Users' mental models of cross-device search under controlled and autonomous motivations
| DOI | https://doi.org/10.1108/AJIM-02-2022-0057 |
| Published date | 31 May 2022 |
| Date | 31 May 2022 |
| Pages | 68-89 |
| Subject Matter | Library & information science,Information behaviour & retrieval,Information & knowledge management,Information management & governance,Information management |
| Author | Jing Dong,Ruoyang Duan,Shaobo Liang |
Users’mental models
of cross-device search under
controlled and
autonomous motivations
Jing Dong and Ruoyang Duan
School of Information Management, Central China Normal University,
Wuhan, China, and
Shaobo Liang
School of Information Management, Wuhan University, Wuhan, China
Abstract
Purpose –Existing literature has elicited the external behaviors of cross-device search but not much is known
about users’cognition of cross-device search. The study aims to explore how users perceive the cross-device
search by combining with the mental models and how motivations affect the users’understanding of cross-
device search.
Design/methodology/approach –The research questions are addressed through semi-structured face-to-
face interviews with 59 users. Prior to the interview, the user is asked to complete a cross-device search task
designed with the simulation of controlled and autonomous motivations to gain a real experience. The concepts
of mental models are coded according to the constructivist grounded theory method.
Findings –The study finds the users’mental models of cross-device search consist of four dimensions:
Element, Quality, Function and Issue. The effect of motivation on the mental models is tested as significant in
terms of the Quality and Function aspects. The controlled motivation affects the user’s perception of how the
device switch influence the search and the autonomous motivation influences the user’s opinion of search
system functions.
Originality/value –The contribution of this study is found to extend the existing knowledge of cross-device
search and update the mental models of information search in the current multi-device environment. The
findings inform the future study of cross-device search and practices of search system design.
Keywords Cross-device search, Mental models, Controlled motivation, Autonomous motivation, Self-
determination theory, Grounded theory
Paper type Research paper
1. Introduction
With the continuous popularization of intelligent search devices, we have entered a multi-
device world. In China, the Internet users’proportion of using desktop computers, laptops,
and tablets is 34.6, 30.8, and 24.9%, respectively (CNNIC, 2021). In such a multi-device
environment, when performing the same search task, users adopt another device to continue
the search behavior, which is called a cross-device search. The cross-device search spawned
by the multi-device environment has gradually become popular, and it has received extensive
attention from scholars. Related researches focus on developing techniques to help the
effective search in the multi-device world (Santos et al., 2018;Zagermann et al., 2020).
However, they often ignore the observation of the users. Due to the lack of awareness, users in
reality often do not realize they are performing a cross-device search. For example, after using
the mobile phone to search, the user chose the computer to continue. In this case, it is possible
for the searcher in mind to feel he/she just searched by different devices, not aware that is a
AJIM
75,1
68
Funding: The research is supported by the Fundamental Research Funds for the Central Universities
(CCNU22XJ027).
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/2050-3806.htm
Received 1 February 2022
Revised 10 May 2022
Accepted 12 May 2022
Aslib Journal of Information
Management
Vol. 75 No. 1, 2023
pp. 68-89
© Emerald Publishing Limited
2050-3806
DOI 10.1108/AJIM-02-2022-0057
cross-device search. This observation wakes up the necessary study to deeply understand the
user’s cognition of cross-device search.
The mental model is defined as “the same structure as the perceived system, and the
relatively persistent, limited and internal conceptual representation of the external system.”
(Doyle and Ford, 1998) Mental models mirror an individual’s psychological situation and
action patterns. In other words, the mental models can well reflect the cognition of the
searcher, being a part of the search behavior. Among studies of search behavior, the
investigation of mental models can be seen, like Thomas et al. (2019) studied searchers’mental
models of result selection and ranking. In this sense, we study the cognition of cross-device
search by observing the users’mental models.
To obtain a concrete understanding from users, we introduce the motivation to provide
the specific context of cross-device search. Self-determination theory (SDT) is a motivational
process theory proposed by Ryan and Deci (2000), which has been widely applied in studying
information behaviors (Chiu, 2021;Savolainen, 2018). We adopt the SDT’s framework of
motivation classification: autonomous motivation and controlled motivation to simulate the
cross-device search of different motivations. Based on the experience of the simulation, we
investigate the users’ideas about cross-device search under different motivations and
establish the mental models. The study provides novel knowledge of cross-device search
from the user’s perspective and implications of how the search system can facilitate cross-
device search activity.
2. Literature review
2.1 Search behavior in cross-device interaction
Cross-device interaction is a form of practice in the multi-device environment. Geronimo et al.
(2016) investigated the scenarios of cross-device interaction in which users are most
interested were when watching YouTube videos and planning a trip. Another research
(Zagermann et al., 2020) found that the impact of different interaction technologies is smaller
than expected, which means work behavior and equipment use depend on the task at hand
rather than the interaction technologies. In addition, in the process of cross-device, users will
encounter some difficulties, a survey identified the challenges of switching between different
devices (Alvina et al., 2020). To support cross-device interaction, researchers have designed
systems and frameworks, like the YanuX framework (Santos et al., 2018) and the FLUID
platform (Oh et al., 2019). Taking properties of cross-device systems and fixed and semi-fixed
features of the environment into consideration, Grønbæk et al. (2020) adopted the method
based on proximity to design cross-device interaction. Recently, the cross-device interaction
is extended by connecting with AR/VR devices. For example, Reichherzer et al. (2021)
designed the SecondSight framework focusing on cross-device AR systems by combining
smartphone display and input with AR HMD viewing. In the design of cross-device
interaction, two key issues are summarized: information transmission (Sohn et al., 2010a,b)
and user matching (Kim et al., 2017;Tanielian et al., 2018).
Cross-device search behavior is becoming more co mmon, with more researchers
conducting relevant research on this topic. Han et al. (2017) used the hidden Markov model
to model cross-device search behavior and found that the main behaviors in the later stage of
the search process are: querying, exploration, and exploration. Unlike the other studies that
pay attention to cross-device web search, Wu et al. (2018) identified query reformulation (QR)
patterns during device transitions in OPAC users’cross-device behavior. Gomes and Hoeber
(2021) were interested in the cross-device search behavior in the digital library search,
introducing an interface supporting cross-session and cross-device search on the academic
digital library, which aims to solve a key challenge, namely, restoring the previously started
search task.
Users
perceiving the
cross-device
search
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