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The purpose of this paper is to develop a content analysis framework and from that derive a process model of knowledge construction in the context of virtual product user communities, organization sponsored online forums where product users collaboratively construct knowledge to solve their technical problems.

The study is based on a deductive and qualitative content analysis of discussion threads about solving technical problems selected from a series of virtual product user communities. Data are complemented with thematic analysis of interviews with forum members.

The research develops a content analysis framework for knowledge construction. It is based on a combination of existing codes derived from frameworks developed for computer-supported collaborative learning and new categories identified from the data. Analysis using this framework allows the authors to propose a knowledge construction process model showing how these elements are organized around a typical “trial and error” knowledge construction strategy.

The research makes suggestions about organizations’ management of knowledge activities in virtual product user communities, including moderators’ roles in facilitation.

The paper outlines a new framework for analysing knowledge activities where there is a low level of critical thinking and a model of knowledge construction by trial and error. The new framework and model can be applied in other similar contexts.

Social network sites are emerging as a popular communication tool for knowledge sharing and construction. LinkedIn, which concentrates on professional networking, is reported to generate great informational benefits to its users. The purpose of this paper is to explore product users’ knowledge construction in solving technical problems on LinkedIn, which was chosen as a case example.

Discussion threads with rich knowledge elements were selected from an interest group about solving technical problems with laptops. Adopting a qualitative content analysis method, selected threads were analysed with a prior analysis framework built in the context of traditional IT company sponsored peer user support forums.

The analysis revealed that the iterative and progressive knowledge construction process and associated trial-and-error strategy used on LinkedIn are similar to those found in peer support forums. However, LinkedIn members are more engaged in knowledge construction episodes. Meanwhile, the sub-category “proposing a new idea” accounts for a larger portion of discussions reflecting the high-level of expertise. One-to-one direct interaction is quite salient. Therefore, LinkedIn can support knowledge construction in a more efficient way due to the character of its social capital, including trust, sense of belonging, norms of cooperation, visible identity, knowledge articulation skills, one-to-one direct interaction and suitable strength of ties.

This research is novel in empirically revealing how LinkedIn attributes and its social capital attributes interact with each other and together facilitate an efficient knowledge construction process.

In the extant research on online knowledge communities (OKCs), little attention has been paid to the influence of membership fluidity on the coevolution of the social and knowledge systems. This article aims to fill this gap.

Based on the attraction-selection-attrition (ASA) framework, this paper constructs a simulation model to study the coevolution of these two systems under different levels of membership fluidity.

By analyzing the evolution of these systems with the vector autoregression (VAR) method, we find that social and knowledge systems become more orderly as the coevolution progresses. Furthermore, in communities with low membership fluidity, the microlevel of the social system (i.e. users) drives the coevolution, whereas in communities with high membership fluidity, the microlevel of the knowledge system (i.e. users' views) drives the coevolution.

This paper extends the application of the ASA framework and enriches the literature on membership fluidity of online communities and the literature on driving factors for coevolution of the social and knowledge systems in OKCs. On a practical level, our work suggests that community administrators should adopt different strategies for different membership fluidity to efficiently promote the coevolution of the social and knowledge systems in OKCs.

Accidents resulting from poorly planned or setup work environments are a major concern within the construction industry. While traditional education and training of personnel offer well-known approaches for establishing safe work practices, serious games in virtual reality (VR) are increasingly being used as a complementary approach for active learning experiences. By taking full advantage of data collection and the interactions possible in the virtual environment, the education and training of construction personnel improves by using non-biased feedback and immersion.

This research presents a framework for the generation and automated assessment of VR data. The proposed approach is tested and evaluated in a virtual work environment consisting of multiple hazards. VR requires expensive hardware, technical knowledge and user acceptance to run the games effectively. An effort has been made to transfer the advantages VR gives to a physical setup. This is done using a light detection and ranging sensing system, which collects similar data and enables the same learning experiences.

Encouraging results on the participants’ experiences are presented and discussed based on actual needs in the Danish construction industry. An outlook presents future avenues towards enhancing existing learning methods.

The proposed method will help develop active learning environments, which could lead to safer construction work stations in the future, either through VR or physical simulations.

The utilization of run-time data collection and automatic analysis allows for better personalized feedback in the construction safety training. Furthermore, this study investigates the possibility of transferring the benefits of this system to a physical setup that is easier to use on construction sites without investing in a full VR setup.

This study aims to explore the influence of topic familiarity on the four stages of college students' learning search process.

This study clarified the effects of topic familiarity on students' learning search process by conducting a simulation experiment based on query formulation, information item selection, information sources and learning output.

The results characterized users' interaction behaviors in increasing topic familiarity through their use of more task descriptions as queries, increased reformulation of queries, construction of more purposeful query formulation, reduced attention to a topic's basic concept content and increased exploration of academic platform contents.

This study proposed three innovative indicators which were proposed to evaluate the effects of topic familiarity on college students' learning search process, and the adopted metrics were useful for observing differences in college students' learning output as their topic familiarity increased. It contributes to the understanding of a user's search process and learning output to support the optimization function of learning-related information search systems and improve their effect on the user's search process for learning.

The purpose of this study was to investigate and propose the appropriate K‐mapping models as an approach to integrating key project components and technologies for the effective improvement of project performance within and across construction projects.

In this holistic, single‐case study, one of the largest construction consulting firms in South Korea has been studied by conducting 15 semi‐structured interviews and the different loci for each of the K‐mapping components are identified and analyzed. Based on the different loci, four types of the K‐mapping model are provided and elucidated.

Research findings indicate that these four types of the K‐mapping model provide the criteria to identify the appropriate types of K‐map for construction project organizations, according to the characteristics and conditions of their own construction personnel, construction processes, and K‐transfer technologies. With the K‐mapping models, an appropriate knowledge management system (KMS) can be developed more effectively.

First, as interpretivism was adopted as the research philosophy, the case study findings were subjective and qualitative to both the interviewees in the case study company and the researchers, though this study provided an important underpinning for future research on K‐mapping within construction project organizations. Second, the theory developed in this study was based on an investigation of the appropriate K‐mapping models with only a single case study. Nevertheless, this case study provided sufficient data and information to develop and propose a theory for successful K‐mapping model development within construction project organizations.

In the KM area, the definition, benefits, purposes, principles and types of K‐map have been already provided by many KM researchers and practitioners. However, no industry (practical)‐based K‐mapping model has been developed and proposed, especially in the construction industry. Accordingly, the originality of this study to be presented in one of the paper's conclusions: construction processes must be considered and adopted as a key component in the K‐mapping process, and the discussion of the four types of K‐map this research have generated, which significantly expands the existing literature on K‐mapping.

This paper aims to establish a theoretical search model on academic social networking sites (ASNSs).

Based on the characteristics of ASNSs and a previous extended sense-making model, this paper first presented an initial model of searching on ASNSs. Next, an online survey was conducted on ResearchGate to understand the search processes and outcomes with the help of a survey questionnaire. In total, 359 participants from 70 countries participated in this online survey. The survey results provided a basis for modifying the initial model.

Results showed that the theoretical model of searching on ASNSs included motives for searching on ASNSs, identification of needs, search triggered by information needs, search triggered by social needs and outcomes. The search triggered by information needs was significantly positively correlated with learning outcomes. Besides learning outcomes, searching on ASNSs could help user amplify their social networks and promote research dissemination.

Understanding users’ search habits and knowledge acquisition can provide insights for ASNSs to design an interface to support searching and enhance learning. Moreover, the proposed model can help users recognize their knowledge status and learning effects and improve their learning efficiency.

This paper contributes to establishing a theoretical model to understand users’ search process and outcomes on ASNSs.

Although social media has gained prominence as a communication and marketing tool in various sectors, its adoption and utilization within the construction industry remain relatively underexplored. Therefore, this study fills this gap by evaluating the level of awareness and the extent of adoption of social media within the Nigerian construction industry, shedding light on its current status and potential impact.

This objective was attained via a quantitative research approach that utilized a structured questionnaire to obtain responses from construction professionals such as architects, builders, engineers, quantity surveyors and estate managers. Frequencies and percentages and the mean item score (MIS) were used to analyze the questionnaire responses and assess the overall awareness and adoption of social media among construction professionals. Additionally, the Kruskal–Wallis H-test provided valuable insights into the variations in social media adoption levels among different professional categories within the construction industry.

The results indicate that construction professionals possess a generally high level of awareness regarding various social media platforms. However, despite this awareness, the extent of adoption does not align with the level of awareness, suggesting that adoption rates are not as widespread as anticipated.

The findings of this study underscore the importance of not just awareness but also effective adoption and utilization of social media platforms. While awareness is a crucial first step, construction firms should focus on implementing strategies to encourage greater adoption and integration of these platforms into their daily operations. This can go a long way in bridging the awareness – adoption gap which was revealed in this study.

While the limited existing research on social media in the construction industry has predominantly concentrated on areas such as marketing, addressing the root causes of fatalities, data environment tools and business branding, none have undertaken a thorough evaluation of social media awareness and adoption within the sector. This study fills a critical gap by narrowing its focus to the adoption dynamics and the technology’s potential impact on communication, collaboration and knowledge sharing among construction professionals.

The purpose of this paper is to introduce a multi-level framework for semantic modeling (MFSM) based on four signification levels: objects, classes of entities, instances and domains. In addition, four fundamental propositions of the signification process underpin these levels, namely, classification, decomposition, instantiation and contextualization.

The deductive approach guided the design of this modeling framework. The authors empirically validated the MFSM in two ways. First, the authors identified the signification processes used in articles that deal with semantic modeling. The authors then applied the MFSM to model the semantic context of the literature about lean manufacturing, a field of management science.

The MFSM presents a highly consistent approach about the signification process, integrates the semantic modeling literature in a new and comprehensive view; and permits the modeling of any semantic context, thus facilitating the development of knowledge organization systems based on semantic search.

The use of MFSM is manual and, thus, requires a considerable effort of the team that decides to model a semantic context. In this paper, the modeling was generated by specialists, and in the future should be applicated to lay users.

The MFSM opens up avenues to a new form of classification of documents, as well as for the development of tools based on the semantic search, and to investigate how users do their searches.

The MFSM can be used to model archives semantically in public or private settings. In future, it can be incorporated to search engines for more efficient searches of users.

The MFSM provides a new and comprehensive approach about the elementary levels and activities in the process of signification. In addition, this new framework presents a new form to model semantically any context classifying its objects.

User‐created metadata, often referred to as folksonomy or social classification, has received a considerable amount of attention in the digital library world. Social tagging is perceived as a tool for enhancing description of digital objects and providing a venue for user input and greater user engagement. This article seeks to examine the pros and cons of user‐generated metadata in the context of digital image collections and compares it to professionally created metadata schema and controlled vocabulary tools.

The article provides an overview of challenges to concept‐based image indexing. It analyzes the characteristics of social classification and compares images described by users to a set of images indexed in a digital collection.

The article finds that user‐generated metadata vary in the level of description, accuracy, and consistency and do not provide a solution to the challenges of image indexing. On the other hand, they reflects user's language and can lead toward user‐centered indexing and greater user engagement.

Social tagging can be implemented as a supplement to professionally created metadata records to provide an opportunity for users to comment on images.

The article introduces the idea of user‐centered image indexing in digital collections.