Abstract
Purpose
Hedonic value is commonly conceded as a determinant of behavioral intentions toward location-based advertising (LBA). However, the careful consideration of a mechanism behind hedonic motivation and its subsequent impact on continuance intention is inadequate. This study aims to explore the formation of hedonic value and its motivation for prolonged usage toward LBA.
Design/methodology/approach
A sample of 486 mobile users was recruited to evaluate the research model using structural equation modeling (SEM).
Findings
Results reveal that perceived utility and promotional offers are the strongest indicators of hedonic value. Moreover, social support and contextual convenience play an essential role in heightening hedonic value. Furthermore, the research lenses attempt to clarify the direct, indirect influences of hedonic value, irritation and perceived credibility on continuance intention.
Practical implications
The findings offer practitioners an understanding of how to improve hedonic value and continuance intention and develop effective LBA strategies in emerging markets.
Originality/value
This study narrows the gap of current literature by formulating a hedonic value-based continuance intention model based on uses and gratifications theory (UGT). Additionally, this work illuminates the insights into hedonic value toward LBA by identifying its motivations, including perceived utility, promotional offers, social support and contextual convenience.
Keywords
Citation
Le, X.C. (2024), "A hedonic value-based consumer continuance intention model toward location-based advertising", Revista de Gestão, Vol. 31 No. 1, pp. 34-49. https://doi.org/10.1108/REGE-08-2021-0165
Publisher
:Emerald Publishing Limited
Copyright © 2023, Xuan Cu Le
License
Published in Revista de Gestão. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
Introduction
The advent of innovative technologies have opened opportunities to communicate with customers (Le & Wang, 2022). Statista (2022) showed that mobile advertising spending is projected to amount to US$413bn by 2024, an increase of US$125bn compared to 2021 levels. Firms are increasingly interested in conveying mobile advertising in an endeavor to attract greater customer base. Location-based advertising LBA is an important part of location-based services (Le, 2022). LBA allows advertisers to deliver advertisements to customers in a specific context based on positioning technology (Lin & Bautista, 2018). Firms should consider the effectiveness of LBA through customer behaviors including acceptance, information seeking, recommendation, and purchase (Le & Wang, 2020). Additionally, continuance intention is alluded as one of the best predictors of actual behavior as it is beneficial in prolonging customer usage (Le & Nguyen, 2021). Otherwise, hedonic value reflects the fulfillment of hedonic expectations (Hirschman & Holbrook, 1982). It is a key indicator of customer value and is a critical precursor of prolonged usage (Le & Nguyen, 2021). Therefore, it is crucial for advertisers to understand how hedonic value determines continuance intention toward LBA.
Hedonic value and behavioral intentions have become an enduringly attention-grabbing topic in academia. Extant studies have focused on investigating how sub-values and hedonic values motivate behavioral intentions in mobile advertising (Lin & Bautista, 2018; Martins, Costa, Oliveira, Gonçalves, & Branco, 2019). Consumers provoke behavioral responses as they wish to experience pleasure rather than complete tasks and obtain perceived utility (Martín-Consuegra, Díaz, Gómez, & Molina, 2018). Consumers elicit emotions when various components of advertisements (e.g. information, images and promotions) arouse the connection between firms and consumers. Emerging technologies can compensate consumers for their insufficient sensory perceptions (Martín-Consuegra et al., 2018). For example, positioning technologies on mobile devices enable them to identify where they are standing and what the real time is. Messages are conveyed to consumers when requests are generated. When messages are suitable, consumers will feel served as individualized peers. Consequently, they reinforce consumers’ closeness to advertisements. In this context, this study will contribute to the understanding of LBA research and especially from a customer perspective by enlightening a holistic mechanism of hedonic value and strengthening current knowledge base about the relationship between hedonic value and continuance intention.
Researchers investigated the relationships between antecedents and hedonic value in mobile advertising, namely location (Bues, Steiner, Stafflage, & Krafft, 2017), incentives (Hongyan & Zhankui, 2017), personalization (Kim & Han, 2014), social support (Erber & Erber, 2000) and informativeness (Liu, Sinkovics, Pezderka, & Haghirian, 2012). To the best of our knowledge, the critical factors, including promotions, perceived utility, personalization and social support strongly influence hedonic value. Furthermore, contextual convenience is an important characteristic of LBA and a predictor of perceived value (Le & Nguyen, 2021) and attitude (Le & Wang, 2022). It leverages emotional responses when consumers view advertisements in real time and in the relevant context (Bues et al., 2017). Hence, we find some solid empirical and theoretical supports for considering them as the drivers of hedonic value in LBA.
In addition to the direct relationship between hedonic value and behavioral intention toward mobile advertising, scholars indicated the significant influence of hedonic value on behavioral intention through perceived value (Le & Nguyen, 2021), flow experience (Martins et al., 2019) and attitude (Hongyan & Zhankui, 2017). Nevertheless, they paid little attention to this relationship through mediating factors, such as irritation and perceived credibility. Hedonic value negatively affects irritation in mobile advertising (Ha, Park, & Lee, 2014) and positively influences perceived credibility in technological systems (Chen & Barnes, 2007). Furthermore, behavioral intentions are influenced by hedonic value (Peng, Yuan, & Ma, 2018), irritation (Richard & Meuli, 2013) and credibility (Lin & Bautista, 2018). In this context, we consider irritation and perceived credibility as mediators between hedonic value and continuance intention because they are the essential characteristics of LBA and the crucial constructs in UGT (Lin & Bautista, 2018). UGT affirmed a close relationship between hedonic value and continuance intention (Gan & Li, 2018). If the relationship between predictors and criterion factors is strong, mediators offer a richer understanding of this relationship (Baron & Kenny, 1987). In the light of this, when the significant association between hedonic value and continuance intention is affirmed by current literature, an introduction of a mediator is better to offer profound insights. Therefore, we consider irritation and perceived credibility as mediators between hedonic value and continuance intention to explore the deeper insights into continued usage. In that case, this study, for the first time in LBA, evidently uncovers that hedonic value performs a vital role in alleviating the annoyance and fostering the credibility, which leads to consumers’ continuance intention.
This study examines (a) what leads to hedonic value and (b) the effects of hedonic value, irritation and perceived credibility on continuance intention. The research questions are as follows:
What are the factors that foster hedonic value?
Do hedonic value reduce irritation and boost perceived credibility, subsequently motivate continuance intention?
To answer these questions, this research applies UGT for identifying factors affecting hedonic value and its motivation for continuance intention. A hedonic value-based continuance intention model is empirically examined. This study’s main contributions are threefold. First, it offers insights into how to devise hedonic value among consumers. Second, the study reveals various weights of hedonic value, irritation and perceived credibility for continuation intention. Third, it identifies irritation and perceived credibility as mediators between hedonic value and continuance intention, while all of preceding studies present a direct influence of the two as per our knowledge. The findings provide advertisers with far-reaching practical implications to understand the importance of hedonic value for prolonged usage.
Theoretical background
The UGT is a communication theory that explicates users’ acceptance of media (Eighmey & McCord, 1998). It is used in interactive advertisements to understand the usage of advertisements for gratifications and a psychological mechanism of behavioral responses (Calder, Malthouse, & Schaedel, 2009). UGT emphasizes an important demand for “strengthening aesthetic, pleasure, and emotional experience” or hedonic value. It reflects the intrinsic experience of enjoyment and entertainment (Calder et al., 2009). Moreover, “information, knowledge, and understanding” serve as the demand-satisfying function in UGT (Aydin, 2017). The salient examples of information are perceived utility and promotional offers. Perceived utility reflects the evaluation of functional benefits and sacrifices (Martín-Consuegra et al., 2018), whereas promotional offers attract consumers with emotions to stimulate behavioral responses (Calder et al., 2009). Meanwhile, personalization enables advertisers to disperse customized advertisements to consumers, which leads to individualized value and personal identity (Calder et al., 2009). Additionally, content with relevant, timely information must be distributed to users; hence, contextual convenience is presented in LBA (Aydin, 2017). Moreover, social interaction is the center of UGT (Le & Wang, 2022). In this study, social support demonstrates a social aspect and depicts a psychological protective mechanism to regulate emotions. On a different note, the two antecedents of UGT are perceived credibility and irritation (Lin & Bautista, 2018). Irritation delineates consumers’ annoyance to advertisements, whilst perceived credibility is the belief in advertisements to be valid with no privacy concern (Ha et al., 2014). These factors are introduced as mediators in our model between hedonic value and continuance intention. Ha et al. (2014) investigated mediators, including entertainment, irritation and informativeness that influence behavioral responses (i.e. purchase and continuous intention) in mobile advertising. In a different study, advertising effectiveness was found through a mechanism of use intention by content-oriented drivers (Lin & Bautista, 2018).
Research model
Promotions allow advertisers to persuade consumers to embrace advertisements (Kim & Han, 2014). Consumers are interested in various promotions, including monetary (e.g. coupons and rebates) and non-monetary (e.g. extra products and gifts) (Le & Wang, 2022). Promotions evoke emotions and increase purchase intention (Le & Wang, 2022). Moreover, promotions leverage consumer responses and offer incentives to consumers who are willing to view advertisements (Kim & Han, 2014). Earlier studies showed that promotional offers improve consumers’ assessment of hedonic value in mobile advertising (Hongyan & Zhankui, 2017) and mobile in-store advertising (Bues et al., 2017). Appota (2018) indicated that Vietnamese consumers express favorable feelings due to freebies and make purchase decisions toward advertised brands. This report noted that individuals show their interests in promotions and pay attention to advertisements for incentive benefits. Hence,
Promotional offers positively impact hedonic value.
Meanwhile, perceived utility reflects the evaluation of functional benefits and sacrifices (Martín-Consuegra et al., 2018). In this study, it contains informativeness and perceived benefit. Informativeness provides valuable information and the differences of products to consumers (Le & Wang, 2022). Martins et al. (2019) asserted that consumers react positively to mobile advertising and not feel annoyed when the advertisements provide relevant information. Thus, consumers increase engagement to the advertisements. Furthermore, perceived benefit is an essential component of perceived utility (Merisavo et al., 2007). It allows consumers to maximize the efficiency and economy of shopping activities (Lin & Bautista, 2018). Le and Wang (2022) revealed a close link between perceived benefit and favorable assessment toward mobile context-aware advertising. Lin and Bautista (2018) stated that perceived utility strongly affects advertising value. Consequently, consumers exhibit a greater sense of hedonic value when they perceive greater convenience, time saving and financial management (Ha et al., 2014). Thus,
Perceived utility positively impacts hedonic value.
Personalization is the dissemination of advertisements to designated individuals based on their personal information (Kim & Han, 2014). Wireless devices and positioning technologies enable advertisers to reach customers via socio-location information (Lin & Bautista, 2018). The advertisers’ preference and context appropriately create personalized advertisements. Therefore, advertisements are delivered to consumers at the right time and in the relevant context with the appropriate content (Martins et al., 2019). When consumers receive customized advertisements, they may feel that the contents pertain to their demands. Mobile advertising easily engages customers’ attentiveness, and hedonic motivations are provided accordingly (Kim & Han, 2014). According to Appota (2018), 82% of Vietnamese respondents disclose personal information for incentives. Customized advertisements increase hedonic value with the help of user information (Ha et al., 2014). Kim and Han (2014) illustrated a significant relationship between personalization and hedonic value in smartphone advertising. This means that customers perceive advertising content to be succinct and entertaining if the advertisements are personalized based on their needs and contexts. Therefore,
Personalization positively impacts hedonic value.
Social support describes reciprocal communication with individuals that give mental encouragement rather than physical and financial help. It incites people’s performance based on acquired knowledge from social relationships. Social support helps people meet information requirement (Le & Wang, 2022), regulate feelings (Erber & Erber, 2000) and spur value perceptions (Le & Nguyen, 2021). Le and Wang (2022) explored that other people’s experience and feedback in a wireless environment deepen the relationship among participants. Consequently, people’s favorable judgment affects emotional responses and intentions to execute actions. Social support makes a pivotal contribution for increasing favorable assessment, adoption, and purchase behaviors by comprehending other individuals’ suggestions and opinions (Le & Wang, 2020). Based on the current literature, we find some empirical and theoretical supports for identifying social support as a predictor of hedonic value. Hence,
Social support positively impacts hedonic value.
Perceived convenience supports individuals in accepting services and helps reach information and use advertisements from a hedonic perspective (Ha et al., 2014). It is considered contextual convenience in LBA, increases information acquirement and stimulates usage and purchase decisions. Contextual convenience reinforces usage effectiveness due to individual-oriented advertisements in the vicinity of consumers. It provides the integration of timeliness when consumers receive advertisements and relevant context in which they easily find to consume products. It becomes significant when individuals’ locations in real time are positioned accurately and advertisements have contextual relevance. Thus, contextual convenience boosts more value for consumers and makes usage experience more enjoyable. Lin and Bautista (2018) found that timely LBA heightens advertising value. Le and Wang (2022) illustrated that location congruity and temporal appropriateness motivate attitude toward LBA. Bues et al. (2017) investigated a significant relationship between location and hedonic value in mobile in-store advertising. Thus,
Contextual convenience positively impacts hedonic value.
Irritation delineates the degree to which advertisements make consumers feel messy and irritating (Ha et al., 2014). In mobile advertising, it provokes displeasure and momentary impatience. Consumers criticize advertisements for being unnecessary because of the usage of offending, insulting or overly manipulative techniques and a large amount of unnecessary information (Martins et al., 2019). Moreover, advertisements generate insufficient, poor, inaccurate content, so they cause the disturbance and hinder consumers’ concentration (Kim & Han, 2014). Lin and Bautista (2018) substantiated that advertisements mitigate irritation because consumers feel that the advertisements are fun, appropriate and relevant. When advertisements match consumer needs for aesthetic enjoyment and sensation release, they curb complaints. Ha et al. (2014) revealed that entertaining advertisements lower irritation. If mobile advertisements are shoddy or not fun, irritation is bound to result. Additionally, irritation negatively impacts behavioral intentions toward LBA (Richard & Meuli, 2013). Irritation significantly influences continuance intention toward mobile advertising (Ha et al., 2014; Le & Nguyen, 2021). Thus, irritation lessens advertising value and its effectiveness (Martins et al., 2019). When customers feel confused about mobile advertising and react negatively to it, and annoyance causing by unwanted advertising content may adversely affect prolonged use toward mobile advertising. The greater irritation in LBA, the less likely customers would express continued usage toward it. Therefore,
Hedonic value negatively impacts irritation.
Irritation negatively impacts continuance intention.
Perceived credibility is the belief in advertisements to be valid with no privacy concern (Ha et al., 2014). It is considered perceived reliability and believability of advertisements. Consumers assess advertising credibility through advertising content, firms’ credibility and creators (Martins et al., 2019). Credibility reinforces advertising value and satisfaction, which results in continuance intention (Hsiao & Chang, 2014). Chen and Barnes (2007) indicated that hedonic value strongly influences perceived credibility in technological systems. Credibility is a motivation underlying advertising value (Kim & Han, 2014). When consumers perceive messages to be credible, they feel captivated (Martins et al., 2019). Consistent with these findings, if consumers’ enjoyment and interests in individualized advertisements are aroused via positioning technologies, they will raise credibility to LBA.
Furthermore, Lin and Bautista (2018) stated that perceived credibility significantly affects consumer responses. Mobile advertising engages consumers’ attention due to perceived credibility (Kim & Han, 2014). Reliable advertisements are essential and individuals adopt behavioral intentions with perceived credible toward mobile advertising. Gong, Liu, Zheng, and Wu (2018) argued that credibility positively drives continuance intention toward social media apps. Hence,
Hedonic value positively impacts perceived credibility.
Perceived credibility positively impacts continuance intention.
Hedonic value reflects the fulfillment of hedonic expectations (Hirschman & Holbrook, 1982). It is motivated by the desire for experiential consumption, fun, pleasure and excitement. In this research, hedonic value provides hedonistic motivation while using LBA. It is viewed as a crucial dimension of emotional responses that determine behavioral responses. Prior studies revealed that it strongly influences purchase intention (Curvelo, Watanabe, & Alfinito, 2019) and continuance intention toward social media (Le, 2021). A significant relationship between hedonic value and usage intention was found in LBA (Richard & Meuli, 2013). For this study, consumers can adopt continued usage toward LBA if they find some activities associated with positioning technologies to be personally enjoyable. Consequently,
Hedonic value positively impacts continuance intention.
Otherwise, this study surmises two mediating factors, including irritation and perceived credibility in the effect of hedonic value on continuance intention. As mentioned above, current literature asserted that hedonic value significantly affects continuance intention in mobile advertising. To offer a stronger understanding of this effect, it is necessary to employ a mediator (Baron & Kenny, 1987). Irritation and perceived credibility are the crucial dimensions of mobile advertising (Le & Wang, 2022). Although prior studies found that the relationship between hedonic value and continuance intention is strengthened by perceived value (Ha et al., 2014; Le & Nguyen, 2021), the introduction of irritation and perceived credibility as the mediators of this relationship remains scarce in LBA. Moreover, hedonic value negatively affects irritation in mobile advertising (Ha et al., 2014) and positively influences perceived credibility in technological systems (Chen & Barnes, 2007). Extant studies demonstrated that behavioral intentions are influenced by hedonic value (Peng et al., 2018), irritation (Richard & Meuli, 2013) and credibility (Lin & Bautista, 2018). We know fairly little about these two mediating factors in the model between hedonic value and continuance intention. Therefore,
Irritation (a) and perceived credibility (b) perform a mediation role in strengthening the relationship between hedonic value and continuance intention.
A research model is presented in Figure 1.
Methodology
Data collection
A survey was conducted to test the research model. Voluntary participants were invited to participate in the survey. Data were collected at a university campus in Hanoi, Vietnam. Students were selected because they are identified as target customers in the market of mobile devices and advertising in Vietnam (Appota, 2018). Moreover, they are ground-breaking users of mobile Internet and have experienced using LBA. With the limited number of LBA users, other participants were employed via Facebook. The questionnaire was uploaded via Google Forms. To ensure participants’ understanding and usage experience, we provided an instance of LBA before asking the participants to answer the survey. It assists them to acquire knowledge and thinking recall. To avoid the over-claim use of the respondents, they were given the flexible time to complete the questionnaire. They had the rights to participate or withdraw at any time within the study.
To test the validity of all items, this study conducted pretest and pilot test. Pretest was applied for establishing the appearance and content validities of the constructs for the items due to the deliberations with experts in the advertising field. The study performed the pretest based on the following method and randomization sequence of the measurement items. Pilot test with 36 respondents was included to evaluate questionnaire suitability. Initial outcomes indicated that coefficient alpha values exceeded 0.7, thus illustrating an appropriate level of content validity.
The responses for all questions were carefully scrutinized. The sample-to-variable ratio suggests a minimum observation-to-variable ratio of 5:1, but ratios of 15:1 or 20:1 are preferred (Hair, Black, Barbin, & Anderson, 2010). This means that although a minimum of five respondents should be considered for each independent variable, 10 to 20 observations per each independent variable are highly recommended. Thus, the acceptable number of respondents must be greater than 45. Though 499 respondents returned the survey, a portion of the respondents whose data were deemed improper was excluded. Improper responses (e.g., the same answers, incomplete responses) were removed from the samples. Consequently, 486 respondents successfully completed the questionnaire, which remained adequate for a further analysis. As illustrated in Table 1, 54.1% were male, 67.1% were in the age range of 21–40 years and 58% were undergraduate, followed by 32.9% for postgraduate and 9.1% for high school students. Moreover, a total of 77.8% have used smartphones for three to 10 years.
Measurement
All constructs were extracted from previous studies and slightly amended to avoid cultural bias and correspond better to the current context (see Appendix). For all items, a five-point Likert scale was anchored, ranging from 1 (“strongly disagree”) to 5 (“strongly agree”).
INT was adapted from Mouakket (2015), and PRO were extracted from Ünal, Erci, and Keser (2011). Meanwhile, ULT was replicated from Calder et al. (2009) and Merisavo et al. (2007). PER was adopted from Zeithaml, Parasuraman, and Malhotra (2000), and SOC was modified from Calder et al. (2009). CON was developed from Ko, Cho, and Roberts (2005), and HED was derived from Ku, Chu, and Tseng (2013). Lastly, IRR was adapted from Varnali, Yilmaz, and Toker (2012), whereas the CRE was offered by Liu et al. (2012).
Results
Common method variance
Common method variance (CMV) may occur as the data were collected from the same subjects in the survey. We applied Harman’s single-factor (HSF) test based on a principal component analysis method to detect CMV. HSF should be below 50.00% (Harman, 1976). The result showed that HSF is 38.49% of the total variance. Thus, there is no significant problem of CMV in this research.
Measurement model
Internal consistency reliability reflects the extent to which all items measure different aspects of the same characteristic. It is assessed using Cronbach’s alpha (CA), with values exceeding 0.7 representing good reliability (Hair et al., 2010) (see Appendix).
To estimate the measurement model’s parameters, we conducted confirmatory factor analysis. Factor loading serves as an indicator of convergent validity. The results indicated that factor loadings exceeded 0.5, ranging from 0.745 to 0.932 (Hair et al., 2010). Composite reliability (CR) and average variance extracted (AVE) were used to assess internal consistency. CR and AVE values exceeded 0.7 and 0.5 respectively, surpassing the convergent validity test (see Appendix).
The results of discriminant validity testing were shown in Table 2 in which the values on the diagonal were the square root of AVE for each construct, and other values were the correlations with other constructs. Discriminant validity was satisfactory when the square root of AVE for each construct was higher than the intersecting value of the construct and all other constructs in the research model (Fornell & Larcker, 1981).
Model fitness
Six indices were used to determine model fitness, namely the ratio between Chi-square and degree of freedom (X2/df), Tucker-Lewis index (TLI), goodness-of-fit index (GFI), comparative fit index (CFI), normed fit index (NFI), and root mean square error of approximation (RMSEA). The recommendations suggest that TLI, GFI, CFI and NFI values exceeding 0.9 present a good fit (Hair et al., 2010). RMSEA must be < 0.08, suggesting goodness-of-fit (Hair et al., 2010). All results met the criteria, illustrating the validity of the measurement model (see Table 3).
Structural model
This study utilized a bootstrapping to examine the hypothesized relationship at a significance level of 0.05. The AMOS path model is estimated using the subsample. This method is continued until a significant number of random subsamples, generally 5000, have been generated (Hair et al., 2010). The standard error values acquired by bootstrapping determine whether or not the coefficient is significant. The results of path coefficients among constructs, levels of significance and explanatory abilities support the research model (see Table 4 and Figure 2). Most of the 10 hypotheses, except for H3, are supported.
First, PRO (β = 0.274, p < 0.001), ULT (β = 0.275, p < 0.001), SOC (β = 0.27, p < 0.001), and CON (β = 0.099, p < 0.05) positively influence HED, thus supporting H1, H2, H4 and H5. The outcomes show that structural model with these constructs explains 55.3% of the variance in HED. However, the relationship between PER (β = 0.078, p > 0.05) and HED is not significant, hence H3 is not supported.
Second, HED (β = −0.536, p < 0.001) negatively influences IRR, and IRR (β = −0.093, p < 0.01) adversely affects INT, thereby supporting H6 and H7. IRR is explained by 28.8% of the variance in the model.
Third, HED (β = 0.561, p < 0.001) positively impacts CRE, thus supporting H8. Moreover, the relationship between CRE (β = 0.174, p < 0.001) and INT is positive, hence supporting H9. CRE is explained by 31.5% of the variance in the model.
Lastly, HED (β = 0.718, p < 0.001) positively influences INT, thereby supporting H10. INT is explained by 77.7% of the variance in the model. Furthermore, this study identifies excellent results when the mediation analysis is conducted. Table 4 indicates that IRR performs a competitive mediation role in the relationship between HED and INT as mediated effects (HED→IRR and IRR→INT) and direct effect (HED→INT) both exist and show contrary direction. The indirect impact of CRE on INT is −0.083 (significant at 0.001). Meanwhile, the result reveals that CRE plays a complementary mediation role in the link between HED and INT as mediated effects (HED→CRE and CRE→INT) and direct (HED→INT) both exist and indicate the similar direction. This indirect impact is 0.155 (significant at 0.001).
Discussion and implications
Theoretical implications
Several theoretical implications are drawn. First, the motivations underlying hedonic value were collectively investigated. Perceived utility exerted the most important predictor of hedonic value. This hints that hedonic values are stimulated by relevant information and advertising benefits. Consumers’ increased pleasant sensations when they recognized LBA to have potential advantages, such as time saving, cost reduction, informativeness, and good purchase experience. Lin and Bautista (2018) affirmed this relationship in mobile advertising. The findings showed the importance of promotional offers for hedonic value, consistent with earlier results (Bues et al., 2017). Consumers are engrossed with the generation of incentives. Hence, this enticement elevates moods, desires to closely connect with advertised products, and reinforces the continuance intention. Otherwise, social support contributed to hedonic value. The result ascertains that aesthetic enjoyment escapes from information acquisition via others’ experiences and opinions. Meanwhile, societal commendation is viewed as a kind of appraisal encouragement, leading to diminishing possible LBA threats. This result follows current literature (Le & Wang, 2022). Moreover, hedonic value was positively affected by contextual convenience, which is in line with past research (Ha et al., 2014). Consumers generally exhibit greater hedonic value as they perceive greater convenience of spatial-temporal offers, which enables them to find vendors close to them and in real time. Furthermore, this study did not support the relationship between personalization and hedonic value. A probable explanation is that consumers perceive LBA to be personalized based on socio-location information, but less personalized than other advertisements. However, advertisements fulfill little requirements for accurate contextual identification because of LBA’s embryonic stage and the immaturity of positioning technologies. Consequently, it abates personalization, and consumers derive less pleasure from LBA.
Second, hedonic value negatively influenced irritation, which corroborates the findings of Ha et al. (2014). When customers perceive LBA as being fun and entertaining, the possibility of being irritated with advertisements diminishes. Therefore, this research advocates that positive emotions and appropriate information reduce annoyance (Lin & Bautista, 2018). Additionally, irritation negatively affected continuance intention, which confirms the arguments of Richard and Meuli (2013). It hints the increase in the distribution of multiple sources of information to users via the positioning technologies. Also, advertising content can be criticized as an insufficient, poor, and inaccurate information source. Hence, irritation precludes customers from prolonging LBA usage.
Third, hedonic value positively influences perceived credibility, which corroborates the result of Martins et al. (2019). Consumers obtain enjoyment when they perceive LBA as a reliable, valid and convincing source of information. Therefore, they would adopt continuance intention. Although earlier works revealed a close relationship between credibility and consumer responses (Le, 2022), the linkage between credibility and continuance intention is rather limited. Moreover, the promulgation of liabilities and principles of mobile advertising in Vietnam bolsters the rigorous enforcement of legislative compliance and improves consumer trust. Therefore, the implementation navigates better continuance intention. Additionally, continuous usage was motivated by hedonic value, which expands the arguments of Le (2021). Hedonic value enables customers to create intrinsic motivation, fun, and entertainment. Thus, consumers experience more interesting features from advertisements and thus continue using advertisements. Finally, this study extended existing literature on continuance intention by suggesting that irritation and perceived credibility mediates the relationship between hedonic value and continuance intention. These results reinforce those of Ha et al. (2014) and Chen and Barnes (2007) regarding the essential roles that irritation and perceived credibility enrich the understanding of this relationship.
Practical implications
This study has some practical implications. Overall, marketers should understand how to boost continuance intention toward LBA. Marketers should gain insights into the formulation of hedonic value. First, marketers leverage advertising dispensation through contextual focus. This characteristic makes LBA more valuable and different from other advertisements due to relevant information about consumer location. Advertisers ensure that the convenience of timing and context motivates emotions before consumers view advertisements. Furthermore, positioning technologies must be available to accurately track users’ location and make advertisements useful in the congruent context and in real time.
Second, this study suggests that an effective way to heighten hedonic value is to focus on social support. Vietnamese users are not familiar with LBA, and they accumulate immature experience and insufficient knowledge about the systematic evaluation of positioning technologies and services at the early adopter phase and in the idiosyncrasies of oriental culture. At this point, they often prioritize beliefs in social links rather than the heuristic process of using advertisements. Marketers make an effort to strengthen the connection with social circles (Garcia, Freire, Santos, & Andrade, 2020). Accordingly, it increases the chance to understand consumer-related issues. Moreover, influencers in video advertisements and clienteles via social media promote consumer beliefs and confidence.
Furthermore, marketers should consider promotional offers and perceived utility as the motivations for hedonic value. They diversify promotions and offer different incentives for products and advertising approaches to different customers. Firms provide appropriate promotions by using positioning technologies and other communication means (e.g., mobile apps and social networks) that resonate with target consumers. Moreover, LBA must be dispensed via various types, namely MMS, mobile websites, and mobile apps, that allow consumers to view content in multimedia formats, including texts, audio and videos. Advertisers strive to deliver personalized advertisements using consumers’ authorized information. Valuable, high-quality and entertaining messages must be provided to arouse hedonic value. Moreover, marketers should improve the friendly interface and integrate different multimedia features and heuristic cues into advertising content. Advertisers are better off avoiding a spray advertising method that causes annoyance and adopt a customized advertising method that raises value perceptions of LBA. Moreover, LBA can be associated with online games to boost entertainment. Along with perceived utility serving as a valuable incentive, hedonic value diminishes irritation and perpetuates the usage. Lastly, the importance of credibility in continuance intention suggests the attempts of firms to heighten trust and diminish irritation by developing privacy guarantee policies, complying with government’s laws of consumer information rights, and letting consumers understand how firms use their personal information. When privacy concerns are high among users, advertisers should distribute pull LBA. Firms consider that consumers can make adjustments of advertising activities (e.g. report content, turn it on/off) in a transparent mechanism. Advertisers create push LBA when they seek target customers and promote new services/products. Firms try to know customers’ avoidance of push LBA and resolve their issues (e.g. privacy and intrusiveness) to boost continuance intention.
Conclusion, limitations and future research
This study contributes to the theoretical background of hedonic value-based continuance intention toward LBA based on UGT. A convenience sampling method was employed to examine the model. This research assists practitioners to formulate a primary foundation for the future benchmark of leveraging hedonic value and continuance intention and improving advertising campaigns in Vietnam.
Some limitations are acknowledged. First, additional hypotheses will be proposed because the work identifies factors underlying hedonic value. The result from the structural model for hedonic value (R2 = 55.3%) indicates that other variables are added to surmise hedonic value. Therefore, this work will provide more empirical significance due to several multidimensional motivational constructs. Second, this study calls for replaceable research contexts or cross-cultural explorations to portray a far-reaching picture of continuance intention. Moreover, the study recruited individuals who used and viewed LBA. Further studies can examine this model in the business-to-business setting as businesses may be a potential segment of mobile advertising markets. Thus, to enhance advertising strategies, future studies conduct the model in this context and compare the investigations.
Figures
Sample profiles
| Measure | Item | Frequency | Percentage |
|---|---|---|---|
| Gender | Female | 223 | 45.9 |
| Male | 263 | 54.1 | |
| Age (years old) | Under 20 | 130 | 26.7 |
| 21−30 | 188 | 38.7 | |
| 31−40 | 138 | 28.4 | |
| 41−50 | 21 | 4.3 | |
| Above 51 | 9 | 1.9 | |
| Education | High school | 44 | 9.1 |
| Undergraduate | 282 | 58 | |
| Postgraduate | 160 | 32.9 | |
| Occupation | Business | 48 | 9.9 |
| Self-employed | 66 | 13.6 | |
| Student | 221 | 45.5 | |
| Administrative staff | 110 | 22.6 | |
| Others | 41 | 8.4 | |
| Smartphone usage experience (years) | 1−under 3 | 16 | 3.3 |
| 3−under 5 | 116 | 23.9 | |
| 5−10 | 262 | 53.9 | |
| Above 10 | 92 | 18.9 |
Source(s): Table by the author
Discriminant validity
| Construct | PRO | ULT | PER | SOC | CON | HED | IRR | CRE | INT |
|---|---|---|---|---|---|---|---|---|---|
| PRO | 0.86 | ||||||||
| ULT | 0.44 | 0.87 | |||||||
| PER | 0.36 | 0.29 | 0.85 | ||||||
| SOC | 0.45 | 0.45 | 0.34 | 0.89 | |||||
| CON | 0.42 | 0.38 | 0.43 | 0.46 | 0.83 | ||||
| HED | 0.53 | 0.52 | 0.35 | 0.52 | 0.43 | 0.86 | |||
| IRR | −0.58 | −0.37 | −0.29 | −0.54 | −0.39 | −0.47 | 0.90 | ||
| CRE | 0.33 | 0.44 | 0.33 | 0.48 | 0.29 | 0.52 | −0.41 | 0.84 | |
| INT | 0.59 | 0.61 | 0.39 | 0.61 | 0.47 | 0.82 | −0.55 | 0.61 | 0.88 |
Note(s): Diagonal terms (in italics) are the square roots of AVEs
Source(s): Table by the author
Model fitness statistics
| Index | Recommended criteria | Structural model |
|---|---|---|
| X2/df | ≤2.00 | 1.62 |
| TLI | ≥0.90 | 0.97 |
| GFI | ≥0.90 | 0.92 |
| CFI | ≥0.90 | 0.98 |
| NFI | ≥0.90 | 0.94 |
| RMSEA | ≤0.08 | 0.04 |
Source(s): Table by the author
Outcomes of hypotheses
| Hypotheses | Relationships | β-values | p-values | Results |
|---|---|---|---|---|
| Direct effects | ||||
| H1 | PRO→HED | 0.27*** | 0.000 | Supported |
| H2 | ULT→HED | 0.28*** | 0.000 | Supported |
| H3 | PER→HED | 0.08 n.s | 0.070 | Not supported |
| H4 | SOC→HED | 0.27*** | 0.000 | Supported |
| H5 | CON→ HED | 0.09* | 0.036 | Supported |
| H6 | HED→IRR | −0.54*** | 0.000 | Supported |
| H7 | IRR→INT | −0.09** | 0.010 | Supported |
| H8 | HED→CRE | 0.56*** | 0.000 | Supported |
| H9 | CRE→INT | 0.17*** | 0.000 | Supported |
| H10 | HED→INT | 0.72*** | 0.000 | Supported |
| Mediation analysis | ||||
| H11a | HED→IRR→INT | −0.083*** | 0.013 | Competitive mediation |
| H11b | HED→CRE→INT | 0.155*** | 0.002 | Complementary mediation |
Note(s): *p < 0.05; **p < 0.01; ***p < 0.001; n.s: not supported
Source(s): Table by the author
Items, AVE, CR and CA
| Constructs | Items | CR | AVE | CA |
|---|---|---|---|---|
| Promotion offers | 0.89 | 0.74 | 0.91 | |
| PRO1 | I am satisfied to receive LBA that offers rewards | |||
| PRO2 | I execute actions to receive LBA that offers rewards | |||
| PRO3 | I respond to LBA to obtain promotions | |||
| Perceived utility | 0.93 | 0.76 | 0.93 | |
| ULT1 | I accumulate good shopping experience using LBA | |||
| ULT2 | LBA provides useful information | |||
| ULT3 | LBA helps manage better my pocket | |||
| ULT4 | LBA helps save time | |||
| Personalization | 0.89 | 0.73 | 0.90 | |
| PER1 | LBA provides me with personalized advertisements to my context | |||
| PER2 | LBA provides me with relevant promotional information to my preferences | |||
| PER3 | LBA provides me with advertisements that I might like | |||
| Social support | 0.94 | 0.79 | 0.92 | |
| SOC1 | A main reason I concern LBA is what I get from others | |||
| SOC2 | I bring up things I have seen on LBA in conversations with others | |||
| SOC3 | My friends who receive LBA and their consumption makes me use LBA and have buying decisions | |||
| SOC4 | LBA gives me something to talk about | |||
| Contextual convenience | 0.86 | 0.68 | 0.84 | |
| CON1 | LBA is easy to use | |||
| CON2 | LBA is convenient to use when it is delivered at a relevant position | |||
| CON3 | LBA is convenient to use when it is delivered at specific time | |||
| Hedonic value | 0.89 | 0.73 | 0.89 | |
| HED1 | LBA is entertaining | |||
| HED2 | LBA is pleasant | |||
| HED3 | LBA is fun | |||
| Irritation | 0.93 | 0.81 | 0.95 | |
| IRR1 | LBA is irritating | |||
| IRR2 | LBA is annoying | |||
| IRR3 | LBA is intrusive | |||
| Perceived credibility | 0.87 | 0.69 | 0.87 | |
| CRE1 | LBA is convincing | |||
| CRE2 | LBA is believable | |||
| CRE3 | LBA is credible | |||
| Continuance intention | 0.91 | 0.77 | 0.92 | |
| INT1 | I will continue using LBA rather than discontinue using it | |||
| INT2 | I will continue using LBA instead of other alternative tools | |||
| INT3 | I will keep using LBA as regularly as I do now | |||
Source(s): Appendix by the author
References
Appota (2018). Vietnam mobile app market report 2018. Available from: https://appota.com/uploads/report/Vietnam_mobile_app_market_Report_2018_EN.pdf [accessed 12 August 2020].
Aydin, G. (2017). A comparative study on attitudes towards SMS advertising and mobile application advertising. International Journal of Mobile Communications, 15(5), 514–536.
Baron, R., & Kenny, D. (1987). The moderator-mediator variable distinction in social psychological research. Journal of Personality and Social Psychology, 51, 1173–1182.
Bues, M., Steiner, M., Stafflage, M., & Krafft, M. (2017). How mobile in-store advertising influences purchase intention: Value drivers and mediating effects from a consumer perspective. Psychology and Marketing, 34(2), 157–174.
Calder, B. J., Malthouse, E. C., & Schaedel, U. (2009). An experimental study of the relationship between online engagement and advertising effectiveness. Journal of Interactive Marketing, 23(4), 321–331.
Chen, Y. H., & Barnes, S. (2007). Initial trust and online buyer behaviour. Industrial Management & Data Systems, 107(1), 21–36.
Curvelo, I. C. G., Watanabe, E. A. d.M., & Alfinito, S. (2019). Purchase intention of organic food under the influence of attributes, consumer trust and perceived value. Revista de Gestão, 26(3), 198–211.
Eighmey, J., & McCord, L. (1998). Adding value in the information age: Uses and gratifications of sites on the world wide web. Journal of Business Research, 41(3), 187–194.
Erber, R., & Erber, M. W. (2000). The self-regulation of moods: Second thoughts on the importance of happiness in everyday life. Psychological Inquiry, 11(3), 142–148.
Fornell, C., & Larcker, D. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.
Gan, C., & Li, H. (2018). Understanding the effects of gratifications on the continuance intention to use WeChat in China: A perspective on uses and gratifications. Computers in Human Behavior, 78, 306–315.
Garcia, J. M., Freire, O. B. D. L., Santos, E. B. A., & Andrade, J. (2020). Factors affecting satisfaction and loyalty to online group buying. Revista de Gestão, 27(3), 211–228.
Gong, X., Liu, Z., Zheng, X., & Wu, T. (2018). Why are experienced users of WeChat likely to continue using the app?. Asia Pacific Journal of Marketing and Logistics, 30(4), 1013–1039.
Ha, Y. W., Park, M. C., & Lee, E. (2014). A framework for mobile SNS advertising effectiveness: User perceptions and behaviour perspective. Behaviour & Information Technology, 33(12), 1333–1346.
Hair, J. F., Black, W. C., Barbin, B. J., & Anderson, R. E. (2010). Multivariate data analysis. New Jersey: Prentice Hall.
Harman, H. H. (1976). Modern factor analysis. Chicago: University of Chicago Press.
Hirschman, E. C., & Holbrook, M. B. (1982). Hedonic consumption: Emerging concepts, methods and propositions. Journal of Marketing, 46(3), 92–101.
Hongyan, L., & Zhankui, C. (2017). Effects of mobile text advertising on consumer purchase intention: A moderated mediation analysis. Frontiers in Psychology, 8, 1–14.
Hsiao, W. H., & Chang, T. S. (2014). Understanding consumers’ continuance intention towards mobile advertising: A theoretical framework and empirical study. Behaviour & Information Technology, 33(7), 730–742.
Kim, Y. J., & Han, J. Y. (2014). Why smartphone advertising attracts customers: A model of web advertising, flow, and personalization. Computers in Human Behavior, 33, 256–269.
Ko, H., Cho, C. H., & Roberts, M. S. (2005). Internet uses and gratifications: A structural equation model of interactive advertising. Journal of Advertising, 34(2), 57–70.
Ku, Y. C., Chu, T. H., & Tseng, C. H. (2013). Gratifications for using CMC technologies: A comparison among SNS, IM, and e-mail. Computers in Human Behavior, 29(1), 226–234.
Le, X. C. (2021). Charting sustained usage toward mobile social media application: The criticality of expected benefits and emotional motivations. Asia Pacific Journal of Marketing and Logistics, 34(3), 576–593.
Le, X. C. (2022). Refining mobile location-based service adoption: The lens of pull effect- and push effect-related motivations. Journal of Asian Business and Economic Studies, ahead-of-print(ahead-of-print). doi:10.1108/JABES-09-2021-0159.
Le, X. C., & Nguyen, T. H. (2021). A framework of location-based advertising effectiveness: Perspectives of perceived value and satisfaction. Asian Journal of Business Research, 11(3), 14–32.
Le, X. C., & Wang, H. (2020). Integrative perceived values influencing consumers’ attitude and behavioral responses toward mobile location-based advertising: An empirical study in Vietnam. Asia Pacific Journal of Marketing and Logistics, 33(1), 275–295.
Le, X. C., & Wang, H. (2022). Context-aware and social integrative-related factors as the precursors of efficient context aware advertising via mobile applications. International Journal of Mobile Communications, 20(3), 332–348.
Lin, T., & Bautista, J. R. (2018). Content-related factors influence perceived value of location-based mobile advertising. Journal of Computer Information Systems, 60(2), 184–193.
Liu, C. L. E., Sinkovics, R. R., Pezderka, N., & Haghirian, P. (2012). Determinants of consumer perceptions toward mobile advertising – a comparison between Japan and Austria. Journal Interact Marketing, 26(1), 21–32.
Martín-Consuegra, D., Díaz, E., Gómez, M., & Molina, A. (2018). Examining consumer luxury brand-related behavior intentions in a social media context: The moderating role of hedonic and utilitarian motivations. Physiology & Behavior, 200, 104–110.
Martins, J., Costa, C., Oliveira, T., Gonçalves, R., & Branco, F. (2019). How smartphone advertising influences consumers' purchase intention. Journal of Business Research, 94, 378–387.
Merisavo, M., Kajalo, S., Karjaluoto, H., Virtanen, V., Salmenkivi, S., Raulas, M., & Leppäniemi, M. (2007). An empirical study of the drivers of consumer acceptance of mobile advertising. Journal of Interactive Advertising, 7(2), 41–50.
Mouakket, S. (2015). Factors influencing continuance intention to use social network sites: The facebook case. Computers in Human Behavior, 53, 102–110.
Peng, W., Yuan, S. P., & Ma, W. J. (2018). Moderating effects of app type on the intention of continued use of mobile apps among college students. International Journal Mobile Communications, 16(6), 715–734.
Richard, J. E., & Meuli, P. G. (2013). Exploring and modelling digital natives' intention to use permission-based location-aware mobile advertising. Journal of Marketing Management, 29(5-6), 698–719.
Statista (2022). Mobile advertising spending worldwide from 2007 to 2024. Available from: https://www.statista.com/statistics/303817/mobile-internet-advertising-revenue-worldwide/[accessed 01 April 2022].
Ünal, S., Erci, A., & Keser, E. (2011). Attitudes towards mobile advertising−a research to determine the differences between the attitudes of youth and adults. Procedia Social and Behavioral Sciences, 24, 361–377.
Varnali, K., Yilmaz, C., & Toker, A. (2012). Predictors of attitudinal and behavioral outcomes in mobile advertising: A field experiment. Electronic Commerce Research and Applications, 11(6), 570–581.
Zeithaml, V., Parasuraman, A. P., & Malhotra, A. (2000). A conceptual framework for understanding e-service quality: Implications for future research and managerial practice. Journal of Marketing, 49, 41–50.
Corresponding author
About the author
Xuan Cu Le is a PhD in Department of Economic Information System and E-commerce at Thuongmai University, Vietnam. His research areas encompass consumer behaviors and technology innovation in emerging markets. His works have been published in academic outlets, such as Asia Pacific Journal of Marketing and Logistics, International Journal of Emerging Markets, International Journal of Mobile Communications, Library Hi-tech, Asia-Pacific Journal of Business Administration, VINE Journal of Information and Knowledge Management Systems, Global Knowledge, Memory and Communication, Journal of Asian Business and Economic Studies, International Journal of Internet Marketing and Advertising, Asian Journal of Business Research, Organizations and Markets in Emerging Economies, and Indian Journal of Finance.