“…size of order, order rate, and money spent) has derived an attention over the related body of mobile shopping literature (i.e. Kim et al, 2017).…”
Section: Literature Reviewmentioning
confidence: 99%
“…A comparative study of Chinese and American mobile shopping adopters was conducted by Lu et al (2017), who found that there are significant differences between United States (US) and Chinese customers in terms of the impact of perceived privacy on the customer's intention to keep using mobile shopping, which could be attributed to cultural values relating to individualism and collectivism. Kim et al (2017) aimed to discover the impact of a customer's digital and mobile experience on the customer's mobile buying behaviour. They found that smartphone users familiar with online and mobile applications are more likely to engage with the purchasing process of mobile shopping.…”
PurposeThis study aims to examine the impact of mobile interactivity dimensions (active control, personalization, ubiquitous connectivity, connectedness, responsiveness and synchronicity) on customer engagement.Design/methodology/approachA quantitative field survey study was conducted to collect the required data from actual users of mobile shopping in three countries: Jordan, the United Kingdom (UK) and Saudi Arabia.FindingsThe results are based on structural equation modelling and support the impact of five dimensions of mobile interactivity: active control, personalization, ubiquitous connectivity, responsiveness and synchronicity. The impact of connectedness is not supported. The results also support the significant impact of customer engagement on customer loyalty.Research limitations/implicationsThis study only considered the shopping activities conducted by mobile channels, while other channels (e.g., online channels, traditional channels and social media shopping channels) are not considered. Furthermore, the current model does not consider the impact of personal factors (e.g., technology readiness, self-efficacy and user experience). The results of the current study present a foundation that can guide marketers and practitioners in the area of mobile shopping.Originality/valueThis study enriches the current understanding of the impact of mobile interactivity on mobile shopping, as well as how mobile interactivity can enhance the level of customer engagement.
“…size of order, order rate, and money spent) has derived an attention over the related body of mobile shopping literature (i.e. Kim et al, 2017).…”
Section: Literature Reviewmentioning
confidence: 99%
“…A comparative study of Chinese and American mobile shopping adopters was conducted by Lu et al (2017), who found that there are significant differences between United States (US) and Chinese customers in terms of the impact of perceived privacy on the customer's intention to keep using mobile shopping, which could be attributed to cultural values relating to individualism and collectivism. Kim et al (2017) aimed to discover the impact of a customer's digital and mobile experience on the customer's mobile buying behaviour. They found that smartphone users familiar with online and mobile applications are more likely to engage with the purchasing process of mobile shopping.…”
PurposeThis study aims to examine the impact of mobile interactivity dimensions (active control, personalization, ubiquitous connectivity, connectedness, responsiveness and synchronicity) on customer engagement.Design/methodology/approachA quantitative field survey study was conducted to collect the required data from actual users of mobile shopping in three countries: Jordan, the United Kingdom (UK) and Saudi Arabia.FindingsThe results are based on structural equation modelling and support the impact of five dimensions of mobile interactivity: active control, personalization, ubiquitous connectivity, responsiveness and synchronicity. The impact of connectedness is not supported. The results also support the significant impact of customer engagement on customer loyalty.Research limitations/implicationsThis study only considered the shopping activities conducted by mobile channels, while other channels (e.g., online channels, traditional channels and social media shopping channels) are not considered. Furthermore, the current model does not consider the impact of personal factors (e.g., technology readiness, self-efficacy and user experience). The results of the current study present a foundation that can guide marketers and practitioners in the area of mobile shopping.Originality/valueThis study enriches the current understanding of the impact of mobile interactivity on mobile shopping, as well as how mobile interactivity can enhance the level of customer engagement.
“…Moreover, the feedback of past behavior (see, Anesbury, Talbot, Day, Bogomolov, & Bogomolova, 2020;Sharp et al, 2012) should be taken into account, as it is likely to underpin continued use, irrespective of it resulting from a more thoughtful evaluation of the app's benefits or simpler decision-making based on recognizing the branded app. On occasion, past research on mobile apps has considered the feedback of past behavior primarily in relation to the initial adoption of apps (e.g., Kim, Kim, Choi, & Trivedi, 2017;Liu, Zhao, & Li, 2017;Newman, Wachter, & White, 2018;Viswanathan et al, 2017). Therefore, there is scope for considering the role of past behavior in the decisionmaking process leading to the continued use of branded apps.…”
Section: Introductionmentioning
confidence: 99%
“…In the specific instance of mobile apps, past research has seldom considered the influence of past behavior and, when accounting for it, it has been considered primarily as a determinant of apps' adoption and initial use. For example, a few studies have highlighted as important drivers of apps' discovery and use: (a) mobile experience and browsing behavior (e.g., Kim et al, 2017); (b) acquisition frequency and recency (Liu et al, 2017); (c) usage frequency and recency (Newman et al, 2018;Viswanathan et al, 2017); and (d) active app usage or consumer voluntary participation (Chung, 2015;Mäki & Kokko, 2017). Moreover, empirical studies based on the analysis of panel data revealed that many categories of apps are characterized by high levels of usage concentration.…”
This study investigates two decision‐making paths that underpin the continued use of branded apps. One path originates from past use of a category of apps and leads to continued use of a branded app from that category via recognition. The second path also starts with past use, but leads to continued use through the evaluation of the app's benefits. Two empirical studies test and subsequently validate the resulting conceptual model, confirming that both paths underpin continued use; however, the strength of the theoretical links varies, and the two paths warrant separate investigation. These outcomes support the generalizability of the proposed model, highlighting its potential as a tool to advance the understanding of consumer decision‐making leading to the continued use of branded apps. The findings of this study also yield practical relevance, especially for the delineation of strategies to enhance the chances of market survival of branded apps.
“…Smart devices and technologies have enabled consumers to display their shopping power 24/7/365 from everywhere, at their convenience. Consumers skillfully conduct research prior to buying across retailers, manage their orders, contribute to Word of Mouth, or conduct other transactions simply with mobile devices using m-wallets [20][21][22][23][24][25]. Correia named these individuals "Fluid consumers" who use mobile technologies to flow easily between different transactions at any time and any place [26].…”
Today’s smart consumers are intelligent consumers with multiple roles in the digital consumption environment. Consumer smartness refers to the multi-dimensional qualities that support various roles. Aiming to discover who the smart consumers are in the digital consumption context, this study classifies consumer segments based on consumer smartness and explores each segment’s profile in terms of demographic and behavioral characteristics. Using the data of 541 adult consumers, a clustering analysis generated four optimal clusters: Go-getters, Socialites, Realists, and Shopping-pococurante. Consumers with a higher level of consumer smartness were likely to have stronger shopping and sharing intentions, which indicates that smart consumers are active entities in the digital consumption context. This is the first attempt to segment today’s consumers carrying out multiple roles based on the concept of consumer smartness.
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