Starring in your own Snapchat advertisement: Influence of self-brand congruity,self-referencing and perceived humor on brand attitude and purchase intention of advertised brands

Snapchat geofilter advertisements use augmented reality (AR) technology to place consumers in photographs embellished with branded content. This study examined the joint influence of self-brand congruity, self-referencing and perceived humor in these self-endorsed geofilter brand advertisements on consumers’ brand-related preferences. Results revealed that self-brand congruity, self-referencing and perceived humor exerted significant main effects on consumers’ post-use brand attitude and purchase intention. Self-brand congruity also significantly interacted with self-referencing and perceived humor to affect brand attitude and purchase intention, while self-referencing significantly interacted with perceived humor to affect purchase intention, but not brand attitude. Theoretical and managerial implications of the research are discussed.     

Immersive interactive technologies and virtual shopping experiences: Differences in consumer perceptions between augmented reality (AR) and virtual reality (VR)

Based on the concepts of the Reality-Virtuality (RV) continuum and the stimulus-organism-response (SOR) framework, this study investigated differences between AR and VR in their effects on vividness/interactivity, a sense of presence, users’ sensory brand app experience, attitude, and behavioral intention. For AR, participants downloaded an app to mobile phones. For VR, participants downloaded a VR app to smartphones attached to a Google Cardboard VR headset. Vividness and interactivity directly (or indirectly) impacted a sense of presence, sensory brand app experience, attitude towards technology, and behavioral intention. However, AR and VR differed in how vividness and interactivity influenced sensory brand app experience and attitude towards technology. Overall, a sense of presence was a significant mediator for 1) the relationship between vividness and sensory brand app experience and 2) the relationship between interactivity and attitude towards technology for VR but no such relationship was observed with AR. The relationships among sensory brand app experience, attitude, and behavioral intention were significant in both AR and VR settings. Employing real AR/VR shopping situations, the study provides for practitioners deep understandings of consumers’ actual perceptions of applications of AR/VR and offers practical insights as to the efficiency of AR and VR in improving consumer virtual shopping experiences.     

Identifying relevant segments of AI applications adopters – Expanding the UTAUT2’s variables

Artificial intelligence (AI) is a future-defining technology, and AI applications are becoming mainstream in the developed world. Many consumers are adopting and using AI-based apps, devices, and services in their everyday lives. However, research examining consumer behavior in using AI apps is scant. We examine critical factors in AI app adoption by extending and validating a well-established unified theory of adoption and use of technology, UTAUT2. We also explore the possibility of unobserved heterogeneity in consumers’ behavior, including potentially relevant segments of AI app adopters. To augment the knowledge of end users’ engagement and relevant segments, we have added two new antecedent variables into UTAUT2: technology fear and consumer trust. Prediction-orientated segmentation was used on 740 valid responses collected using a pre-tested survey instrument. The results show five segments with different behaviors that were influenced by the variables of the proposed model. Once known, the profiles were used to propose apps to AI developers to improve consumer engagement. The moderating effects of the added variables—technology fear and consumer trust—are also shown. Finally, we discuss the theoretical and managerial implications of our findings and propose priorities for future research.     

Download or swipe left: The role of complexity,future-oriented emotions and feature overload

With the intense competition in the mobile applications (apps) market, it is imperative for app providers to understand how visual stimuli from their apps can create a positive first impression and enhance the app download rates. In the present study, the mechanism through which visual complexity influences mobile app download intention is examined. Using a combination of convenience and snowball sampling, 218 participants were recruited to take part in a single-group post-test only quasi-experimental design. The findings of the study showed that visual complexity influences mobile app download intentions through the mediating role of two future-oriented emotions (i.e. hope and anticipated regret). Additionally, the study showed that the indirect effect of visual complexity on download intentions was moderated by feature overload with the importance of visual complexity significantly reducing as perceptions of feature overload increases. The proposed model explained 46% variance in the intentions to download a mobile app. The findings not only provide practical insights for mobile app developers and publishers but also theoretical insights on consumer decision making in the pre-use context of mobile apps.     

The role of brand community identification and reward on consumer brand engagement and brand loyalty in virtual brand communities

With the advent of interactive Web 2.0 (and beyond) technologies, the role of consumer brand engagement (CBE), which focuses on the consumer’s investment in their brand interactions, is of rapidly growing importance. Despite growing recognition of CBE’s importance in virtual brand communities, empirically-derived insight into its drivers, dynamics, and outcomes remains limited, as investigated in this study. Responding to this gap, we explore the effect of consumers’ brand community identification and reward on CBE, which we posit to subsequently affect brand loyalty. To investigate our hypotheses, we conducted a survey with 602 Facebook users. Our structural equation modeling results reveal brand community identification’s and reward’s positive effect on CBE, and a positive effect of CBE on brand loyalty. In addition, our findings reveal CBE’s partial mediating effect in the association of brand community identification and reward with brand loyalty. Theoretically, our findings further insight into CBE’s virtual community-based dynamics, with a focus on the role of community identification and reward. Practically, our results identify the key role of nurturing virtual community identification and the offering of reward to engage consumers.     

Examining the antecedents and consequences of mobile app engagement

Understanding how and why consumers engage with mobile apps is critical to the success of ubiquitous mobile marketing. This study proposed and tested a structural model to investigate the antecedents and consequences of mobile app engagement. Results show that time convenience, interactivity, and compatibility positively influenced mobile app engagement, in turn leading to strong relationship commitment and self-brand connections. Furthermore, informational and experiential mobile apps moderated the effects of time convenience, interactivity, and compatibility on mobile app engagement. Theoretical and practical implications for effective app engagement strategies are discussed.     

A Mobile App Search Engine

With the popularity of mobile apps on mobile devices based on iOS, Android, Blackberry and Windows Phone operating systems, the numbers of mobile apps in each of the respective native app stores are increasing in leaps and bounds. Currently there are close to one million mobile apps across these four major native app stores. Due to the enormous number of apps, both the constituents in the app ecosytem, consumers and app developers, face problems in ‘app discovery’. For consumers, it is a daunting task to discover the apps they like and need among the huge number of available apps. Likewise, for developers, enabling their apps to be discovered is a challenge. To address these issues, Mobilewalla (MW) an app search engine provides an independent unbiased search for mobile apps with semantic search capabilities. It has also developed an objective scoring mechanism based on user and developer involvement with an app. The scoring mechanism enables MW to provide a number of other ways to discover apps—such as dynamically maintained ‘hot’ lists and ‘fast rising’ lists. In this paper, we describe the challenges of developing the MW platform and how these challenges have been mitigated. Lastly, we demonstrate some of the key functionalities of MW.     

Why should I pay? Exploring the determinants influencing smartphone users’ intentions to download paid app

The objectives of this study are to investigate (1) the positive effects of smartphone users’ core self-evaluations (CSEs), online consumer conformity, and the social identity of their emotions, (2) the effects of their positive emotions on their trust, and (3) the effects of their trust on their intention to purchase paid apps and willingly engage in co-production. A questionnaire was employed to examine the relationship between the users’ CSEs, online consumer conformity, and social identity, on the one hand, with their intention to purchase paid apps and willingly engage in co-production on the other hand. The findings were as follows: (1) CSE, online consumer conformity, and social identity positively affected the users’ emotions, (2) positive emotions had a significantly positive effect on user trust, and (3) trust had significantly positive effects on the users’ intention to purchase paid apps.     

Systematic evaluation of mobile fitness apps: Apps as the Tutor,Recorder, Game Companion,and Cheerleader

Mobile fitness applications are innovating the ways in which smartphone users self-manage their health. Prior research found that app functions may impact app efficacy. However, research to date has not sought to systematically investigate how different combinations of app functions impact user response to apps, especially adoption intent. This article describes two studies on mobile fitness app characteristics and user attitudes. Study One used content analysis and hierarchical cluster analysis on 98 iPhone fitness apps and identified four app clusters: “Tutor”, “Recorder”, “Game Companion”, and “Cheerleader.” Tracking was the predominant function in current market, but tracking-focused Recorder apps received lowest user ratings among all app clusters. Users favored Tutor apps that combine exercise education and tracking, and Game Companion apps that combine gamification, tracking, and social functions. Function combinations, rather than standalone functions, impact app success. Following a Reasoned Action Approach, Study Two found various effects of individual differences (age, gender, BMI, eHealth literacy, smartphone experience, function preference) on user attitude toward different fitness app types. A comparison between two studies demonstrated a mismatch between market offerings and user needs regarding app functions. Implications of results for mobile fitness app design to improve consumer health and for theories are discussed.     

The associate impact of individual internal experiences and reference groups on buying behavior: A case study of animations,comics, and games consumers

Among the entertainment and media market, it can be observed that animations, comics, and video games (hereinafter abbreviated as ACG) have the highest output value and most market influence. Moreover, ACG also incorporates various industries and creates many derivative products. As the ACG industry emphasizes acousto-optics, imagery, and storylines, personal impressions derived from consumer experiences will influence consumer decisions. In addition, the ACG industry is mainly marketed towards younger age groups, with younger people being the main consumers; as such, these consumers’ decisions are more easily affected by peer behavior.This study aims to analyze the effects of internal cognitions and external influences on buying behavior of ACG consumers by applying the uncomplicated decision tree data mining algorithm. We analyze and develop the target attributes on measures of customer loyalty for ACG industry to set up the decision trees from the collected questionnaire data. The decision tree data mining method is applied to analyze the hidden association rules between the target attributes (i.e., consumer loyalty) and the critical influencing factors of consumer’s internal impressions and external influences for ACG consumers. The results and suggestions of this paper can be used as a reference for enterprises in the ACG industry to help with business policies concerning products’ extensional design, marketing, and CRM, and to further strengthen customer satisfaction and loyalty, thus increasing company profits.     

Elucidating the determinants of purchase intention toward social shopping sites: A comparative study of Taiwan and Japan

As the market potential of social shopping is increased by social networking sites (SNSs), the social shopping research can be interesting and valuable for social shopping firms to clearly understand the determinants affecting consumers’ purchase intention toward social shopping sites (SSSs). The objectives of this study are to examine consumers’ purchase intention toward a target SSS in different countries and to understand these differences from a cross-cultural perspective. In order to explore the cross-cultural differences in consumers’ purchase intention toward a target SSS, we propose an analytic framework that consists of website quality (i.e. system, information, and service quality) and relationship quality (i.e. satisfaction, commitment, and trust) that the SSS provides. To identify the determinants of purchase intention of SSS, large-scale online surveys were conducted in Taiwan and Japan simultaneously with the same questionnaire. Results show that perceptions of the two types of quality for a target SSS are significantly different between Taiwan and Japan, and effects of the quality types on the degree of purchase intention derived from the SSS also vary across the two countries. This paper ends with a discussion and limitations of the study results, as well as future research directions.     

Determinants of the intention to use Buy-Online,Pickup In-Store (BOPS): The moderating effects of situational factors and product type

Despite the wider introduction of the buy online and pick up in-store (BOPS) service by retailers, research on BOPS is still sparse, especially those from the consumer perspective. This paper employs the scenario-based factorial survey method to investigate how the perceived characteristics of innovation and the perceived risk of online shopping influence the consumers’ intention to use BOPS while also considering the moderating effects of situational factors (location convenience) and product type (involvement). Our findings indicated that the consumer perceptions of relative advantage, complexity, compatibility, and risks involved in online shopping are important antecedents to intention to use BOPS, and that these relationships were significantly moderated by locational convenience and product involvement. The implications of the findings and suggestions for future research are discussed in detail.     

Crafting a smartphone repurchase decision making process: Do brand attachment and gender matter?

In this fast changing digital era, smartphones users tend to change their smartphones regularly. Therefore, retaining existing consumers has been a challenging task for smartphone manufacturer brands. Given the status quo in smartphone industry and deficiencies in smartphone research, this study proposes a model that integrates Expectation Confirmation Model and brand attachment to shed light on smartphone repurchase decision making process. Besides, gender differences are also accounted for in the model. This study employs a two-staged approach, namely PLS-ANN, in analysing the data collected from 510 existing smartphone owners. Partial least squares structural equation modelling was firstly engaged, and followed by artificial neural network analysis. Besides, a non-parametric partial least squares multi-group analysis has also been conducted to discover gender differences in such decision making process. The results suggest that brand attachment is the most influential and relevant driver of consumers’ intention to repurchase smartphones from current brand (measured by continuance intention). Moreover, male consumers are more sensitive to the influence of brand attachment in developing their satisfaction and continuance intention, while female consumers are relying more on perceived usefulness. Interestingly, perceived usefulness does not significantly influence male consumers in continuance intention development. Practical and theoretical implications are subsequently offered.     

Enhancing customer loyalty to mobile instant messaging: Perspectives of network effect and self-determination theories

Mobile instant messaging (MIM) apps are popular information and communication technologies (ICTs). As known for ICTs, network effect variables such as perceived network size and perceived complementarity are important for determining user loyalty. However, there is insufficient research on why they are determinants, i.e., knowing the underlying mechanism. Grounded in network effect and self-determination theories, we study such underlying mechanism. Based on an analysis of responses from an online survey of 292 participants using structural equation modelling, we find that the network effect variables of perceived network size and perceived complementarity are positively related to three user-perceived values, namely user-perceived functional, self-expressive, and social values. They are, in turn, positively related to satisfaction of the needs for competence, autonomy, and relatedness, respectively, that further leads to user loyalty. This study contributes to the MIM literature by being the first to use the three user-perceived values and satisfaction of the three needs as novel process variables to construct a model for explaining the impacts of network effect variables on user loyalty to MIM apps. This study provides the insight that MIM providers can more effectively enhance user loyalty by providing the three values that users appreciate and satisfying the three needs, when increasing and advertising their network sizes and availability of complementary offerings.     

A relook at the mobile advertising landscape: What are the new consumer expectations in the retailing industry?

Following the rapid proliferation of mobile devices and the phenomenal advancements in mobile commerce, mobile advertising has since overtaken traditional advertising and seized the lead as the primary advertising channel for businesses. This research seeks to unveil the salient factors that affect the efficacy of mobile advertising in driving purchase intention by expanding the Web Advertising Model to include additional mobile advertising attributes (i.e., credibility and incentive) while considering the role of brand attitude in the process. A cross-sectional study was conducted with quantitative data obtained from 300 eligible respondents through an offline questionnaire survey. The empirical data were subsequently validated using the Structural Equation Modelling Artificial Neural Network (SEM-ANN) method to determine the non-compensatory non-linear relationships. All four ANN models show consistent results with the SEM analysis. The results indicate that consumer perceptions and expectations toward mobile advertising have shifted over time, such that the qualities of informativeness, irritation, and entertainment no longer play a critical role in determining mobile advertising value. On the other hand, the credibility and incentive of mobile advertising were found to be far more crucial in swaying such evaluations. Moreover, mobile advertising value was found to positively affect consumer attitudes towards the mobile advertisement and the advertised brand, which ultimately leads to purchase intention. This study offers an insightful overview of the modern mobile advertising environment that practitioners may use as a handy guide to optimise their mobile advertising strategies to suit the current perceptions and expectations of consumers.     

The impact of online brand community characteristics on customer engagement: An application of Stimulus-Organism-Response paradigm

The advent of interactive digital platforms has led people to progressively interact on such platforms, urging organizations to create online communities to engage customers with them and with each other to enhance brand loyalty. This study attempts to investigate what motivates customers to engage in these brand communities. Through a questionnaire survey of 430 Facebook users, this study investigates whether and how the unique characteristics (information quality, system quality, virtual interactivity, and rewards) of online brand communities affect customer engagement. The consequent effect of customer engagement on brand loyalty is also examined. This study frames and empirically validates a model for engaging customers with online brand communities on Facebook, considering the moderating role of gender. The Stimulus-Organism-Response paradigm is solicited to justify the theoretical background of this study. The data were analyzed using structure equation modelling. Results reveal that each of the characteristics positively influences customer engagement, with information quality and virtual interactivity bearing the strongest influence. Customer engagement also exhibits a strong positive impact on brand loyalty. This results further reveal that gender gap in the online environment is declining as the impact of all the four characteristics of online brand communities on customer engagement was invariable across male and female members.     

Religion-focused dating apps: A Q methodology study on the uses of mutual

Dating apps have become an increasingly viable option for individuals seeking interpersonal romantic relationships. While there is significant research regarding user motivation on dating apps such as Bumble, Tinder, and Match.com, there is no published research that discusses the motivations of Mutual app users. Developed as a dating app to target members of The Church of Jesus Christ of Latter-Day Saints, Mutual allows users to find potential mates who share their religious background and specify their relationship readiness (from “Into Dating I Guess” to “Ready for a Ring”). This research aims to illuminate the various motivations, attitudes, and opinions of Mutual app users through Q methodology, which identifies perceptual groups among homogeneous populations through a factor analysis of participants’ agreement with similar statements regarding Mutual use. Findings indicated four factor groups: the Relationship Readies (i.e., those serious about dating), the Swipeaholics (i.e., those looking for entertainment), the Faithless (i.e., those who felt pressured to use Mutual), and the Eligible Optimists (i.e., those who saw the app as a convenient, entertaining way to date). Different from other research on dating apps, this study indicates that people may use a niche religion-focused dating app to find individuals with similar moral values or due to external pressure from others. Results warrant further investigation into niche dating apps.     

BeWell: Sensing Sleep,Physical Activities and Social Interactions to Promote Wellbeing

Smartphone sensing and persuasive feedback design is enabling a new generation of wellbeing apps capable of automatically monitoring multiple aspects of physical and mental health. In this article, we present BeWell+ the next generation of the BeWell smartphone wellbeing app, which monitors user behavior along three health dimensions, namely sleep, physical activity, and social interaction. BeWell promotes improved behavioral patterns via feedback rendered as an ambient display on the smartphone’s wallpaper. With BeWell+, we introduce new mechanisms to address key limitations of the original BeWell app; specifically, (1) community adaptive wellbeing feedback, which generalizes to diverse user communities (e.g., elderly, children) by promoting better behavior yet remains realistic to the user’s lifestyle; and, (2) wellbeing adaptive energy allocation, which prioritizes monitoring fidelity and feedback responsiveness on specific health dimensions (e.g., sleep) where the user needs additional help. We evaluate BeWell+ with a 27 person, 19 day field trial. Our findings show that not only can BeWell+ operate successfully on consumer smartphones; but also users understand feedback and respond by taking steps towards leading healthier lifestyles.     

AdaptiveMutate: a technique for privacy preservation

Mobile apps are known to be rich sources for gathering privacy-sensitive information about smartphone users. Despite the presence of encryption, passive network adversaries who have access to the network infrastructure can eavesdrop on the traffic and therefore fingerprint a user’s app by means of packet-level traffic analysis. Since it is difficult to prevent the adversaries from accessing the network, providing secrecy in hostile environments becomes a serious concern.In this study, we propose AdaptiveMutate, a privacy-leak thwarting technique to defend against the statistical traffic analysis of apps. First, we present a method for the identification of mobile apps using traffic analysis. Further, we propose a confusion system in which we obfuscate packet lengths, and/or inter-arrival time information leaked by the mobile traffic to make it hard for intruders to differentiate between the altered app traffic and the actual one using statistical analysis. Our aim is to shape one class of app traffic to obscure its features with the minimum overhead. Our system strives to dynamically maximize its efficiency by matching each app with the corresponding most dissimilar app. Also, AdaptiveMutate has an adaptive capability that allows it to choose the most suitable feature to mutate, depending on the type of apps analyzed and the classifier used, if known.We evaluate the efficiency of our model by conducting a comprehensive simulation analysis that mutates different apps to each other using AdaptiveMutate. We conclude that our algorithm is most efficient when we mutate a feature of one app to its most dissimilar one in another app. When applying the identification technique, we achieve a classification accuracy of 91.1%. Then, using our obfuscation technique, we are able to reduce this accuracy to 7%. Also, we test our algorithm against a recently published approach for mobile apps classification and we are able to reduce its accuracy from 94.8% to 17.9%. Additionally, we analyze the tradeoff between the shaping cost and traffic privacy protection, specifically, the associated overhead and the feasibility for real-time implementation.