Given the pace at which emerging technologies are changing retailing, we build an analytical model with one retailer and one technology provider to analyze the cross-period strategies of introducing two complementary technologies. Under the mode choices without cross-period strategies, we find that introducing one technology with low energy consumption by an independent R D mode (IRD) has a greater market advantage for retailers; introducing another technology with high energy consumption by a direct purchase mode (DP) has a greater market advantage. When adopting cross-period strategies of introducing two complementary technologies, retailers face a tradeoff between market dominance and technology upgrading. Market dominance prioritizes technologies with higher market advantages via purchase, and technology upgrading focuses on developing technologies with greater functionality. The preferred cross-period strategy is affected by technological functionality contribution in each period, so that the mode choice of period 1 may not be consistent with that of period 2. Thus, the retailer should incentivizes technology providers to enhance the technological contribution, reduce the energy consumption of emerging technologies. This paper mainly contributes to the important guidelines for improving benefits from any emerging technology and helps retailers better understand the strategic role of emerging technologies.
A 25-year bibliometric retrospective of Information Technology and Management (ITM) characterizes publication growth, citation structure, knowledge foundations, and thematic evolution. A Scopus corpus of 398 citable documents (articles, reviews, and conference papers) for 2000–2024 underpins all ranked performance tables and keyword analyses, complemented by WoS (2007–2024) for science-mapping networks in VOSviewer. The workflow follows a SPAR-4-SLR design and combines productivity and impact indicators with co-citation (journals and documents), bibliographic coupling (documents, authors, institutions, and countries), and keyword co-occurrence analyses implemented via VOSviewer and bibliometrix/biblioshiny, alongside SciVal topic-cluster signals for 2015–2024. Results show stepwise changes in annual output, with a pronounced expansion around 2011–2012 and subsequent stabilization. Citation distributions are highly skewed, with early cohorts containing the strongest outliers and the highest citation density, while recent cohorts exhibit tighter dispersion consistent with shorter citation windows. ITM’s citation canon is anchored in trust, privacy, technology adoption, and IT value creation, while high-velocity recent contributions align with payments, logistics, analytics, and domain-specific IS applications. WoS co-citation structures place ITM at the intersection of core IS journals, decision analytics, enterprise/industrial systems, and behavioral/marketing outlets. Country and institutional patterns indicate a persistent US hub and a marked post-2012 rise in China, reinforcing a dense US–China collaboration axis. SciVal topic clusters highlight strong recent alignment with globally prominent fronts in digital consumer behavior and digital innovation, with particularly high field-weighted impact in recommendation-oriented and selected human-centered analytics clusters. Collectively, the findings firmly position ITM as a mature, internationally integrated journal with durable socio-technical pillars and evolving methodological and topical breadth.
Advanced technologies like Artificial Intelligence (AI) have shown great potential in achieving Sustainable Development Goals (SDGs), yet, little is known on how they influence the key factors that drive firm SDG achievement. This study conducts a systematic literature review and synthesizes scholarly articles, identifying nine theoretical concepts and presenting testable propositions. It argues that the extent of AI implementation strengthens both the internal and external factors driving SDG achievement, ultimately enhancing firm’s environmental and financial performance. The study also identifies key theories and perspectives used in extant literature and proposes multiple research questions to guide future research. Our findings hold implications for practitioners and scholars, demonstrating how organizations can effectively leverage AI for SDG achievement while also benefitting from superior environmental and financial performance.
Internet of Things (IoT) services have developed rapidly in recent years. However, existing research has not yet proposed a mature method for quantitatively evaluating IoT services, nor has it correctly considered the impact of cooperation and competition among IoT service providers on IoT service pricing strategies. This paper constructs a duopolistic Cournot competition model comprising data providers, service providers, and consumers. IoT service providers can utilize machine learning to train models that generate user-oriented IoT services, then market and monetize them, thereby generating profits. This paper introduces a logistic-curve-based service quality function, using the amount of data employed for machine learning as the principal metric of IoT service quality. We then describe the competitive behavior between IoT service providers in a Cournot duopoly market and derive the IoT service pricing and machine learning training data procurement strategies that maximize profits for both parties. Subsequently, we explore the possibility of bundling IoT services at discounted prices and categorize the scenarios into four cases based on the relationship between service quality and pricing. We calculate the pricing of the IoT service bundle and the optimal data quantities for both parties to maximize their profits. Finally, we incorporate a price fluctuation factor to account for market uncertainty, refining the profit function model to better reflect actual market dynamics.
Accurately identifying core technologies is essential for driving technological progress, informing strategic decision-making, and enhancing industrial competitiveness. Existing methods, ranging from expert assessments and quantitative analyses to machine learning techniques, are often hampered by limited adaptability, shallow semantic understanding, and poor time efficiency. To overcome these limitations, we introduce a novel and universally applicable framework that integrates continuous pre-training with multi-task curriculum learning to significantly improve the ability of large language models (LLMs) to identify core technologies and their associated International Patent Classification (IPC) codes across diverse domains. By incorporating extensive, multi-source domain knowledge within a unified continuous pre-training and fine-tuning pipeline, our approach achieves markedly superior semantic comprehension and identification accuracy relative to conventional methods, offering a scalable and effective solution for real-time core technology identification across a wide range of technological domains.
