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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 purpose of this study is to examine the interplay between the curatorial practices of consumers as collectors and the materiality of the collected objects. In particular, this study explores how the material substances of collected objects shapes curatorial practices and how the ongoing use of the collected objects challenges curatorial practices.

Taking advantage of the publicization of once-private collections on social media, we collect 111 YouTube videos created by plastic shoe aficionados. Drawing from visual anthropology and theorizations of materiality, we analyze consumer interactions with the objects they collect.

This study’s findings elucidate consumers’ interactions with the material substances of the objects they collect and demonstrate how these interactions shape the ways in which consumers curate their collections, including how they wear, care for, catalog, and display the collected objects.

Our findings have implications for theorization on consumer collections, consumer identity, and consumer participation in brand communities and are relevant for consumer researchers who study the interactions and relationships between consumers and consumption objects.

This study is the first to re-examine consumers as collectors to extend and update consumer research on the curatorial practices of physical, wearable collectibles. This study sets the foundations for further research to advance our understanding of consumers as collectors as well as to illuminate other theories and aspects of consumer research that consider consumer–object interactions.

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.

Advances in material agency driven by artificial intelligence (AI) have facilitated breakthroughs in material adaptivity enabling smart objects to autonomously provide individualized smart services, which makes smart objects act as social actors embedded in the real world. However, little is known about how material adaptivity fosters the infusion use of smart objects to maximize the value of smart services in customers' lives. This study examines the underlying mechanism of material adaptivity (task and social adaptivity) on AI infusion use, drawing on the theoretical lens of social embeddedness.

This study adopted partial least squares structural equation modeling (PLS-SEM), mediating tests, path comparison tests and polynomial modeling to analyze the proposed research model and hypotheses.

The results supported the proposed research model and hypotheses, except for the hypothesis of the comparative effects on infusion use. Besides, the results of mediating tests suggested the different roles of social embeddedness in the impacts of task and social adaptivity on infusion use. The post hoc analysis based on polynomial modeling provided a possible explanation for the unsupported hypothesis, suggesting the nonlinear differences in the underlying influencing mechanisms of instrumental and relational embeddedness on infusion use.

The formation mechanisms of AI infusion use based on material adaptivity and social embeddedness help to develop the business strategies that enable smart objects as social actors to exert a key role in users' daily lives, in turn realizing the social and economic value of AI.

This study advances the theoretical research on material adaptivity, updates the information system (IS) research on infusion use and identifies the bridging role of social embeddedness of smart objects as agentic social actors in the AI context.

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.

Tourism studies have conceptualized social media as artifacts and networks of tangible objects based on neat distinctions and categorizations. These neat ontological distinctions and categorizations have been discussed within the academic field of actor-network theory. Several scholars have most significantly investigated the spatialities of messier ways of conceptualizing and approaching societal objects and the trajectories of societal phenomena. Efforts are being made to widen the ontological register that has traditionally dominated social science research, including tourism studies. The purpose of this chapter is to address and problematize the social media pertaining to tourism, focusing on a research project as analytical and methodological lens.

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.