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Research objects, such as datasets and classification standards, are difficult to be incorporated into a document-centric framework of citations, which relies on unique citable works. The Diagnostic and Statistical Manual for Mental Disorder (DSM)—a dominant classification scheme used for mental disorder diagnosis—however provides a unique lens on examining citations to a research object, given that it straddles the boundaries as a single research object with changing manifestations.

Using over 180,000 citations received by the DSM, this paper analyzes how the citation history of DSM is represented by its various versions, and how it is cited in different knowledge domains as an important boundary object.

It shows that all recent DSM versions exhibit a similar citation cascading pattern, which is characterized by a strong replacement effect between two successive versions. Moreover, the shift of the disciplinary contexts of DSM citations can be largely explained by different DSM versions as distinct epistemic objects.

Based on these results, the authors argue that all DSM versions should be treated as a series of connected but distinct citable objects. The work closes with a discussion of the ways in which the existing scholarly infrastructure can be reconfigured to acknowledge and trace a broader array of research objects.

This paper connects quantitative methods and an important sociological concept, i.e. boundary object, to offer deeper insights into the scholarly communication system. Moreover, this work also evaluates how versioning, as a significant yet overlooked attribute of information resources, influenced the citation patterns of citable objects, which will contribute to more material-oriented scientific infrastructures.

The purpose of this paper is to contribute to the theoretical reflection on learning study as a research approach. The focus is on description and reflection on the methodology of learning study as paedeutic research. This research is for, not on, teachers, i.e. research into problems and challenges faced by teachers in their professional practice. Learning study as paedeutic research is about the content and processes of formation/Bildung in relation to specific learning objects. Its focus is on subject-specific ways of knowing as well as how such knowing is enabled through teaching.

The point of departure is a perspective on research approaches as practices, i.e. as activities with certain aims as well as ways of “making” knowledge. Based on a description of the knowledge machinery in a learning study, i.e. those mechanisms that together generate new knowledge, the knowledge claims that can be made are discussed together with the theoretical underpinning of the arguments. The knowledge machinery is described in relation to how it is organised around the delimitation and analysis of an object of Learning as well as designing and evaluating ways to make the critical aspects of this object of learning visible. As an epistemological underpinning, some aspects of pragmatic philosophical thinking regarding the relationship between theory and practice are outlined. Based on that the research process may be described as a development of means-ends relationships – from unconscious empirical relationships to conscious staging of internal and theoretical relations. Abduction is an important tool for this meaning-making.

Learning study can be described as a particularistic, theory-building research approach concerning the knowing of specific learning objects as well as how they can be taught and learnt. The knowledge that is generated in learning study is theoretical and describes aspects of the teaching and learning of specific objects of learning. The research process can be described in terms of specification where practice is gradually supplied with a more differentiated meaning. A learning study is organised around a specific object of learning that functions as an open and unfolding object of knowledge. It combines a practice-based development of theory with a theory-based development of practice.

The development of the thinking about learning study as research for, rather than on, teachers is paedeutical research. A contribution to reflection on the knowledge machinery and knowledge claims of such research.

The practice of artificial intelligence (AI) is increasingly being promoted by technology developers. However, its adoption rate is still reported as low in the construction industry due to a lack of expertise and the limited reliable applications for AI technology. Hence, this paper aims to present the detailed outcome of experimentations evaluating the applicability and the performance of AI object detection algorithms for construction modular object detection.

This paper provides a thorough evaluation of two deep learning algorithms for object detection, including the faster region-based convolutional neural network (faster RCNN) and single shot multi-box detector (SSD). Two types of metrics are also presented; first, the average recall and mean average precision by image pixels; second, the recall and precision by counting. To conduct the experiments using the selected algorithms, four infrastructure and building construction sites are chosen to collect the required data, including a total of 990 images of three different but common modular objects, including modular panels, safety barricades and site fences.

The results of the comprehensive evaluation of the algorithms show that the performance of faster RCNN and SSD depends on the context that detection occurs. Indeed, surrounding objects and the backgrounds of the objects affect the level of accuracy obtained from the AI analysis and may particularly effect precision and recall. The analysis of loss lines shows that the loss lines for selected objects depend on both their geometry and the image background. The results on selected objects show that faster RCNN offers higher accuracy than SSD for detection of selected objects.

The results show that modular object detection is crucial in construction for the achievement of the required information for project quality and safety objectives. The detection process can significantly improve monitoring object installation progress in an accurate and machine-based manner avoiding human errors. The results of this paper are limited to three construction sites, but future investigations can cover more tasks or objects from different construction sites in a fully automated manner.

This paper’s originality lies in offering new AI applications in modular construction, using a large first-hand data set collected from three construction sites. Furthermore, the paper presents the scientific evaluation results of implementing recent object detection algorithms across a set of extended metrics using the original training and validation data sets to improve the generalisability of the experimentation. This paper also provides the practitioners and scholars with a workflow on AI applications in the modular context and the first-hand referencing data.

This paper aims to implement and extend the You Only Live Once (YOLO) algorithm for detection of objects and activities. The advantage of YOLO is that it only runs a neural network once to detect the objects in an image, which is why it is powerful and fast. Cameras are found at many different crossroads and locations, but video processing of the feed through an object detection algorithm allows determining and tracking what is captured. Video Surveillance has many applications such as Car Tracking and tracking of people related to crime prevention. This paper provides exhaustive comparison between the existing methods and proposed method. Proposed method is found to have highest object detection accuracy.

The goal of this research is to develop a deep learning framework to automate the task of analyzing video footage through object detection in images. This framework processes video feed or image frames from CCTV, webcam or a DroidCam, which allows the camera in a mobile phone to be used as a webcam for a laptop. The object detection algorithm, with its model trained on a large data set of images, is able to load in each image given as an input, process the image and determine the categories of the matching objects that it finds. As a proof of concept, this research demonstrates the algorithm on images of several different objects. This research implements and extends the YOLO algorithm for detection of objects and activities. The advantage of YOLO is that it only runs a neural network once to detect the objects in an image, which is why it is powerful and fast. Cameras are found at many different crossroads and locations, but video processing of the feed through an object detection algorithm allows determining and tracking what is captured. For video surveillance of traffic cameras, this has many applications, such as car tracking and person tracking for crime prevention. In this research, the implemented algorithm with the proposed methodology is compared against several different prior existing methods in literature. The proposed method was found to have the highest object detection accuracy for object detection and activity recognition, better than other existing methods.

The results indicate that the proposed deep learning–based model can be implemented in real-time for object detection and activity recognition. The added features of car crash detection, fall detection and social distancing detection can be used to implement a real-time video surveillance system that can help save lives and protect people. Such a real-time video surveillance system could be installed at street and traffic cameras and in CCTV systems. When this system would detect a car crash or a fatal human or pedestrian fall with injury, it can be programmed to send automatic messages to the nearest local police, emergency and fire stations. When this system would detect a social distancing violation, it can be programmed to inform the local authorities or sound an alarm with a warning message to alert the public to maintain their distance and avoid spreading their aerosol particles that may cause the spread of viruses, including the COVID-19 virus.

This paper proposes an improved and augmented version of the YOLOv3 model that has been extended to perform activity recognition, such as car crash detection, human fall detection and social distancing detection. The proposed model is based on a deep learning convolutional neural network model used to detect objects in images. The model is trained using the widely used and publicly available Common Objects in Context data set. The proposed model, being an extension of YOLO, can be implemented for real-time object and activity recognition. The proposed model had higher accuracies for both large-scale and all-scale object detection. This proposed model also exceeded all the other previous methods that were compared in extending and augmenting the object detection to activity recognition. The proposed model resulted in the highest accuracy for car crash detection, fall detection and social distancing detection.

The purpose of this study is to explore consumers’ profiles for and purchase intentions towards smart environmental objects. It segments consumers according to two apparently contradictory dimensions of smart environmental objects: environment (i.e. environmental concern and environmental beliefs) and technology (i.e. materialistic values and technological beliefs).

A cluster analysis was conducted among 658 French consumers based on their environmental concern, environmental beliefs, materialistic values, perceived usefulness and perceived ease of use. A regression analysis identifies the variables with the greatest influence on purchase intentions.

Four segments result from the analysis: unconcerned, retro eco-friendly, non-materialistic converted and converted. The converted consumer segment had the highest purchase intentions and exhibited high levels of both environmental beliefs and perceived usefulness compared with the other segments. Both environmental and technological beliefs and environmental concern influence purchase intentions more broadly.

A combined consideration of both environmental and technological beliefs is necessary to influence purchase intentions towards smart environmental objects. This study challenges some previous research that assumes a clear opposition between materialism and environmentalism.

This study proposes tailored managerial recommendations for each of the four consumer segments in the context of smart environmental objects.

This study provides novel insights into consumers’ concerns, beliefs and values in the rapidly expanding context of smart environmental objects.

Behind the simple connotation “business exchange” a complex empirical phenomenon can be observed, including using, producing and developing activities, taking place in different contexts, influenced by ideas stemming from both practice and mainstream economic thinking. The purpose of this paper is to discuss the methodological challenges of research on business exchange in general and of IMP research in particular. Furthermore, to discuss how the authors can avoid the contemporary “methodomania” trend, where the researchers’ focus is directed toward accounting for which rules were followed.

The paper is based on a methodological distinction made by Peter Galison (1997) in his investigation of the interdependence among research approach, methodology, and research object in microphysics. Studies based on: “image,” allows data in its original form, and “logic,” requires the translation of original data and therefore relies “fundamentally on statistical demonstrations.” This distinction is utilized to investigate what is specific with business exchange as a research object, and how IMP researchers have dealt with the methodological challenges it presents. Furthermore, the paper considers these different methodological approaches in relation to theory and understanding of the research object.

The main conclusion is the huge importance the image-based methodology has had for the development of the IMP network approach. From the very start the IMP project has been focused on the production of a large set of, in Galison’s terminology, “hard facts” about the existence, substance and importance of interaction and the relationships it is creating. This image-based methodology has been utilized in the development of a set of imaging instruments, each with an ability to picture the content and consequences of business exchange.

Two methodological challenges which are specific for business research are identified. One is that “images” in terms of personal accounts on the organizing of production and use of economic resources are marbled with ideas, stemming from a mix of theories, textbooks and practice on how to do this. The second is that established theories create a “logic” in terms of the combination of “assumptions” and established “accounting principles” that produce a number of outputs interpreted as primary data and objective accounts of the characteristics of the production and use of economic resources.

IMP’s image-based methodology and the development of specific imaging instruments can increase the exactness in the pictures of the content and consequences of business interaction, and also, catch the range of its substance. Considering this circumstance could be a way to avoid “methodomania” and to breed awareness of the relationship among research object, methodology, and research approach.

IMP’s image-based methodology can increase the awareness that the logic-based model of business exchange has been ascribed an advisory role in terms of how companies should act in order to survive and prosper: as sellers and buyers in relation to each other, and also in relation to others.

First, the paper underlines that image-based methodologies can be used to produce “hard facts” about the existence, substance, and importance of business interaction. Second, the paper shows how the methodology of mainstream economics tends to be “the elephant in the room,” both in approaches resting on “image” and “logic.” It addresses the importance of making the elephant visible and investigates what is happening in its shadow.

The purpose of this paper is to explore how relationships between different actors are being shaped to allow industry to come to acceptable and desirable uses of research and innovation (R&I) that address societal challenges.

Building on existing notions of responsibility proposed in the literature, the paper develops a theoretical account of “networks of responsibility” which capture the interlinked nature of responsibility relationships. The usefulness of the approach is evaluated by exploring two cases of R&I in industry deploying a qualitative research approach that involves interviewing and document analysis. For this, a multinational company from Germany was involved, as well as a small- and medium-sized company from Denmark.

The study surfaced 68 responsibility relationships involving a range of different objects, subjects, authorities and norms. By describing overlaps in objects, subjects and other aspects across relationships, the theoretical model proved adequate in untangling and displaying interrelatedness of responsibilities. Furthermore, the analysis surfaced characteristics of responsible research and innovation (RRI) that are already in place in the R&I processes of two innovative companies, such as anticipation, foresight and stakeholder engagement. Not all aspects of responsibility outlined in the theoretical model could be extracted from the interview data for every responsibility relationship, pointing to the need for further research.

The paper is practically relevant because it supports policy development on an organisational, as well as societal level. Moreover, the networks of responsibility model offer a fine-grained assessment of responsibilities in R&I practice by mapping existing responsibilities which supports translating RRI principles into everyday organisational practices.

RRI sets an ambitious agenda to ensure a more social and ethical R&I. Much work is still needed to bridge the gap between these theoretical and political aspirations and daily R&I practice, especially in non-academic contexts such as industry. By offering a way to understand and untangle the complexity of responsibility relationships, the networks of responsibility model seem to offer a promising approach that can support this endeavour.

The paper offers a novel theoretical approach to understanding and analysing responsibility allocations in R&I in industry. It demonstrates the reliability of this theoretical position empirically. It is practically important because it supports policy development on an organisational as well as societal level.

This study aims to contribute to the clarification of core concepts in information experience research and to the consolidation of information experience as a distinctive research object.

This study adopts a series of techniques from Wilson's toolkit of concept analysis.

This study finds that there exist tensions between different uses of the term information experience, giving rise to two fundamentally different conceptions of this particular human experience which this study names, respectively, the posterior conception and the a priori conception. It also finds that it is linguistically more useful, practically more consonant with LIS's concerns and unitarily more consistent to define information experience following the a priori conception. It postulates that information experience can be defined as a person's subjective, pre-reflective living through of his/her life as an information user in the information sphere of the lifeworld.

If adopted by future research, the concept proposed in this study is likely to push information experience research toward a more prominent phenomenological turn on the one hand, and a return to conventional LIS concerns on the other.

The clarified concept may help user experience librarians and system designers to see the relevance of information experience research for their work more clearly.

By identifying, comparing and discussing different existing uses of information experience, and by suggesting a redefinition of the concept, this study has brought the core concepts of information experience research to a new level of clarity, and has verified information experience as a distinctive object for LIS research.

The complexity of the integration of non-financial benefits (NFB) in major infrastructure projects (MIP) engenders the formulation of networked knowledge between researchers and practitioners. The authors’ research question is as follows: To what extent does scientific knowledge about the integration of NFB into MIP support engaged scholarship or co-construction of knowledge between researchers and practitioners?

The paper uses a review of literature published in academic journals on the integration of NFB in MIP. Nearly 300 papers are analysed in depth, based on categories (aspects and sub-aspects) inspired from engaged scholarship and paradoxical participation approaches. The culture of collaboration and the notion of boundary objects are the two main aspects of this categorization of journal papers.

First, research on the integration of NFB into MIP is either project-oriented or society-oriented but in a larger proportion for society-oriented. Second, a lot of researches favour an analytic over a holistic approach, despite their openness to dialogue with practitioners through the complexity and conflict.

It contributes to the theorization of the engaged scholarship. It also provides insights about research avenues to be exploited where these aspects were not sufficiently exploited, as it is often the case with sustainability, for a better collaboration between researchers and practitioners. Linking the culture of collaboration, boundary objects and knowledge co-creation in the engaged scholarship setting encourages a better understanding of the needs (problem to be resolved) of practitioners, by themselves and the researchers.

The systematic review was conducted in parallel with the organization of two workshops with participants concerned by the integration of NFB into MIP. The paper identified four clusters from their level of compatibility with engaged scholarship.

The purpose of this study is to find the most effective objects used in Ramadan TV ads of banks operated in Turkey and to determine superiority of the objects among these ads.

This research examines used objects in bank TV advertisements for the month of Ramadan by applying content analysis and fuzzy logic method.

The most prominent of the findings, as a result of the research, are kids and the elder objects in bank TV commercials. The research findings also reveal the importance of religious objects of Ramadan ads.

The limitation of this research is non-generalization of the results because of sample size. Future research could explore the impact of objects used in TV ads on consumers with a larger sample size.

The banks are eager to look for opportunities by launching campaigns during the period of Ramadan. Special occasions such as religious holidays are seen as a new market that should not to be missed by the finance sector, and therefore, the number of bank TV commercials increase dramatically in this term. However, it is important for banks to know which objects are more effective. In this study, it is revealed that objects used in TV ads are more effective.

This is the first study to examine bank TV commercials during the period of Ramadan. It is also important to reflect Turkish consumers’ perspective and banks’ promotion activities in a Muslim country.