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The emergence of collaborative delivery models and working practices in construction industry has created a potential area for project success research. Previous studies have addressed success factors of various collaborative delivery models (e.g. alliance and partnering). However, there is currently very limited research-based knowledge concerning core success factors for different collaborative delivery models, exploring the commonalities. Thus, this study aims to conceptualize a success model for collaborative construction projects by identifying and structuring their core success factors through the lens of project delivery elements.

A systematic literature review was conducted, and thematic as well as content analysis of the relevant studies led to the identification of mentioned success factors in the literature for different collaborative delivery models. Then, those common success factors were structured in a model based on factors' relation to project delivery elements.

The obtained results present eight core success factors (e.g. equality, mutual trust and commitment to win–win philosophy) for collaborative construction projects, structured in a model based on their contribution toward project organization, contractual relationships, and operational system in construction project delivery. Moreover, the differences between success factors for traditional and collaborative construction projects are discussed.

This study's findings provide insightful theoretical contributions on collaborative construction project success and providing a departure point for future studies based on the discussed differences between success factors of collaborative and traditional construction projects. The findings can be also practically insightful for the project professionals in collaborative construction projects to succeed in managing project organization, contractual relationships, and operational system.

Collaborative consumption, such as car sharing, specifically implicates customer-to-customer interaction, which must be regulated by service providers (companies, peers and self-regulating communities), comprising different challenges for business organizations. While in conventional business relations, consumers are protected from undesirable customer behavior by laws, regulations (power) in the context of collaborative consumption are rare, so that trust becomes more relevant. It is the purpose of the study to investigate possible mechanisms to prevent undesirable customers in collaborative consumption.

In between subject designs, samples of 186 and 328 consumers filled in experimental online questionnaires with vignettes. Analyses were made of differences among car sharing companies, private persons and car sharing communities in terms of the power of providers, trust in providers and trust in other users of the shared goods, undesirable customer behavior and consumer–provider relations.

Companies, private persons and self-regulating communities differ in terms of perceived power and trust. Participants specifically perceive mainly coercive power with the car sharing company, but with the private person and the community, reason-based trust in other users is perceived as prevalent. Nevertheless, undesirable customer behavior varies only marginally over the models.

The present study is the first to investigate measures to prevent undesirable customer behavior over different collaborative consumption models. This enables appropriate identification of market segments and tailoring of services. The study identifies opportunities for companies in contrast to private persons and self-regulating communities and, in doing so, provides important stimulation for marketing strategy and theory development.

The purpose of this paper is to alleviate the problem of poor robustness and over-fitting caused by large-scale data in collaborative filtering recommendation algorithms.

Interpreting user behavior from the probabilistic perspective of hidden variables is helpful to improve robustness and over-fitting problems. Constructing a recommendation network by variational inference can effectively solve the complex distribution calculation in the probabilistic recommendation model. Based on the aforementioned analysis, this paper uses variational auto-encoder to construct a generating network, which can restore user-rating data to solve the problem of poor robustness and over-fitting caused by large-scale data. Meanwhile, for the existing KL-vanishing problem in the variational inference deep learning model, this paper optimizes the model by the KL annealing and Free Bits methods.

The effect of the basic model is considerably improved after using the KL annealing or Free Bits method to solve KL vanishing. The proposed models evidently perform worse than competitors on small data sets, such as MovieLens 1 M. By contrast, they have better effects on large data sets such as MovieLens 10 M and MovieLens 20 M.

This paper presents the usage of the variational inference model for collaborative filtering recommendation and introduces the KL annealing and Free Bits methods to improve the basic model effect. Because the variational inference training denotes the probability distribution of the hidden vector, the problem of poor robustness and overfitting is alleviated. When the amount of data is relatively large in the actual application scenario, the probability distribution of the fitted actual data can better represent the user and the item. Therefore, using variational inference for collaborative filtering recommendation is of practical value.

With the continuous increase of users, resources and tags, social tagging systems gradually present the characteristics of “big data” such as large number, fast growth, complexity and unreliable quality, which greatly increases the complexity of recommendation. The contradiction between the efficiency and effectiveness of recommendation service in social tagging is increasingly becoming prominent. The purpose of this study is to incorporate topic optimization into collaborative filtering to enhance both the effectiveness and the efficiency of personalized recommendations for social tagging.

Combining the idea of optimization before service, this paper presents an approach that incorporates topic optimization into collaborative recommendations for social tagging. In the proposed approach, the recommendation process is divided into two phases of offline topic optimization and online recommendation service to achieve high-quality and efficient personalized recommendation services. In the offline phase, the tags' topic model is constructed and then used to optimize the latent preference of users and the latent affiliation of resources on topics.

Experimental evaluation shows that the proposed approach improves both precision and recall of recommendations, as well as enhances the efficiency of online recommendations compared with the three baseline approaches. The proposed topic optimization–incorporated collaborative recommendation approach can achieve the improvement of both effectiveness and efficiency for the recommendation in social tagging.

With the support of the proposed approach, personalized recommendation in social tagging with high quality and efficiency can be achieved.

The research problem in this study is how a client (as a project owner) should organise early stakeholder involvement and integration in the front-end phase of a project. This study aims to create normative managerial statements as propositions from the client's perspective and to combine them into a set of activities enabling efficient organisation in the front-end phase of a hospital construction project.

Action design research (ADR) was carried out in a large hospital construction project where the first author acted as an “involved researcher” and the other authors acted as “outside researchers”.

The authors created seven normative managerial propositions that were verified by the case project stakeholders and developed a managerial framework describing the client's essential stakeholder involvement and integration activities in the front-end phase of a hospital construction project based on these propositions. The authors have also depicted the subphases of the front-end phase: value definition phase in the client permanent organisation, value proposition phase in the client Programme Management Office (PMO) and finally development phase in the alliance organisation ending on the final investment decision.

The collaborative contract delivery model enables the early involvement and integration of stakeholders. It has been somewhat surprising to note the extent to which collaborative contracts change the client role in the project front-end. The results offer practical activities for how clients can manage front-end activities in collaborative contracts.

The case project offered a platform to analyse how the collaborative contract delivery model changes the emphasis of activities in the front-end of a project. One of the key benefits of collaborative contracts is that development, design and delivery occur partially in parallel, thereby enabling contributions from production to be included in the design and development. The benefit of having a real-life case under study provides the possibility to triangulate and analyse rich data, however limited by the qualitative case method.

The purpose of this paper is to explore the key features of fashion rental from a business model perspective with a focus on the role played by digital platforms.

A qualitative research design was used based on multiple case studies of three Italian fashion rental enterprises.

The findings reveal the key aspects characterizing fashion rental business models and the centrality of digital platforms in value creation, configuration and capture activities. The study also found that fashion rental platforms are likely to exhibit further successful development in the future.

The academic literature on fashion rental has been mainly focussed on examining consumers' motivations and concerns, paying little attention to the enterprise's viewpoint. To date, no previous study has examined fashion rental business models from the retailer's perspective. This paper is the first to explore the key components of fashion rental business models and how digital platforms influence them from the perspective of retailers. The originality of the study is further strengthened by the unique context of analysis, namely, Italy, a leading country for global fashion.

It has been verified that companies are using crowdsourcing to carry out tasks related with the market, concentrating on three main areas: product development, advertising and promotion and market research. This study aims to carry out qualitative research using the case study method. The analysis unit that will be used will be marketing activities based on crowdsourcing. Finally, the study subjects used to understand the phenomenon are Coca Cola, PepsiCo, Netflix and Amazon.

For this study, the decision was made to use the case study method due to its qualitative character. Thus, the analysis unit of the study is composed of the activities of the company investigated related to the activities of the marketing information system. For this study, a unique case study was used. The object companies were Coca Cola, PepsiCo, Netflix and Amazon.

A significant number of advantages are therefore perceived for companies that adopt mass collaboration practices in the research and development of products. The strategic importance of this concept for companies that launch themselves into these surprising endeavors is ever more clear and consolidated in their planning. It is up to those sectors that are responsible for communication to think of strategies that incorporate the collaborative work of consumers into their actions.

The study presents some limitations that give rise to the opportunity to embark on future studies. As it is an exploratory study, there was no validation of causality between variables, which may diminish the empirical value of the findings. In this context, the sample is small and non-probabilistic, which on one hand made applying the interviews in the field study viable, but on the other does not enable the results to be generalized.

The transfer of power to crowds of consumers has led to a reduction in costs. This combination of innovative and cost reduction constitutes value innovation, a basic factor for customer retention, standing apart from the competition and obtaining appreciable results, thus reducing mortality.

A collaborative action is an integrated marketing and communication action. In the same way, using bloggers to evaluate products today is a basic form of research and development, networking and communication with consumers, who become co-creators of products and services, as well as interlocutors with those responsible for corporate communication.

A significant number of advantages are therefore perceived for companies that adopt mass collaboration practices in the research and development of products. The strategic importance of this concept for companies that launch themselves into these surprising endeavors is even more clear and consolidated in their planning. It is up to those sectors that are responsible for communication to think of strategies that incorporate the collaborative work of consumers into their actions.

This article theoretically analyzes how response to intervention (RTI) can be used as a tool in lesson study (LS) to enhance student learning and how RTI can be made more user-friendly by teachers in LS. The focus is on how RTI can be adapted to teachers' daily work by including it in the LS model and how LS can benefit by introducing a scientific approach in analyzing student learning outcomes through RTI. The article also highlights how this approach can contribute to learning for children with special educational needs (SEN).

This theoretical paper describes and compares the characteristics of the LS model with the RTI framework. The comparison highlights the design of models related to teachers’ development and learning outcomes. The benefits and challenges with the models are described. A previous research study related to the models is also briefly reviewed.

There are benefits and challenges with both the RTI and LS models but parts of the models appear to complement one another to some extent. Teachers' professional development and a better control of learning outcomes could be gained by combining the models. This could also lead to educational improvement.

There has been almost no research about a combined LS and RTI model.

This study aims to focus on a key unanswered question about how digitalization and the knowledge processes it enables affect firms’ strategies in the international arena.

The authors conduct a systematic literature review of relevant theoretical and empirical studies covering over 20 years of research (from 2000 to 2023) and including 73 journal papers.

This review allows us to highlight a relationship between firms’ international strategies and the knowledge processes enabled by applying digital technologies. Specifically, the authors discuss the characteristics of patterns of knowledge flows and knowledge processes (their origin, the type of knowledge they carry on and their directionality) as determinants for the emergence of diverse international strategies embraced by single firms or by populations of firms within ecosystems, networks, global value chains or alliances.

Despite digital technologies constituting important antecedents and critical factors for the internationalization process, and international businesses in general, and operating cross borders implies the enactment of highly knowledge-intensive processes, current literature still fails to provide a holistic picture of how firms strategically use what they know and seek out what they do not know in the international environment, using the affordances of digital technologies.

This paper aims to provide top-level design and basic platform for intelligent application in China high-speed railway.

Based on the analysis for the future development trends of world railway, combined with the actual development needs in China high-speed railway, The definition and scientific connotation of intelligent high-speed railway (IHSR) are given at first, and then the system architecture of IHSR are outlined, including 1 basic platform, 3 business sectors, 10 business fields, and 18 innovative applications. At last, a basic platform with cloud edge integration for IHSR is designed.

The rationality, feasibility and implementability of the system architecture of IHSR have been verified on and applied to the Beijing–Zhangjiakou high-speed railway, providing important support for the construction and operation of the world’s first IHSR.

This paper systematically gives the definition and connotation of the IHSR and put forward the system architecture of IHSR for first time. It will play the most important role in the design, construction and operation of IHSR.