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Customers’ attitudes towards in-store location sharing prompts and its influence on purchase decision making This report was prepared for Australian Retailers Association October 2021 This report was prepared by Dr Di Wang Dr Frank Mathmann 1 Page
Cite this report Wang, D., & Mathmann, F. (2021) Customers’ attitudes towards in-store location sharing prompts and its influence on purchase decision making. Queensland University of Technology, School of Advertising, Marketing and Public Relations. Note This report is not for external publication without approval from the Australian Retailers Association. This report is a deliverable under the terms of the QUT Services Agreement with Australian Retailers Association, which sought to investigate customers’ reactions to in- store location sharing prompts and collect data from Australian customers. Funding This report was funded by Australian Retailers Association Consumer Research Committee Grants. Acknowledgements We would like to acknowledge those members of the Queensland University of Technology team for providing support on this project. Prof. Gary Mortimer – Research guidance on industrial project and report Mr Toyohiko Sugimoto – Research Assistance Dr Marilyn Healy – Faculty Research Ethics / Research Integrity Advisor (FREA /RIA) 2 Page
Executive Summary
In recent years, there has been a growing interest in location data sharing via smartphone by
the retail industry. A smartphone accesses customers’ geographic locations in the store,
tracks their movements, and allows retailers’ mobile applications to offer meaningful,
personalised services which are more valuable, accurate, and useful information for both
customers and retailers (Aguirre, Mahr, Grewal, De Ruyter, & Wetzels, 2015; Riedlinger,
Chapman, & Mitchell, 2019). Currently over 92% of Australians own a smartphone, about 95%
of global firms rely on location-based services (LBMA, 2020), and about 74% of retailers intend
to spend on novel technologies like location data sharing (TotalRetail, 2019).
A successful personalised retail offering improves customers’ loyalty and reduces their
switching intention to a competitor (Martin & Palmatier, 2020), but personal data sharing
with retailers makes customers concerned about the loss of privacy (Martin & Palmatier,
2020). 87% of U.S. customers doubt if retailers are effectively able to manage their customers’
personal data, and about 50% of them do not trust using retailers’ new technologies such as
location data tracking (PwC, 2018). Thus, it is crucial to reduce customers’ privacy concerns if
retailers want their customers to share their location data.
Prior research has looked at customers’ reaction towards location sharing. However, most of
the studies were restricted to online setting (e.g., Aiello et al., 2020; Bidler, Zimmermann,
Schumann, & Widjaja, 2020; Markos, Milne, & Peltier, 2017). Little is known about customers’
attitude towards location sharing request when they are visiting a physical store (Bues,
Steiner, Stafflage, & Krafft, 2017). In addition, research has revealed that customers are more
willing to exchange data with the retailer when they perceive the value of the data exchange
(Pallant, Pallant, Sands, Ferraro, & Afifi, 2022). However, it is still unclear what specific value
are most effective to customers’ willingness to share their location data with the retailers and
whether articulating the specific value will change the attitude towards location sharing
request of those customers who initially rejects sharing their location with the retailer.
This project aims to fill these research gaps and understand general customers’ attitudes
towards a location sharing prompt on the mobile phone when visiting a store (e.g., looking
for a product). The research examines how the prompt (explaining the benefit of data
exchange) will influence purchase decision-making.
There are four research questions.
• RQ1: What is customers’ response towards a location sharing prompt requested by a
retailer’s app?
• RQ2: What are the determining factors influencing customers’ response towards a
location sharing prompt?
• RQ3: How do retailers change the attitudes towards the location sharing request of
customers who initially reject the location sharing request.
• RQ4: Is there any association between the response towards a location sharing
prompt and purchase intention?
3 PageThe outcomes of this research answer the proposed questions. The first and second analyses revealed that highlighting the potential benefits customers would receive from the retailer’s app will increase their willingness to share location data with retailers, especially when customers will receive financial benefits (e.g., personalised product offers, individual discounts, and personalised loyalty points). The third analysis found that 5% of the customers who stated they would "never share their location", changed their minds to "would share location" if they were presented with an in-app benefit being considered 'most important'. The fourth analysis confirmed that customers’ willingness to share location data is positively associated with their attitudes towards the retailer and purchase intentions. The present report documents an online survey investigating customers’ responses toward an in-store location data sharing prompt and starts by providing eight key findings and six actionable recommendations. It next offers a detailed overview of key aims, a following method summary, and then a detailed illustration of key findings and related actionable recommendations along with statistical analyses required. Lastly, it concludes with limitations and future research directions. Key findings and recommendations Finding 1: Without mentioning the benefit of using the retailer’s app, 55.4% of customers would ‘opt in’ by accepting the invitation to share their location with the app. However, 44.6% of customers indicated they would not ‘opt-in’. Finding 2: When mentioning the benefit of using the app, 68.8% of customers chose to turn on the location-based service, with 31.2% of customers advising they would never turn on location-based service. Finding 3: Among the customers who indicated they would not ‘opt-in’ to location sharing initially, 39% of them changed their minds, indicating they would ‘opt-in’, when the retailer mentioned the in-app benefits. Actionable Recommendation A: Retailers should clearly articulate the benefits customers will receive when using the app, which will increase customers’ willingness to share their location. Finding 4: Financial benefits increased intentions to grant location sharing while using the app, and privacy concerns reduced those intentions. Finding 5: Both financial benefits and perceived trust increased intention to always grant location sharing. Actionable Recommendation B: Highlight financial benefits of using the app to customers when requesting location sharing permissions. These financial benefits may include personalised product offers, individual discounts, and personalised loyalty points. 4 Page
Actionable Recommendation C: Convey clear information to customers that the location data will not be misused by the retailer and will not be shared with third party. Clarify to customers that the app strictly follows privacy and security regulations. Actionable Recommendation D: Building trust is the key to encourage customers to “always grant location sharing”. One way to build trust is to communicate to customers that the retailer sets cybersecurity and privacy as the forefront of business strategy and implements robust governance and privacy protection policies. Finding 6: Within six in-app benefits (i.e., product offers, price discount, loyalty points, recommendations, price comparisons, location guidance), price discount was the most important benefit to customers, product offers, and loyalty points were the two follow-ups. Finding 7: When customers were told explicitly that they would get the most important benefit from using the app if they share their location data, 5% of them who initially chose to “never share their location” changed their response to “share their location in using the app”. 6% of them who initially chose to “share their location in using the app” changed their response to “always share their location”. 20% of them who initially chose to “always share their location” changed their response to “share their location in using the app”. Actionable Recommendation E: Highlighting the financial benefits (e.g., price discount, product offer, loyalty points) customers can get if sharing their location will change 5% of “never sharing location” customers’ response to “share their location”. Findings 8: Customers who granted location sharing while using the app had a more positive attitude towards the retailer than those who never granted location sharing. Customers who always granted location sharing had an even more positive attitudes towards the retailer than those who never granted location sharing. A similar pattern was found for purchase intention. Actionable Recommendation F: To increase purchase instore, retailers may consider encouraging customers to share their location with the retailer’s app. This can be done through providing personalised financial incentives such as product discount coupons in the retailer’s app. 5 Page
Table of Contents Executive Summary 3 Key Aims 7 Literature Review 9 Method Summary 10 Key Findings 15 Limitations and future research 22 References 23 Appendices 26 Appendix 1. Ethics approval letter 26 Appendix 2. Scale items 27 6 Page
Key Aims
The rapid technological development brought a new retail era that retailers gather more
customer data than ever before. With data sharing by customers’ technological devices,
retailers obtain a wealth of customer data: search history, decision-making, and geographic
movements. One such type of data, currently receiving attention in practice, is location data
collected by a smartphone app. A smartphone is now owned by over 92% of Australians, and
the usage increased through the COVID 19 pandemic (Deloitte, 2020). With Built-in GPS, Wi-
Fi, and Bluetooth beacons, a smartphone accesses the owners’ geographic locations, tracks
the movements, and allows mobile software applications (apps) to offer customised
geolocation services such as navigation, transportation search, and social connection.
Retailers also utilise location data sharing to offer personalised customer services (Riedlinger
et al., 2019). When a customer visits a large retail store (e.g., Kmart, Target, and Bunnings),
the retailer app communicates with electric devices in the store. If the app recognises that
s/he is looking for a particular product at the specific store section, the retailer sends the
customers discount offers/recommendations matching their preference. For example, a
customer looking for a toaster at the home appliance section receives a discount offer of
kitchen appliances like a kettle.
Retailers are realising the significant value of customers’ location-based data. About 74% of
retailers now intend to spend on novel technologies like emotional tracking or location data
sharing (TotalRetail, 2019). Third-party location data marketers are proliferating. The New
York Times reported that a U.S. firm, inMarket, covers most smartphones’ location data,
tracks 50 million people each month and sells them to retailers (Michael, 2019). Academic
research in retailing also underlines the importance of customers’ data sharing. Previous
research finds that customers expect personalised benefits from retailers (Aguirre et al., 2015;
Rust, 2020), which improves customer loyalty and reduces switching intentions to a
competitor (Martin et al., 2020). Meanwhile, customers have concerns about the loss of
privacy or personal safety by sharing their personal information with retailers in exchange of
customised offerings (Martin & Palmatier, 2020). Prior literature has identified ways to
alleviate such concerns such as trust-building (Grosso, Castaldo, Li, & Larivière, 2020),
personal control (Murphy et al., 2020), and setting limits on data use and sharing (Riedlinger
et al., 2019). However, most studies investigate customers reactions in online settings and
fewer has considered in-store situations (Aiello et al., 2020; Bidler, Zimmermann, Schumann,
& Widjaja, 2020; Markos, Milne, & Peltier, 2017). The knowledge about customers’ general
responses to location data sharing is still scarce (Bues et al., 2017), especially when they are
visiting a store (i.e., in-store location sharing). Furthermore, although abundant research
studies factors associated with personal data sharing, it is still unclear how customers value
the benefits of data exchange with retailers (Pallant et al., 2022). Additionally, customers’
responses to share personal information vary across situations (Grosso et al., 2020; Li, Lin, &
Wang, 2015). For example, we do not know if customers change their initial response to
location data sharing in the situation after they know they will receive the benefit that matters
most to them through data exchange.
Hence, this research aims to fill the research gaps and offer the following insights:
• Customers' general response towards a location sharing prompt requested by a
retailer's app when visiting a store;
7 Page• Determining factors influencing customers' response towards a location sharing
prompt when visiting a store;
• Situations in which customers, in particularly those who initially reject the location
sharing request, would change their response towards an in-store location sharing
prompt;
• Insights about the relationship between the response towards a location sharing
prompt and attitudes towards the retailer and purchase intention
This report is situated within the Australian context. We conducted an online survey of 202
Australian adult smartphone users. The insights guide retailers in how in-store location data
sharing prompts should be requested and in which situations customers are likely to accept
the location data sharing prompt.
8 PageLiterature Review Personal data sharing and customers’ privacy concerns Retailers access various types of customer data (e.g., identification, preference, location- based data) and gathering rich personal data is essential for retailers because it enables them to offer customers personalised retail services and relevant marketing communications (Aguirre et al., 2015; Bradlow, Gangwar, Kopalle, & Voleti, 2017). Effective personalised retail offerings satisfy customers’ individual preferences and improve their experiences (Rust, 2020), enhancing loyalty, reducing switching intention, and encouraging customers to share more personal data (Martin & Palmatier, 2020). While customers expect customised shopping experiences, they are also reluctant to share their personal information to receive personalised offers (Aguirre et al., 2015). Customers feel anxious and uncomfortable when disclosing data due to the potential loss of privacy (Martin & Palmatier, 2020; Thomaz, Salge, Karahanna, & Hulland, 2020) and data breaches (PwC, 2018). This paradox between customers’ desire for a better-personalised service and their unwillingness to share personal information is called the personalisation-privacy paradox (Pallant et al., 2022). This paradox impedes the value creation process by customers and retailers (Aiello et al., 2020; Martin & Palmatier, 2020), and academics have investigated how to ease privacy concerns to make customers more willing to share their personal information. Prior research revealed factors that stimulate customers privacy concerns, making them unwilling to share their personal data, include loss of personal safety (Martin & Palmatier, 2020), uncertainty in data usage (Acquisti, Brandimarte, & Loewenstein, 2015), question sensitivity (Acquisti, John, & Loewenstein, 2012), information sensitivity (Phelps, Nowak, & Ferrell, 2000), risk awareness (Olivero & Lunt, 2004), and perceived vulnerability (Acquisti et al., 2012). Research also offered recommendations how to alleviate these concerns, including perceived warmth (Aiello et al., 2020), perceived control (Pallant et al., 2022), data usage transparency (Mazurek & Małagocka, 2019), trust-building (Martin & Murphy, 2017), perceived benefits (Olivero & Lunt, 2004), firms’ privacy practice (Kim, Barasz, & John, 2019), setting limits on data use and data sharing (Riedlinger et al., 2019), gamification (Bidler, Zimmerman, Schumann, & Widjaja, 2020). Prior works offer some insights, but these tend to focus on positive/negative factors associated with customers’ willingness to share personal information. It is still unknown how customers value the benefit in exchange for sharing their personal data with retailers (Martin & Palmatier, 2020; Pallant et al., 2022). For example, the research has not fully answered how customers react to data sharing prompts when retailers articulate the potential benefits (e.g., discounts or recommendations) customers will receive by sharing their personal data with retailers. Location data sharing and customers’ reactions Location-based data is recently gaining attention. Over 92% of global firms currently rely on location-based services (LBMA, 2020). Over 70% of retailers intend to invest in innovative technologies like location tracking (TotalRetail, 2019). Recent academic research has investigated customers’ reactions to personal data sharing across different phases of the retail journey: prepurchase, purchase, post-purchase phase (Aiello et al., 2020; Martin & Palmatier, 2020). Specifically, Martin & Palmatier (2020) emphasise that location data sharing 9 Page
is crucial in the prepurchase phase, guiding retailers to determine which product solutions should be recommended during the product search. Like other type of personal data exchange, location data sharing also involves the personalisation-privacy paradox between customers’ expectation of personalisation and their concern for data privacy. Location data itself does not have individual identifiers (e.g., name, date of birth). However, it is aggregated with other personally identifying data to detect specific customers and track their habitual behaviours with the real-time location (Riedlinger et al., 2019). The general knowledge offered by previous personal information sharing studies may help avoid/alleviate such customers’ concerns. But current available studies tend to examine customers reactions in the online context, and few of them has explored customers reactions in in-store environments (Aiello et al., 2020; Bidler, Zimmermann, et al., 2020; Markos et al., 2017). Prior research also indicates that customers’ reactions to personal information sharing vary depending upon the situation (Acquisti et al., 2015; Grosso et al., 2020; Li et al., 2015). For instance, customers tend to share their personal information in offline environments (e.g., in-store environment) rather than online settings (e.g., online shopping) (Nguyen, Bin, & Campbell, 2012), in particularly when they observe other customers also share personal information (Acquisti et al. 2015). Aiello et al. (2020) observed that customers are more inclined to provide their personal data at the end of the purchase phase of retail journeys. Thus, research specifically focusing on customers’ reactions to location data sharing is needed as available knowledge could not fully explain customers’ reactions in the in-store location data sharing context. Personal data sharing and individual differences Prior research found that customers’ reactions to share personal information depend upon situational factors but also vary across individual difference factors (Acquisti et al., 2015; Grosso et al., 2020; Li et al., 2015), including age (Li et al., 2015; Murphy et al., 2020), gender (Grosso et al., 2020), self-confidence (Li et al., 2015), country of origin (Markos et al., 2017), readiness to technologies (Pallant et al., 2022), and loyalty levels with the retailer (Grosso et al., 2020). Li et al. (2015) reported that young females with self-confidence are more open to disclose their personal information, even sensitive information. Grosso et al. (2020) found that younger and loyal customers are more likely to provide their data with retailers. More recently, Pallant et al. (2022) proposed a framework for categorising customers into three broad segments: data exchange advocate, data exchange neutral, data exchange protector. Each segment exhibits different levels of concerns on value, risk, vulnerability, transparency perceptions about personal data sharing and show different inclinations to share personal data. Pallant et al. (2022) also mentioned that there is still much room to explore other factors moderating customers’ data sharing intention. Specifically, they recommend testing more individual difference factors related to motivations. One such example can be regulatory focus (Higgins et al, 2001). The present research aims to fulfil these research gaps identified by assessing how customers respond to location sharing prompt requested by a retailer’s app when visiting a store; the determining factors, in terms of both situational and individual difference, influencing 10 Page
customer responses; and whether providing benefit will change customers response towards a location sharing prompt and if so, what kind of benefits are more effective. 11 Page
Method Summary
In October 2021, we recruited 202 Australian adults who currently own or use a smartphone
through Qualtrics, one of the largest online panel companies in the world. The age ranged
from 18 to 81 with an average of 50.70 years with a standard deviation of 17.52 years.
Please see Table 1 for the demographic details of the sample.
Table 1: Demographic characteristics.
Item n %
Gender
Male 91 45
Female 110 54.5
Prefer not to say 1 .5
Education
Less than high school 20 9.9
High school 46 22.8
Some post-secondary 61 30.2
education
Bachelor’s degree 53 26.2
Master’s degree 17 8.4
Doctorate 3 1.5
Other 2 1.0
Family annual income
Below $10,000 6 3.0
$10,001~$50,000 81 40.1
$50,001~$100,000 69 34.2
$100,001~$150,000 24 11.9
Over $150,001 22 10.9
Participants completed a self-administered online questionnaire, which consisted of six
sections: (1) an information page, (2) screening question, (3) general questions; (4) specific
questions; (5) individual difference measure; and (6) demographic questions.
Participants first read the information page and confirmed their willingness to participate,
then they answered screening question as the kind of smartphone they currently own or use.
Only those who did not answer “I do not use smartphone” proceeded with the main survey.
In the general question section, participants answered a three-item scale for attitudes
towards technology (Riedlinger et al., 2019), what kind of retailer’s app installed on their
smartphone, whether they grant location sharing with any retailer’s app (yes or no). In the
specific question section, they read a scenario as below.
12 PageImagine you visit a large brick-and-mortar retailer (e.g., department store, hardware store, supermarket, warehouse store) to buy some products. In the store you find the retailer is currently promoting its new app, which enables customers to discover the store, receive product discount/offers, locate the product, check the price, provide product recommendation, become a loyalty member etc. To download the app, the customers may have an opportunity to receive the following in-app benefits. 1. Product offers 2. Price discount 3. Extra loyalty points 4. Product recommendation 5. Convenience to check/compare prices 6. Product location guidance to reduce searching time of products Participants were then asked to rank the order of the above six potential in-app benefits in terms of importance. They further read that after opening the app they saw a prompt asking them whether to share their location with the app. The three-option included: Never (never turn on location-based services); While using the app (turn on location-based services to automatically share your precise location with the app while using the app); and always (turn on location-based services to transmit your precise location to the app all the time). They chose one option. Following that, participants answered the factors informing their choice to grant (or not grant) access to their location in terms of value-orientated factors, including three-item financial usefulness, three-item convenience, two-item ease of use; risk-oriented factors including three-item privacy concerns, two-item perceived trust, and three-item perceived control. These measures were adapted from Riedlinger et al. (2019) and Pallant et al. (2022). They also answered their attitudes towards the retailer in the scenario (Wang, Martin, & Yao, 2021) and purchase intention (Oliver & Swan, 1989). Next, participants were told that if they share the location with the app, the app will give them the benefit, which is based on the most important one they indicated earlier in the survey. And then, they were asked whether they would change their location access setting and if so, what is the new setting they changed to. Finally, before they answered their demographic information, participants completed the Regulatory Focus Questionnaire (Higgins et al., 2001). To ensure the validity of the scale items used in this survey, we performed reliability analysis and factor analysis for all the key constructs. For the reliability test, the Cronbach’s alpha (for scale with three or more items) or the Spearman-Brown Coefficient (for scale with two items) for all the constructs is above 0.7 except for the three-item perceived control scale, the Cronbach’s alpha of which is 0.4, below the acceptance level. By a close review of the items, the low coefficient may be because of the reverse coded first item. We dropped the first item, and the Spearman-Brown Coefficient of the remaining two items is 0.64. For factor analysis, all the items in each scale are loaded onto a single factor with more than 60% of variance explained. 13 Page
According to Hair, Black, Babin, & Anderson (2018), the minimum acceptable level of
reliability score is 0.6, and the acceptable variance explained in the factor analysis for a
construct to be valid is 60%. So based on this standard, the scales used in this research has
good reliability and validity. Please see Table 2 for the descriptive analysis, reliability score,
and percentage of variance explained in the factor analysis for each scale.
Table 2: Descriptive analysis, reliability, and factor analysis of scale items
Scale Mean SD Reliability % of variance
Attitudes towards technology 4.63 1.19 α = .70 63
Financial usefulness 4.52 1.58 α = .92 86
Convenience 4.73 1.53 α = .92 86
Ease of use 4.41 1.61 r =.93 94
Privacy concern 4.92 1.40 α =.86 78
Perceived trust 4.21 1.57 r =.81 84
Perceived control 4.89 1.30 r =.64 74
Attitudes towards the retailer 4.79 1.50 α =.96 93
Purchase intention 4.89 1.49 α =.95 91
We received the ethics clearance before the formal launch of the survey (QUT Ethics Approval
Number 4612). The scale items and ethics approval letter are in the Appendices.
14 PageKey Findings
Key findings for RQ1:
1. Without mentioning the benefit of using the app, 55.4% (n = 112) of customers indicated
they would grant access to location sharing data with the retailer’s app, 44.6% (n = 90) of
customers indicated they would not.
2. When mentioning the benefit of using the app, 68.8% (n = 139) of customers chose to turn
on the location-based service (61.4% of customers chose to turn on the location-based service
while using the app; 7.4% of customers chose to always turn on the location-based service)
and 31.2% (n = 63) of customers never turned-on location-based service. Figure 1 presents
customers’ responses towards a location sharing prompt.
Figure 1: Customers’ responses towards a location sharing prompt.
7.40%
31.20%
61.40%
Never turn on Turn on while using the app Always turn on
3. Among the customers (n = 90) who did not grant location sharing initially, when they were
advised of the in-app benefits, 39% (n = 35) of them changed their mind to grant location
sharing. While for those who granted location sharing initially, when mentioning the potential
benefit, only 7% (n = 8) of customers changed their mind not to grant location sharing. The
difference is significant (χ2 (2) = 68.69%, p < 0.001).
Actionable recommendation:
• Retailers should highlight the benefit that customers can attain when using the app
to increase the likelihood of granting location sharing.
15 PageKey findings for RQ2:
1. We submitted the data to a multinominal logistic regression, where we modelled the
probability of granting the location sharing as a function of three value factors (i.e., financial
benefit, convenience to locate products, ease of use), three risk factors (i.e., privacy concerns,
app trust, and perceived control), and two individual difference factors (i.e., attitudes towards
the technology and regulatory focus). Overall statistical measures confirmed the adequacy of
the model as it represents a significant improvement in fit over a null model (χ2 (16) = 138.34,
p < .001) and exhibits a sufficiently high interpretation power of McFadden’s pseudo R2 which
is 0.4.
2. Table 3 reports the estimated parameters relative to the baseline of never turn on location-
based services.
2.1 when considering the comparison between grant location sharing while using the app and
never turn on location sharing, financial benefits (b = .80, SE = .29, p < .01) and privacy
concerns (b = -.44, SE = .24, p = .06) are the significant predictors in the model. This indicates
that
• a customer scoring higher on the financial benefit in sharing their location with the
app was more likely to grant location sharing while using the app.
• a customer scoring lower on privacy concern in sharing their location with the app was
more likely to grant location sharing while using the app.
2.2 when considering the comparison between always grant location sharing and never turn
on location sharing, financial benefits (b = 1.82, SE = .61, p < .01) and perceived trust (b = .74,
SE = .38, p = .052) are the significant predictors in the model. This indicates that
• a customer scoring higher on the financial benefit in sharing their location with the
app was more likely to always grant location sharing.
• a customer scoring higher on the perceived trust of the app was more likely to always
grant location sharing.
16 PageTable 3: Parameter estimates of location sharing
Location sharing B Std. Error Sig. (p value)
While using the app vs. never
Financial benefits .80 .29 .01*
Convenience .35 .28 .21
Ease of use .21 .23 .36
Privacy concern -.44 .24 .06^
Perceived trust .27 .21 .20
Perceived control .21 .22 .34
Attitudes towards technology .33 .23 .16
Regulatory focus .15 .26 .58
Always vs. never
Financial benefits 1.82 .61 .01*
Convenience .19 .55 .73
Ease of use .61 .40 .13
Privacy concern -.23 .31 .46
Perceived trust .74 .38 .05^
Perceived control .11 .37 .77
Attitudes towards technology .31 .38 .41
Regulatory focus .09 .45 .83
Note: * p < .05; ^ p < .1
Actionable recommendation:
• Highlighting financial benefits of using the app when requesting location sharing:
these financial benefits include personalised product offers, individual discounts, and
personalised loyalty points.
• Convey clear information to customers that the location data will not be misused by
the retailer and will not be provided to the third party. Make it clear to customers
that the app strictly follows privacy and security regulations.
• Building trust is the key to encourage customers to “always grant location sharing”.
One way to build trust is to communicate to customers that the retailer sets
cybersecurity and privacy as the forefront of business strategy and implements
robust governance and privacy protection policies.
17 PageKey findings for RQ3:
1. We analysed customers’ ranking of six listed in-app benefits they feel important. Results
show that price discount is the most important benefit to customers, product offers, and
loyalty points are the two follow-ups. Comparing price, product recommendation, and
location guidance are the least important benefit they expect from using the app. Figure 2
presents the importance score of benefits.
Figure 2: Importance of in-app benefits
1300
1100
Importance
900
700
500
In-app benefits
2. We counted the number of respondents who changed their initial response of granting
location sharing to the app, after they were told the app would provide the benefit, the most
important one they had selected. In total, 15 respondents (4 initially chose never to share
location; 8 initially chose to share location in using the app; 3 initially chose to always share
location) changed their initial responses. Table 4 presents how the 15 respondents changed
their new response.
The results show that when advising customers, they can get the benefit that is most
important to them,
• 5% of customers who initially chose to “never share their location” changed their
response to “share their location in using the app”.
• 6% of customers who initially chose to “share their location in using the app” changed
their response to “always share their location”.
• 20% of customers who initially chose to “always share their location” changed their
response to “share their location in using the app”.
18 PageTable 4: Change of response
Initial response of location New response of location sharing
sharing Never Using the app Always Delete the app
Never (n = 63) 59 3 0 11
Using the app (n = 124) 1 116 7 0
Always (n = 15) 0 3 12 0
Actionable recommendations:
• Highlighting the price discount customers can get if sharing their location will change
5% of “never sharing location” customers’ response to share their location.
Key findings for RQ4:
1. We ran a one-way between-group analysis of variance (ANOVA) to explore the effect of
self-reported location sharing on attitudes towards the retailer. Results show that there was
a significant difference in attitudes towards the retailer for the three location sharing options
(F(2, 199) = 39.70, p < .001). Simple contrasts show that customers who granted location
sharing while using the app (M = 5.21, SD = 1.10) had a more positive attitude towards the
retailer than those who never granted location sharing (M = 3.66, SD = 1.63, 95% CI = [1.17,
1.94]). Customers who always granted location sharing (M = 6.11, SD = .87) had an even more
positive attitude towards the retailer than those who never granted location sharing (95% CI
= [1.73, 3.18]). The difference between those who always granted location sharing and those
who granted location sharing while using the app is also significant (95% CI = [.21, 1.59]).
Figure 3 presents the attitudes towards the retailers across three groups of customers with
different location sharing options.
1
The 1 single case selected deleting the app in the new response could be due to the fact that his/her initial
response was to delete the app but there was no such option in the initial response question. So, this case
could be ignored.
19 PageFigure 3: Attitudes towards the retailer relative to location sharing options
7
6.11
6
Attitudes towards the retailer
5.21
5
4 3.66
3
2
1
0
Never turn on Turn on while using the app Always turn on
Location sharing options
2. We ran a similar analysis using purchase intention as a dependent variable. Results show
that there was a significant difference in purchase intention for the three location sharing
options (F(2, 199) = 30.77, p < .001). Simple contrast shows that customers who granted
location sharing while using the app (M = 5.29, SD = 1.16) had a higher purchase intention
than those who never granted location sharing (M = 3.85, SD = 1.63, 95% CI = [1.05, 1.85]).
Customers who always granted location sharing (M = 5.96, SD = .97) had an even higher
purchase intention than those who never granted location sharing (95% CI = [1.37, 2.85]).
Despite a positive pattern, the difference between those who always granted location sharing
and those who granted location sharing while using the app, however, is not significant (95%
CI = [-.04, 1.37]). Figure 4 presents the purchase intention in three groups of customers with
different location sharing options.
Figure 4: Purchase intention relative to location sharing options
7
5.96
6
5.29
Purchase intention
5
3.85
4
3
2
1
0
Never turn on Turn on while using the app Always turn on
Location sharing options
20 PageActionable recommendations:
• There is a positive association between location sharing and attitudes towards the
retailers and purchase intention. Customers willing to share their location with the
app tend to have a positive attitude towards the retailer and have higher purchase
intention.
• To increase purchase instore, retailers may consider encouraging customers to share
their location with the retailer’s app. This can be done through providing in-app
personalised financial incentives such as product discount coupons.
21 PageLimitations and future research Our research is not without limitation, and our findings provide several potential avenues for future research. First, our study only considers large retailers, typical large brick-and-mortar retailers, such as Kmart, Target, and Bunnings. Customers’ reactions to personal data sharing are contingent upon situational factors (Grosso et al., 2020; Li et al., 2015). Differences accompanied by store type and size may influence customers’ willingness to share their location data. For example, store-type (e.g., relatively small discount stores, large high-end stores) with different store layouts, the assortment, and the price level affects customer decision-making (Van Horen & Pieters, 2013). Future studies could examine this angle to see whether customers' location data sharing varies with smaller retailers. Second, from the methodological perspective, our analysis based on multinominal logistic regression does not consider the interaction effects of factors (i.e., value factors, risk factors, and individual difference factors). These factors may interact with each other to generate different results. Recently, Pallant et al. (2022) found that different factors (e.g., value, risk, vulnerability, transparency perceptions) with different levels to personal data sharing shape unique customer segments and exhibit different reactions to personal data sharing. So, testing interaction effects in the location data sharing context may be of value for future research. Lastly, the method we chose in this study is a survey, which is appropriate because we are more interested in investigating a natural view of the phenomena with little control (i.e., ecological validity). However, our results only describe associations of location sharing and attitudes/purchase intention. Future research can apply experimental method by randomly assigning customers to conditions to test whether there is a causal relationship between location data sharing and purchase intention. 22 Page
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25 PageAppendices Appendix 1. Ethics approval letter 26 Page
Appendix 2. Scale items
Scale Items (1 = strongly disagree; 7 = strongly agree)
Attitudes I think it is important to keep up with the latest trends in technology.
towards New technology makes life more complicated.
technology I feel that I get more done because of technology.
Sharing my location with the app will enable me to receive the
personalized product offer.
Financial Sharing my location with the app will enable me to receive the
benefits individual discount.
Sharing my location with the app will enable me to receive personalized
loyalty points.
Sharing my location with the app will enable me to use location-based
product recommendation.
Sharing my location with the app will enable me to check and compare
Convenience price of the products with other nearby retailers.
Sharing my location with the app will enable me to locate the product I
am looking for quickly.
Location-based services make retailer’s app more intuitive to use.
Ease of use It will be easier for me to use the app after sharing the location with the
app.
I am concerned that my location data may be misused by the app.
I am concerned about providing location information to this service
Privacy provider because it could be used in a way I did not foresee.
concerns I am concerned the app has not clearly disclosed its privacy and security
regulations.
There is little personal risk to me in disclosing my location information
Perceived trust to this app.
I trust the app to operate with the best interests of its user in mind.
Perceived I can control when and if I choose to share my location data.
control I know how to change my location sharing preferences if I change my
mind.
Items
Attitude 1 = Bad; 7 = Good
towards the 1 = Unfavorable; 7 = favorable
retailer 1 = Negative; 7 = Positive
Purchase 1 = Unlikely; 7 = Likely
Intention 1 = Impossible; 7 = Possible
1 = Improbable; 7 = Probable
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