Social commerce continues to grow as a channel for firms to engage with consumers. However, few studies have attempted to provide a comprehensive understanding of the role of customer engagement in generating business outcomes. We utilize a sequential mixed methods design and develop a model illustrating the mechanism through which social commerce generates business outcomes via customer engagement based on the structure–conduct–outcome (SCO) framework from the consumer perspective. We use a qualitative study to conceptualize and develop a contextualized structure, consumer conduct, and outcomes in the context of social commerce. We collected data from American consumers using three rounds of surveys and used quantitative analysis to validate our research. The results show that social ties and social media commitment increase brand community commitment, which is positively related to both social media brand recommendation and brand community interaction (two important types of customer engagement). Accordingly, consumers’ double adoption of social commerce leads to brand commitment, in turn affecting brand loyalty and word of mouth. Our study provides an enhanced understanding of customer engagement in social commerce by validating the SCO framework and integrating the dual model of environmental perception and double adoption. It delivers practical insights into how social commerce can be used as a strategic tool for gaining business benefits.

Consumers continue to rely on social commerce for shopping purposes; however, one challenge for businesses in effectively implementing social commerce is the lack of consumer trust. Thus, the goal of this study is to investigate the mechanisms through which social commerce companies can successfully build consumer trust and increase business. From the socio-technical perspective, we identify social commerce usability and sociability as the two technological and social functions, respectively, of social commerce. We investigate the impacts of social commerce usability and sociability on consumers’ cognitive and affective (emotional) appraisals, which in turn affect their trust and social commerce use. The empirical results indicate that social commerce usability and sociability are positively related to cognitive and affective appraisal, both of which positively affect customer trust in social commerce. The results also demonstrate the positive impacts of consumer trust on social commerce use. Our study advances the social commerce literature.

Sequential recommendation (SR) aims to infer users' future interests and suggest the next items for them. Most SR methods learn one single vector to represent a user's recent interests, i.e., a user representation. Despite their great success, most of them do not explicitly consider the phenomenon of users' interest drift when learning user representations. Moreover, interest drift presents two critical challenges for these SR methods: (1) how to explore the potential distributions of the users' varying interest drift levels; and (2) how to capture the interest drift-aware collaborative knowledge among the users. In this paper, we delve into the issue of interest drift in SR and propose a novel and generic framework, i.e., Interest Drift-driven User Representation Learning (IDURL), to enhance SR methods to tackle the above two challenges. Specifically, our IDURL contains an interest drift quantization (IDQ) module to enable a quantitative measurement of the interest drift. Moreover, a drift representation generation module models the users' latent varying levels of interest drift, and an interest drift-guided representation disentanglement module optimizes the distributions of the interest drift levels under the guidance of IDQ. Furthermore, an interest drift-aware representation alignment module helps to capture the interest drift-aware collaborative knowledge among users. Finally, the users' overall interest representations are obtained to calculate the preference scores on the candidate items. Extensive experiments on four public datasets show the effectiveness of our IDURL. The source code of our IDURL is available at: https://github.com/xiaolLIN/IDURL.

Consumers are increasingly relying on consumer-generated information when making purchase decisions; therefore, for consumers to make informed and accurate decisions, the honesty of consumer-generated information is essential. This study aims to provide an understanding about consumers’ honest information sharing in social commerce. Drawing on the dual model of environmental perception, we developed a model of honest information sharing by examining consumers’ perceptions regarding Facebook’s ethics, their trust in social commerce, and social commerce ethicality. Using a survey with 969 participants, our results strongly suggest that the hypotheses were supported. Consumers’ perceptions regarding Facebook’s ethics are positively associated with their trust in social commerce and perceived social commerce ethicality, both of which positively affect honest information sharing. Our study advances both the information sharing and social commerce literature. Practically, our study delivers insights for businesses into how to use social commerce as a strategic tool for supporting consumers’ honest information sharing.

Sequential recommendation has been widely used to predict users’ potential preferences by learning their dynamic user interests, for which most previous methods focus on capturing item-level dependencies. Despite the great success, they often overlook the stage-level interest dependencies. In real-world scenarios, user interests tend to be staged, e.g., following an item purchase, a user’s interests may undergo a transition into the subsequent phase. And there are intricate dependencies across different stages. Meanwhile, users’ behaviors are usually heterogeneous, including auxiliary behaviors (e.g., examinations) and target behaviors (e.g., purchases), which imply more fine-grained user interests. However, existing methods have limitations in explicitly modeling the relationships between the different types of behaviors. To address the above issues, we propose a novel framework, i.e., dynamic stage-aware user interest learning (DSUIL), for heterogeneous sequential recommendation, which is the first solution to model user interests in a cross-stage manner. Specifically, our DSUIL consists of four modules: (1) a dynamic graph construction module transforms a heterogeneous sequence into several subgraphs to model user interests in a stage-wise manner; (2) a dynamic graph convolution module dynamically learns item representations in each subgraph; (3) a behavior-aware subgraph representation learning module learns the heterogeneous dependencies between behaviors and aggregates item representations to represent the staged user interests; and (4) an interest evolving pattern extractor learns the users’ overall interests for the item prediction. Extensive experimental results on two public datasets show that our DSUIL performs significantly better than the state-of-the-art methods.

Side-information integrated sequential recommendation incorporates supplementary information to alleviate the issue of data sparsity. The state-of-the-art works mainly leverage some side information to improve the attention calculation to learn user representation more accurately. However, there are still some limitations to be addressed in this topic. Most of them merely learn the user representation at the item level and overlook the association of the item sequence and the side-information sequences when calculating the attentions, which results in the incomprehensive learning of user representation. Some of them learn the user representations at both the item and side-information levels, but they still face the problem of insufficient optimization of multiple user representations. To address these limitations, we propose a novel model, i.e., Multi-Sequence Sequential Recommender (MSSR), which learns the user's multiple representations from diverse sequences. Specifically, we design a multi-sequence integrated attention layer to learn more attentive pairs than the existing works and adaptively fuse these pairs to learn user representation. Moreover, our user representation alignment module constructs the self-supervised signals to optimize the representations. Subsequently, they are further refined by our side information predictor during training. For item prediction, our MSSR extra considers the side information of the candidate item, enabling a comprehensive measurement of the user's preferences. Extensive experiments on four public datasets show that our MSSR outperforms eleven state-of-the-art baselines. Visualization and case study also demonstrate the rationality and interpretability of our MSSR.

Social media has been integrated into traditional e-commerce, creating an innovative technology-based approach to changing business practice and service, yet few researchers have attempted to provide an understanding of how this approach is changing consumer decision-making and purchase behaviours. Combining the expectation-confirmation theory and the expectation-confirmation model and information systems continuance, we propose a model for social commerce, illustrating how social media is utilised in online shopping as a three-fold process (pre-purchase, purchase, and post-purchase stages) from a consumer perspective. The model consists of three phases: social commerce motivation, social commerce adoption, and e-commerce effectiveness. Using two rounds of surveys, we find that (1) autonomous motivation and controlled motivation have positive effects on social commerce information seeking and sharing, and (2) social commerce information seeking formulates consumers’ pre-purchase decisions, thus affecting their actual purchase outcomes, repurchase intentions, and social commerce information-sharing intentions in the post-purchase stage. In addition, our study indicates that consumers may play dual roles in social commerce: as information seekers in the pre-purchase stage and as information providers in the post-purchase stage. Our findings have important implications for literature and practice.

Abstract An enhanced understanding of the innovative use of artificial intelligence (AI) is essential for organizations to improve work design and daily business operations. This study's purpose is to offer insights into how AI can transform organizations' work practices through diving deeply into its innovative use in the context of a primary AI tool, a chatbot, and examining the antecedents of innovative use by conceptualizing employee trust as a multidimensional construct and exploring employees' perceived benefits. In particular, we have conceptualized employee trust in chatbots as a second‐order construct, including three first‐order variables: trust in functionality, trust in reliability, and trust in data protection. We collected data from 202 employees. The results supported our conceptualization of trust in chatbots and showed that three dimensions of first‐order trust beliefs have relatively the same level of importance. Further, both knowledge support and work–life balance enhance trust in chatbots, which in turn leads to innovative use of chatbots. Our study contributes to the existing literature by introducing the new conceptualization of trust in chatbots and examining its antecedents and outcomes. The results can provide important practical insights regarding how to support innovative use of chatbots as the new way we organize work.

Recently, sequential recommendation has become a research hotspot while multi-behavior sequential recommendation (MBSR) that exploits users' heterogeneous interactions in sequences has received relatively little attention. Existing works often overlook the complementary effect of different perspectives when addressing the MBSR problem. In addition, there are two specific challenges remained to be addressed. One is the heterogeneity of a user's intention and the context information, the other one is the sparsity of the interactions of target behavior. To release the potential of multi-behavior interaction sequences, we propose a novel framework named NextIP that adopts a dual-task learning strategy to convert the problem to two specific tasks, i.e., next-item prediction and purchase prediction. For next-item prediction, we design a target-behavior aware context aggregator (TBCG), which utilizes the next behavior to guide all kinds of behavior-specific item sub-sequences to jointly predict the next item. For purchase prediction, we design a behavior-aware self-attention (BSA) mechanism to extract a user's behavior-specific interests and treat them as negative samples to learn the user's purchase preferences. Extensive experimental results on two public datasets show that our NextIP performs significantly better than the state-of-the-art methods.

As social networking sites (SNSs) have become quite popular, organizations have used SNSs to support their various business processes. A growing trend is that organizations increasingly encourage consumers to contribute knowledge via SNSs. The contributed knowledge can help organizations to improve their products/services and therefore these contributions have great business value. As consumers are outside of the organization and may not maintain a close relationship, it can be quite challenging to encourage consumer engagement with knowledge sharing on SNSs. This study develops a research model to examine how community identification and trust support consumer knowledge sharing on SNSs, as well as their antecedents. The results demonstrate that community identification and trust facilitate consumer knowledge sharing behavior. This study contributes to the literature by highlighting the important role of community identification in the process of knowledge sharing. The results provide practitioners with guidelines for SNSs and to encourage consumer knowledge sharing.

Organizations increasingly use virtual teams to support their business processes. With software development as its context, this study aims to examine how software engineers are motivated to work in virtual teams, as well as the subsequent impacts this has on their programming and collaboration skills development. A theoretical model was developed based on self-determination theory. Data were collected from longitudinal surveys taken by software engineers in China. Our research results show that trust is positively related to software engineers’ autonomous motivation, whereas social influence is positively related to their controlled motivation. Besides, autonomous motivation enhances the amount of effort software engineers put into programming, whereas controlled motivation does not. Programming effort, in turn, increases their programming and collaboration skills. These research findings can advance our understanding about software engineers’ motivations in the context of software development. Our work also has important implications for organizations and software engineers.

Sharing economy services such as bicycle-sharing have become quite popular. In these services, companies maintain systems which allow consumers to conduct sharing activities. Based upon expectation-confirmation theory, we develop a model investigating the antecedents of consumer confirmation and its consequences in the sharing economy context. Specially, we identify two antecedents including perceived performance and perceived risk. Using an empirical study, our results show that perceived performance has a positive impact on confirmation while perceived risk has a negative effect. Confirmation positively affects service satisfaction, which in turn increases continuance intention and recommendation intention. Further, confirmation is negatively related to dissatisfaction, which in turn increases switch intention. Our study clarifies the process of consumers’ decision-making about using sharing economy, and thus making contribution to the sharing economy literature. Practically, it delivers insights for companies into how to retain customers through increasing the value and reducing the risk associated with sharing economy.

The paper draws on customer engagement literature to propose an organic approach to achieving customer loyalty. The organic approach is reflected in customers’ volitional engagement in an online brand community without any deliberate marketing endeavors and incentives from the brand organization. This research employs a longitudinal study focusing on consumers in the United States of America (USA) and examines the relationships between organic customer-engagement behaviors and customer loyalty. Based on expectancy theory, customer perceived benefits are proposed to intervene in these relationships. Results from the longitudinal investigation support the study propositions. Discussion and implications are provided for researchers and practitioners.