Mapping online App hate: Determinants and consequences

https://doi.org/10.1016/j.tele.2020.101401Get rights and content

Highlights

  • The present study Mapping online hate.

  • Symbolic avoidance is a significant predictor of App hate.

  • Relationship quality Avoidance is a significant predictor of App hate.

  • Moral Avoidance is a significant predictor of App hate.

  • App hate is negatively related with negative word of mouth and App switching Behavior.

Abstract

According to Lee’s emergent brand avoidance theoretical framework and self-congruity theory, this research conceptualizes and examines the application (App) hate framework. Data are collected from Chinese smartphone users who provided the possible reasons for hating a specific App. This study first validates Lee’s brand avoidance framework in relation to App hate. The empirical investigation certifies that four key determinants, namely, symbolic, relationship quality, moral, and deficit-value avoidance, are responsible for App hate among online users. Moreover, findings show that App hate is positively and significantly related to negative word of mouth and switching App behavior. This study also discusses the theoretical and managerial implications of Apps.

Introduction

At present, individuals download applications (Apps) to manage their personal and professional affairs (Kim et al., 2015). Scholars have confirmed that users express their virtual identities/social self-concept through the Apps they use (Peng et al., 2014, Zhao and Balagué, 2015). Apps refer to “software downloadable to a mobile device which prominently displays a brand identity, often via the name of the app and the appearance of a brand logo or icon, throughout the user experience” (Bellman et al., 2011). This type of software has completely changed the way users connect, shop, and socialize (Kim et al., 2016). As a result, companies are keen to launch competitive Apps that can provide smooth, comfortable, and delightful user experiences (Liu et al., 2016, Sheikh et al., 2019). However, no single research has focused on the reverse notion, wherein users hate particular Apps to prevent objectionable congruence to their lives. For instance, Chen et al. (2019) claimed that two-thirds out of the millions of Apps in various App stores are seldom downloaded. Hsu et al. (2015) demonstrated that Google Play Store held more than 1 million Apps, in which 90% have been installed less than 50,000 times. Economic developers conducted a survey of 10,000 App developers from 137 countries worldwide and summarized that only 1.6% of these Apps produce business in the App industry, and 2.9 million App developers are in a handful of situations. In addition, the Pew Research Center released a report on the smartphone app users in the USA and stated that 20% of these users abandoned their installed Apps. Therefore, concerns about App avoidance of hate have increased, and researchers are eager to discover the possible answers to this phenomenon (see Fig. 1).

Over the last decade, researchers have focused on empirical and theoretical attempts to identify either the cause or consequences of brand hate or avoidance (Hegner et al., 2017, Islam et al., 2018a; Lee et al., 2009b, Zarantonello et al., 2016), but the traditional hate literature has predominantly investigated the brick and mortar stores, see in Table 1. Recently, businesses have switched from brick and mortar to click and click (Braojos et al., 2019, Huang and Benyoucef, 2013), and companies and App developers are increasingly concerned in finding the reasons behind App hate. Therefore, to address this research gap, this study concentrates on App hate because App success in the digital economy indicates remarkable firm performance (Huang and Benyoucef, 2013). Customers hate brands, and brand managers do their best to transform hate into love as it severely influences the firms’ sales. Knittel et al. (2016) confirmed that brand hate or avoidance is an approach applied when individuals immediately and globally express their negative emotions in the digital context. Extant literature found that growing website hate has influenced customers’ cognitive judgment toward the brand’s image and status and their buying decision (Pleşea et al., 2011, Strebe, 2016). Social psychologists believe that emotions are associated with personal experience and behavioral actions (Fischer et al., 2018, Lewis, 1993, Tracy and Robins, 2007). They confirmed that hate ranked third among the 213 emotions. Alden et al. (2013) claimed that brand hate is a more aggressive emotional reaction that customers have toward the firms offering brands than brand animosity. Chen et al. (2019) claimed that Apps were treated as significant assets when they added financial worth to the firm by helping boost and maintain cash inflow for the business. As a result, an in-depth investigation is required to search for the critical determinant and consequences of App hate. Two points are emphasized by considering the significance of Apps in a business context. First, the mobile app is clearly considered a critical determinant of substantial brand equity and customer loyalty and an essential factor in the firm’s assets because of the exponential increase in the use of smartphones (Fang, 2017, Islam et al., 2017, Peng et al., 2014, Zhao and Balagué, 2015). Second, Apps negatively affect brand equity, although this concept has not been adequately investigated.

Hate is a self-conscious emotion (Muris and Meesters, 2014), whereby a customer forms extreme negative emotions about brands generated from unpleasant experiences (Lee et al., 2009b). Koenderink (2014, p. 4) stated that brand avoidance and hate were associated with the perception of intentionally rejecting a brand. Lee et al. (2009b) explained that four types of key avoidance (i.e., experiential, identity, moral, deficit-value) were likely to affect brand hate. In addition, Hegner et al. (2017) found that similar determinants (i.e., symbolic avoidance and ideological incompatibility) affect brand hate. According to the self-congruity theory, symbolic incongruence contains refused brands because of self-image incongruence (Sirgyet al., 2015). Zhao et al. (2008) explored the identity construction on Facebook and found that symbolic congruence was a significant factor of self-concept. This research found that online users display their extended self-concept through branded social apps. Lee et al. (2009b) found that symbolic avoidance happened when an individual felt the risk of self-image from using spurious brands.

Sirgy et al. (2015) explained that functional congruence demonstrated the ideal characteristic of an individual looking for a product. Scholars have examined the critical role of functional congruence in anticipating various consumer behavior expressed as an attitude toward brand choice and product preference (Ahn et al., 2013, Islam et al., 2018a). Extant literature explained various reasons for functional incongruence for brand hate, such as negative shopping experience, poor brand quality, negative experience with the brand, unattractive packaging, and unpleasant color themes (Hegner et al., 2017, Kucuk, 2019c). According to expectancy disconfirmation theory, experiential avoidance is generated because of unsatisfactory user experience and unmet expectations from the brands (Pizam and Milman, 1993). These unmet expectations increase because of the undelivered brand promise (Tripathi, 2009). In an online environment, brand experience is interchanged with the relationship quality of the Apps, which is an important variable in relationship marketing and positively related to customer loyalty (Hajli, 2014). Extant literature determined that high relationship quality was a key determinant of an App’s continuance intention (Fang, 2017, Peng et al., 2014, Tam et al., 2018). However, limited research on relationship quality avoidance in the context of App hate has been performed.

According to Hegner et al. (2017), individuals morally avoid brands because of social needs rather than personal ones. In addition, Kucuk (2019c) explored the main antecedent of brand avoidance because of ideological incompatibility based on social and moral issues. Lee et al. (2009b) emergent model of brand avoidance explained that moral avoidance was a key factor that influenced brand hate. This marketing researcher has long noted that moral avoidance was divided into two facets, namely, anti-hegemony and country of origin (COO). Anti-hegemony indicates that customers avoid leading brands because of their desire to stop cartels and unethical actions (Alnawas and Aburub, 2016). COO includes the feeling of avoidance toward certain brands because of their political, economic, and diplomatic events (Leong et al., 2016). Rooksby et al. (2016) examined a mobile App’s ethical implementation in the App Store and found that the App store arrangement must be conducted ethically. Specifically, Lee et al. (2009a) introduced the new idea called deficit-value avoidance, which aroused when customers felt that brands are functionally inadequate because of unfamiliarity and aesthetic insufficiency. Knittel et al. (2016) found that individuals might use brand appearance as a key determinant of functional value and reject aesthetically deficient brands with poor presentation. Scholars found that branded mobile apps were rejected because of the Apps’ interface overburden of information and different screen themes (Wang and Li, 2017). Hoehle and Venkatesh (2015) explored mobile App usability and confirmed that the appropriate combination of colors helps produce a positive image in the user’s eyes.

Psychologists agree that negative emotions, such as individual protests and boycotts, have a stronger influence on individual behavior than positive ones (Fetscherin, 2019). Recently, hate literature found a significant relationship between hate emotions and behavioral outcomes, such as brand rejection (Cromie and Ewing, 2009, Moore, 2019), brand revenge (Fetscherin, 2019), brand switching, and negative word of mouth (Hegner et al., 2017). Likewise, in the online environment, App users’ negative emotions originate from unpleasant experiences or services, and such failure may affect the user relationship with particular apps (Biucky and Harandi, 2017). According to Rahmani et al. (2018), Web 2.0 has empowered App users to share negative experiences or emotions with particular Apps with only one click. The research identified that any breach of promise is a key determinant of brand hate and a major reason for the negative word of mouth and switching behavior. In addition, Hegner et al. (2017) stated that unsatisfied customers spread negative comments toward the brands in public. Several researchers have found that brand hate is a key determinant of the negative word of mouth and switching behavior (Oliva et al., 1992).

To date, studies on Apps have focused primarily on determining the reasons behind their choice of mobile Apps (Tarute et al., 2017, Zhao and Balagué, 2015) based on the technology adoption model (Ayeh, 2015, Davis, 1989, Muk and Chung, 2015, Ooi and Tan, 2016, Persico et al., 2014) and unified theory of acceptance and use of technology (1 & 2) (Benbasat and Barki, 2007, Islam et al., 2017, McLean et al., 2018, Parameswaran et al., 2015) by investigating the factors responsible for the continuance purchase intention (Chen, 2017, Hsu et al., 2015). Extant literature extensively examined the App adoption, and to the best of our knowledge, no study on App hate has been conducted. To fill this research gap, this study provided various significant contributions to the extant literature. First, this study contributes to practice and literature. Second, although previous research in the offline context has examined the important role of brand hate on behavioral- and organizational-level outcomes, hate has not been explored in the context of the online environment, such as App hate. Moreover, according to Lee’s avoidance framework and self-congruence theory, our research provides an in-depth understanding of App hate constructs. This research extends the literature on App hate in the digital environment context because extant literature on hate is generally discussed in terms of the offline environment. Finally, the findings of this research can be helpful for researchers and practitioners in understanding the procedure of App hate or anti-consumption of mobile apps (e.g., under what circumstances do users hate these apps). In addition, some scholars have proposed that the knowledge of what online users do not need is equally important as that on what they need due to the exponential increase in smartphone usage. (Sheikh et al., 2019)

Research on hate is typically restricted to the food and tourism sectors (Delzen, 2014, Hussain et al., 2019, Popp et al., 2016, Sheikh et al., 2019). Research on the topic of App hate in digital economies is still in its introductory phase. In addition, Chinese App users are selected as the research subjects of this study because they are part of the leading online business worldwide. At approximately 715 million smartphone users, China has the largest iOS apps download market in the world and has approximately 50% of the total iOS downloads in 2018 (Annie, 2019). This research offers a major contribution to the literature and advances the existing knowledge on brand hate in the context of Apps. The proposed App hate framework provides App developers and corporations with key insights.

Section snippets

Theoretical development

Lee et al. (2009a) brand avoidance framework is extensively used in research that examines the reasons of anti-consumption, brand avoidance, or brand hate (Hegner et al., 2017, Islam et al., 2018b; Kucuk, 2019b, Kucuk, 2019c, Zarantonello et al., 2016, Zarantonello et al., 2018). Lee et al. (2009a) demonstrate a complete construct conceptualization measure development and acceptance framework. This conceptualization combined prior literature (Fine, 1987, Klein and Dawar, 2004), and divided

Research settings

This research selects the users in Shanghai, China, as the research setting for various reasons. First, China is the largest smartphone market in the world, with an estimated 800 million smartphone users; an abrupt increase of 70% has been observed over the last decade. According to the App Annie’s annual report in 2018, the total app downloads exceed 113 billion, and China accounted for approximately 50% of the total downloads, with an estimated $76 billion user spending (Annie, 2019). Second,

Respondents’ profile and characteristics

A total of 350 questionnaires were distributed to smartphone users who are active in using various mobile apps. In response, we received 330 questionnaires. Out of the received responses, 18 responses were deleted due to incomplete responses or missing values. The final sample consisted of 312 responses. Table 2 displays the demographic characteristics of the final sample.

Data analysis

To test this research structure and measurement model, we employed structural equation modeling using the AMOS (version

Discussion

The current research first discussed and empirically validated the determinant and outcomes of app hate. To date, the phenomenon of hate has been deliberated to physical brand hate in the existing marketing literature but with inadequate research in the online environment. The main idea of the present study is to enhance the existing literature in app hate and to understand the user behavior in a digital era. To develop and confirm our conceptual framework of app hate, we first looked into

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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