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In the era of artificial intelligence (AI), algorithms have gained unprecedented importance. Scientific studies have shown that algorithms are frequently mentioned in papers, making mention frequency a classical indicator of their popularity and influence. However, contemporary methods for evaluating influence tend to focus solely on individual algorithms, disregarding the collective impact resulting from the interconnectedness of these algorithms, which can provide a new way to reveal their roles and importance within algorithm clusters. This paper aims to build the co-occurrence network of algorithms in the natural language processing field based on the full-text content of academic papers and analyze the academic influence of algorithms in the group based on the features of the network.

We use deep learning models to extract algorithm entities from articles and construct the whole, cumulative and annual co-occurrence networks. We first analyze the characteristics of algorithm networks and then use various centrality metrics to obtain the score and ranking of group influence for each algorithm in the whole domain and each year. Finally, we analyze the influence evolution of different representative algorithms.

The results indicate that algorithm networks also have the characteristics of complex networks, with tight connections between nodes developing over approximately four decades. For different algorithms, algorithms that are classic, high-performing and appear at the junctions of different eras can possess high popularity, control, central position and balanced influence in the network. As an algorithm gradually diminishes its sway within the group, it typically loses its core position first, followed by a dwindling association with other algorithms.

To the best of the authors’ knowledge, this paper is the first large-scale analysis of algorithm networks. The extensive temporal coverage, spanning over four decades of academic publications, ensures the depth and integrity of the network. Our results serve as a cornerstone for constructing multifaceted networks interlinking algorithms, scholars and tasks, facilitating future exploration of their scientific roles and semantic relations.

This study aimed to examine the mental health information entities and associations between the biomedical, psychological and social domains of bipolar disorder (BD) by analyzing social media data and scientific literature.

Reddit posts and full-text papers from PubMed Central (PMC) were collected. The text analysis was used to create a psychological dictionary. The text mining tools were applied to extract BD entities and their relationships in the datasets using a dictionary- and rule-based approach. Lastly, social network analysis and visualization were employed to view the associations.

Mental health information on the drug side effects entity was detected frequently in both datasets. In the affective category, the most frequent entities were “depressed” and “severe” in the social media and PMC data, respectively. The social and personal concerns entities that related to friends, family, self-attitude and economy were found repeatedly in the Reddit data. The relationships between the biomedical and psychological processes, “afraid” and “Lithium” and “schizophrenia” and “suicidal,” were identified often in the social media and PMC data, respectively.

Mental health information has been increasingly sought-after, and BD is a mental illness with complicated factors in the clinical picture. This paper has made an original contribution to comprehending the biological, psychological and social factors of BD. Importantly, these results have highlighted the benefit of mental health informatics that can be analyzed in the laboratory and social media domains.

This research paper aims to investigate detailed relationships between market selection and product positioning decisions and their associated short- and long-term product performance outcomes in the context of the music category: a cultural goods industry with high amounts of product introductions. Market selection decisions are defined by the size, competitiveness and age of market subcategories within an overall product category. Positioning decisions include where a product’s attributes are located spatially in the category (periphery versus the market center), whether a product resides within a single subcategory or spans multiple ones and what brand strategy (single versus co-branding) is used.

Data are from multiple sources for the US music industry (aka product category) from 1958 to 2019 to empirically test the hypotheses: genres (rock, blues, etc.) correspond to subcategories; artists to brands; and songs to products. Regression analyses are used.

A complex set of nuanced results are generated and reported, finding that key marketing decisions drive short-term new product success differently and frequently in opposing ways than long-term success. Launching into very new, well-established or very competitive markets leads to the strongest long-term success, despite less attractive short-run prospects. Positioning a product away from the market center and spanning subcategories similarly poses short-run challenges, but long-run returns. Brand collaborations have reverse effects. Short-run product success is found, overall, to be difficult to predict even with strong data inputs, which has substantial implications for how firms should manage portfolios of products in cultural goods industries. Long-run product success is considerably more predictable after short-run success is observed and accounted for.

While managers and firms in cultural goods industries have long relied on intuition to manage market selection and product positioning decisions, this research tests the hypothesis that objective data inputs and empirical modeling can better predict short- and long-run success of launched products. Specific insights on which song characteristics may be associated with success are found – as are more generalizable, industry-level results. In addition, by distinguishing between short- and long-run success, a more complete picture on how key decisions holistically affect product performance emerges. Many market selection and product positioning decisions have differential impacts across these two frames of reference.

To answer the questions: what roles windows of opportunity act in the catchup process of latecomers, what strategies latecomer enterprises should adopt to size windows of opportunity to catch-up with incumbents even going beyond?

This paper studies the catch-up history of the Chinese mobile phone industry and proposes a sectoral innovation system under scenario of technology paradigm shifts. Then a history-friendly simulation model and counterfactual analysis are conducted to learn how different windows of opportunity and catch-up strategies influence the catch-up performance of latecomers.

Results show latecomers can catch up with technology ability by utilizing technology window and path-creating strategy. However, catching up with the market is not guaranteed. Demand window can help latecomers to catch up with market as it increases their survival rates, different sized windows benefit different strategies. However, it also enlarges incumbents' scale effect. Without technology window technology catch up is not guaranteed. Two windows have combination effects. Demand window affects the “degree” of change in survival rates, while the technology window affects the “speed” of change. Demand window provides security; technology window provides the possibility of a breakthrough for technology ability.

The findings of this paper provide theoretical guidance for latecomer enterprises to choose appropriate catch-up strategies to seize different opportunity windows.

This paper emphasizes the abrupt change of industrial innovation system caused by technology paradigm shifts, which makes up for the shortcomings of previous researches on industrial innovation system which either studied the influence of static factors or based on the influence of continuous changes.

Underpinned by the legitimacy perspective, this study explores how ride-hailing services are legitimized through resource exchange among the industry players. The authors explore the types of legitimacy involved in the legitimation process. The authors also examine the political games being played by the actors to attain legitimacy.

This qualitative study involves thirty-one stakeholders/interviewees from emerging Asia. The interview data are supported by online documents and observations.

Thematic analysis shows that the industry players collaborate to achieve political, market, alliance, social, and investment legitimacy. The collaborations also legitimize industry players' existence through an eclectic mix of the numerous stakeholders' actions. This study shows how Dacin's proposed four types of legitimacy are coexisting and interconnected. It also highlights the neglected political legitimacy.

The findings guide the policymakers and ride-hailing operators experiencing competing requests to legitimize sustainable ride-hailing service development in urban cities.

The purpose of this paper is to discuss how enterprises can effectively perceive and use the digital opportunities brought about by digital technologies and dynamic environments and how they can enhance their capabilities to realize digital transformation and adapt to the development of the digital economy era.

Based on the windows of opportunity theory and strategic cognition theory, this paper conducts an empirical analysis of the questionnaire data of 268 enterprises and discusses the influence of external windows of opportunity and internal windows of opportunity on the digital transformation of enterprises, as well as the action mechanism of strategic cognition and entrepreneurship.

The results show that both the external windows of opportunity and the internal windows of opportunity have significant positive effects on the digital transformation of enterprises. Strategic cognition plays a partial mediating role in the external windows of opportunity and the internal windows of opportunity influencing the enterprise digital transformation process. Entrepreneurship plays a positive regulatory role in the process of external windows of opportunity and internal windows of opportunity influencing strategic cognition.

This paper deepens the relationship between internal and external windows of opportunity and enterprise digital transformation and contributes a new theoretical cognition. This paper integrates the strategic cognition theory to clarify the complex process mechanism of digital transformation using external situational opportunities and internal capabilities. This paper introduces entrepreneurship into the path mechanism of digital transformation and expands the characteristics of the study of digital transformation antecedents to the individual level within the enterprise.

This study aims to investigate the effects of entrepreneurial leadership on service innovation in the hospitality industry and examine the mediating effects of market-sensing capability and knowledge acquisition. Additionally, the study explores the moderating role of competitive intensity in the relationships between market-sensing capability, knowledge acquisition and service innovation, drawing on the dynamic capability theory and resource dependence theory.

The data for this study were obtained from 322 employees and 137 leaders working in 103 hotels in Vietnam, using a time-lagged approach. The collected data were analyzed using structural equation modeling in SPSS Amos 28.

The results of this study reveal a significant positive association between entrepreneurial leadership and service innovation, with mediation effects observed through both knowledge acquisition and market-sensing capability. Moreover, the findings demonstrate that competitive intensity moderates the association between knowledge acquisition and service innovation.

The results of this study provide implications for hospitality firms to cultivate entrepreneurial leadership through leadership training and development programs and enhance their dynamic capabilities (i.e. market-sensing capability and knowledge acquisition) to allow them to survive and develop in a competitive market.

This study advances entrepreneurial leadership research in the hospitality context by identifying mediating and moderating mechanisms that translate entrepreneurial leadership into hospitality firms’ service innovation.

Knowledge management in any organization is important in enhancing the organization’s performance. This study aims to explore the influence of knowledge management capability on organizational performance with the mediation role of opportunity recognition and the moderation effect of environmental dynamism.

This study used an effective and well-developed survey to collect data from 387 managers working in different service organizations in the Kingdom of Saudi Arabia. Further, SmartPLS software was used for data analysis.

The outcomes confirm that knowledge management capability is directly and indirectly (via opportunity recognition) related to organizational performance. Environmental dynamism moderates the linkage between knowledge management capability and opportunity recognition, as well as between opportunity recognition and organizational performance.

This study provides a valuable and novel managerial and theoretical approach for management to improve firm performance with the help of knowledge management capabilities, opportunity recognition, and environmental dynamism. The findings of this paper can be implemented not only in the service sector but also in the production sector. Future studies can use this conceptual framework in different countries with the current data. Moreover, other analytical approaches can be used to check the variability of these findings.

This study contributes to connecting research gaps in the literature and advancing insights into how knowledge management capabilities affect organizational performance. The study explores the mediating role of opportunity recognition and environmental dynamics as moderators between knowledge management capability and opportunity recognition and between opportunity recognition and organizational performance.

This review paper seeks to enhance knowledge of how pre-loading affects reinforced concrete (RC) beams under fire. It investigates key factors like deflection and load capacity to understand pre-loading's role in replicating RC beams' actual responses to fire, aiming to improve fire testing protocols and structural fire engineering design.

This review systematically aggregates data from existing literature on the fire response of RC beams, comparing scenarios with (WP) and without pre-loading (WOP). Through statistical tools like the two-tailed t-test and Mann–Whitney U-test, it assesses deflection extremes. The study further examines structural responses, including flexural and shear behavior, ultimate load capacity, post-yield behavior, stiffness degradation and failure modes. The approach concludes with a statistical forecast of ideal pre-load levels to elevate experimental precision and enhance fire safety standards.

The review concludes that pre-loading profoundly affects the fire response of RC beams, suggesting a 35%–65% structural capacity range for realistic simulations. The review also recommended the initial crack load as an alternative metric for determining the pre-loading impact. Crucially, it highlights that pre-loading not only influences the fire response but also significantly alters the overall structural behavior of the RC beams.

The review advances structural fire engineering with an in-depth analysis of pre-loading's impact on RC beams during fire exposure, establishing a validated pre-load range through thorough statistical analysis and examination of previous research. It refines experimental methodologies and structural design accuracy, ultimately bolstering fire safety protocols.