Product recommendation in online social networking communities: An empirical study of antecedents and a mediator

Inclusive Digital Innovation in South Africa: Perspectives from Disadvantaged and Marginalized Communities

Inclusive digital innovation (IDI) entails rolling out policies and digital innovations to ensure equal access to services and new goods by previously excluded and marginalized societies. Digital commerce (d-commerce) has the potential to foster an inclusive community through IDI empowerment in emerging economies; however, the literature on inclusive digital innovation and citizen empowerment is limited on the effect of e-strategy policies and empowerment on d-commerce adoption, use, and recommendation propensity. Underpinned by three theories: the Extended Unified Theory of Acceptance and Use of Technology, the citizen empowerment theory, and the affective decision-making theory of optimism bias and risk, this study proposes a model to establish the determinants of use intention, use behavior, and propensity to recommend d-commerce in disadvantaged and marginalized communities in South Africa. Using survey data from 983 disadvantaged d-commerce users to test the proposed model and hypotheses using IBM Amos for Windows and structural equation modeling (SEM), this paper provides a unique narrative to the empowerment discourse of marginalized people. Results show that privacy, security, trust, and citizen empowerment positively influence use intention and optimism bias, use behavior, and citizen empowerment mediate the propensity to recommend. Factors that promote or hinder citizens’ choices to use and recommend d-commerce are pertinent to scholars, government agencies, and regulators seeking better policy implementations to eradicate socio-economic inequalities, sustainable societies, and the empowerment of disadvantaged and marginalized people.

Social Commerce Intention, Social Interaction, and Social Support

A higher number of socially anxious users were found as more users joined social network sites. Since social commerce has become an important issue, this study investigated the effect of social anxiety on online users’ social commerce intention. Online social interactions are hypothesized to influence social commerce intention directly or indirectly through online social support. 427 effective samples were collected from Facebook users, and the results confirmed most of the causal effects. The study also tested the moderating effect of social anxiety on the causal effects. Of the eight relationships, social anxiety significantly moderates six of them. The relationships between online social interaction and emotional support and between online social interaction and social commerce intention are stronger for users with higher social anxiety. For users with lower social anxiety, the relationship between social support and the receiving of social commerce intention is stronger. The research findings lead to significant theoretical contributions and managerial implications.

The Mediated Effect of Social Presence on Social Commerce WOM Behavior

Based on expectation disconfirmation theory, this study analyzes how attitudes (satisfaction and loyalty) influence interaction intention (sWOM) and, consequently, active and passive sWOM behavior. It does so by assessing the mediating role of social presence on sWOM intention and behavior. The empirical results provide several contributions. First, knowing how to increase active sWOM contributes to bridging the gap regarding how to enhance interactions between users. Second, fostering active sWOM on social commerce websites will provide companies with more positive user-generated content, since this active sWOM comes from satisfied and loyal users, and it is assumed that they will rate the product positively and report a good experience. Third, companies can benefit more from users if users interact with other users by sharing their experiences. This study sheds light on how social presence can mediate the relationship between intention and behavior, particularly when it comes to increasing active participation and brand promotion.

The role of customers in the gig economy: how perceptions of working conditions and service quality influence the use and recommendation of food delivery services

This research examines how customers’ perceptions about controversial labor practices of food delivery platforms may affect their intentions to use and recommend these services. Three studies reveal that customers’ behavioral intentions depend on their perceptions of the working conditions for the delivery workers, as well as service quality. This influence is higher among customers with a high level of social conscious consumption. Our research also explores the costs that customers would be willing to assume to be served by a food delivery service that offers better working conditions. These insights reveal several relevant managerial implications for gig economy firms.

Mobile Apps Use and WOM in the Food Delivery Sector: The Role of Planned Behavior, Perceived Security and Customer Lifestyle Compatibility

This research examines the phenomenon of food delivery services from the mobile app user’s perspective and how consumers’ lifestyles are changing because of the convenience provided by the apps. By means of an online survey targeted at US food delivery app customers, our study analyzes the main motivations that lead them to use and recommend these technology-based services. The results of the study revealed that some of the theory of planned behavior model variables (i.e., attitude toward the behavior, subjective norms), influence customer use and word-of-mouth (WOM) intentions. Security influences intention to spread WOM, whereas customer lifestyle compatibility influences intention to use the food delivery apps. A post hoc analysis revealed that perceived control is only important for older customers, who need to perceive that they control the apps before they will recommend them to other customers. The findings of the study are discussed and contrasted with previous research in the field. The managerial implications derived from the findings provide practical guidance for food delivery app companies. Further research avenues are suggested to encourage scholars to continue investigating the challenge of the diffusion of mobile apps in the food delivery and related sectors.

Indoor Location Prediction Method for Shopping Malls Based on Location Sequence Similarity

Fast and accurate indoor location prediction plays an important part in indoor location services. This work proposes an indoor location prediction framework named Indoor-WhereNext. First, a novel algorithm, “indoor spatiotemporal density-based spatial clustering of applications with noise” (Indoor-STDBSCAN), is proposed to detect the stay points in an indoor trajectory and convert them into a location sequence. Then, a spatial-semantic similarity (SSS) method for measuring the similarity between location sequences is defined. SSS comprehensively considers the spatial and semantic similarities between location sequences. Finally, a clustering algorithm is used to obtain similarity user groups based on SSS. These groups are used to train different prediction models to achieve improved results. Extensive experiments were conducted using real indoor Wi-Fi positioning datasets collected in a shopping mall. The results show that the Indoor-WhereNext model markedly outperforms the three existing baseline methods in terms of prediction accuracy and precision.