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The purpose of this research is to develop a framework of an ontology-based Asset Information Model (AIM) for a Digital Twin (DT) platform and enhance predictive maintenance practices in building facilities that could enable proactive and data-driven decision-making during the Operation and Maintenance (O&M) process.

A scoping literature review was accomplished to establish the theoretical foundation for the current investigation. A study on developing an ontology-based AIM for predictive maintenance in building facilities was conducted. Semi-structured interviews were conducted with industry professionals to gather qualitative data for ontology-based AIM framework validation and insights.

The research findings indicate that while the development of ontology faced challenges in defining missing entities and relations in the context of predictive maintenance, insights gained from the interviews enabled the establishment of a comprehensive framework for ontology-based AIM adoption in the Facility Management (FM) sector.

The proposed ontology-based AIM has the potential to enable proactive and data-driven decision-making during the process, optimizing predictive maintenance practices and ultimately enhancing energy efficiency and sustainability in the building industry.

The research contributes to a practical guide for ontology development processes and presents a framework of an Ontology-based AIM for a Digital Twin platform.

The purpose of this study is to focus on structured data provision and asset information model maintenance and develop a data provenance model on a blockchain-based digital twin smart and sustainable built environment (DT) for predictive asset management (PAM) in building facilities.

Qualitative research data were collected through a comprehensive scoping review of secondary sources. Additionally, primary data were gathered through interviews with industry specialists. The analysis of the data served as the basis for developing blockchain-based DT data provenance models and scenarios. A case study involving a conference room in an office building in Stockholm was conducted to assess the proposed data provenance model. The implementation utilized the Remix Ethereum platform and Sepolia testnet.

Based on the analysis of results, a data provenance model on blockchain-based DT which ensures the reliability and trustworthiness of data used in PAM processes was developed. This was achieved by providing a transparent and immutable record of data origin, ownership and lineage.

The proposed model enables decentralized applications (DApps) to publish real-time data obtained from dynamic operations and maintenance processes, enhancing the reliability and effectiveness of data for PAM.

The research presents a data provenance model on a blockchain-based DT, specifically tailored to PAM in building facilities. The proposed model enhances decision-making processes related to PAM by ensuring data reliability and trustworthiness and providing valuable insights for specialists and stakeholders interested in the application of blockchain technology in asset management and data provenance.

The purpose of this paper is to shed light on different types of knowledge created and how this links to the project design, process, and content.

In this paper the authors investigate participants' experiences from a three-year interactive research project, designed to trigger reflection among the participants. They apply a knowledge creation perspective on experiences expressed by participants as a result of different research project activities.

The study resulted in five categories of insights with potential for sustainable influence on the participating organizations: an understanding of concepts and theories; an understanding of the impacts of collaborative, reflective work processes; an understanding of the meaning of one's own organizational context; an understanding of the importance of increased organizational self-awareness; and an understanding of the potential for human interaction and communication.

The author’s findings suggest that it is possible to design a project to promote more profound and sustainable effects on a business beyond the explicit purpose of the project. They advise practitioners to make room for iterative reflection; be mindful to create a trustful and open environment in the team; challenge results with opposing views and theories; and make room for sharing experiences and giving feedback.

This study contributes to unraveling key practices which can nurture conditions for knowledge creation in interactive research projects and business projects alike.

The purpose of this study is to identify the key data categories and characteristics defined by asset information requirements (AIR) and how this affects the development and maintenance of an asset information model (AIM) for a blockchain-based digital twin (DT).

A mixed-method approach involving qualitative and quantitative analysis was used to gather empirical data through semistructured interviews and a digital questionnaire survey with an emphasis on AIR for blockchain-based DTs from a data-driven predictive analytics perspective.

Based on the analysis of results three key data categories were identified, core data, static operation and maintenance (OM) data, and dynamic OM data, along with the data characteristics required to perform data-driven predictive analytics through artificial intelligence (AI) in a blockchain-based DT platform. The findings also include how the creation and maintenance of an AIM is affected in this context.

The key data categories and characteristics specified through AIR to support predictive data-driven analytics through AI in a blockchain-based DT will contribute to the development and maintenance of an AIM.

The research explores the process of defining, delivering and maintaining the AIM and the potential use of blockchain technology (BCT) as a facilitator for data trust, integrity and security.

The purpose of this paper is to assess the impact and effectiveness of the LearnOvation leadership development program in the welfare services sector in Sweden.

LearnOvation was based on ambidexterity theory for the program content and the research study design. A mixed-method design was applied, using questionnaires among staff (n = 523) and written evaluations with the management teams (n = 60).

Quantitative analysis of the questionnaires indicated little change in managers' and staffs' innovation behaviors, though employee exploration behaviors were strongly and positively correlated with their innovation behaviors. Qualitative leader-written evaluations reported increased understanding of innovation management and the use of exploration and exploitation activities to involve staff in the implementation of creative ideas within the organization.

The authors argue that innovating is about creating a fertile ground for exploration and exploitation processes of learning that support staff's willingness to meet goals, as well as their capability to explore new ideas and experiment in new ways of working. Leadership development activities that engage the entire management team can build the necessary capacity and power to lead innovation processes in highly structured welfare services and free the employees' innovativeness, potentially leading to improved services and employee satisfaction.

With the goal of enhancing the innovation capacity in daily practice, this study adds to the scarcity of research in welfare services on how to actually support management's work on leading successful implementation of creative ideas.

This study aims to form a new concept of power-to-gas-based virtual power plant (GVPP) and propose a low-carbon economic scheduling optimization model for GVPP considering carbon emission trading.

In view of the strong uncertainty of wind power and photovoltaic power generation in GVPP, the information gap decision theory (IGDT) is used to measure the uncertainty tolerance threshold under different expected target deviations of the decision-makers. To verify the feasibility and effectiveness of the proposed model, nine-node energy hub was selected as the simulation system.

GVPP can coordinate and optimize the output of electricity-to-gas and gas turbines according to the difference in gas and electricity prices in the electricity market and the natural gas market at different times. The IGDT method can be used to describe the impact of wind and solar uncertainty in GVPP. Carbon emission rights trading can increase the operating space of power to gas (P2G) and reduce the operating cost of GVPP.

This study considers the electrical conversion and spatio-temporal calming characteristics of P2G, integrates it with VPP into GVPP and uses the IGDT method to describe the impact of wind and solar uncertainty and then proposes a GVPP near-zero carbon random scheduling optimization model based on IGDT.

This study designed a novel structure of the GVPP integrating P2G, gas storage device into the VPP and proposed a basic near-zero carbon scheduling optimization model for GVPP under the optimization goal of minimizing operating costs. At last, this study constructed a stochastic scheduling optimization model for GVPP.

The purpose of this research is to support the customization ability for industrial house building companies striving to offer individualized products but with a strategy which includes a production facility. This is accomplished by analyzing the as-is state in terms of existing engineering assets and by proposing a to-be state using the design platform and product lifecycle management (PLM) support.

This study is based on design research methodology and collected data are in-depth interviews, document reviews and workshops and method development. The theoretical baseline is product platforms and the design platform.

The analysis showed that despite use of a platform, inherent assets are disorganized. Still, the identified object-based engineering assets were possible to include in a conceptual proposal for better management, both in the process and product view, using an asset relationship matrix and a PLM system.

The results should be applicable for industrial house building and off-site construction companies and offers an approach to identify and manage their assets and platforms which are crucial to stay competitive.

Previous research on design platforms has focused on engineer-to-order companies within the mechanical industry. The contribution of this paper lies in the application and support of the design platform for industrial house building and the introduction of PLM system support.

Regionalisation in education has gained increased interest and importance because of the increasing collaborations among neighbouring nations. Definitions of the term vary, and more so the regionalisation practices and initiatives of higher educational institutions. In the Philippines, the emphasis on regionalisation has become even more pronounced with the Association of Southeast Asian Nations (ASEAN) Integration Vision. This vision of the ASEAN is geared towards enhancing regional collaboration and the creation of an ASEAN identity and puts education at the forefront, considering it as a strategic objective to achieve the region’s development agenda of economic, social and cultural growth. It becomes of paramount importance then to examine how regionalisation in education is understood by university constituents, its manifestations in terms of institutional activities and especially, how the ASEAN Integration shapes these initiatives and constructions of regionalisation. The paper aims to discuss these issues.

Using a multiple case study design that looked at three higher education institutions (HEIs) in the Philippines, this study found that regionalisation is associated and interchanged with internationalisation in terms of purpose, goal and activities, but is differentiated in dimensions of geographical location and orientation.

Institutional initiatives pertaining to regionalisation were largely functional and mostly open and soft collaborations. The ASEAN Integration creates an ASEAN-centric consciousness, and functions as an opportunity for expanding partnerships, institutional niches and programmatic initiatives; and for legitimising regionalisation and internationalisation goals.

These definitions and approaches to regionalisation have significant policy implications as HEIs strive to respond to the challenges of the Integration.

The purpose of the paper is to describe ambidextrous learning in organizations within the customer order-based context (COBC), here based on a dynamic view of work processes. The study focuses on how organizations can learn while working with customer orders, considering learning in organizations as both a process and an outcome.

This conceptual article focuses on learning in the COBC, where the individual customer requirements represent a key input into the organization’s work processes, thus limiting the possibilities to plan and standardize. The COBC brings about challenges and potentials for learning in organizations where task variety and complexity are high and in which the contradictory interplay between efficiency and responsiveness is apparent not only at a strategic level but also at an operative level in the customer order fulfillment processes. Depending on the variations in tasks and parallel complex work processes between different units in the organization, the ambidextrous learning dynamic can appear in the COBC.

Five propositions were made from the analysis: Proposition 1: Learning in the COBC can occur both in real-time but also in retrospect and with sporadic and recurrent interventions. Proposition 2: Learning in the COBC can occur for, as well as from, customer order processes. Proposition 3: Learning in the COBC varies and will depend on the delivery strategy. Proposition 4: Learning can be stimulated by the variation in priorities among customer orders in the COBC because the work characteristics for the back office and front office differ between customer order fulfillment processes. Proposition 5: Learning in the COBC can occur both within the back office and front office but also between these organizational units. The paper discusses the importance of building learning infrastructure in COBC and how that can be supported by a suggested learning office.

The present study demonstrates the importance of functions being able to act both as back office and front office in relation to delivery strategy. It also shows the ambidextrous learning process for the sake of improving both the internal efficiency and external effectiveness across the organization.

The shipper selects a suitable shipping route and plans for a voyage in order to import and export cargo on the basis of published sailing schedules. The reliability of the sailing schedule will influence the shipper’s logistics expense, which means that the logistics costs will depend on the reliability of schedules published by container shipping companies. Therefore, it is important to consider factors which can cause delays would for container ships sailing on sea routes. The reliability of published sailing schedules can be affected by a number of different factors. This study adopts the multi-criteria decision making (MCDM) method to estimate the importance of the delaying factors in a sailing schedule. In addition, the consistent fuzzy preference relations (CFPR) method is applied to identify the subjective importance (weights) of the delaying factors. The entropy weight method combined with the actual performance of the container shipping company are both used when estimating the objective importance (weights) of the delaying factors. According to the analysis results, the criteria can be divided into four quadrants with different management implications, which indicate that instructions for chase strategy, sailing schedule control, fleet allocation, transship operation arrangement and planning for ports in routes are often ignored by container shipping companies. Container shipping companies should consider adjusting their operational strategies, which would greatly improve their operational performance.