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The purpose of this study is to explore the coopetition relationships between platform owners and complementors in complementary product markets. Drawing on the coopetition theory, the authors examined the evolutionary trends of the coopetition relationships between platform owners and complementors and explore the main influence factors.

The authors used Lotka–Volterra model to analyze the coopetition relationship between platform owners and complementors, including the evolutionary trends as well as the results. Considering the feasibility of sample data collection, simulation is used to verify the effects of different factors on the evolution of coopetition relationships.

The results show that there are four possible results of the competition in the complementary products market. That comprises “winner-take-all for platform owners,” “winner-take-all for complementors,” “stable competitive coexistence” and “unstable competitive coexistence,” where “stable competitive coexistence” is the optimal evolutionary state. Moreover, the results of competitive evolution are determined by innovation subjects’ interaction parameters. However, the natural growth rate, the initial market benefits of the two innovators and the overall benefits of the complementary product markets influence the time to reach a steady state.

The study provides new insights into the entry of platform owners into complementary markets, and the findings highlight the fact that in complementary product markets, platform owners and complementors should seek “competitive coexistence” rather than “winner-takes-all.” Moreover, the authors also enrich the coopetition theory by revealing the core factors that influence the evolution of coopetition relationships, which further enhance the analysis of the evolutionary process of coopetition relationships.

The study aims to explore the digital fashion trend within the Metaverse, characterized by non-fungible tokens (NFTs), across Twitter networks. Integrating theories of diffusion of innovation, two-step flow of communication and self-efficacy, the authors aimed to uncover the diffusion structure and the influencer's social roles undertaken by social entities in fostering communication and collaboration for the advancement of Metaverse fashion.

Social network analysis examined the critical graph metrics to profile, visualize, and cluster the unstructured network data. The authors used the NodeXL program to analyze two hashtag keyword networks, “#metaverse fashion” and “#metawear,” using Twitter API data. Cluster, semantic, and time series analyses were performed to visualize the contents and contexts of communication and collaboration in the diffusion of Metaverse fashion.

The results unraveled the “broadcast network” structure and the influencers' social roles of opinion leaders and market mavens within Twitter's “#metaverse fashion” diffusion. The roles of innovators and early adopters among influencers were comparable in collaborating within the competition venues, promoting awareness and participation in digital fashion diffusion during specific “fad” periods, particularly when digital fashion NFTs and cryptocurrencies became intertwined with the competition in the Metaverse.

The study contributed to theory building by integrating three theories, emphasizing effective communication and collaboration among influencers, organizations, and competition venues in broadcasting digital fashion within shared networks. The validation of multi-faceted Social Network Analysis was crucial for timely insights, highlighting the critical digital fashion equity in capturing consumers' attention and driving engagement and ownership of Metaverse fashion.

Container liner shipping companies started expanding their business by investing in container port terminals in the late 1990s. This market entry results in an extensive presence of vertically integrated liners and terminals. This study aims to explore the competition effects of this vertical integration trend based on a regional (European) analysis. In particular, it extracts lessons from the European Commission (EC) cases on the competition effects of vertical integration. The critical analysis of the cases examined at the institutional level intends to reach conclusions on whether liner–terminal vertical integration harmed or advanced competition in the relevant markets and/or the extent that there is a need to revise the current policy practices.

This study critically assesses the EC’s decisional practices in port container terminal vertical mergers in the last 25 years (1997–2021). Based on a literature review comparing maritime and competition economists' perspectives, it reviews the types of mergers examined, the methodology followed for relevant market definition and calculation of market shares and the estimated competition effects. The Hamburg–Le Havre area is the port range used as a case study for comparing the decisional practice with actual market developments. These container ports serve the greatest consuming market of final and intermediate goods in Europe and are gateways to Central and Eastern Europe.

The assessment identifies a need for expanding the investigation as a precondition for reaching conclusions on both the anti- and pro-competitive effects. First, only a limited number of transactions have been notified to the EC. Second, the empirical research identified a gap in this process, as there were no decisions (phase I) on vertical mergers between 2008 and 2016. Third, the exante assessment has not applied a phase II in-depth analysis to any case due to the absence of competition concerns. Finally, due to the absence of complaints, there is a lack of any ex post assessment of the effects of vertical integration.

This assessment is important for understanding the current and emerging features of intra-port and inter-port competition and the potential effects that the continuation and expansion of liner companies' vertical integration strategies will have along maritime supply chains. It also contributes to the broader discussion on liner companies' strategies, such as the research and policy-making efforts around the globe to understand the impact of both vertical and horizontal integration.

These discussions are critical for a diversity of businesses that use liner shipping services or provide facilities and services to container shipping lines or ports. They are important for the interests of customers and consumers as they could inform any needed re-visiting of competition policy to protect from the dominance of any market developments that would lead to conditions limiting competition. Expanding analysis on the competition effects of non-notified mergers would help a better understanding of market changes.

Enhancing competition and limiting monopolies is valuable from a consumer's perspective. This is more so in the case of maritime trade that serves the needs of societies. The study contributes by generating a better understanding of how decision-makers have worked towards that direction and what realignments are worthy.

There are no previous comprehensive reviews and analyses of the ways that policy-makers at the regional level have addressed the competition effects of vertical integration strategies of liner shipping companies when enhancing competition is valuable from a consumer perspective. Comparing maritime economists and competition, the study, via its literature review, also offers a comparison of maritime and competition perspectives on these competition effects, allowing positioning of how effective decisional-making practices have been.

This paper aims to explore the promotion decisions of heterogeneous sellers on a decentralized platform under competitive conditions and analyze how seller behaviors impact platform profit, seller revenue, buyer surplus and social welfare.

This paper considers a Cournot model consisting of a platform charging a commission rate and two sellers with different conversion rates and browsing costs. Promotion efforts by sellers can increase traffic, but they also incur promotion costs for sellers. The sellers decide on promotion effort by weighing these two effects. The authors also explore the equilibrium when the platform charges a fixed usage fee.

The seller’s profit improves as its conversion rate increases and worsens as browsing costs increase. Also, increasing the commission rate charged by the platform makes the seller invest less in promotional efforts. Therefore, the platform must consider this trade-off to determine an optimal rate. The analysis shows that the seller with a high conversion rate and high browsing cost plays a greater role in generating more overall revenue. When the market favors such a seller, the platform tends to charge less in order not to impair its profitability.

This paper incorporates conversion rate, buyer’s browsing cost, unit promotion cost and the fee charged by the platform into the model to study sellers’ promotion decisions on decentralized platforms.

This research paper aims to investigate the critical factors influencing the live commerce industry and their implications for Key Opinion Leaders (KOLs) and brands.

The study comprehensively reviews previous research, develops relevant hypotheses and utilizes personal information from 66 anchors, along with data from 23,000 product links obtained from the backends of live commerce platforms.

The study emphasizes that KOLs with higher traffic significantly influence Gross Merchandise Volume (GMV). Intriguingly, KOLs with lower traffic levels exhibit a more pronounced effect on Return on Investment (ROI), highlighting their significance in driving profitability. Furthermore, the study explores the correlation between KOL hashtags and GMV/ROI and the intricate relationship between product types and KOL hashtags.

The findings significantly enhance the understanding of live shopping behavior and provide valuable insights for business management strategies. Practitioners can leverage this empirical evidence to make informed decisions, utilizing extensive data samples of KOLs and brands.

This research contributes unique insights into the live-streaming commerce industry using backend data from Live Streaming E-commerce platforms. The findings are more accurate based on market data than previous studies that relied on platform reviews or questionnaires. Additionally, this paper investigates the impact of KOLs on the performance of live e-commerce from three perspectives: GMV, ROI and hot-selling products.

This case reviews the development of Dianping. After seeing Zagat's unique business model in the United States, founder Zhang Tao found that he could bring it to China and bring about local innovation. At the beginning of its establishment, the collection and promotion of comment content was the major challenge for Dianping. At the same time, Dianping faced legal issues. To solve these problems, the review mechanism of Dianping was designed to a certain extent to ensure the fairness of the review. With the advent of the mobile Internet era, Dianping began to develop a new business model. Relying on its high-quality “word-of-mouth” content and mass basis, Dianping launched group buying, online restaurant ordering, and other businesses. Dianping has always been open to strategic partners. Since 2015, Dianping has undergone historical changes, merging with Meituan. Since then, Dianping has continuously adjusted its business and organizational structure to maintain its competitiveness. Gradually, Dianping has changed from an independent business entity into a business unit of Meituan.

The study aims to propose an intelligent real-time traffic model to address the traffic congestion problem. The proposed model assists the urban population in their everyday lives by assessing the probability of road accidents and accurate traffic information prediction. It also helps in reducing overall carbon dioxide emissions in the environment and assists the urban population in their everyday lives by increasing overall transportation quality.

This study offered a real-time traffic model based on the analysis of numerous sensor data. Real-time traffic prediction systems can identify and visualize current traffic conditions on a particular lane. The proposed model incorporated data from road sensors as well as a variety of other sources. It is difficult to capture and process large amounts of sensor data in real time. Sensor data is consumed by streaming analytics platforms that use big data technologies, which is then processed using a range of deep learning and machine learning techniques.

The study provided in this paper would fill a gap in the data analytics sector by delivering a more accurate and trustworthy model that uses internet of things sensor data and other data sources. This method can also assist organizations such as transit agencies and public safety departments in making strategic decisions by incorporating it into their platforms.

The model has a big flaw in that it makes predictions for the period following January 2020 that are not particularly accurate. This, however, is not a flaw in the model; rather, it is a flaw in Covid-19, the global epidemic. The global pandemic has impacted the traffic scenario, resulting in erratic data for the period after February 2020. However, once the circumstance returns to normal, the authors are confident in their model’s ability to produce accurate forecasts.

To help users choose when to go, this study intended to pinpoint the causes of traffic congestion on the highways in the Bay Area as well as forecast real-time traffic speeds. To determine the best attributes that influence traffic speed in this study, the authors obtained data from the Caltrans performance measurement system (PeMS), reviewed it and used multiple models. The authors developed a model that can forecast traffic speed while accounting for outside variables like weather and incident data, with decent accuracy and generalizability. To assist users in determining traffic congestion at a certain location on a specific day, the forecast method uses a graphical user interface. This user interface has been designed to be readily expanded in the future as the project’s scope and usefulness increase. The authors’ Web-based traffic speed prediction platform is useful for both municipal planners and individual travellers. The authors were able to get excellent results by using five years of data (2015–2019) to train the models and forecast outcomes for 2020 data. The authors’ algorithm produced highly accurate predictions when tested using data from January 2020. The benefits of this model include accurate traffic speed forecasts for California’s four main freeways (Freeway 101, I-680, 880 and 280) for a specific place on a certain date. The scalable model performs better than the vast majority of earlier models created by other scholars in the field. The government would benefit from better planning and execution of new transportation projects if this programme were to be extended across the entire state of California. This initiative could be expanded to include the full state of California, assisting the government in better planning and implementing new transportation projects.

To estimate traffic congestion, the proposed model takes into account a variety of data sources, including weather and incident data. According to traffic congestion statistics, “bottlenecks” account for 40% of traffic congestion, “traffic incidents” account for 25% and “work zones” account for 10% (Traffic Congestion Statistics). As a result, incident data must be considered for analysis. The study uses traffic, weather and event data from the previous five years to estimate traffic congestion in any given area. As a result, the results predicted by the proposed model would be more accurate, and commuters who need to schedule ahead of time for work would benefit greatly.

The proposed work allows the user to choose the optimum time and mode of transportation for them. The underlying idea behind this model is that if a car spends more time on the road, it will cause traffic congestion. The proposed system encourages users to arrive at their location in a short period of time. Congestion is an indicator that public transportation needs to be expanded. The optimum route is compared to other kinds of public transit using this methodology (Greenfield, 2014). If the commute time is comparable to that of private car transportation during peak hours, consumers should take public transportation.

The purpose of this study is to compare the competition and productivity of the US freight rail transportation industry for the past 41 years (1980 ∼ 2020), which consists of the two periods, before and after the abolishment of the Interstate Commerce Commission (ICC) in 1995.

This study investigates any relationships between the market concentration index values and labor productivity values in the separate two periods, and how the existence of a regulatory body in the freight transportation market impacted the productivity of the freight rail transportation industry by using a Cobb–Douglas production function on annual financial statement data from the US stock exchange market.

This study found that, after the abolishment of the ICC: (1) the rail industry became less competitive, (2) even if the rail industry had an increasing labor productivity trend, there was a strong negative correlation between the market concentration index and labor productivity and (3) the rail industry’s total factor productivity was decreased.

This study is to find empirical evidence of the effect of the ICC abolishment on the competition and productivity levels in the US freight rail transportation industry using a continuous data set of 41-year financial statements, which is unique compared to previous studies.

The study aims to analyze factors impacting firms’ success and persistence in a digital platform competition using the structure-conduct-performance (SCP) framework. The study also includes real-life cases that are beneficial to academicians and practitioners to understand and develop strategies for success and persistence during uncertainty.

A literature review to identify the factors that impact success and persistence in a digital platform competition was conducted following Webster and Watson (2002). Findings were integrated into a SCP framework to examine and understand the identified factors’ relational impact.

While analyzing factors under the SCP framework, all factors were divided into three categories: those impacting positively, those impacting negatively and those with ambiguous impact on the success and persistence in digital platform competition. Digital platform firms can exploit the positively impacting factors to increase market share by being distinctive from other digital platform firms and becoming dominant by withstanding competition. On the other hand, negatively impacting factors increase barriers to entry, intensify competition and reduce the distinctiveness of digital platform firms. Lastly, a few factors may have either a positive or a negative impact depending upon the particular characteristics of the firm/industry.

The study opens the scope for future research on empirically testing the developed conceptual framework and relationships by developing propositions to posit the possible impact of these factors on digital platforms’ success and persistence.

The study contributed to the existing literature by using SCP framework to analyze the factors affecting firm’s success and persistence in a digital platform competition. Also, the study has discussed the relational impact of factors rather than their impact in isolation.

A major source of external funding for US airports comes from issuing municipal bonds. Credit rating agencies evaluate the bonds using multiple factors, but the judgments behind the ratings are not well understood. This paper examines if airport rate-setting methods affect the bond ratings of US airports.

Using a set of unbalanced panel data for 58 hub airports from 2010 to 2019, we examine the effect of the rate-setting methods and other airport characteristics on Fitch’s airport bond rating.

We find that compensatory airports consistently receive a very high bond rating from Fitch. The probability of getting a very high Fitch rating increases by ∼28 percentage points for a compensatory airport. Additionally, the probability of getting a very high rating is about 33 percentage points higher for a legacy hub.

The study uses Fitch bond ratings. Future studies could examine if S&P’s and Moody’s ratings are also influenced by airport rate-setting methods and legacy hub status.

The results uncover the linkage between bond ratings and their determinants for US airports. This information is important for investors when assessing airport creditworthiness and for airport operators as they manage capital project financing.

This is the first study to evaluate the effects of rate-setting methods on airport bond rating and also the first to document a statistically significant relationship between airports’ legacy hub status and bond ratings.