Affective memories and perceived value: motivators and inhibitors of the data search-access process
| Date | 15 March 2023 |
| Pages | 1236-1264 |
| DOI | https://doi.org/10.1108/JD-06-2022-0129 |
| Published date | 15 March 2023 |
| Subject Matter | Library & information science,Records management & preservation,Document management,Classification & cataloguing,Information behaviour & retrieval,Collection building & management,Scholarly communications/publishing,Information & knowledge management,Information management & governance,Information management,Information & communications technology,Internet |
| Author | Qiao Li,Chunfeng Liu,Jingrui Hou,Ping Wang |
Affective memories and perceived
value: motivators and inhibitors
of the data search-access process
Qiao Li
Department of Information Resources Management,
Nankai University Business School, Tianjin, China
Chunfeng Liu
Wuhan University School of Information Management, Wuhan, China
Jingrui Hou
Department of Computer Science, Loughborough University,
Loughborough, UK, and
Ping Wang
Wuhan University Centre for Studies of Information Resources, Wuhan, China and
Wuhan University School of Information Management, Wuhan, China
Abstract
Purpose –As an emerging tool for data discovery, data retrieval systems fail to effectively support users’
cognitive processes duringdata search and access. To uncover the relationship between data search and access
and the cognitive mechanisms underlying this relationship, this paper examines the associations between
affective memories, perceived value, search effort and the intention to access data during users’interactions
with data retrieval systems.
Design/methodology/approach –This study conducted a user experiment for which 48 doctoral students
fromdifferent disciplineswere recruited.The authors collectedsearch logs, screen recordings,questionnairesand
eyemovement data during the interactivedata search. Multiplelinear regressionwas used to test the hypotheses.
Findings –The results indicate that positive affective memories positively affect perceived value, while the
effects of negative affective memories on perceived value are nonsignificant. Utility value positively affects
search effort, while attainment value negatively affects search effort. Moreover, search effort partially
positively affects the intention to access data, and it serves a full mediating role in the effects of utility value and
attainment value on the intention to access data.
Originality/value –Through the comparison between the findings of this study and relevant findings in
information search studies, this paper reveals the specificity of behaviour and cognitive processes during data
search and access and the special characteristics of data discovery tasks. It sheds light on the inhibiting effect
of attainment value and the motivating effect of utility value on data search and the intention to access data.
Moreover, this paper provides new insights into the role of memory bias in the relationships between affective
memories and data searchers’perceived value.
Keywords Data search, Open data discovery, Open data access, Affective memory, Perceived value,
Search effort
Paper type Research paper
1. Introduction
Open data are available data that can be freely used and accessed (National Science
Foundation, 2018). Open data reuse is the key to realizing the value of open data. It allows
researchers to conduct research based on novel data combinations, thus facilitating new
research findings (Kim, 2021) and improving the efficiency of research (Kim and Yoon, 2017).
Open data discovery is a prerequisite for data reuse (Koesten et al., 2017). It covers key stages,
JD
79,5
1236
This work was supported by the National Natural Science Foundation of China [No. 72074171].
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/0022-0418.htm
Received 15 June 2022
Revised 20 November 2022
11 January 2023
Accepted 14 January 2023
Journal of Documentation
Vol. 79 No. 5, 2023
pp. 1236-1264
© Emerald Publishing Limited
0022-0418
DOI 10.1108/JD-06-2022-0129
including data search, evaluation and access (Gregory et al., 2020b), which involve complex
and iterative cognitive processes (Koesten et al., 2017).
The data retrieval system serves as an emerging technical tool for users to discover
relevant data. Many datasets and their descriptions are in the “long tail”of the web and the
“deep web”, and therefore, they cannot be discovered effectively by general information
retrieval (IR) systems (Brickley et al., 2019). As a specialization of information retrieval
systems (Kunze and Auer, 2013), the data retrieval system retrieves data according to user
queries (Khalsa et al., 2018). It returns a list of relevant datasets, resource links and data
descriptions sorted by certain rules (Kacprzak et al., 2019). In addition, data retrieval systems
limit the types of search results to data, which allows searchers to use search strategies
similar to those used in general web search engines to search data (Koesten et al., 2017).
However,finding relevant data is particularlychallenging,and the main reasons include the
lack of effective tools and reliable access to reusable data (Koesten et al., 2017). The data
retrieval system fails to effectively facilitate open data search, evaluation and access and
cognitive processes during these stages. Specifically, keyword search is mainly based on
metadata (L€
offler et al.,2021); however, the completeness, accuracy and consistency of
metadata in current open data practice were found to be relatively low (Marc et al., 2016).
Keywordsearch lacks the abilityto understand the searcher’sintent and the context of datasets
(Jiang et al.,2019). Therefore, it has difficulties in supportingusers in finding the requireddata.
In addition, metadataand data documents have difficulty providing sufficient knowledge for
data searchers to evaluate data (Pasquetto et al., 2019). Users devote considerable cognitive
effort to understanding metadata (Bugajeand Chowdhury, 2017). Moreover, unavailable data
are one of the greatest challenges for datasearchers (Gregory et al., 2020a).It has been found
that among 4,384 open datasets linked to Horizon 2020 projects, 63.9% of them lacked valid
URLs, hampering data access (European Commission, Directorate-General for Research and
Innovation,2021). Users usually findit difficult to browse datasetsand data documents online,
so they download data without fully understanding the content or usefulness of the data
(Bugaje and Chowdhury,2017). As a result, it is challenging for usersto access relevant data.
Understanding the cognitive mechanisms underlying the processes of data search,
evaluation and access is essential to solve the above problems. However, current data
retrieval research mainly focuses on technological exploration and attention to users is
relatively insufficient (Lu et al., 2012). There is a lack of the theory of the data searcher’s
cognition. Information search theories provide a solid theoretical foundation for
understanding users’cognition during the information search process. They are potential
reference theories for data search studies.
There are both connections and differences between information seeking and data search.
Regarding connections, according to an international survey about data discovery and reuse,
only 16.70% of researchers are aware of the differences between literature search and data
search (Gregory, 2020). Kr€
amer et al. (2021) observed the data search behaviour of social
scientists and found that the data search process of these researchers is very similar to their
literature search process. They also suggested that some findings in the information search
might be relevant or transferable to the data search (Kr€
amer et al., 2021). Given these
connections, Gregory et al. (2019) developed a framework for data search behaviours
according to established models of interactive IR. Regarding differences, IR systems were
found as long ago as the 1960s and have been widely studied; however, data retrieval is a
nascent field (Gregory et al., 2019). There are differences between the technology of data
retrieval and general IR, so data retrieval requires different tools and interaction models
(Koesten et al., 2017). Additionally, the process of searching and selecting datasets is more
complex and time-consuming than literature retrieval (Kern and Mathiak, 2015). Given the
connections between data search and information search, data search studies can draw upon
information seeking theories to develop research hypotheses. Given the differences between
Inhibitors of
the data
search-access
process
1237
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