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Effective project learning can prevent projects from repeating the same mistakes; however, knowledge codification is required for project-to-project learning to be up-scaled across the temporal, geographical and organisational barriers that constrain personalised learning. This paper explores the state of practice for the structuring of codified project learnings as concrete boundary objects with the capacity to enable externalised project-to-project learning across complex boundaries. Cross-domain reconceptualisation is proposed to enable further research and support the future development of standardised recommendations for boundary objects that can enable project-to-project learning at scale.

An integrative literature review method has been applied, considering knowledge, project learning and boundary object scholarship as state-of-practice sources.

It is found that the extensive body of boundary object literature developed over the last three decades has not yet examined the internal structural characteristics of concrete boundary objects for project-to-project learning and boundary-spanning capacity. Through a synthesis of the dispersed structural characteristic recommendations that have been made across examined domains, a reconceptualised schema of 30 discrete characteristics associated with boundary-spanning capacity for project-to-project learning is proposed to support further investigation.

This review makes a novel contribution as a first cross-domain examination of the internal structural characteristics of concrete boundary objects for project-to-project learning. The authors provide directions for future research through the reconceptualisation of a novel schema and the identification of important and previously unidentified research gaps.

The purpose of this study is to substantiate the matrix approach to digitalization of management objects based on identification of relevant qualitative characteristics of these objects and its dichotomies, which allowing determine the quantity and quality of their main variants, as well as the relationships between them.

Methods of classification and typology are selected as study methods, and binary matrices are used as the tool to determine the main variants of management objects, assign binary codes to it and form codes of more complex management objects on its basis, depending on the content of study tasks.

The main results of study include the classification of organization components; variants for choosing qualitative characteristics of chains components; adjusted content of methodology of qualitative research of management objects; sequences of “up” and “down” digitization of these objects; actual qualitative characteristics of e components of management objects and dichotomies; and variants of forming of ciphers of these objects.

The use of study results allows to reduce the complexity of substantiating and making managerial decisions in organization and supply chains, to structure these decisions by man-agement levels and positions and to reduce costs, time and lost profits for fulfilling orders of end consumers of products and/or services.

The originality of this study is confirmed by the substantiation of choice and use of actual qualitative characteristics of management objects and its dichotomies, which allow obtaining two variants of these objects and assigning them binary codes processed using computer software for management activities.

The purpose of this paper is to address the challenging issue of developing a quantitative approach for the representation of cultural significance data in heritage information systems (HIS). The authors propose to provide experts in the field with a dedicated framework to structure and integrate targeted data about historical objects' significance in such environments.

This research seeks the identification of key indicators which allow to better inform decision-makers about cultural significance. Identified concepts are formalized in a data structure through conceptual data modeling, taking advantage on unified modeling language (HIS). The design science research (DSR) method is implemented to facilitate the development of the data model.

This paper proposes a practical solution for the formalization of data related to the significance of objects in HIS. The authors end up with a data model which enables multiple knowledge representations through data analysis and information retrieval.

The framework proposed in this article supports a more sustainable vision of heritage preservation as the framework enhances the involvement of all stakeholders in the conservation and management of historical sites. The data model supports explicit communications of the significance of historical objects and strengthens the synergy between the stakeholders involved in different phases of the conservation process.

Authenticity has been studied from a variety of disciplinary perspectives, leading to a rich but confused literature. This study, a review, aims to compare the psychology and sociology/tourism definitions of authenticity to clarify the concept. From a psychological perspective, authenticity is a mental appraisal of an object or experience as valued leading to feelings and summative judgements (such as satisfaction or perceived value). In objective authenticity, a person values the object due to belief in an expert’s opinion, constructive authenticity relies on socially constructed values, while existential authenticity is based on one’s self-identity. The resultant achievement of a valued goal, such as seeing a valued object, leads to feelings of pleasure. Sociological definitions are similar but based on different theoretical antecedent causes of constructed and existential authenticity. The paper further discusses the use of theory in tourism and the project to develop tourism as a discipline. This project is considered unlikely to be successful and in turn, as argued, it is more useful to apply theory from other disciplines in a multidisciplinary manner. The results emphasise that it is necessary for tourism researchers to understand the origins and development of the concepts they use and their various definitions.

Robots face fundamental challenges in achieving reliable and stable operations for complex home service scenarios. This is one of the crucial topics of robotics methods to imitate human beings’ advanced cognitive characteristics and apply them to solve complex tasks. The purpose of this study is to enable robots to have the ability to understand the scene and task process in complex scenes and to provide a reference method for robot task programming in complex scenes.

This paper constructs a task modeling method for robots in complex environments based on the characteristics of the perception-motor memory model of human cognition. In the aspect of episodic memory construction, the task execution process is included in the category of qualitative spatio-temporal calculus. The topology interaction of objects in a task scenario is used to define scene attributes. The task process can be regarded as changing scene attributes on a time scale. The qualitative spatio-temporal activity graphs are used to analyze the change process of the object state with time during the robot task execution. The tasks are divided according to the different values of scene attributes at different times during task execution. Based on this, in procedural memory, an object-centered motion model is developed by analyzing the changes in the relationship between objects in the scene episode by analyzing the scene changes before and after the robot performs the actions. Finally, the task execution process of the robot is constructed by alternately reconstructing episodic memory and procedural memory.

To verify the applicability of the proposed model, a scenario where the robot combines the object (one of the most common tasks in-home service) is set up. The proposed method can obtain the landscape of robot tasks in a complex environment.

The robot can achieve high-level task programming through the alternating interpretation of scenarios and actions. The proposed model differs from traditional methods based on geometric or physical feature information. However, it focuses on the spatial relationship of objects, which is more similar to the cognitive mechanism of human understanding of the environment.

Successful implementation of a building information modeling (BIM) for building operation and maintenance (O&M) requires purposeful, early-design identification of end-user-specific model exchange requirements. This paper aims to provide a semantic data-rich classification system for model objects to convey facilities management (FM) requirements in BIM guidelines in support of efficient FM-BIM data workflows.

A modularized, repeatable and technical solution for semantic requirements of BIM exchange objects was developed through ontology-based data mapping of the industry foundation classes. The proposed solution further contextualizes syntax per the buildingSMART Data Dictionary schema and provides an implementation agreement to address the quality issues of discipline BIMs and establish consistent modeling and naming conventions to facilitate automated BIM data workflow.

The level of semantics (LOS) development framework and the results of LOS implementation focusing on a building mechanical system case project are presented and discussed to showcase the increased efficiency resulting from its implementation throughout the BIM data management workflows.

This study represents a pioneering effort to create and implement the LOS schema as a modularized solution in support of automatic BIM data creation, adjustment, verification and transition across the design, construction and O&M workflows of a large owner organization in the Midwest USA.

The compliance checking of Building Information Modeling (BIM) models is crucial throughout the lifecycle of construction. The increasing amount and complexity of information carried by BIM models have made compliance checking more challenging, and manual methods are prone to errors. Therefore, this study aims to propose an integrative conceptual framework for automated compliance checking of BIM models, allowing for the identification of errors within BIM models.

This study first analyzed the typical building standards in the field of architecture and fire protection, and then the ontology of these elements is developed. Based on this, a building standard corpus is built, and deep learning models are trained to automatically label the building standard texts. The Neo4j is utilized for knowledge graph construction and storage, and a data extraction method based on the Dynamo is designed to obtain checking data files. After that, a matching algorithm is devised to express the logical rules of knowledge graph triples, resulting in automated compliance checking for BIM models.

Case validation results showed that this theoretical framework can achieve the automatic construction of domain knowledge graphs and automatic checking of BIM model compliance. Compared with traditional methods, this method has a higher degree of automation and portability.

This study introduces knowledge graphs and natural language processing technology into the field of BIM model checking and completes the automated process of constructing domain knowledge graphs and checking BIM model data. The validation of its functionality and usability through two case studies on a self-developed BIM checking platform.

The outbreak of COVID-19 pandemic has posed severe challenges to infrastructure construction in China. Particularly, the complex technology and high process uncertainty of deep foundation pit construction make its safety risk identification a challenging issue of general concern. To address these challenges, Building Information Modeling (BIM) can be used as an important tool to enhance communication and decision-making among stakeholders during the pandemic. The purpose of this study is to propose a knowledge management and BIM-integrated safety risk identification method for deep foundation pit construction to improve the management efficiency of project participants.

This paper proposes a risk identification method that integrates BIM and knowledge management for deep foundation pit construction. In the framework of knowledge management, the topological relationships between objects in BIM are extracted and visualized in the form of knowledge mapping. After that, formal expressions of codes are established to realize the structured processing of specification provisions and special construction requirements. A comprehensive plug-in for deep foundation pit construction is designed based on the BIM software.

The proposed method was verified by taking a sub-project in deep foundation pit project construction as an example. The result showed the new method can make full use of the existing specification and special engineering requirements knowledge. In addition, the developed visual BIM plug-in proves the feasibility and applicability of the proposed method, which can help to increase the risk identification efficiency and refinement.

The deep foundation pit safety risk identification is challenged by the confusion of deep foundation pit construction safety knowledge and the complexity of the BIM model. By establishing the standardized expression of normative knowledge and special construction requirements, the efficiency and refinement of risk identification are improved while ensuring the comprehensiveness of results. Moreover, the topology-based risk identification method focuses on the project objects and their relations in the way of network, eliminating the problem of low efficiency from the direct BIM-based risk identification method due to massive data.

Negative brand engagement represents a pervasive and persistent feature of interactivity in online contexts. Although existing research suggests that consumer negativity is potentially more impactful or detrimental to brands than its positive counterpart, few studies have examined negative brand-related cognitions, feelings and behaviours. Building on the concept of brand engagement, this study aims to operationalise negative online brand engagement.

This paper presents the results of nine studies that contributed to the development and validation of the proposed scale. Building on the concept of engagement, Studies 1–3 enhanced the construct conceptualisation and generated items. Study 4 involved validation with an academic expert panel. The process of measure operationalisation and validation with quantitative data was completed in Studies 5–8. Finally, the scale's nomological validity was assessed in Study 9.

The results confirm the multidimensional nature of negative online brand engagement. The validated instrument encompasses four dimensions (cognition, affection, online constructive behaviour and online destructive behaviour), captured by 17 items.

Progress in understanding and dealing with negative online brand engagement has been hampered by disagreements over conceptualisation and the absence of measures that capture the phenomenon. This work enhances managerial understanding of negativity fostering strategies that protect brand engagement and improve firm performance.

This research explores the differential effects of pixel-level and object-level visual complexity in firm-generated content (FGC) on consumer engagement in terms of the number of likes and shares, and further investigates the moderating role of image brightness.

Drawing on a deep learning analysis of 85,975 images on a social media platform in China, this study investigates visual complexity in FGC.

The results indicate that pixel-level complexity increases both the number of likes and shares. Object-level complexity has a U-shaped relationship with the number of likes, while it has an inverted U-shaped relationship with the number of shares. Moreover, image brightness mitigates the effect of pixel-level complexity on likes but amplifies the effect on shares; contrarily, it amplifies the effect of object-level complexity on likes, while mitigating its effect on shares.

Although images play a critical role in FGC, visual data analytics has rarely been used in social media research. This study identified two types of visual complexity and investigated their differential effects. We discuss how the processing of information embedded in visual content influences consumer engagement. The findings enrich the literature on social media and visual marketing.