test, all models were significant.

Table 3.

Multiple linear regression: the intention to reuse open data and the intention to discover open data (independent variable: Attention measured by fixation duration).

	B	SE	β	p
The intention to reuse open data
R² = 0.411, adjusted R² = 0.371 F = 10.229, p = 0.000
Data title	0.330	0.072	0.592	0.000
DOI	−3.015	0.880	−0.444	0.001
Citation frequency and usage frequency	1.359	0.609	0.269	0.031
The intention to discover open data
The intention to find open data
R² = 0.333, adjusted R² = 0.287 F = 7.311, p = 0.000
Data type	0.284	0.079	0.481	0.001
Subject category	−0.215	0.090	−0.320	0.021
Query information	0.016	0.008	0.259	0.045
The intention to access open data
R² = 0.344, adjusted R² = 0.299 F = 7.683, p = 0.000
Data type	0.204	0.077	0.343	0.011
Data abstract	−0.013	0.004	−0.429	0.001
Search results analysis	0.331	0.146	0.299	0.028

B: coefficient of nonstandard regression; SE: B standard errors; β: coefficient of standard regression.

Regarding the stepwise models in which the dependent variables were the intention to discover open data, the variance inflating factors of all variables in all stepwise models (min = 1.035, max = 1.192) were <10, indicating the absence of multicollinearity. The Durbin–Watson statistic for all models (min = 1.759, max = 1.937) was between 1.5 and 2.5, indicating the independence of observations. According to the model F test, all models were statistically significant.

According to Table 3, the attention to the data title and the data citation frequency and usage frequency positively affects the intention to reuse open data. However, attention to the DOI is negatively related to the intention to reuse open data. Therefore, H4 is partially supported. Attention to the data type and the query information function positively influence the intention to find open data. However, attention to the subject category negatively impacts the intention to find open data. The attention to the data type and search results analysis function positively impact the intention to access open data. However, attention to the data abstract has a negative effect on the intention to access open data. Therefore, H5 is partially supported.

The effect of perceived value on the intention to reuse data and the intention to discover data

Stepwise regression analysis, in which the dependent variables were the intention to reuse open data and the intention to discover open data and the independent variable was perceived value, was performed (Table 4). Regarding the stepwise models in which the dependent variables were the intention to reuse open data, as the results are not significant, the results of the variance inflating factors of all variables and the Durbin-Watson statistic are not reported here. Regarding the stepwise models in which the dependent variables were the intention to discover open data, the variance inflating factors of all variables in all stepwise models (min = 1.000, max = 2.357) were less than 10, indicating the absence of multicollinearity. The Durbin-Watson statistic for all models (min = 1.889, max = 2.433) was between 1.5 and 2.5, indicating the independence of observations. According to the model F test, all models were statistically significant.

Table 4.

Multiple linear regression: The intention to discover data.

	B	SE	β	p
The intention to find open data
R² = 0.609, adjusted R² = 0.601 F = 71.661, p = 0.000
Perceived ease of use	0.830	0.098	0.780	0.000
The intention to evaluate open data
R² = 0.530, adjusted R² = 0.509 F = 25.373, p = 0.000
Perceived ease of use	0.477	0.168	0.445	0.007
Perceived usefulness	0.389	0.185	0.330	0.041
The intention to access open data
R² = 0.335, adjusted R² = 0.320 F = 23.161, p = 0.000
Perceived ease of use	0.705	0.146	0.579	0.000

B: coefficient of nonstandard regression; SE: B standard errors; β: coefficient of standard regression.

According to the Table 4, perceived usefulness, perceived ease of use, and perceived enjoyment have no significant effect on the intention to reuse data, rejecting H2(a), H2(b), and H2(c). Perceived usefulness is positively related to the intention to find open data, supporting H3(a). Perceived ease of use is positively related to the intention to find, evaluate, and access open data, supporting H3(b). Perceived enjoyment has no significant effect on the intention to discover data, rejecting H3(c).

Discussion

This paper aims to test the relationships among attention to metadata and retrieval functions, perceived value, data discovery intention, and data reuse intention. We proposed our research model and used stepwise regression analysis to test these hypotheses.

Users’ attention to specific retrieval functions (i.e. query information and search results analysis) is found to be positively associated with perceived value. This finding supports previous findings on the positive effect of attention on perceived usefulness (Gloria and Achyar, 2021). Moreover, when users have longer fixation durations for the query information, they may have high perceived enjoyment. This finding aligns with the research finding that attention focus positively impacts perceived enjoyment (Liu and Li, 2011; Wang and Lee, 2020). However, attention to subject category negative affects perceived usefulness, and attention to data abstract negatively affects perceived ease of use. This result may be explained by the multiplicative model of the attentional drift-diffusion model. According to this model, the effect of attention on the perceived value of the looked-at option is influenced by the actual value of the looked-at option itself (Krajbich et al., 2010). The quality of metadata in current open data practice was found to be relatively low (Marc et al., 2016). Users have been found to put much cognitive effort into understanding the metadata of open data (Bugaje and Chowdhury, 2017). The subject category may be too broad and vague to obtain useful information for data evaluation, while data abstracts may be too complex to be easily understood. Our findings suggest that the actual value of these two metadata elements (i.e. subject category and data abstract) is relatively low, which eliminates the promoting effect of attention on perceived value, resulting in more effort and less perceived benefit for users.

Regarding the effects of data searchers’ attention to metadata and retrieval functions on their intentions to discover and reuse data, attention to specific elements of metadata (i.e. data title and citation and usage frequency) significantly and positively affects the intention to reuse data. Attention to specific elements of metadata (i.e. data type) and specific retrieval functions (i.e. query information and search results analysis) positively affects the intention to find and access data. These results support and expand previous findings on the positive effect of attention on system adoption (Liu et al., 2009; Webster et al., 1993). However, attention to specific elements of metadata (i.e. subject category) is negatively correlated with the intention to find data. Attention to data abstracts is negatively correlated with the intention to access data. Attention to the DOI element negatively affects open data reuse intention. This finding is inconsistent with previous findings on the positive effect of attention on behavioral intention (Chandra et al., 2009). These findings may also be explained by the multiplicative model of the attentional drift-diffusion model. Smith and Krajbich (2019) noted that the interaction between the value of the option itself and attention positively impacts evidence accumulation in the decision-making process. Specifically, the decision maker’s gaze on the higher value option has a greater impact on choice (Smith and Krajbich, 2019). Our findings suggest that the actual value of these metadata elements (i.e. subject category, data abstract, and DOI) is relatively low, which eliminates the promoting effect of attention on choice related to data discovery and reuse, leading to a reduction in data searchers’ intentions to discover and reuse data. This implies the interaction effect of the actual value of metadata and attention to metadata on open data discovery and reuse intention.

Perceived usefulness and perceived ease of use are found to be important factors positively influencing the intention to discover data. This finding is consistent with the research results of Saxena and Janssen (2017). However, this study did not find any significant relationship between perceived enjoyment and researchers’ intentions to discover data. This result is inconsistent with previous findings that perceived enjoyment is an important driver of information seeking (Savolainen, 2012). A possible explanation may be that the difference in research contexts leads to the elimination of perceived enjoyment’s motivating effect. In addition, surprisingly, the effect of perceived value on data reuse intention is statistically insignificant. This finding is inconsistent with those of other studies (Khurshid et al., 2022; Talukder et al., 2019). This result may be explained by action theories. Intentional action is action driven by intention, which can reflect an actor’s intention (Burks, 2001). Intentional action is mainly measured by the correlation between intention and subsequent behavior (Burks, 2001). This study used the number of documents saved as a behavioral indicator to measure open data reuse intention. Saving open data documents is a prerequisite to reusing open data (Bugaje and Chowdhury, 2017). This indicates that open data reuse intention is closely correlated with data saving behavior. Therefore, saving open data is an intentional action driven by open data reuse intention and directed toward reuse intention. Perceived value has been found to be an antecedent of intention (Liang et al., 2021). However, intentional action has been found to be affected by self-efficacy, motivation, will power, social pressures, outside resources, social norms, emotional state, and the concept of self as the active agent (Burks, 2001). Our findings suggest that perceived value is not a salient antecedent of intentional behavior in the context of open data reuse.

Theoretical implication

This study reveals the effects of researchers’ attention allocations among metadata elements and retrieval functions on their perceived value of data retrieval systems and the role of the actual value of metadata and retrieval function in these effects. It offers novel insights into the implication of the diversity and quality of metadata and retrieval functions for researchers’ attention allocations and perceived value. Specifically, during interaction with data retrieval systems, researchers allocate their attention across various metadata elements and retrieval functions. This study uncovers the positive effects of data searchers’ attention to specific retrieval functions on their perceptions of value and the negative effects of attention to specific metadata elements on perceived value. These findings support the multiplicative model of the attentional drift-diffusion model, which reveals the interaction effect of attention and the actual value of the looked-at option itself on perceived value (Krajbich et al., 2010). Researchers’ attention to high-value functions amplifies the perceived value of data retrieval systems. In contrast, attention to low-value metadata elements hampers the perceived value. These findings suggest that attention allocation among diverse metadata and retrieval functions significantly affects perceived value. The actual value of metadata and retrieval functions determines the direction of attention effects. Our findings highlight the importance of attention allocation in perceived value during interactive data retrieval, expanding the current understanding of perceived value and the role of attention in information retrieval.

This study uncovers the attention mechanisms in data discovery and reuse intention by analyzing the effect of attention allocation on open data discovery and reuse intention. The role of the actual value of metadata and retrieval functions in these mechanisms is identified. It puts forward a new lens for exploring cognitive processes underlying data discovery and reuse intention. Specifically, attention to the elements of title and citation frequency and usage frequency positively affects data reuse intention, while attention to the DOI element negatively affects data reuse intention. Attention to the data type element and specific functions positively affects data discovery intention, while attention to subject category and abstract elements negatively affects data discovery intention. According to flow theory, focused attention promotes behavioral intention. Our findings suggest that attention can serve as both a motivator and an inhibitor for data discovery and reuse intention. The actual value of metadata and retrieval functions is a determinant of the direction of attention effects. It contributes to existing knowledge of the impact of attention on behavioral intention by highlighting the importance of the quality of gazed objects.

This study reveals the distinct effects of perceived value on researchers’ intentions to discover and reuse open data. The findings identify the positive effect of perceived ease of use on researchers’ intention to find, evaluate, and access open data and the positive effect of perceived usefulness on researchers’ intentions to evaluate open data. This suggests a role for perceived ease of use in promoting open data discovery intention. In addition, we use behavioral indicators to reveal open data reuse intention, which provides a new lens for understanding researchers’ intentions to reuse open data and intentional action. Inconsistent with previous findings of the positive effects of perceived value on open data reuse intention, our findings suggest that perceived value is not a salient motivator of intentional action. It sheds light on the distinct motivating mechanisms underlying perceived intention and intentional action in open data reuse.

Practical implications

This study identifies several low-value metadata elements (i.e. subject category, data abstract, and DOI) and proposes optimizing strategies. Specifically, attention to data abstract negatively affects perceived ease of use. Thus, we suggest that data retrieval systems should make data abstract easier to understand. There is evidence that providing adequate information about the data creation process and data content is the key for users to understand data (Gebru et al., 2021). Structured abstract formats, including elements such as data creation motivation, background, data collection and processing, data content, and recommended use, can be adopted to improve data abstract quality and comprehensibility. Additionally, users’ attention to subject category negatively affects perceived usefulness, which indicates that it is important to improve the usefulness of subject categories in supporting data evaluation. Controlled vocabularies can be used to effectively represent the topic of metadata (Marc et al., 2016), and topic-specific controlled vocabularies can help users search for domain-specific content (Chipangila et al., 2021). This study suggests that a data subject-controlled vocabulary should be designed for subject-specific data searchers so that they can accurately judge the position of data in the subject knowledge system or data resource system. Furthermore, attention to DOI negatively affects data reuse intention. This suggests that the design of DOIs should be optimized to improve users’ willingness to access data. The DOI label should be made easier to understand by changing it to a more intuitive download link label to support data access.

Previous studies have mainly focused on improving the quality of metadata and retrieval functions based on users’ needs (Wu et al., 2019). This study provides a new vision for designing and optimizing metadata and retrieval functions by highlighting the role of users’ attention in the diversity of metadata and functions. The results indicate that data searchers’ attention to high-value metadata elements and retrieval functions (i.e. data type, data title, citation frequency and usage frequency, query information, and search results analysis) is positively related to perceived value, data discovery intention, and data reuse intention. Individuals pay more attention to visually salient options (Kahn, 2017). Data retrieval system designers can improve the visual salience of high-value retrieval function icons and metadata elements by increasing their brightness and color to direct and retain users’ attention.

Conclusion

This paper comprehensively investigates user attention to metadata and retrieval functions and its impact on perceived value and open data discovery and reuse intention during interaction with data retrieval systems. To test the research hypotheses, a controlled experiment was designed to collect data, and stepwise regression analysis was used to analyze data. The findings show that users’ attention to metadata and retrieval functions significantly affects perceived value and open data discovery and reuse intention. Perceived value affects open data discovery intention.

Despite these contributions, the present study has several limitations. First, in this study, the number of documents saved is used as a behavioral indicator to measure open data reuse intention. However, other behavior indicators, such as the number of bookmarks and average bookmark rank, may be suitable for measuring open data reuse intention. Future studies can collect and analyze various behavioral indicators to fully investigate open data reuse intention. Second, although there are various types of data retrieval systems, such as data search engines, data repositories, and open government data platforms, this study chose DCI as the experimental system. Further investigation can examine our research findings in other types of data retrieval systems.

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 National Natural Science Foundation of China [No. 72074171].

ORCID iD

Qiao Li

Author biographies

Ping Wang is a professor of the School of Information Management at Wuhan University, China. He received his Ph.D. degree in information resource management from Wuhan University. His research interests include information system and information resource planning, data curation, information quality assessment, and user studies. His works has appeared in Journal of the Association for Information Science and Technology, Information Processing & Management, Decision Support Systems, Journal of Knowledge Management, Journal of Documentation, Journal of Information Science, Government Information Quarterly, and Library & Information Science Research.

Yufei Xie is a master student of the School of Information Management at Wuhan University, China. Her current research focuses on user studies and open science.

Xueyi Li is a master student of the School of Information Management at Wuhan University, China. Her current research focuses on open data and human computer interaction.

Qiao Li is an assistant professor of the Department of Information Resources Management, Business School, Nankai University, China. Her research interests include open science, human computer interaction, and user studies. Her works have appeared in Information Processing & Management, Journal of Knowledge Management, Journal of Documentation, Journal of Librarianship and Information Science, Aslib Journal of Information Management, and The Electronic Library.

References

Agarwal

Karahanna

(2000) Time flies when you’re having fun: Cognitive absorption and beliefs about information technology usage. MIS Quarterly 24(4): 665–694.

Belkin

Cool

Kelly

, et al. (2001) Iterative exploration, design and evaluation of support for query reformulation in interactive information retrieval. Information Processing & Management 37(3): 403–434.

Belkin

Croft

(1992) Information filtering and information retrieval. Communications of the ACM 35(12): 29–38.

Bishop

Hank

Webster

, et al. (2019) Scientists’ data discovery and reuse behavior: (Meta)data fitness for use and the FAIR data principles. Proceedings of the Association for Information Science and Technology 56(1): 21–31.

Bugaje

Chowdhury

(2017) Is data retrieval different from text retrieval? An exploratory study. In: Choemprayong

Crestani

Cunningham

(eds) Digital Libraries: Data, Information, and Knowledge for Digital Lives. Berlin: Springer International Publishing, pp.97–103.

Burks

(2001) Intentional action. Journal of Advanced Nursing 34(5): 668–675.

Cavanagh

Wiecki

Kochar

, et al. (2014) Eye tracking and pupillometry are indicators of dissociable latent decision processes. Journal of Experimental Psychology General 143(4): 1476–1488.

Chandra

Srivastava

Theng

(2009) Role of cognitive absorption and trust for collaboration in virtual world. In: 2009 Pacific Asia conference on information systems. Hyderabad, India, 10–12 July 2009, pp.1–13. Illinois: AIS Electronic Library.

Cheung

Hong

Thong

(2017) Effects of animation on attentional resources of online consumers. Journal of the Association for Information Systems 18(8): 605–632.

10.

Chipangila

Liswaniso

Mawila

, et al. (2021) Improved discoverability of digital objects in institutional repositories using controlled vocabularies. In: 2021 ACM/IEEE joint conference on digital libraries (eds Downie

McKay

Suleman

, et al.), Champaign, September 2021, pp. 100–109. Piscataway, NJ: IEEE.

11.

Chiu

Wang

ETG

(2008) Understanding web-based learning continuance intention: The role of subjective task value. Information Management 45(3): 194–201.

12.

Cho

Chun

Choi

(2020) The paradox of smoking & perceived stress: Do graphic health warnings influence smokers under high stress in adverse ways? Health Communication 35(11): 1368–1375.

13.

Csikszentmihalyi

(1988) The flow experience and its significance for human psychology. In: Csikszentmihalyi

Csikszentmihalyi

(eds) Optimal Experience: Psychological Studies of Flow in Consciousness. Cambridge: Cambridge University Press, pp.15–35.

14.

Davis

(1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly 13(3): 319–340.

15.

Davis

Bagozzi

Warshaw

(1992) Extrinsic and intrinsic motivation to use computers in the workplace1. Journal of Applied Social Psychology 22(14): 1111–1132.

16.

Devarakonda

Guntupally

Thornton

, et al. (2021) FAIR interfaces for geospatial scientific data searches. In: Proceedings of the 1st ACM SIGSPATIAL international workshop on searching and mining large collections of geospatial data (eds Cavallaro

Heras

), Beijing, China, November 2021, pp.1–4. New York: Association for Computing Machinery.

17.

European Commission (2011) Open data an engine for innovation, growth and transparent governance. Available at: https://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=COM:2011:0882:FIN:EN:PDF (accessed 20 July 2021).

18.

Eysenck

Keane

(2020) Cognitive Psychology: A Student’s Handbook. London: Psychology Press.

19.

Fei

Tan

Peng

, et al. (2021) Promoting or attenuating? An eye-tracking study on the role of social cues in e-commerce livestreaming. Decision Support Systems 142: 113466.

20.

Gallarza

Gil-Saura

Holbrook

(2011) The value of value: Further excursions on the meaning and role of customer value. Journal of Consumer Behaviour 10(4): 179–191.

21.

Gebru

Morgenstern

Vecchione

, et al. (2021) Datasheets for datasets. Communications of the ACM 64(12): 86–92.

22.

Gloria

Achyar

(2021) Effects of externalities and flow on perceived usefulness, satisfaction, and loyalty in mobile instant messaging promotion. ASEAN Marketing Journal 8(2): 85–103.

23.

Gregory

(2020) A dataset describing data discovery and reuse practices in research. Scientific Data 7(1): 232.

24.

Gregory

Cousijn

Groth

, et al. (2020a) Understanding data search as a socio-technical practice. Journal of Information Science 46(4): 459–475.

25.

Gregory

Groth

Cousijn

, et al. (2019) Searching data: A review of observational data retrieval practices in selected disciplines. Journal of the Association for Information Science and Technology 70(5): 419–432.

26.

Gregory

Groth

Scharnhorst

, et al. (2020b) Lost or found? Discovering data needed for research. Harvard Data Science Review. Epub ahead of print 2 April 2020. DOI: 10.1162/99608f92.e38165eb

27.

Henderson

Denison

(1989) Stepwise regression in social and psychological research. Psychological Reports 64(1): 251–257.

28.

Hilberink-Schulpen

Nederstigt

van Meurs

, et al. (2016) Does the use of a foreign language influence attention and genre-specific viewing patterns for job advertisements? An eye-tracking study. Information Processing & Management 52(6): 1018–1030.

29.

Hinkin

(1998) A brief tutorial on the development of measures for use in survey questionnaires. Organizational Research Methods 1(1): 104–121.

30.

Holbrook

(1999) Consumer Value: A Framework for Analysis and Research. London: Psychology Press.

31.

Hou

Sarigöllü

(2020) Feelings of satiation as a mediator between a product’s perceived value and replacement intentions. Journal of Cleaner Production 258: 120637.

32.

Hsiao

Chang

Tang

(2016) Exploring the influential factors in continuance usage of mobile social apps: Satisfaction, habit, and customer value perspectives. Telematics and Informatics 33(2): 342–355.

33.

Husin

NNFA

Zakaria

Dahlan

(2019) Factors influencing open data adoption in Malaysia based on users perspective. In: 2019 6th international conference on research and innovation in information systems (ICRIIS). Johor, Malaysia, 2–3 December 2019, pp. 1–5. Piscataway: IEEE.

34.

Janssen

Charalabidis

Zuiderwijk

(2012) Benefits, adoption barriers and myths of open data and open government. Information Systems Management 29(4): 258–268.

35.

Jurisch

Kautz

Wolf

, et al. (2015) An international survey of the factors influencing the intention to use open government. In: 2015 48th Hawaii international conference on system sciences (eds Tung

Sprague

). Kauai, Hawaii, 5–8 January 2015, pp. 2188–2198. Piscataway: IEEE.

36.

Kaasenbrood

Zuiderwijk

Janssen

, et al. (2015) Exploring the factors influencing the adoption of open government data by private organisations. International Journal of Public Administration in the Digital Age 2(2): 75–92.

37.

Kacprzak

Koesten

Ibáñez

, et al. (2017) A query log analysis of dataset search. In: 17th international conference on web engineering (eds Bertino

Gao

Steffen

, et al.). Rome, Italy, 5–8 June 2017, pp. 429–436. Berlin: Springer International Publishing.

38.

Kahn

(2017) Using visual design to improve customer perceptions of online assortments. Journal of Retailing 93(1): 29–42.

39.

Kazancoglu

Demir

(2021) Analysing flow experience on repurchase intention in e-retailing during COVID-19. International Journal of Retail & Distribution Management 49(11): 1571–1593.

40.

Kern

Mathiak

(2015) Research and advanced technology for digital libraries. In: Kapidakis

Mazurek

Werla

(eds) Are There Any Differences in Data Set Retrieval Compared to Well-Known Literature Retrieval? Berlin: Springer International Publishing, pp.197–208.

41.

Khalsa

Cotroneo

(2018) A Survey of Current Practices in Data Search Services. Report to Research data alliance data (RDA) discovery paradigms interest group.

42.

Khatri

Samuel

Dennis

(2018) System 1 and system 2 cognition in the decision to adopt and use a new technology. Information Management 55(6): 709–724.

43.

Khurshid

Zakaria

Arfeen

, et al. (2022) Factors influencing citizens’ intention to use open government data—a case study of Pakistan. Big Data and Cognitive Computing 6(1): 31.

44.

Kim

Hall

(2019) A hedonic motivation model in virtual reality tourism: Comparing visitors and non-visitors. International Journal of Information Management 46: 236–249.

45.

Kim

Yoon

(2017) Scientists' data reuse behaviors: A multilevel analysis. Journal of the Association for Information Science and Technology 68(12): 2709–2719.

46.

Koesten

Gregory

Groth

, et al. (2021) Talking datasets – Understanding data sensemaking behaviours. International Journal of Human-Computer Studies 146: 102562.

47.

Koesten

Kacprzak

Tennison

JFA

, et al. (2017) The trials and tribulations of working with structured data: A study on information seeking behaviour. In: Proceedings of the 2017 CHI conference on human factors in computing systems (eds Mark

Fussell

), Denver, CO, 6–11 May 2017, pp. 1277–1289. New York: Association for Computing Machinery.

48.

Koesten

Singh

(2017) Searching data portals-more complex than we thought? In: CHIIR 2017 workshop on supporting complex search tasks, Oslo, Norway, 11 March 2017, pp. 25–28. CEUR-WS.

49.

Krajbich

Armel

Rangel

(2010) Visual fixations and the computation and comparison of value in simple choice. Nature Neuroscience 13(10): 1292–1298.

50.

Krämer

Papenmeier

Carevic

, et al. (2021) Data-seeking behaviour in the social sciences. International Journal on Digital Libraries 22(2): 175–195.

51.

Lee

Ahn

(2012) Attention to banner ads and their effectiveness: An eye-tracking approach. International Journal of Electronic Commerce 17(1): 119–137.

52.

Léger

Sénecal

Courtemanche

, et al. (2014) Precision is in the eye of the beholder: Application of eye fixation-related potentials to information systems research. Journal of the Association for Information Systems 15(10): 651–678.

53.

Liang

Lin

Hou

(2021) What drives consumers to adopt a sharing platform: An integrated model of value-based and transaction cost theories. Information Management 58(4): 103471.

54.

Liu

Cole

, et al. (2012) Task difficulty and domain knowledge effects on information search behaviors. Proceedings of the American Society for Information Science and Technology 49(1): 1–10.

55.

Liu

(2021) Deconstructing search tasks in interactive information retrieval: A systematic review of task dimensions and predictors. Information Processing & Management 58(3): 102522.

56.

Liu

Zhou

(2017) Empirical study of influencing factors of the users’ intention based on the survey of apple pay users. Journal of Interdisciplinary Mathematics 20(6–7): 1391–1395.

57.

Liu

Liao

Pratt

(2009) Impact of media richness and flow on e-learning technology acceptance. Computers & Education 52(3): 599–607.

58.

Liu

(2011) Exploring the impact of use context on mobile hedonic services adoption: An empirical study on mobile gaming in China. Computers in Human Behavior 27(2): 890–898.

59.

Liu

Chen

Kato

, et al. (2021) Data discovery and reuse in data service practices: A global perspective. Proceedings of the Association for Information Science and Technology 58(1): 610–612.

60.

Liu

(2019) Information resource, interface, and tasks as user interaction components for digital library evaluation. Information Processing & Management 56(3): 704–720.

61.

Löffler

Wesp

König-Ries

, et al. (2021) Dataset search in biodiversity research: Do metadata in data repositories reflect scholarly information needs? PLoS One 16(3): e0246099.

62.

Marc

Beattie

Herasevich

, et al. (2016) Assessing metadata quality of a federally sponsored health data repository. AMIA Annual Symposium Proceedings 2016: 864–873.

63.

Marchionini

(2004) From information retrieval to information interaction. In: 26th European conference on IR research: Advances in information retrieval (eds McDonald

Tait

), Sunderland, 5–7 April 2004, pp. 1–11. Berlin, Heidelberg: Springer.

64.

Maughan

Gutnikov

Stevens

(2007) Like more, look more. Look more, like more: The evidence from eye-tracking. Journal of Brand Management 14(4): 335–342.

65.

Tan

Hei

, et al. (2016) A prediction method based on stepwise regression analysis for train axle temperature. In: 2016 12th International Conference on Computational Intelligence and Security (CIS), Wuxi, China, 16–19 December 2016, pp. 386–390. Piscataway: IEEE.

66.

Megler

Maier

(2012) When big data leads to lost data. In: Proceedings of the 5th Ph.D. workshop on information and knowledge (eds Aparna

Suchanek

), Maui, Hawaii, 2 November 2012, pp. 1–8. New York: Association for Computing Machinery.

67.

Mormann

Russo

(2021) Does attention increase the value of choice alternatives? Trends in Cognitive Sciences 25(4): 305–315.

68.

Neves

de Oliveira

RGG

Leme

LAPP

, et al. (2018) Empirical analysis of ranking models for an adaptable dataset search. In: 15th international conference: The semantic web (eds Gangemi

Navigli

Vidal

,et al.), Heraklion, Crete, Greece, 3–7 June 2018, pp. 50–64. Heidelberg: Springer International Publishing.

69.

Orquin

Mueller Loose

(2013) Attention and choice: A review on eye movements in decision making. Acta Psychologica 144(1): 190–206.

70.

Osakwe

Okeke

Kwarteng

(2021) Trust building in mobile money and its outcomes. European Business Review 34(2): 244–262.

71.

Pasquetto

Borgman

Wofford

(2019) Uses and reuses of scientific data: The data creators’ advantage. Harvard Data Science Review 1(2): 1–32.

72.

Pelet

JÉ

Ettis

Cowart

(2017) Optimal experience of flow enhanced by telepresence: Evidence from social media use. Information Management 54(1): 115–128.

73.

Pieters

Wedel

(2004) Attention capture and transfer in advertising: Brand, pictorial, and text-size effects. Journal of Marketing 68(2): 36–50.

74.

Posner

(2012) Attention in a Social World. New York: Oxford University Press.

75.

Rayner

(1998) Eye movements in reading and information processing: 20 years of research. Psychological Bulletin 124(3): 372–422.

76.

Rzepka

Berger

(2018) User interaction with AI-enabled systems: A systematic review of IS research. In: 39th international conference on information systems, San Francisco, CA, 13–16 December 2018, pp. 1–17. Atlanta, GA: Association for Information Systems.

77.

Saadé

Bahli

(2005) The impact of cognitive absorption on perceived usefulness and perceived ease of use in on-line learning: An extension of the technology acceptance model. Information Management 42(2): 317–327.

78.

Sánchez-Fernández

Iniesta-Bonillo

MÁ

(2007) The concept of perceived value: A systematic review of the research. Marketing Theory 7(4): 427–451.

79.

Sánchez

Callarisa

Rodríguez

, et al. (2006) Perceived value of the purchase of a tourism product. Tourism Management 27(3): 394–409.

80.

Savolainen

(2012) Expectancy-value beliefs and information needs as motivators for task-based information seeking. Journal of Documentation 68(4): 492–511.

81.

Saxena

Janssen

(2017) Examining open government data (OGD) usage in India through UTAUT framework. Foresight 19(4): 421–436.

82.

Sheth

Newman

Gross

(1991) Why we buy what we buy: A theory of consumption values. Journal of Business Research 22(2): 159–170.

83.

Shuja

Alanazi

Alasmary

, et al. (2021) COVID-19 open source data sets: A comprehensive survey. Applied Intelligence 51(3): 1296–1325.

84.

Simon

(1971) Computers, communication, and the Public Interest. In: Greenberger

(ed.) Designing Organizations for an Information-Rich World. Baltimore, MD: Johns Hopkins Press, pp.37–72.

85.

Smith

Krajbich

(2019) Gaze amplifies value in decision making. Psychological Science 30(1): 116–128.

86.

Sweeney

Soutar

(2001) Consumer perceived value: The development of a multiple item scale. Journal of Retailing 77(2): 203–220.

87.

Talukder

Shen

Hossain Talukder

, et al. (2019) Determinants of user acceptance and use of open government data (OGD): An empirical investigation in Bangladesh. Technology and Society 56: 147–156.

88.

Tella

Oyewole

Tella

(2017) An analysis of perceived usefulness of google scholar by the postgraduate students of the university of Ilorin, Nigeria. South African Journal of Information Management 19(1): a793.

89.

Turel

Serenko

Bontis

(2007) User acceptance of wireless short messaging services: Deconstructing perceived value. Information Management 44(1): 63–73.

90.

Tzafilkou

Protogeros

(2017) Diagnosing user perception and acceptance using eye tracking in web-based end-user development. Computers in Human Behavior 72: 23–37.

91.

Ulahannan

Narayanan

Thalhath

, et al. (2020) A citizen science initiative for open data and visualization of COVID-19 outbreak in Kerala, India. Journal of the American Medical Informatics Association 27(12): 1913–1920.

92.

United Nations Educational Scientific and Cultural Organization (2015) UNESCO Science Report: Towards 2030. Report for the United Nations Educational, Scientific and Cultural Organization, 12 July. Paris: UNESCO Publishing.

93.

Venkatesh

Morris

Davis

, et al. (2003) User acceptance of information technology: Toward a unified view. MIS Quarterly 27(3): 425–478.

94.

Venter

Swart

(2018) An integrated model for the continuous use intention of microsoft office simulation software. In: 2018 IEEE global engineering education conference (EDUCON), Santa Cruz de Tenerife, Spain, 17–20 April 2018, pp. 320–329. Piscataway: IEEE.

95.

Vlachopoulou

Ferryman

Zhou

, et al. (2013) A stepwise regression method for forecasting net interchange schedule. In: 2013 IEEE Power & Energy Society General Meeting (PES), Vancouver, Canada, 21–25 July 2013, pp. 1–5. Piscataway: IEEE.

96.

Wang

Lee

(2020) Getting in the flow together: The role of social presence, perceived enjoyment and concentration on sustainable use intention of mobile social network game. Sustainability 12(17): 6853.

97.

Wang

Yeh

Liao

(2013) What drives purchase intention in the context of online content services? The moderating role of ethical self-efficacy for online piracy. International Journal of Information Management 33(1): 199–208.

98.

Webster

Trevino

Ryan

(1993) The dimensionality and correlates of flow in human-computer interactions. Computers in Human Behavior 9(4): 411–426.

99.

Weerakkody

Kapoor

Balta

, et al. (2017) Factors influencing user acceptance of public sector big open data. Production Planning and Control 28(11–12): 891–905.

100.

Wen

PJH

Ebrahimi

, et al. (2021) Key factors affecting user adoption of open-access data repositories in intelligence and security informatics: An affordance perspective. ACM Transactions on Management Information Systems 13(1): 1–24.

101.

Wiegand

García

(2007) A task-based ontology approach to automate geospatial data retrieval. Transactions in GIS 11(3): 355–376.

102.

Psomopoulos

Khalsa

, et al. (2019) Data discovery paradigms: User requirements and recommendations for data repositories. Data Science Journal 18(3): 1–24.

103.

Lee

, et al. (2017) User acceptance of media tablets: An empirical examination of perceived value. Telematics and Informatics 34(4): 206–223.

104.

Zeithaml

(1988) Consumer perceptions of price, quality, and value: A means-end model and synthesis of evidence. Journal of Marketing 52(3): 2–22.

105.

Zhang

Liu

Cole

, et al. (2015) Predicting users’ domain knowledge in information retrieval using multiple regression analysis of search behaviors. Journal of the Association for Information Science and Technology 66(5): 980–1000.

106.

Zhou

(2008) The impact of perceived value on user acceptance of mobile commerce. In: 2008 international symposium on electronic commerce and security, Guangzhou, China, 3–5 August 2008, pp. 237–240. Piscataway: IEEE.

107.

Zhou

(2011) Examining mobile instant messaging user loyalty from the perspectives of network externalities and flow experience. Computers in Human Behavior 27(2): 883–889.

108.

Zuiderwijk

Janssen

Dwivedi

(2015) Acceptance and use predictors of open data technologies: Drawing upon the unified theory of acceptance and use of technology. Government Information Quarterly 32(4): 429–440.