Blockchain technology (BCT) has emerged as a promising solution for ensuring supply chain traceability. However, not all consumers have a comprehensive understanding of the benefits associated with BCT-enabled traceability. In this article, we investigate the impacts of consumer awareness on the adoption of BCT within a supply chain comprising a manufacturer and a retailer. We develop two distinct scenarios: Scenario B, where the supply chain traceability is managed via traditional digital systems, and Scenario E, where the supply chain traceability is managed via BCT-enabled systems. We introduce the concept of consumer traceability awareness level, representing the proportion of the consumer population that is knowledgeable about the advantages of these traceability technologies. The findings reveal that the adoption of BCT enhances the overall performance of the supply chain and makes it more sensitive to the consumer traceability awareness level. Nonetheless, the manufacturer consistently experiences advantages from BCT adoption, whereas the retailer's situation may deteriorate. In both scenarios, a low traceability awareness level prompts the retailer to target all consumers, whereas a high-traceability awareness level shifts its focus solely to the knowledgeable consumers. Intriguingly, the adoption of BCT shifts the retailer's inclination toward targeting the knowledgeable consumers rather than all consumers.

Although the transformative potential of artificial intelligence (AI) in driving innovation is widely acknowledged, the relationship between AI-adoption intensity and firm innovation performance remains underexplored. To address this gap, this study investigates the impact of AI-adoption intensity on firm innovation performance through the lens of technology affordance theory (TAT), emphasizing the role of AI affordances. Additionally, it examines the moderating role of data quality in shaping the relationship between AI-adoption intensity and AI affordances. A conceptual model was developed and tested through structural equation modeling (SEM) using data collected from 396 Chinese firms. The findings demonstrate that AI-adoption intensity significantly enhances firm innovation performance. AI affordances—conceptualized along three dimensions: mobility affordance, interactivity affordance, and autonomy affordance—mediate the effect of AI-adoption intensity on firm innovation performance. These affordances collectively support AI's dual roles as a supportive tool and an innovation agent. Furthermore, data quality positively moderates the relationship between AI-adoption intensity and AI affordances, underscoring the importance of data quality in realizing AI's potential benefits. This article advances our understanding of leveraging AI to bolster innovation outcomes and extends the theoretical foundations of the TAT within the context of AI innovation. These insights provide valuable guidance for firms seeking to enhance their innovation outcomes through AI adoption.

IT-enabled transformation (ITT) programs are increasingly vulnerable to creeping disruptions. Previous studies have proposed a range of project team resilience activities to address types of disruption, suggesting that different team resilience capacities require different resources. Drawing on the capacity view of team resilience, this study adopts a mixed-methods research approach. The qualitative phase reveals that (1) goal volatility is the most common creeping disruption, while anticipating change, planning adjustment, and adapting to new practices are the three components of ITT program team resilience; and (2) change potency, program psychological safety, business understanding, and IT improvisation are key resources in fostering ITT program resilience capacity. A quantitative survey of 177 ITT programs was used to externally validate and extend the qualitative findings. Results confirmed that ITT program team resilience significantly enhances program performance, particularly under conditions of high program goal volatility, and four identified resources are positively associated with ITT program resilience capacity. This study extends the literature on IT project management by distinguishing program team resilience in the ITT program context from project team resilience.

In an era where generative AI (GenAI) is reshaping industries, public understanding of this phenomenon remains limited. This study addresses this gap by analyzing public beliefs about GenAI using the Technology Acceptance Model and Diffusion of Innovations Theory as frameworks. We adopted a big-data approach, utilizing machine-learning techniques to analyze 21,817 public comments extracted from an initial set of 32,707 on 44 YouTube videos discussing GenAI. Our investigation surfaced six pivotal themes: concerns over job and economic impacts, GenAI's potential to revolutionize problem-solving, its perceived shortcomings in creativity and emotional intelligence, the proliferation of misinformation, existential risks, and privacy decay. Emotion analysis showed that negative emotions dominated at 58.46%, including anger (22.85%) and disgust (17.26%). Sentiment analysis echoed this negativity, with 70% negative. The triangulation of thematic, emotional, and sentiment analyses highlighted a polarized public stance: recognition of GenAI's transformative potential is tempered by significant concerns about its implications. The findings offer actionable insights for engineering managers and policymakers. Strategies such as awareness-building, transparency, public engagement, balanced communication, governance, and human-centered development can address polarization and build trust. Ongoing research into public opinion remains essential for aligning technological advancements with societal expectations and acceptance.

As e-commerce platforms play an increasingly important intermediary role in supply chains, manufacturers must decide between two sales models offered by platforms: Business-to-Consumer (B2C), where manufacturers sell directly to consumers, and Business-to-Business-to-Consumer (B2B2C), where manufacturers sell to a platform that markets products to consumers. Manufacturers operating in the B2B2C channel typically sign <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">profit-margin-guarantee</i> (PMG) contracts requiring them to ensure minimum profit margins for the platform under specified conditions. While providing manufacturers with sales opportunities, this exacerbates the financial burden on small- and medium-size manufacturers (SMEs) with limited capital. This study explores the optimal financing decisions and sales channel choices for manufacturers under the Factoring Financing (FF) model, where manufacturers sell their accounts receivable to a factor entity to alleviate liquidity constraints. We find: 1) The initial accounts receivable level significantly influences manufacturers' sales channel choice—when accounts receivable are low, the B2B2C channel's PMG contracts may not be advantageous for manufacturers, but when they are high, dual-channel operations (B2C and B2B2C) may benefit the platform, although manufacturers' gains might be limited. 2) Higher FF ratios do not consistently enhance manufacturers' performance, as overreliance on financing can increase operational risks. 3) Although conventional wisdom suggests that manufacturers should lower wholesale prices to mitigate the pressures of PMG contracts, platforms can foster beneficial arrangements by reducing the PMG rate, which improves overall supply chain efficiency. We develop a robust theoretical framework for channel choice, contract design, and financing strategies, providing actionable insights for SMEs to optimize e-commerce supply chain operations.

To enhance competitiveness and curb sourcing costs, many manufacturers now turn to investments in independent R&D of core components (e.g., chips for smartphones or batteries for electrical vehicles). However, R&D is expensive, lengthy and inherently uncertain. Knowing that R&D success is not guaranteed, should manufacturers abandon the supplier and turn to in-house production (the Abandon policy) or maintain the supplier as a complementary source (the Retain policy)? We model this situation as a two-stage game, derive equilibrium solutions under both policies, and identify the manufacturer's optimal strategy under convex production costs. Our results reveal interesting managerial insights. The Retain policy always provides sourcing cost relief to the manufacturer, a benefit absent under the Abandon policy. An increase in the manufacturer's technology level can always benefit the manufacturer under the Abandon policy, which is not the case under the Retain policy. A counter-intuitive finding is that an increase in supplier's cost may possibly benefit all supply chain members under the Retain policy. Furthermore, we provide guidance on policy choices that the manufacturer with a sufficiently high level of technology should adopt the Abandon policy. Conversely, the manufacturer with a low level of technology should adopt the Retain Policy. Finally, we find that the timing of supplier pricing and linear in-house production costs do not affect the main conclusions, but the manufacturer consistently prefers the Abandon policy under concave costs.

With the maturity of digital technology, manufacturing platforms that achieve the connection between productions and sales are becoming increasingly common. For a two-level manufacturing platform supply chain comprising a manufacturing platform, a manufacturer of the check-in platform, and retailers, this paper constructs a noncooperative-cooperative biform game model to study the green R&D with technology spillovers and pricing in co-opetition situation. The co-opetition is reflected in price competition and green competition between the manufacturing platform and the manufacturer, and the revenue sharing between online and offline channels. The coupling mechanism between competition and cooperation is revealed. Cooperation stability is proved by convexity of the cooperative game and individual rationality of allocation values. By a numerical example, our results reveal that increasing online channel acceptance will decrease the manufacturing platform's profit, which is surprising and counter-intuitive. A unidirectional technology spillover will reduce the price, green R&D level, and profit of the enterprise. But moderate technology spillover of the manufacturer can improve social welfare. In order to accomplish a win-win situation for enterprises and society, the manufacturing platform can get low-level technology spillover of the manufacturer by green R&D cooperation. Our paper provides theoretical guidance for the co-opetition in platform supply chain.

Scientific breakthroughs often emerge not only from formal R&D project structures, but also from emergent collaborations beyond those boundaries. This study theorizes that network stability and inconsistency jointly influence scientific performance via two distinct search scopes at the paper and project levels. While moderate network stability facilitates both coordination and integration, this curvilinear effect is amplified only at high levels of network inconsistency, allowing teams to leverage stable ties without becoming rigid. To explain the tradeoff of stable collaborations, we identify two opposing mechanisms: stability expands paper search scope yet narrows project search scope by limiting the team to similar research trajectories. We test hypotheses using a dual-network dataset of 5566 papers derived from 12 562 government-funded biotechnology projects in South Korea (2012–2020), based on project and research collaboration networks. Our findings advance research on boundary-spanning, search behavior, and project-based organizing by revealing how structural configurations in temporary collaborations influence both the direction and diversity of scientific performance.

Introducing a new product into a retail assortment presents significant challenges for inventory planning, particularly when an existing product is nearing the end of its life cycle. These challenges are amplified by fluctuating daily demand and the need for optimal replenishment strategies. Our study addresses these challenges through a system dynamics modeling approach that simulates the impact of new product demand patterns on inventory decisions. By examining the interplay between new and existing product demands, the model highlights the critical importance of setting a minimum demand threshold to prevent premature phase-out and mitigate stockouts. The analysis also emphasizes the need to incorporate substitution effects into inventory planning frameworks to enhance decision-making and develop robust management systems. Additionally, our research explores how demand patterns for newly introduced competing products can determine whether an existing product is phased out early or retained for an extended period. The study reveals that assortment choices are more intricate for competing products characterized by fluctuating demand patterns. The study also highlights the role of safety stock adjustments, review periods, and demand forecasting time windows to mitigate stockouts and reduce lost sales. Using empirical data from a fashion retailer, the simulations yield actionable insights, emphasizing the need for accurate demand forecasting and effective replenishment strategies. More importantly, this work offers practical guidance for retail operations managers to optimize product assortment timelines and manage safety stock levels to reduce lost sales and improve inventory management systems.