Topical Landscape Research Articles

Mapping the effect of healthy and unhealthy food and beverages marketing: two decades of bibliometric analysis

PurposeWith multiple theoretical traditions, diverse topical landscape and rapid regulatory advancements galvanising the ongoing discourse, the emergent marketing scholarship on healthy and unhealthy food and beverages (F&B) has become exhaustive, fragmented and almost non-navigable. Accordingly, this study aims to synthesise and trace two decades of research focused on healthy and unhealthy F&B marketing.Design/methodology/approachThis study conducts a bibliometric analysis of papers published between 2000 and 2020. The data was retrieved from Web of Science (WoS) and Scopus, yielding 338 papers for final analysis. Using VOSviewer software and the Biblioshiny package, the authors performed a detailed bibliometric analysis comprising performance analysis and science mapping.FindingsThe study delineated the contribution, theoretical and thematic structure of healthy and unhealthy F&B marketing scholarship. The authors also mapped the evolution trajectory of the thematic structure, which helped us contemplate the research gaps.Research limitations/implicationsBy delving deeper into the “who”, “where”, “how”, “what” and “when” of healthy and unhealthy F&B marketing, the study enhances the current understandings and future developments for both theorists and practitioners. However, the selection of literature is confined to peer-reviewed papers available in WoS and Scopus.Practical implicationsThe findings delineate the existing scholarship which could guide F&B marketers and policymakers towards designing consumer-centric marketing/policy interventions.Originality/valueTo the best of the authors’ knowledge, this study is the first to perform a bibliometric analysis of healthy and unhealthy F&B marketing, likely to provide valuable guidelines for future scholars, policymakers and practitioners.

Exploring sentiment divergence on migrant workers through the lens of Sina Weibo

PurposeSince the opening of China (aka, reform and opening-up), a great number of rural residents have migrated to large cities in the past 40 years. Such a one-way population inflow to urban areas introduces nontrivial social conflicts between urban natives and migrant workers. This study aims to investigate the most discussed topics about migrant workers on Sina Weibo along with the corresponding sentiment divergence.Design/methodology/approachAn exploratory-descriptive-explanatory research methodology is employed. The study explores the main topics on migrant workers discussed in social media via manual annotation. Subsequently, guided LDA, a semi-supervised topic modeling approach, is applied to describe the overall topical landscape. Finally, the authors verify their theoretical predictions with respect to the sentiment divergence pattern for each topic, using regression analysis.FindingsThe study identifies three most discussed topics on migrant workers, namely wage default, employment support and urban/rural development. The regression analysis reveals different diffusion patterns contingent on the nature of each topic. In particular, this study finds a positive association between urban/rural development and the sentiment divergence, while wage default exhibits an opposite relationship with sentiment divergence.Originality/valueThe authors combine unique characteristics of social media with well-established theories of social identity and framing, which are applied more to off-line contexts, to study a unique phenomenon of migrant workers in China. From a practical perspective, the results provide implications for the governance of urbanization-related social conflicts.

Construction health and safety: A topic landscape study

AbstractWe aim to draw in-depth insights into the current literature in construction health and safety and provide perspectives for future research efforts. The existing literature on construction health and safety is not only diverse and rich in sight, but also complex and fragmented in structure. It is essential for the construction industry and research community to understand the overall development and existing challenges of construction health and safety to adapt to future new code of practice and challenges in this field. We mapped the topic landscape followed by identifying the salient development trajectories of this research area over time. We used the topic modeling algorithm to extract 10 distinct topics from 662 abstracts (filtered from a total of 895) of articles published between 1991 and 2020. In addition, we provided the most cited references and the most popular journal per topic as well. The results from a time series analysis suggested that the construction health and safety would maintain its popularity in the next 5 years. Research efforts would be devoted to the topics including “Physical health and disease”, “Migrant and race”, “Vocational ability and training”, and “Smart devices.” Among these topics, “Smart devices” would be the most promising one.

PND66 Topic Landscape Analysis of Reddit Social Media Submissions in Insomnia

Patient experiences in insomnia are largely unknown due to limitations of traditional data sources. Recent advances in natural language processing (NLP) provide new opportunities to analyze these data beyond conventional approaches. Our objective was to discover a topic landscape of insomnia social media submissions and to determine any impact on trending topics due to the COVID-19 pandemic. Insomnia subreddit www.reddit.com/r/insomnia is a community of 72K+ anonymous subscribers detailing their insomnia experience. Submissions were collected from Jan 2017 to Sept 2020 (n=18,251). Text was transformed into vector embeddings (d=768) via a deep learning Bidirectional Encoder Representations from Transformers (BERT) model pretrained on document similarity detection. These embeddings were then reduced to 64 dimensions using Uniform Manifold Approximation and Projection (UMAP) dimensional reduction technique, then clustered using Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) and sub-clustered. Sentiment analysis using BERT was also performed, specifically focusing on treatment use. Of the 18.3k posts, 45% were grouped to 7 high level clusters: Short medication inquiries (n=2.3k), Sleep complaints (n=1.6k), Sleep questions (n=790), Insomnia questions (n=530), Long descriptions of dreams/hallucinations (n=516), Medication journeys (n=1.1k) and General insomnia journeys (n=1.5k). The rest were identified as noise. Specific treatment inquiry sub-clusters were identified such as marijuana, cognitive behavior therapy, melatonin, Ambien, trazadone, etc. Sentiment analysis on treatment use revealed that CBT, marijuana, and melatonin use had the smallest negative sentiment score while z-drugs had larger negative sentiment scores. When comparing topic trends pre- and post-COVID pandemic, no substantial difference was found in the topic share distribution. A novel combination of NLP deep and machine techniques enabled organization of social media data into distinct topic groups. Results indicated negative sentiment among z-drugs and unchanged trends post-COVID, possibly due to the shorter data timeframe. Further granular analysis is needed to confirm trends observed.

Research on product-service systems: topic landscape and future trends

PurposeThe paper attempts to address the following research questions (RQs): RQ1: What are the main research topics within PSS research? RQ2: What are future trends for PSS research?Design/methodology/approachTwenty years of research (1999–2018) on product-service systems (PSS) produced a significant amount of scientific literature on the topic. As the PSS field is relatively new and fragmented across different disciplines, a review of the prior and relevant literature is important in order to provide the necessary framework for understanding current developments and future perspectives. This paper aims to review and organize research contributions regarding PSS. A machine-learning algorithm, namely Latent Dirichlet Allocation, has been applied to the whole literature corpus on PSS in order to understand its structure.FindingsThe adopted approach resulted in the definition of eight distinct and representative topics able to deal adequately with the multidisciplinarity of the PSS. Furthermore, a systematic review of the literature is proposed to summarize the state-of-the-art and limitations in the identified PSS research topics. Based on this critical analysis, major gaps and future research challenges are presented and discussed.Originality/valueOn the basis of the results of the topic landscape, the paper presents some potential research opportunities on PSSs. In particular, challenges, transversal to the eight research topics and related to recent technology trends and digital transformation, have been discussed.

Ethics and privacy of artificial intelligence: Understandings from bibliometrics

Artificial intelligence (AI) and its broad applications are disruptively transforming the daily lives of human beings and a discussion of the ethical and privacy issues surrounding AI is a topic of growing interest, not only among academics but also the general public This review identifies the key entities (i.e., leading research institutions and their affiliated countries/regions, core research journals, and communities) that contribute to the research on the ethical and privacy issues in relation to AI and their intersections using co-occurrence analysis. Topic analyses profile the topical landscape of AI ethics using a topical hierarchical tree and the changing interest of society in AI ethics over time through scientific evolutionary pathways. We also paired 15 selected AI techniques with 17 major ethical issues and identify emerging ethical issues from a core set of the most recent articles published in Nature, Science, and Proceedings of the National Science Academy of the United States. These insights bridging the knowledge base of AI techniques and ethical issues in the literature, are of interest to the AI community and audiences in science policy, technology management, and public administration.

The Highs in Communication Research: Research Topics With High Supply, High Popularity, and High Prestige in High-Impact Journals

More and more scholarly attention is paid to dissecting discipline of communication research under the microscope thereby aiming at revealing foci of scientific interest. The lion’s share of research has hereby focused either on the supply side of research examining what topics scholars write about or at the popularity side of research shedding light on what scientific publications receive the most citations. Building up on this, we argue that these research strands are inadequate to the task of exhaustively identifying foci of scientific interest. Tailoring for the fragmented topical landscape of communication research, we propose an integrative combination of three metrics: supply, popularity, and prestige of research topics. By means of topic modeling, citation counts and citation networks, our study showcases how our approach is able to reveal the intellectual architecture of our discipline in order to identify relevant paths for further scientific inquiry.

Trait‐based ecology of fishes: A quantitative assessment of literature trends and knowledge gaps using topic modelling

AbstractSpecies traits are a new data currency to enhance our understanding of ecological patterns and processes. Trait‐based studies of fishes are numerous in comparison with other animal groups, reflecting the diversity of fish forms and functions they provide to aquatic ecosystems. We conduct a retrospective examination of literature to identify knowledge gaps and provide guidance for future research in trait‐based fish ecology. We apply an automated text mining and topic modelling to track the evolution of research topics within peer‐reviewed articles of functional traits in marine and freshwater fishes published over the past half century, explore the inter‐connections among those topics and identify emerging avenues for investigation. By mapping the topic landscape of the literature, 16 latent topics emerged that vary in their prevalence. Our results show a decline in the frequency of studies using reproductive traits to model and explore the way fish allocate energy for reproduction, and increase in studies reporting functional diversity metrics and utilizing the concept of multivariate functional space. Research focused on contributions of fish traits to ecosystem functioning also has increased in frequency. We revealed large gaps in information between growing and decreasing topics and that these gaps were derived from different types of traits being considered. We suggest that scientists break‐free from the traditions of their research field by targeting investigations that: (a) apply functional diversity metrics to a broader assortment of traits, (b) focus on traits influencing energy allocation to growth/reproduction and (c) integrate trophic‐web and behavioural studies with other topics.

The geographic and topical landscape of medical education research

BackgroundWhether medical education research (MER) is primarily conducted in wealthy countries (in the “Realm of the Rich”) is the subject of an ongoing debate. Previous studies of the geography of MER publication output have relied upon proprietary databases, have not compared MER with other fields of study, and have not studied the relationship between authorship geography and topics of study. This study was designed to evaluate the geographic distribution of MER authorship and to relate this to the topics studied in MER.MethodsAuthors’ countries of affiliation were identified from PubMed records by parsing and cleaning the text of affiliations and submitting them to the google maps geocoding API. The geography of publication output in MER was compared to other fields using the chi-square goodness-of-fit test. Country income classifications and medical subject heading (MeSH) terms were used to evaluate the topical contributions of countries at different income levels, and simulation was used to compute significance of MeSH term enrichment in MER papers from low income and lower middle income countries.ResultsThe vast majority of MER papers were contributed by authors based in high income countries. The top four countries were the United States, the United Kingdom, Canada, and Australia, with listed author affiliations in 80% of all MER papers. This percentage was greater in MER than in several other categories, including Biological Science Disciplines (48%), Medicine (69%) and Education (74%), which is a parent category of MER. Authors from low income countries contributed significantly to the topical diversity of MER. MeSH terms associated with government, community health, and health delivery were enriched in papers from low income countries, while terms associated with specialty and clinical training, technology in teaching, and professional obligations (such as workload, burnout, and empathy) were enriched in papers from high income countries.ConclusionsGeographic disparities in publication output are greater in MER than in any other field examined. The historical origins of MER in North America might explain disproportionate publication output by authors from this region. This study suggests that the MER field benefits from research contributed by authors from low income countries, and also points to potential gaps in MER (and medical education as a whole) in the developing world.

The research topic landscape in the literature of social class and inequality.

The literature of social class and inequality is not only diverse and rich in sight, but also complex and fragmented in structure. This article seeks to map the topic landscape of the field and identify salient development trajectories over time. We apply the Latent Dirichlet Allocation topic modeling technique to extract 25 distinct topics from 14,038 SSCI articles published between 1956 to 2017. We classified three topics as “hot”, eight as “stable” and 14 as “cold”, based on each topic’s idiosyncratic temporal trajectory. We also listed the three most cited references and the three most popular journal outlets per topic. Our research suggests that future effort may be devoted to Topics “urban inequalities, corporate social responsibility and public policy in connected capitalism”, “education and social inequality”, “community health intervention and social inequality in multicultural contexts” and “income inequality, labor market reform and industrial relations”.

Uncovering the Topic Landscape of Product-Service System Research: from Sustainability to Value Creation

As the product-service system (PSS) is considered a promising business model that can create more value for customers, PSS research has enjoyed remarkable growth in its volume and coverage over the last decade. This study aims to delineate the thematic landscape of PSS research by identifying latent topics from a large amount of scholarly data. Ten topics of PSS research are identified by applying the Latent Dirichlet Allocation (LDA) model to 1229 PSS publications published between 2000 and 2016. The ten PSS topics are briefly reviewed to provide an overview of what has previously been studied in PSS research. We also investigate which topics rise or fall in popularity by identifying hot and cold topics of PSS research. It is observed that the focus of discussions on the benefits of PSS has shifted from sustainability to value creation. Also, increasing attention has been paid to more practical topics such as PSS implementation. The areas of subspecialty of the top ten PSS journals are also examined to explore the interdisciplinary nature of PSS research and thematic differences across disciplines. The findings of this study can provide rich implications for both academia and practice in the field of PSS.

The Topic Landscape of Disruption Research—A Call for Consolidation, Reconciliation, and Generalization

Using a natural language processing approach, the paper takes stock of extant disruption research and analyzes the full‐text corpus of 1078 journal articles published on disruption between 1975 and 2016. This yields a topic map composed of 84 distinct topics that present the overall topic structure of this dynamic field. Topic network analyses uncover the existence of two increasingly disconnected subnetworks centered around disruptive innovation at the macro level and radical innovation at the micro level. This disconnect is consequential, as both perspectives appear to be highly interdependent and conceptually constitute two sides of the same coin. To counteract the threat of growing fragmentation and reconnect these subnetworks, three research priorities are proposed, firmly grounded in a systematic literature analysis: (1) the need to consolidate peripheral and decoupled topics, (2) the necessity to reconcile competing terminologies and refocus the theoretical core, and (3) the imperative to strengthen the generalizable empirical evidence base.

Functional Synchronization: The Emergence of Coordinated Activity in Human Systems.

The topical landscape of psychology is highly compartmentalized, with distinct phenomena explained and investigated with recourse to theories and methods that have little in common. Our aim in this article is to identify a basic set of principles that underlie otherwise diverse aspects of human experience at all levels of psychological reality, from neural processes to group dynamics. The core idea is that neural, behavioral, mental, and social structures emerge through the synchronization of lower-level elements (e.g., neurons, muscle movements, thoughts and feelings, individuals) into a functional unit—a coherent structure that functions to accomplish tasks. The coherence provided by the formation of functional units may be transient, persisting only as long as necessary to perform the task at hand. This creates the potential for the repeated assembly and disassembly of functional units in accordance with changing task demands. This perspective is rooted in principles of complexity science and non-linear dynamical systems and is supported by recent discoveries in neuroscience and recent models in cognitive and social psychology. We offer guidelines for investigating the emergence of functional units in different domains, thereby honoring the topical differentiation of psychology while providing an integrative foundation for the field.

When Form Trumps Substance: A Dynamic Analysis of Microblogging During the 2016 U.S. Presidential Primary Debates

The 2016 presidential election illustrated the growing role that micro-blogging sites such as Twitter play in electoral politics. In this paper we report an analysis of a unique dataset that characterizes how the substantive and affective content of Tweets evolved during the course of three pivotal Republican Primary debates leading up to the 2016 Presidential election. We find that as the debates progressed Tweets provided an increasingly backward-looking account of the debates, as original content gradually gave way to retweets of the most popular earlier posts. Moreover, whereas during the debate Tweets focused on a mix of substantive topics, the Tweets that had the longest staying power after the debates were those that focused on the more sensationalist news events, often through pictures and videos. As such, a user coming to Twitter after the debate was over would have encountered a different topical and emotional landscape than one who had been following the site in real-time, one more closely resembling a tabloid than a substantive discussion forum. We explore the potential implications of the findings for the role that micro-blogging sites may have on shaping voter opinion in elections.

Automated Text Data Mining Analysis of Five Decades of Educational Leadership Research Literature

Purpose: The purpose of this study is to describe the underlying topics and the topic evolution in the 50-year history of educational leadership research literature. Method: We used automated text data mining with probabilistic latent topic models to examine the full text of the entire publication history of all 1,539 articles published in Educational Administration Quarterly ( EAQ) from 1965 to 2014. Given the computationally intensive data analysis required by probabilistic topic models, relying on high-performance computing, we used a 10-fold cross-validation to estimate the model in which we categorized each article in each year into one of 19 latent topics and illustrated the rise and fall of topics over the EAQ’s 50-year history. Findings: Our model identified a total of 19 topics from the 1965 to 2014 EAQ corpus. Among them, five topics—inequity and social justice, female leadership, school leadership preparation and development, trust, and teaching and instructional leadership—gained research attention over the 50-year time period, whereas the research interest appears to have declined for the topic of epistemology of educational leadership since the 2000s. Other topics waxed and waned over the past five decades. Implications: This study maps the temporal terrain of topics in the educational leadership field over the past 50 years and sheds new light on the development and current status of the central topics in educational leadership research literature. More important, the panoramic view of topical landscape provides a unique backdrop as scholars contemplate the future of educational leadership research.

Mining Twitter to monitor invasive alien species — An analytical framework and sample information topologies

Social online media increasingly emerge as important informal information sources that can contribute to the detection of trends and early warnings in critical fields such as public health monitoring or emergency management. In the face of global environmental challenges the utilization of this information in ecological monitoring contexts has been called for, but examples remain sparse. This can be attributed to the significant technical challenges in processing this data and concerns about the quality, reliability and applicability of information mined from social media to the ecological domain.Here the strength and weaknesses of social media mining for ecological monitoring are assessed using the micro-blogging service Twitter and invasive alien species (IAS) monitoring as an example. The assessment is based on a manual analysis of 2842 Tweets sampled from Twitter data with potential direct or descriptive references to IAS impacting forest ecosystems, which was collected over a period of nearly three years. The results are presented as information topologies for Twitter messages of observational and non-observational character for three IAS with distinctive characteristics (Oak Processionary Moth, Emerald Ash Borer, Eastern Grey Squirrel).The results show that the social media channel Twitter is a rich source of primary and secondary observational biodiversity information. It also provides useful insights in the topical landscape of public communications on IAS as well as the public perception of IAS and IAS management. The analysis suggests broad application opportunities in IAS monitoring and management, and points at applications for related environmental questions. The results highlight that social media mining for ecological monitoring needs to be approached with the same best practices as ecological monitoring in general, requiring a good understanding of the monitored subjects and specific monitoring questions.The challenges in utilizing this information for operational systems are of technical rather than conceptual nature and include extending the degree of automation, especially with regard to image recognition and the automatic provisioning of location information.

Mapping the Topic Landscape of JPIM, 1984–2013: In Search of Hidden Structures and Development Trajectories

During the three decades since its inception in 1984, the JPIM has shaped the evolution of innovation research as a scientific field. It helped create a topic landscape that is not only more diverse and rich in insights, but also more complex and fragmented in structure than ever before. We seek to map this landscape and identify salient development trajectories over time. In contrast to prior citation‐based studies covering the first two decades of JPIM research, we benefit from recent advances in natural language processing and rely on a topic modeling algorithm to extract 57 distinct topics and the corresponding most common words, terms, and phrases from the entire full‐text corpus of 1008 JPIM articles published between 1984 and 2013. Estimating the development trajectory of each topic based on yearly publication counts in JPIM allows us to identify “hot,” “cold,” “revival,” “evergreen,” and “wall‐flower” topics. We map these topics onto the Product Development and Management Association (PDMA) Body of Knowledge categories and discover that these categories differ significantly not only in terms of their internal topic diversity and relative prevalence, but also—and arguably more importantly—in terms of their publication and citation trajectories over time. For instance, the PDMA category “Codevelopment and Alliances” exhibits only moderate topic diversity (7 out of 57 topics) and prevalence in JPIM (161 out of 1008 articles). That said, it is among the most dynamic categories featuring two evergreen topic (“Users and Innovation” and “Tools and Systems for Technology Transfer”) and three hot topics (“Open Innovation,” “Alliances and Cooperation,” and “Networks and Network Structure”) as well as a sharply growing annual number of citations received. Our findings are likely to be of interest to all those who are keen to (re)discover JPIM's topic landscape in search of hidden structures and development trajectories.

Assessing citizen science opportunities in forest monitoring using probabilistic topic modelling

With mounting global environmental, social and economic pressures the resilience and stability of forests and thus the provisioning of vital ecosystem services is increasingly threatened. Intensified monitoring can help to detect ecological threats and changes earlier, but monitoring resources are limited. Participatory forest monitoring with the help of “citizen scientists” can provide additional resources for forest monitoring and at the same time help to communicate with stakeholders and the general public. Examples for citizen science projects in the forestry domain can be found but a solid, applicable larger framework to utilise public participation in the area of forest monitoring seems to be lacking. We propose that a better understanding of shared and related topics in citizen science and forest monitoring might be a first step towards such a framework. We conduct a systematic meta-analysis of 1015 publication abstracts addressing “forest monitoring” and “citizen science” in order to explore the combined topical landscape of these subjects. We employ ‘topic modelling’, an unsupervised probabilistic machine learning method, to identify latent shared topics in the analysed publications. We find that large shared topics exist, but that these are primarily topics that would be expected in scientific publications in general. Common domain-specific topics are under-represented and indicate a topical separation of the two document sets on “forest monitoring” and “citizen science” and thus the represented domains. While topic modelling as a method proves to be a scalable and useful analytical tool, we propose that our approach could deliver even more useful data if a larger document set and full-text publications would be available for analysis. We propose that these results, together with the observation of non-shared but related topics, point at under-utilised opportunities for public participation in forest monitoring. Citizen science could be applied as a versatile tool in forest ecosystems monitoring, complementing traditional forest monitoring programmes, assisting early threat recognition and helping to connect forest management with the general public. We conclude that our presented approach should be pursued further as it may aid the understanding and setup of citizen science efforts in the forest monitoring domain.

Assessing citizen science opportunities in forest monitoring using probabilistic topic modelling

With mounting global environmental, social and economic pressures the resilience and stability of forests and thus the provisioning of vital ecosystem services is increasingly threatened. Intensified monitoring can help to detect ecological threats and changes earlier, but monitoring resources are limited. Participatory forest monitoring with the help of “citizen scientists” can provide additional resources for forest monitoring and at the same time help to communicate with stakeholders and the general public. Examples for citizen science projects in the forestry domain can be found but a solid, applicable larger framework to utilise public participation in the area of forest monitoring seems to be lacking. We propose that a better understanding of shared and related topics in citizen science and forest monitoring might be a first step towards such a framework. We conduct a systematic meta-analysis of 1015 publication abstracts addressing “forest monitoring” and “citizen science” in order to explore the combined topical landscape of these subjects. We employ ‘topic modelling’, an unsupervised probabilistic machine learning method, to identify latent shared topics in the analysed publications. We find that large shared topics exist, but that these are primarily topics that would be expected in scientific publications in general. Common domain-specific topics are under-represented and indicate a topical separation of the two document sets on “forest monitoring” and “citizen science” and thus the represented domains. While topic modelling as a method proves to be a scalable and useful analytical tool, we propose that our approach could deliver even more useful data if a larger document set and full-text publications would be available for analysis. We propose that these results, together with the observation of non-shared but related topics, point at under-utilised opportunities for public participation in forest monitoring. Citizen science could be applied as a versatile tool in forest ecosystems monitoring, complementing traditional forest monitoring programmes, assisting early threat recognition and helping to connect forest management with the general public. We conclude that our presented approach should be pursued further as it may aid the understanding and setup of citizen science efforts in the forest monitoring domain.

Semi-automated collection evaluation for large-scale aggregations

Library and museum digital collections are increasingly aggregated at various levels. Large-scale aggregations, often characterized by heterogeneous or messy metadata, pose unique and growing challenges to aggregation administrators – not only in facilitating end-user discovery and access, but in performing basic administrative and curatorial tasks in a scalable way, such as finding messy data and determining the overall topical landscape of the aggregation. This poster describes early findings on using statistical text analysis techniques to improve the scalability of an aggregation development workflow for a large-scale aggregation. These techniques hold great promise for automating historically labor-intensive evaluative aspects of aggregation development and form the basis for the development of an aggregator's dashboard. The aggregator's dashboard is planned as a statistical text-analysis-driven tool for supporting large-scale aggregation development and maintenance, through multifaceted, automatic visualization of an aggregation's metadata quality and topical coverage. The administrator's dashboard will support principled yet scalable aggregation development.