=Paper=
{{Paper
|id=Vol-3341/wm5103
|storemode=property
|title=Towards an Understanding of the Intention of University Members to Use Indoor Positioning Systems: A Unified Theory of Acceptance and Use Perspective
|pdfUrl=https://ceur-ws.org/Vol-3341/WM-LWDA_2022_CRC_5103.pdf
|volume=Vol-3341
|authors=Thomas Paetow,Johannes Wichmann,Hannes Reil,Michael Leyer
|dblpUrl=https://dblp.org/rec/conf/lwa/PaetowWRL22
}}
==Towards an Understanding of the Intention of University Members to Use Indoor Positioning Systems: A Unified Theory of Acceptance and Use Perspective==
https://ceur-ws.org/Vol-3341/WM-LWDA_2022_CRC_5103.pdf
Towards an Understanding of the Intention of
University Members to Use Indoor Positioning
Systems: A Unified Theory of Acceptance and Use
Perspective
Thomas Paetow1 , Johannes Wichmann2 , Hannes Reil3 and Michael Leyer2,4
1
Wismar University of Applied Sciences, Philipp-Müller-Str. 14, 23966 Wismar, Germany
2
Marburg University, Universitätsstraße 25, 35037 Marburg, Germany
3
Rostock University, Ulmenstraße 69, 18057 Rostock, Germany
4
Queensland University of Technology, 4101 Brisbane, Australia
Abstract
Indoor positioning systems (IPS) have become increasingly important in various sectors (e.g., airports,
malls, hospitals). They are relatively new to universities and have not been adequately addressed in
research. We investigate behavioral intentions to use such IPS in universities to close this gap. The
objective is to investigate the intentions of university members to use IPS in universities. The Unified
Theory of Acceptance and Use of Technology 2 (UTAUT2) is used to investigate the intention to use
IPS in universities. For this, we designed use cases and questionnaires to be handed out to university
members. Until now, we derived certain use cases for IPS in universities to investigate university
members’ intention to use this IPS. Those use cases will be investigated using the UTAUT2 questionnaire.
As conclusion, a questionnaire for university members and use cases as well as an IPS user interface
prototype based on those use cases have been developed. In future research steps we will utilize use
cases and questionnaires to investigate our hypotheses and to measure university members’ intentions
and underlying reasons using partial least squares structural equation modeling.
Keywords
IPS, indoor navigation, LBS, location-based services, UTAUT2, Unified Theory of Acceptance and Use of
Technology 2, digital university, human-computer interaction
1. Introduction
While navigation and spatial orientation are difficult for some individuals [1, 2, 3, 4] location-
based services (LBS) and GPS-based systems help them navigate outside [5]. Due to the benefits
of those LBS, indoor positioning systems (IPS) have become quite popular recently, as they
allow companies to e.g. save time and resources [6]. Universities are important to investigate
with regard to IPS as their building structures are typically complex [6] and members typically
LWDA’22: Lernen, Wissen, Daten, Analysen. October 05–07, 2022, Hildesheim, Germany
Envelope-Open thomas.paetow@hs-wismar.de (T. Paetow); johannes.wichmann@wiwi.uni-marburg.de (J. Wichmann);
hannes.reil@uni-rostock.de (H. Reil); michael.leyer@wiwi.uni-marburg.de (M. Leyer)
Orcid 0000-0001-9503-282X (T. Paetow); 0000-0002-9877-1422 (J. Wichmann); 0000-0001-9200-0646 (H. Reil);
0000-0001-9429-7770 (M. Leyer)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
access different buildings with varying patterns. While some studies address IPS in universities
(e.g., [7]), research is short on designing IPS for universities so that university members intend
to use them. Hence, our research question is which behavioral reasons determine the intention
of university members to use indoor positioning systems. In order to adress this question,
this paper adopts the Unified Theory of Acceptance and Use of Technology (UTAUT2). In
applying the theory, we design use cases and a questionnaire as a first conceptual step in this
work in progress paper. This article is structured as follows: section 2 describes the theoretical
background of the research by explaining IPS, presenting related work, introducing UTAUT2,
and creating the structural model for our study. Section 3 describes the methods used, containing
use cases and questionnaires. Finally, in Section 4 we discuss our results and we provide a
conclusion in Section 5 including an outlook on future research steps.
2. Theoretical Background
2.1. Indoor Positioning Systems
An indoor positioning system determines an individual’s or object’s position within buildings
or building complexes [8] using an algorithm that estimates the live position using a mobile
client. For IPS, various technologies are used, such as Wi-Fi, Bluetooth-Low-Energy (BLE),
Radio Frequency Identification (RFID), or ultrasound [9]. In addition, those technologies use
techniques to determine a specific position, such as triangulation or trilateration [10]. For further
information concerning IPS, we recommend the survey of Zafari et al. [6]. For developing
an IPS, designing use cases and prototypes is necessary [6]. One such study was conducted
by Bucheli Fuentes et al. [11], who used the evolutionary development model [12] to build a
prototype to implement a BLE-based IPS that uses fingerprinting techniques and evaluated
its accuracy. Next to the technology, the IPS user interface has to be considered for sufficient
IPS [6]. Several studies investigated IPS user interface design, such as Aoki et al. [13] for
hospitals or, e.g., [14, 15, 16] for universities. However, most of those studies evaluate IPS from
a technological point of view, as Liu et al. [14] and Zafari et al. [6] proposed, leaving behind
other important factors, such as intention to use. Nonetheless, intention to use concerning
IPS was considered in a study conducted by Wichmann & Leyer [17], who used the Reasoned
Action Approach (RAA) according to Fishbein & Ajzen [18]. They investigated IPS in hospitals
and determined underlying reasons for intention to use IPS in hospitals but did not evaluate use
cases or prototypes. Thus, research is necessary that combines both use cases and prototype
evaluation on one side and intention to use on the other [19].
2.2. Indoor Positioning Systems for Universities
Recently, IPS have been the subject of various studies for different application scenarios, such
as airports, libraries, and hospitals [6, 9]. Further, IPS are important for universities, as, e.g.,
Hammadi et al. [20] and Hadwan et al. [16] determined. Hammadi et al. [20] conducted a study
in a university in the United Arab Emirates by designing an IPS for Android smartphones that are
based on Near Field Communication (NFC) and Quick Response (QR) code technologies. They
proposed several functions for IPS in universities, such as: (i) displaying contact information,
(ii) displaying a campus map, (iii) navigating to POIs, such as the nearest restroom, and (iv)
information about available parking lots. Hadwan et al. [16] determined the preferences of
university members towards IPS and evaluated an IPS prototype in a university in Saudi Arabia.
The authors proposed functions for IPS in universities that are: (i) places and services nearby
(e.g., the closest toilet or cafeteria), (ii) staff timetable and office opening hours, (iii) search
function, e.g., search by name, map, places, and services nearby, (iv) navigating to lecture halls,
(v) emergency exits, and (vi) library. Möller et al. [21] also generated and evaluated an IPS user
interface for complex buildings, such as universities, with AR and VR technology to perform
wayfinding. Their qualitative and quantitative experiments found that AR navigation provides
reliable results even with inaccurate positioning. We consider this finding important, so the
wayfinding and IPS should include AR functions. By conducting participatory workshops
with students from a German university, Paetow et al. [22] determined functions for IPS in
universities that are: (i) indoor navigation (e.g., to points of interest, lecture halls, or toilets), (ii)
unique walkways, due to restrictions (e.g., based on the definition of entry and exit points due
to the Corona Pandemic), (iii) a student organization planner (e.g., for book rooms in university
buildings, arrange consultations with lecturers, integrate timetables), (iv) library (e.g., to find
books in the library), (v) a parking lot finder (e.g., find parking lots near the starting points of
indoor navigation). Based on those studies, we designed use cases that will be presented in the
following. To the best of our knowledge, no research exists that investigates IPS in universities
considering the user’s acceptance and usage behavior. Thus, we apply UTAUT2 to use cases to
close this research gap and identify further aspects when implementing IPS in universities.
2.3. The Unified Theory of Acceptance and Use of Technology
In 2003, Venkatesh et al. [23] established the Unified Theory of Acceptance and Use of Technol-
ogy (UTAUT). It is dedicated to determining the intention (and underlying acceptance) to use
new technologies and can be used by different stakeholders, such as managers. It takes several
acceptance theories and models into account, such as Technology Acceptance Model (see [24]),
Social Cognitive Theory (see [13]), and Innovation Diffusion Theory (see [25]).
Performance expectancy1
Effort expectancy2 Behavioral
Use Behavior
Intention
Social Influence3
Facilitating Conditions4 Notes:
1. Moderated by age and gender.
2. Moderated by age, gender and experience.
Hedonic Motivation
3. Moderated by age, gender and experience.
4. Effect on use behavior is moderated by
Price Value age and experience.
Habit Legend:
UTAUT
Age Gender Experience UTAUT2 +
Figure 1: Unified Theory of Acceptance and Use of Technology 2
Further, it helps to explain underlying reasons for the intention towards a specific behavior.
Since then, UTAUT has been frequently used in research [26]. It has been used for, e.g.,
implementing electronic health records in hospitals [27], several applications for food delivery
services [28], or mobile payment applications [29]. In 2012, Venkatesh et al. revised their initial
UTAUT model and proposed UTAUT2 [14]. UTAUT2 is shown in Figure 1.
2.4. UTAUT and Location-Based Services
Next to the aforementioned use cases, location-based services (LBS) were investigated using
UTAUT2 [30]. LBS are increasingly important since the number of mobile applications evaluated
using UTAUT is rising [26]. Ayuning Budi et al. [24] used the UTAUT model to explain the
use of location-based applications in an emergency situation (the intention to use a ”panic
button” in this study) and proved that UTAUT is applicable to the context. They found that
performance expectancy is one of the key drivers when using LBS like a panic button in an
emergency. Further, privacy concerns and trust towards the provider of mobile applications
are important for determining the intention to use LBS [24]. Those findings are supported by
Yun et al. [31], who used UTAUT to investigate the intention to use LBS while considering
privacy concerns. Their results indicate that privacy concerns are important for determining
the intention and use of LBS. However, as they take groups with different privacy concern
levels into consideration, their results show that the effect of performance expectancy on usage
intention was stronger for users with a low level of privacy concerns [31]. Fu & Ai [32] used
UTAUT to investigate the intention of LBS users to use management information systems. They
added perceived risk according to [33] to their UTAUT model and determined that university
users think LBS are risky regarding privacy concerns [32]. Hence, we add perceived risk to our
study to investigate how privacy concerns of university members affect the intention to use
IPS in universities. Further, Fu & Ai [32] showed differences in the influence of the factors of
the UTAUT when looking at different groups of users. By adding perceived risk as one of the
influential factors on behavioral intention, they showed that especially university users see a
high risk in using LBS [32]. Thus, these studies indicate that users of universities see higher risk
in using LBS due to privacy concerns. Privacy concerns can be defined as the control of personal
information and the fear of losing this control of personal information, like the user’s position to
third parties who will use them for their own benefit [34, 31]. Hence, we add privacy concerns
according to [33] to our UTAUT2 model to determine whether privacy concerns influence the
intention to use IPS in universities. Performance expectancy, facilitating conditions, and effort
expectancy seems to be three of the main drivers when it comes to the intentional use of LBS
in tourism contexts [35, 36]. Further, Uphaus et al. [35] showed that hedonic motivation is an
important factor in determining the intention to use LBS in tourism contexts. Those services
include providing information based on the user’s location or beacons (like QR-Codes), which
interact with the user’s smartphone and give them touristic information about their location
[35]. Gupta & Dogra [36] showed that users’ habits are an important factor to consider for the
intention to use LBS. They investigated existing LBS (called ”mapping apps” in their research)
and the intention of tourists to use such services during vacation [36]. Performance expectancy,
facilitating conditions, and effort expectancy seem to be the three main drivers when it comes
to the intentional use of LBS in tourism contexts [35, 36]. Further, Uphaus et al. [35] showed in
their study that hedonic motivation is an important factor in determining the intention to use
location-based applications in tourism contexts. Those services include providing information
based on the location of the user or provided by beacons (like QR-Codes) which interact with the
smartphone of the user and give them (touristic) information on their location [35, 36]. Hence,
those studies show that UTAUT and UTAUT2 are applicable to the context of LBS. However,
we were unable to determine any research concerning UTAUT2 and location-based applications
for universities. Thus, we aim to close this gap by applying UTAUT2 to the context of IPS for
universities.
2.5. Hypotheses & Research Model
According to Venkatesh et al. [30], performance expectancy is the degree to which using
technology will benefit consumers in performing certain activities. Therefore, performance
expectancy is an essential predictor for adoption studies of LBS. Gupta & Dogra [36] determined
that performance expectancy positively influences behavioral intentions towards using LBS.
This is supported by other studies which determine performance expectancy as one of the key
factors influencing the intention to use LBS [24, 35]. Further, Chen & Tsai [25] determined
that information quality, system quality, and perceived usefulness positively influence the
intention to use LBS. According to UTAUT2, which contains Technology Acceptance Model,
those variables (information quality, system quality, perceived usefulness) are also crucial for
ascertaining performance expectancy [30]. Thus, our first hypothesis is:
H1: Performance expectancy positively influences an individual’s intention to use IPS in
universities.
Effort expectancy is the ”degree of ease/effort associated with consumers’ use of the technol-
ogy” [30]. Regarding LBS, Gupta & Dogra [36] state that effort expectancy positively influences
the behavioral intention to use LBS. This proposition is supported by Uphaus et al. [35], who
state that effort expectancy is one of the critical factors influencing usage behavior. Further,
Wichmann & Leyer [17] determined that effort expectancy (called personal innovativeness in
their study) positively influences the intention to use IPS in hospitals. Chen & Tsai [25] also
state that effort expectancy (called perceived ease of use in their study) positively influences the
intention to use LBS. Hence, our second hypothesis is:
H2: Effort expectancy positively influences an individual’s intention to use IPS in universities.
Venkatesh et al. [23] define social influence as the ”degree to which an individual perceives
that important others believe he or she should use the new system.” For IPS, social influence
refers to the opinions of other individuals that are important to the individual in question,
such as friends, family, colleagues, and superiors [9, 36, 18]. Both, Gupta & Dogra [36] as well
as Wichmann [9] & Leyer [17] state that social influence positively influences the behavioral
intention to use LBS [36] as well as IPS in hospitals [17]. Thus, our third hypothesis is:
H3: Social influence positively influences an individual’s intention to use IPS in universities.
Conditions are ”consumers” perceptions of the resources and support available to perform
a behavior [23]. UTAUT2 suggests that an individual’s perception of facilitating conditions
directly influences technology acceptance. They confirmed that such acceptance was either
supported or hindered by the surrounding environment of an individual [30]. For LBS, Gupta
& Dogra [36] determined that facilitating conditions positively influence the behavioral inten-
tion to use LBS as well as their actual use. Hence, our fourth hypothesis is divided into two parts:
H4a: Facilitating conditions positively influence an individual’s intention to use IPS in uni-
versities.
H4b: Facilitating conditions positively influence an individual’s actual use of IPS in universities.
Venkatesh et al. [30] define hedonic motivation as ”the pleasure or enjoyment derived from
using a technology.” For hedonic motivation, Brown & Venkatesh [37] state that it is an essential
predictor of technology adoption and usage. Concerning LBS in a tourism context, Gupta
& Dogra [36], as well as Uphaus et al. [35], determined that hedonic motivation positively
influences an individual’s behavioral intention. Thus, our fifth hypothesis is:
H5. Hedonic motivation positively influences an individual’s intention to use IPS in universi-
ties.
In UTAUT2, Venkatesh et al. [30] state that price value is essential for behavioral intentions.
They do so as consumers are more cost-sensitive than corporate employees are, as the con-
sumers somehow have to pay for the service provided by the technology. If consumers think
the perceived benefits of the technology are higher than the costs, the price value is positive,
which positively influences behavioral intentions [30]. For LBS, Gupta & Dogra [36] state that
price value positively influences behavioral intentions. Hence, our sixth hypothesis is:
H6: Price value positively influences an individual’s intention to use IPS in universities.
According to Venkatesh et al. [30], habit is the outcome of past behavior and experiences.
Past and reoccurring behavior are main antecedents for predicting present actions [38]. For
technology acceptance, several studies have proved that they are mandatory for technology
acceptance (e.g., [15, 39, 40]). For LBS, Gupta & Dogra [36] determined that habit positively
influences behavioral intentions and the actual use of those apps. Thus, our seventh hypothesis
divides into two parts that are:
H7a: Habit positively influences an individual’s intention to use IPS in universities.
H7b: Habit positively influences an individual’s actual use of IPS in universities.
Ayuning Budi et al. [24] state that privacy concerns of users have a negative impact on the
behavioral intention to use LBS. Further, the intention to use is lower for those individuals who
have high privacy concerns in university contexts [6]. Since we apply our IPS in the context
of a university and members of universities or scientific facilities seem to have a high level
of privacy concerns [18], it is assumable that the privacy concerns influence the behavioral
intention of the users. Thus, our eighth hypothesis is:
H8: Privacy concerns of users negatively influence an individual’s intention to use IPS in
universities.
For the intention to use IPS, Wichmann & Leyer [17] state that spatial abilities negatively
influence intention. They determined that the intention to use IPS is lower for those individuals
who are good at navigating themselves through buildings that are large and unknown to them.
Hence, our ninth hypothesis is:
H9: Spatial abilities negatively influence an individual’s intention to use IPS in universities.
Fishbein & Ajzen [18] state that intentions are an individual’s willingness to engage in a
specific behavior. Behavioral intention is often considered the predecessor of behavior, proven
empirically [41]. For technology acceptance, those intentions are mandatory for predicting a
specific behavior [30]. While Gupta & Dogra [36] determined that positive intentions support
the use of LBS, Wichmann & Leyer [17] prove that intention is also important for IPS. Thus,
our tenth hypothesis is:
H10: Behavioral intentions to use IPS in universities positively influence the actual use of IPS
in universities.
Figure 2 summarizes the resulting research model.
Effort expectancy H1+
Performance expectancy H2+
H4b+
Social Influence H3+
Facilitating Conditions H4a+ Behavioral H10+
Use Behavior
Intention
H5+
Hedonic Motivation
H6+
H7b+
Price Value
H7a+
Habit
Privacy Concerns H8-
Spatial Abilities H9-
Figure 2: Research Model
3. Methods
3.1. Use Cases
We generated different use cases for our measures and created IPS user interface prototypes
based on them. The IPS prototype, developed as a mobile application for smartphones, has five
functions: (i) Search (see Figure 3), (ii) Map, (iii) Library. (iv) Parking lot Finder, (v) Planner. We
developed use cases for each function to determine the intention to use the application due to
the respective use case. For each of those functions, our individuals received a use case that
is like the following for the IPS search function: The IPS has a search function. Thus, the IPS
can find (i) individuals, e.g., professors, (ii) rooms, e.g., lecture halls, or (iii) points of interest.
Concerning the latter, the IPS directs to, e.g., printing/copying stations or restrooms. The search
function can be accessed via a button (”Search”) on the main screen. The next screen contains a
search bar. People, rooms, and point-of-interests can be searched. If the searched item is e.g.,
an individual, her/his room (i.e., assigned office) and the building are displayed as additional
information, as well as the distance in meters based on the navigational route. Additional
options are: (i) displaying her/his room using a 2-D map, (ii) navigating to the respective office,
(iii) making a consultation appointment, and (iv) starting a phone call. Based on the selection of
an additional option, different functions are triggered, e.g., AR navigation or map function.
Figure 3: IPS user interface prototype for the search function
3.2. Questionnaire
To test our hypotheses and to determine the intention to use an IPS, we apply the questionnaire
of Venkatesh et al. [30] that we combine with the propositions of Gupta & Dogra [36], Uphaus
et al. [35], and Tamilmani et al. [26]. Further, we add questions of Xu [34] regarding privacy
concerns. Yun et al. [31] showed that these questions are applicable in the context of LBS. For
spatial abilities, we are using questions of Wichmann & Leyer [17]. Our questionnaire is shown
in Table 1.
Table 1: Survey Questions
Dimension Survey Questions
Performance PE1: I think that this IPS will be useful while being at the university; PE2:
Expectancy I think that this IPS helps me reach my destination conveniently; PE3: I
(PE) think that this IPS saves time for me; PE4: I think that this IPS increases my
productivity and helps me find objects
Effort Ex- EE1: I think that learning how to use this IPS will be easy for me; EE2: I think
pectancy that my interaction with this IPS will be clear and understandable; EE3: I
(EE) think that using this IPS will be easy for me; EE4: I think it will be easy for
me to become an expert/skillful when using IPS
Social Influ- SI1: People who are important to me think that I should use IPS while moving
ence (SI) through universities; SI2: People who influence my behavior think that I
should use IPS while moving through universities; SI3: People whose opinions
I value prefer that I use IPS
Facilitating FC1: I think I have the resources necessary to use IPS; FC2: I think I have the
Conditions knowledge to use IPS; FC3: IPS is compatible with other technologies I use;
FC4: I can get help from others when I have difficulties using IPS
Hedonic HM1: I think using this IPS is fun; HM2: I think using this IPS is enjoyable;
Motivation HM3: I think using this IPS is very entertaining
(HM)
Price Value PV1: I think that the cost of using IPS is reasonable; PV2: Using IPS is worth
(PV) the cost; PV3: At the current price, IPS provides a good value
Habit (HT) HT1: When I compare this IPS with LBS that are familiar to me, I think using
this IPS could become a habit for me; HT2: When I compare this IPS with LBS
that are familiar to me, I think I could get addicted to this IPS; HT3: When I
compare this IPS with LBS that are familiar to me, I think I must use it when
I move through universities
Privacy Con- PC1: I am concerned that the university is collecting too much information
cerns (PC) about me; PC2: I am concerned that the university may not take measures to
prevent unauthorized access to my location information; PC3: I am concerned
that the company may keep my location information in an inaccurate manner
in their database; PC4: I am concerned that the university may share my
location information with other parties without obtaining my authorization;
PC5: Overall, I feel unsafe about providing location information on the
university through the use of the IPS
Table 1 – continued from previous page
Dimension Survey Questions
Spatial abili- SA1: I am good in navigating myself through buildings that are large and
ties (SA) unknown to me; SA2: I am good in navigating myself through buildings that
are known to me; SA3: I always find the shortest way through buildings that
are large and unknown to me, while I am navigating myself; SA4: I always
find the shortest way through buildings that are known to me, while I am
navigating myself; SA5: I do not need assistance while navigating myself
through buildings, that are large and unknown to me; SA6: I do not need
assistance while navigating myself through buildings, that are known to me.
Behavioral BI1: I would definitely use such an IPS during my next visit to a university if
Intention it would be available; BI2: I intend to use such an IPS during my next visit to
(BI) a university if it is available; BI3: I plan to use such an IPS during my next
visit to a university if it is available
Use Behav- UB1: How often do you use IPS?
ior (UB)
Since we could not find a study in which the UTAUT2 was applied for an IPS, we will
contribute to the research by applying the UTAUT2 for an indoor navigation service technology.
We do so as we provide an IPS user interface prototype to university members that are based
on current propositions about how to design IPS for universities [20, 16, 22]. Since our IPS is a
user interface prototype, we consider our approach as an early adoption phase, according to
Tamilmani et al. [26]. For IS adoptions in early phases, UTAUT2 is important, according to
Tamilmani et al. [26], which is why we use UTAUT2 for investigating IPS. After the individuals
have received the use cases, different user interfaces of the IPS functions are displayed. On
this basis, the individuals will answer the questionnaire (see Table 1). A total of 32 items were
obtained. To do so, we adapted studies to suit a questionnaire in the context of IPS in universities.
The responses of the questionnaire participants to each of the items were measured with a
7-point Likert scale for each item (from 1 ”do not agree at all” to 7 ”completely agree”). We also
include control questions (i.e., which building structure has the participant’s university that has
visited most frequently in the past 365 days, and demographic data such as age and gender).
4. Discussion
Our objective is to investigate the intentions of university members to use IPS in universities.
For this purpose, we have chosen UTAUT2 as methodology. In our opinion, it is well fitting
because it was developed to evaluate the use of new technology in the consumer market which
has been shown by several researches (e.g., [35, 36, 24]). UTAUT2 was used because, according
to Tamilmani et. al. [19] it is the better model compared to other theory acceptance models
(e.g., Theory of Reasoned Action (TRA), the Technology Acceptance Model (TAM), theory of
planned behavior (TPB)) for the investigation of the intention to use technology. UTAUT, for
example, would have been unsuitable because it is primarily about evaluating the use of a
new technology within an organization. While there is existing research on IPS in complex
environments such as universities (e.g., [20, 16, 21], these research were focused on developing
an IPS not concentrating on the users but on the systems functions. Our research provides a
use case of LBS in a university which is created based on these researches and focuses on the
acceptance and intention to use such an IPS. Therefore, we extend the research in this field by
adding factors that need to be considered in the development of an IPS in order for this system
to be used. Thus, our research contributes different in that we are approaching social science
research that goes beyond just IPS system functions. Fu & Ai [32] showed that especially users
in an academic environment have a high perceived risk when using LBS. This finding makes it
very interesting and difficult to design an IPS for a university since the risk evaluation of using
an IPS has to be considered in the designing process. Therefore, our research aims to find out
which factors outweigh the perceived risk of academic users. By doing so, we extend existing
research in this field by providing a deeper insight in understanding the acceptance and using
behavior of people with a high education.
5. Conclusion
In summary, we designed a questionnaire for university members based on related research
and existing research using UTAUT2 in the area of LBS. Furthermore, we generated use cases
and based on them IPS user interface prototypes. As a theoretical implication, a UTAUT2
questionnaire was adapted and generated for LBS in the context of universities. Our study design
contributes to the literature as a further research base. As noted, related work addresses UTAUT2
and location-based services but not the explicit use case for universities. The questionnaire
further focuses on the key users whose intention to use will be investigated: University members.
As a practical implication, the questionnaire, in combination with the developed use cases and
IPS prototype, can be the basis for practical investigations in universities. The questionnaire
can be used with the developed use cases and the IPS prototype as a basis for practical studies
in universities. The developed user interfaces and use cases can also be the basis for further
developments. Our study is subject to limitations. Since the result is a developed questionnaire,
there are no results. Thus, items are still subject to change. Furthermore, our questionnaire is
addressed exclusively to users, the university members. Developers of the digital service are
not considered. Finally, in future research steps, we will conduct our developed questionnaire
and the use cases and IPS prototype using an online survey. All participants who fill out
the questionnaire must be affiliated with a university, e.g., by being an employee or student.
To verify this, test questions at the beginning and the end of the questionnaire so that the
participant’s self-declared status can be checked for correctness.
Acknowledgments
This research is funded entirely by the EU European Regional Development Fund, project
number TBI-V-1-329-VBW-113, at the Wismar University of Applied Sciences: Technology,
Business and Design.
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