Purpose E-commerce influencers in China have cultivated a unique strategy that integrates personal brand building on social media with sales conversion on e-commerce platforms through their self-branded products. This study aims to examine how authenticity, along with its antecedents of influencer expertise and self-disclosure characteristics, influences followers’ purchase intention in this cross-platform strategy. Design/methodology/approach An online survey of 244 followers of e-commerce influencers examines the factors influencing their intention to purchase self-branded products promoted by these influencers. Findings The findings demonstrate the significant role of authenticity in balancing an influencer’s self-representation and self-commodification. The results show that authenticity, as the core of audience perception toward e-commerce influencers, mediates the effects of influencer expertise (i.e. product familiarity) and self-disclosure (i.e. disclosure intimacy and narrativity) on follower’s purchase intention. Research limitations/implications The research advances our understanding of e-commerce influencer entrepreneurship by investigating how authenticity mediates expertise, self-disclosure and purchase intention while validating a balanced self-presentation framework that integrates product familiarity with disclosure intimacy and narrativity. The findings also illuminate how influencers’ curated authenticity in personal branding effectively converts into product sales and sustainable brand value. Practical implications The findings provide guidance for influencers on building authentic personal brands through product expertise and self-disclosure, offer recommendations for platforms to enhance cross-platform collaborations and encourage followers to reflect on the rationale behind their purchase decisions. Originality/value The study examines a business model in which e-commerce influencers as entrepreneurs are both producers and endorsers of their own brands. This role duality transcends traditional influencer marketing approaches and reveals a new pattern of self-commodification.

The meteoric growth of social media has driven firms to leverage influencers in their advertising campaigns, with a preference for those possessing a larger follower base. However, existing research has predominantly examined the role of influencer popularity in isolation, neglecting the interdependencies among influencers within a firm’s campaign. In contrast, this study categorizes influencers into two groups—macro- and micro-influencers—based on their popularity, and examines the effect of within- and cross-group spillovers on influencers’ conversion performance. Leveraging data from a real marketing campaign comprising 163 promotional videos of 111 influencers over a 38-month period and utilizing a fixed-effects panel data regression, we found that both within- and cross-group spillovers are significant solely among micro-influencers. Based on these findings, we discuss the theoretical implications for advertising planning and managerial implications for marketing managers seeking to optimize their influencer marketing expenditure.

PurposePaid knowledge products have become prevalent on online platforms; however, consumers' discontinued consumption has plagued paid knowledge platforms, and the factors affecting user discontinuance have not been fully investigated. This study examined how users' post-usage attitudes toward knowledge products were associated with their discontinuance intention and how the associations were moderated by users' goal orientations.Design/methodology/approachIn this cross-sectional study, 352 users of paid knowledge products completed online surveys. Hierarchical multiple regression analysis was used to examine the effects of post-usage attitudes and goal orientation on post-purchase discontinuance intention.FindingsThe results showed that users' post-usage attitudes toward self-improvement were negatively associated with discontinuance intention, whereas users' attitudes toward self-image and conformity to subjective norms were positively associated with discontinuance intention. Simultaneously, learning goal orientation weakened the negative relationship between self-improvement attitudes and discontinuance intention, whereas performance-oriented goal orientation strengthened the positive relationship between self-image attitudes and discontinuance intention.Originality/valueThis study advances the literature on paid knowledge consumption by identifying consumer post-usage attitudes driven by their pre-usage expectations, including self-improvement, self-image and conformity with norms. In addition, this study extends the expectation disconfirmation theory (EDT), which postulates a positive relationship between post-usage attitudes and user continuance by highlighting the intrinsic and extrinsic nature of user expectations as a boundary condition. Consistent with EDT, users with positive attitudes toward intrinsically driven self-improvement were less likely to discontinue their use. However, in contrast to EDT, users with positive attitudes about extrinsically driven self-image and conformity to norms are more likely to discontinue use. These findings have important implications for practitioners designing different paid knowledge products and retaining users with distinct expectations.

We argue that advances in large language models (LLMs) and generative Artificial Intelligence (AI) will diminish the value of Wikipedia, due to a withdrawal by human content producers, who will withhold their efforts, perceiving less need for their efforts and increased "AI competition." We believe the greatest threat to Wikipedia stems from the fact that Wikipedia is a user-generated product, relying on the "selfish altruism" of its human contributors. Contributors who reduce their contribution efforts as AI pervades the platform, will thus leave Wikipedia increasingly dependent on additional AI activity. This, combined with a dynamic where readership creates authorship and readers being disintermediated, will inevitably cause a vicious cycle leading to a staling of the content and diminishing value of this venerable knowledge resource.

We argue that advances in large language models (LLMs) and generative Artificial Intelligence (AI) will diminish the value of Wikipedia, due to a withdrawal by human content producers, who will withhold their efforts, perceiving less need for their efforts and increased “AI competition.” We believe the greatest threat to Wikipedia stems from the fact that Wikipedia is a user-generated product, relying on the “selfish altruism” of its human contributors. Contributors who reduce their contribution efforts as AI pervades the platform, will thus leave Wikipedia increasingly dependent on additional AI activity. This, combined with a dynamic where readership creates authorship and readers being disintermediated, will inevitably cause a vicious cycle leading to a staling of the content and diminishing value of this venerable knowledge resource.

The emergence of crowdsourcing as a new form of work has introduced a paradox among workers who receive small payments for piecemeal microwork yet continue to participate in microwork digital labor platforms (DLPs). To better understand what sustains microworkers’ participation, this study draws upon individual labor supply theory to quantitatively examine the impacts of microworkers’ motivations, perceptions, and preferences on their labor supply and wages. To explore the meaning of monetary rewards for microworkers, a qualitative inquiry explores microworkers’ spending patterns. Based on a survey of 306 microworkers on Amazon Mechanical Turk, our hierarchical regression analysis reveals that while individual motivations for monetary rewards, enjoyment, and microtime structure have some impact on the microwork labor supply and wages, their impact is limited. Our thematic analysis uncovers diverse meanings attached to microwork earnings. The two most noted are meeting subsistence needs and nonessential expenditures, both of which have positive effects on microwork wages. By investigating the elasticity of the microwork labor supply and wages and offering a nuanced understanding of monetary rewards, our study contributes to information management research on DLPs. Moreover, it provides practical insights for various stakeholders, including microworkers, requesters, and DLP operators.

Getting paid for completing microtasks online via crowdsourcing (i.e., microworking) has become a widely accepted way to earn money. Despite disputes over low pay rates, however, little is known about the extent of “lowness” and about the perceptions of microworkers concerning the value of micro-paid online activity. In an online survey on a microtask crowdsourcing platform, respondents demonstrated the dual attitudes of work and leisure toward microworking. Although actual wage rates were lower than microworkers expected, the perceived value of the money earned from microworking was paramount. The monetary equivalent, a newly developed metric calibrating microworkers’ subjective evaluations of monetary and nonmonetary dimensions, of microworking outstripped that of alternative activities, the majority of which were leisure activities. Instead of struggling with below-expectation pay rates, microworkers tend to appreciate the value of small gains, especially in contrast to potential losses incurred by alternatives activities.

Small payments in micro-task crowdsourcing markets appear unreasonable compared with remunerations for regular work in the workplace, yet hundreds of thousands of micro-tasks are completed each day, and frequently by highly educated individuals. To explain this perplexing anomaly, we investigate individuals’ continuous participation in micro-task crowdsourcing from a time allocation perspective. Drawing upon the theory of the allocation of time, relative advantage over alternative activities and reservation wage of micro-task crowdsourcing affect intent to continue and expected wage respectively, which in turn have effects on intent to increase participation level. Based on previous research on time structure, we propose time structure as another indicator of continuous participation in micro-task crowdsourcing. More importantly, the negative moderating effect of time is conjectured as a salient driver of continuous participation in micro-task crowdsourcing. IT-enabled time structuring thus helps individuals fill dead time with micro-tasking online in spite of low payments.