Manufacturers’ investment in digital technologies serves as an important foundation for supporting their online multi-channel sales expansion and optimal channel selection. Against the practical background of rapid advances in digital technologies, this paper focuses on examining how digital technologies influence manufacturers’ sales channel choices. By segmenting the consumer market and constructing game models under six scenarios, this study further analyzes the underlying mechanism through which digital technology development affects manufacturers’ online sales channel selection. The results show that, first, in the context of no channel competition, manufacturers will choose the platform-embedded platform (PEP) model to cooperate with small and medium-sized e-commerce (SMS e-) platforms when the referral fee rate of well-known e-commerce (WK e-) platforms is low and consumer awareness of WK e-platforms is high. In contrast, when both the referral fee rate of WK e-platforms and consumer awareness of SMS e-platforms are at a high level, the reselling will become manufacturers’ optimal choice of cooperation channel instead of the agency selling, due to the fact that the reselling channel facilitates cost control while the agency selling channel is prone to profit squeeze. Second, in the context of channel competition, manufacturers will adopt the reselling channel to cooperate with SMS e-platforms for sales when the service quality of WK e-platforms is moderate. However, manufacturers will opt for the PEP channel when the service quality of WK e-platforms is low. Interestingly, when the service quality of WK e-platforms is high, conducting sales through the PEP channel can drive both manufacturers and SMS e-platforms to achieve optimal profits simultaneously, resulting in a win–win situation. Finally, from an overall perspective, a high service quality of WK e-platforms, low direct selling efficiency of manufacturers, and high consumer awareness of WK e-platforms will all stimulate manufacturers to engage in channel competition. On the contrary, when manufacturers’ direct selling efficiency reaches an extremely high level, they will abandon channel competition and conduct sales exclusively through SMS e-platforms. This study provides theoretical and practical implications for manufacturers to optimize their digital strategies and select optimal channel portfolios.
This study examines the intellectual structure of the journal Electronic Commerce Research over its first twenty-five years and frames a forward-looking research agenda. Using reference co-citation analysis, the study identifies the knowledge base, major thematic domains, temporal evolution, and key turning points that have shaped the journal’s development. The findings show a progressive expansion from early concerns with adoption, usability, trust, reputation, security, and e-finance toward more recent themes related to social influence, cross-border platforms, omnichannel systems, algorithmic personalization, live commerce, and AI-mediated markets. Citation burst analysis further reveals that the field evolves through accumulation and recombination rather than abrupt thematic replacement, as influential references consolidate specific clusters and connect adjacent research streams over time. Building on this trajectory, the article proposes a research agenda centered on agentic commerce, truth signals, platform governance, human–AI and agent-to-agent interaction, foundation models for high-fidelity market simulation, financial-product market convergence, and the AI-enabled reorganization of e-commerce firms.
The rapid integration of artificial intelligence and related emerging technologies has intensified data privacy challenges across the digital economy. Although scholarship has grown substantially, it remains fragmented across technical, legal, ethical, and sector-specific domains, limiting a clear view of the field’s structure and development. This study presents a bibliometric analysis and thematic mapping of 434 peer-reviewed articles published between 2016 and 2026 in the Web of Science Core Collection. It identifies seven thematic clusters and finds that applied and privacy-preserving technical work dominates, while business strategy, trust, and public-sector governance remain underdeveloped. The study extends this mapping with a multi-dimensional privacy taxonomy and a TCCM (theory, context, characteristics, and methodology) synthesis, moving beyond the topic- and domain-specific scope of prior reviews to diagnose where the field is structurally weak. It argues that privacy remains under-theorized as an economic and behavioral phenomenon despite its centrality to digital commerce and sets out a research agenda of economically grounded questions derived from these gaps.
Online consumer reviews are a vital source of market insight, yet existing research largely assumes that reviews reflect only post-consumption evaluations. This study challenges that assumption by revealing that pre-consumption factors, specifically consumers’ self-disclosed needs, play a decisive role in shaping both how reviews are written and how they are perceived. Drawing on person–environment fit theory, we examine how the (mis)alignment between consumer needs and product functions—referred to as consumer–product (mis)match—influences the content and linguistic characteristics of online reviews and their impact on satisfaction and perceived helpfulness. Using text-mining and econometric analyses of reviews from The Body Shop, we find that incorporating pre-consumption needs provides a more complete understanding of review behavior. Consumers with explicit needs write more detailed and diagnostic reviews, while need–function alignment leads to lower linguistic diversity, higher readability, and reduced subjectivity. Moreover, need–function mismatches moderate how review characteristics reflect both reviewer satisfaction and reader judgments of helpfulness. By linking pre- and post-consumption perspectives, this study reframes online reviews as cognitive and linguistic manifestations of expectation confirmation and disconfirmation. The findings offer actionable insights for businesses to better capture, interpret, and respond to consumer needs in designing products and managing online review systems.
Heterogeneous influencers (i.e., strong and weak influencers) coexist on live streaming e-commerce platforms. However, strong influencers with substantial fan base and strong sales abilities could potentially cannibalize the market share of weak influencers, leading to an imbalance in platforms’ ecosystems. To mitigate these negative impacts, platforms have begun to support and subsidize weak influencers with cash and flow subsidies. In this paper, we consider the coexistence and heterogeneity of strong and weak influencers to investigate the optimal subsidy strategy for platforms and for weak influencers, and analyze the impacts of platform subsidies. Our findings indicate that although cash subsidy brings an additional cost to the platform, it still prefers cash subsidy to flow subsidy under low or high hassle cost. Besides, the weak influencer benefits more from flow subsidy than from cash subsidy when the hassle cost is low, and the opposite holds when the hassle cost is high. Interestingly, we find that while the weak influencer does not necessarily benefit, the platform always benefits from its subsidy strategy. This indicates that the platforms leverage subsidy strategies to balance the competitive ecosystem and provides an explanation for why platforms implement subsidies in practice.
