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Built asset management processes require a long transition period to collect, edit and update asset conditions information from existing data sets. This paper aims to explore and explain whether and how digital technologies, including asset information model (AIM), Internet of Things (IoT) and blockchain, can enhance asset conditions assessment and lead to better asset management.

Mixed methods are applied to achieve the research objective with a focus in universities. The questionnaire aims to test whether the integration of AIM, IoT and blockchain can enhance asset condition assessment (ACA). Descriptive statistical analysis was applied to the quantitative data. The mean, median, mode, standard deviation, variance, skewness and range of the data group were calculated. Semi-structured interviews were designed to answer how the integration of AIM, IoT and blockchain can enhance the ACA. Quantitative data was analysed to define and explain the essential factors for each sub-hypothesis. Meanwhile, to strengthen the evaluation of the research hypothesis, the researcher also obtained secondary data from the literature review.

The research shows that the integration of AIM, IoT and blockchain strongly influences asset conditions assessment. The integration of AIM, IoT and blockchain can improve the asset monitoring and diagnostics through its life cycle and in different aspects, including financial, physical, functional and sustainability. Moreover, the integration of AIM, IoT and blockchain can enhance cross-functional collaboration to avoid misunderstandings, various barriers and enhance trust, communication and collaboration between the team members. Finally, costs and risk could be reduced, and performance could be increased during the ACA.

The contribution of this study indicated that the integration of AIM, IoT and blockchain application in asset assessment could increase the efficiency, accuracy, stability and flexibility of asset assessment to ensure the reliability of assets and lead to a high-efficiency working environment. More importantly, a key performance indicator for ACA based on the asset information, technology and people experience could be developed gradually.

This study can break the gap between transdisciplinary knowledge to improve the integration of people, technology (AIM, IoT and blockchain) and process value-based ACA in built asset management within universities.

This study aims to explore auto mechanics awareness of repairs and maintenance related to the car’s cybersecurity and provide insights into challenges based on current practice.

This study is based on an empirical study consisting of semistructured interviews with representatives from both branded and independent auto workshops. The data was analyzed using thematic analysis. A version of the capability maturity model was introduced to the respondents as a self-evaluation of their cybersecurity awareness.

Cybersecurity was not found to be part of the current auto workshop work culture, and that there is a gap between independent workshops and branded workshops. Specifically, in how they function, approach problems and the tools and support available to them to resolve (particularly regarding previously unknown) issues.

Only auto workshop managers in Sweden were interviewed for this study. This role was picked because it is the most likely to have come in contact with cybersecurity-related issues. They may also have discussed the topic with mechanics, manufacturers or other auto workshops – thus providing a broader view of potential issues or challenges.

The challenges identified in this study offers actionable advice to car manufacturers, branded workshops and independent workshops. The goal is to further cooperation, improve knowledge sharing and avoid unnecessary safety or security issues.

As cars become smarter, they also become potential targets for cyberattacks, which in turn poses potential threats to human safety. However, research on auto workshops, which has previously ensured that cars are road safe, has received little research attention with regards to the role cybersecurity can play in repairs and maintenance. Insights from auto workshops can therefore shed light upon the unique challenges and issues tied to the cybersecurity of cars, and how they are kept up-to-date and road safe in the digital era.

This study aims to propose the use of time series autoregressive integrated moving average (ARIMA) models to predict gas path performance in aero engines. The gas path is a critical component of an aero engine and its performance is essential for safe and efficient operation of the engine.

The study analyzes a data set of gas path performance parameters obtained from a fleet of aero engines. The data is preprocessed and then fitted to ARIMA models to predict the future values of the gas path performance parameters. The performance of the ARIMA models is evaluated using various statistical metrics such as mean absolute error, mean squared error and root mean squared error. The results show that the ARIMA models can accurately predict the gas path performance parameters in aero engines.

The proposed methodology can be used for real-time monitoring and controlling the gas path performance parameters in aero engines, which can improve the safety and efficiency of the engines. Both the Box-Ljung test and the residual analysis were used to demonstrate that the models for both time series were adequate.

To determine whether or not the two series were stationary, the Augmented Dickey–Fuller unit root test was used in this study. The first-order ARIMA models were selected based on the observed autocorrelation function and partial autocorrelation function.

Further, the authors find that the trend of predicted values and original values are similar and the error between them is small.

The study explores how Taiwan’s electronic health data systems can be used to build algorithms that reduce or eliminate medical errors and to advance precision medicine.

This study is a narrative review of the literature.

The body of medical knowledge has grown far too large for human clinicians to parse. In theory, electronic health records could augment clinical decision-making with electronic clinical decision support systems (CDSSs). However, computer scientists and clinicians have made remarkably little progress in building CDSSs, because health data tend to be siloed across many different systems that are not interoperable and cannot be linked using common identifiers. As a result, medicine in the USA is often practiced inconsistently with poor adherence to the best preventive and clinical practices. Poor information technology infrastructure contributes to medical errors and waste, resulting in suboptimal care and tens of thousands of premature deaths every year. Taiwan’s national health system, in contrast, is underpinned by a coordinated system of electronic data systems but remains underutilized. In this paper, the authors present a theoretical path toward developing artificial intelligence (AI)-driven CDSS systems using Taiwan’s National Health Insurance Research Database. Such a system could in theory not only optimize care and prevent clinical errors but also empower patients to track their progress in achieving their personal health goals.

While research teams have previously built AI systems with limited applications, this study provides a framework for building global AI-based CDSS systems using one of the world’s few unified electronic health data systems.

Higher energy conversion efficiency of internal combustion engine can be achieved with optimal control of unsteady in-cylinder flow fields inside a direct-injection (DI) engine. However, it remains a daunting task to predict the nonlinear and transient in-cylinder flow motion because they are highly complex which change both in space and time. Recently, machine learning methods have demonstrated great promises to infer relatively simple temporal flow field development. This paper aims to feature a physics-guided machine learning approach to realize high accuracy and generalization prediction for complex swirl-induced flow field motions.

To achieve high-fidelity time-series prediction of unsteady engine flow fields, this work features an automated machine learning framework with the following objectives: (1) The spatiotemporal physical constraint of the flow field structure is transferred to machine learning structure. (2) The ML inputs and targets are efficiently designed that ensure high model convergence with limited sets of experiments. (3) The prediction results are optimized by ensemble learning mechanism within the automated machine learning framework.

The proposed data-driven framework is proven effective in different time periods and different extent of unsteadiness of the flow dynamics, and the predicted flow fields are highly similar to the target field under various complex flow patterns. Among the described framework designs, the utilization of spatial flow field structure is the featured improvement to the time-series flow field prediction process.

The proposed flow field prediction framework could be generalized to different crank angle periods, cycles and swirl ratio conditions, which could greatly promote real-time flow control and reduce experiments on in-cylinder flow field measurement and diagnostics.

This study was conducted to forecast the monthly number of tuna landings between 2023 and 2030 and determine whether the estimated number meets the government’s target.

The ARIMA and seasonal ARIMA (SARIMA) models were employed for time series forecasting of tuna landings from the Malaysian Department of Fisheries. The best ARIMA (p, d, q) and SARIMA(p, d, q) (P, D, Q)¹² model for forecasting were determined based on model identification, estimation and diagnostics.

SARIMA(1, 0, 1) (1, 1, 0)¹² was found to be the best model for forecasting tuna landings in Malaysia. The result showed that the fluctuation of monthly tuna landings between 2023 and 2030, however, did not achieve the target.

This study provides preliminary ideas and insight into whether the government’s target for fish landing stocks can be met. Impactful results may guide the government in the future as it plans to improve the insufficient supply of tuna.

The outcome of this study could raise awareness among the government and industry about how to improve efficient strategies. It is to ensure the future tuna landing meets the targets, including increasing private investment, improving human capital in catch and processing, and strengthening the system and technology development in the tuna industry.

This paper is important to predict the trend of monthly tuna landing stock in the next eight years, from 2023 to 2030, and whether it can achieve the government’s target of 150,000 metric tonnes.

Many town centres in England exhibit high retail property vacancies and require regeneration. Several alternatives for the replacement of town centre retail (TCR) have been suggested, one of which is healthcare. The healthcare sector in England is in distress, with the National Health Service (NHS) tackling extensive patient waiting lists, whilst operating from an ageing estate. This paper is an introductory study that uses seven carefully selected personalised surveys to raise academic awareness of the importance and potential of integrating healthcare into town centres and calls for large-scale research to establish the statistical validity of the reported observations.

This study is developed from an interpretative standpoint. Through semi-structured interviews with key stakeholders specific to retail-to-healthcare conversions, this study reports stakeholders' perspectives on opportunities and limitations for such conversions to give direction for large statistical research in the future.

All participants support the integration of healthcare into town centres and agreed that diagnostic services, mental health support and primary care services are appropriate for provision within town centres. The participants advocate large-scale change in town centres in England, with integrated healthcare co-located with complementary services to fit with wider regeneration plans. Participants prefer adaptation of existing buildings where technically feasible and emphasise the importance of obtaining the buy-in of other stakeholders whilst expressing concerns about the uncertainty of capital funding availability.

This is the first study to analyse the practice of retail-to-healthcare conversions in town centres. These are still rare in England and projects are complex. The market experience is limited, and thus, the literature is scarce. This study fills this void and provides a starting point for future quantitative research in this area and informs the new town-planning policies.

Exponential organizations (ExOs) are purpose-driven companies that leverage exponential technologies and exponential business practices to grow and scale rapidly, transform industries and create massive value and impact. In contrast, non-ExOs follow a linear approach to business and organizational strategy design and execution. This study aims to validate the hypothesis, based on financial metrics, that ExOs outperform their competitors and linear counterparts. Furthermore, it also brings a new understanding of the gap raised in the past eight years about how ExOs can achieve significantly better performance, measured with financial metrics.

For measuring how exponential an organization is, this study elaborated a completely new assessment tool called Exponential Quotient (ExQ). This study applied ExQ to the 100 largest US headquartered companies as ranked by Fortune magazine in 2014. Calculating the ExQ enabled this study to rank these Fortune 100 companies and identify the most and the least exponential firms. This study tracked these companies as to how they performed on different financial metrics over the eight years of 2014–2021 and analyzed the results.

Through the analysis, this study revealed that the top 10 ExOs have significantly outperformed their bottom 10 non-exponential peers, delivering 40x higher shareholder returns, 2.6x better revenue growth, 6.8x higher profitability and 11.7x better asset turnover. Furthermore, this study could identify commonalities and similarities between the two groups. This means that ExOs can thrive even in tough times and that accelerating technologies unlock abundance and allow every organization to become a disruptive innovator and stay ahead of the competition. These are novel results in the research focusing on the gap between exponential and traditional organizations.

Using the ExQ diagnostics tool, every organization can see how flexible, scalable and agile they are, which is the starting point for an exponential transformation program. Although this approach has already found its way into practice and is applied globally by thousands of organizations (startups, scaleups and incumbents), so far, the academic establishment is in its nascent phase. With this research, the authors wanted to extend this field of science. On the other hand, because of its novelty, no appropriate previous studies existed to compare the results.

The possible implications showed that there is a plannable way for significantly increasing an organization’s ExQ and advance it from a linear toward an exponential organizational model.

The results validated the robustness of the ExO framework and philosophy and shed light on the importance of exponential transformation – a proven method to increase an organization’s ExQ. This framework is not a “how to be successful” guide. Instead, it uncovered some of the previously unknown and universal mechanisms of scalability – which, in turbulent times, make companies successful (based on financial metrics). To the best of the authors’ knowledge, this study was among the first kind of in-depth analyses to validate the whole ExO model.

This paper aims to explore the relationship between owner-manager or leader’s ambidextrous leadership style and its effect on human resource management (HRM) practices, contextual ambidexterity and knowledge-intensive small- and medium-enterprises (SMEs) strategic agility.

This study presents an in-depth qualitative case study analysis of two knowledge-intensive SMEs from India’s information technology and health-care products industry serving a range of global clients. Using the theoretical lenses of empowerment-focused HRM practices, ambidextrous leaders, contextual ambidexterity and strategic agility, semi-structured interview data of leaders, managers and employees of the case organizations were analysed. Through a two-staged analytical process, we abductively developed a novel conceptual framework at the intersection of the above theoretical lenses.

The findings suggest that the knowledge-intensive SME’s strategic agility, ambidexterity and empowerment-focussed HRM approach was influenced by the owner-manager or leader’s ambidextrous leadership style and their philosophy towards managing people and had a positive impact in creating a culture of trust, participation, risk-taking and openness, and led to delivering innovative products and services as well as several positive employee-level outcomes.

Recent literature reviews on HRM In SMEs highlight several gaps, including the impact of owner-manager or leader’s philosophy of managing people in shaping HRM practices and employee outcomes. This paper thus adds to the existing literature on HRM and knowledge-intensive SMEs.

The phenomenon of growth spillover occurs because of domestic shocks, global shocks and shocks to a foreign country or region, and these are transmitted through specific channels. This study investigates the strength of the economic linkages between Caribbean Community (CARICOM) economies and its main traditional partners, including the European Union (EU-27), and emerging trading partners, such as China, with a view to determining the presence and extent of spillover growth which results from the interdependence among these economies. The paper hypothesizes that the presence of these spillovers can be leveraged to chart the future for the region's integration in the global sphere.

Based on the existing theoretical and empirical literature, a structural vector autoregressive (SVAR) model was developed and employed to examine the strength of the economic linkages between CARICOM economies and its main trading partners, such as the United States (US), the United Kingdom (UK) and the EU-27, alongside some of the non-traditional partners such as China. This method has been widely used by institutions, such as the International Monetary Fund (IMF) and World Bank, to profile economic linkages between economies. To this end, the methodology was formulated based on the IMF Spillover Reports which were produced from 2011 to 2015.

The model suggests that positive spillovers are likely to occur from continued deepened integration with the US, EU-27 and the UK, as traditional trade partners, but that opportunities also exist from a deliberate deepening of relations with non-traditional trade partners, for example, China. This becomes even more apparent when CARICOM is separated into categories consisting of more developed countries (MDCs) and less developed countries (LDCs). In addition, from the perspective of any trading partner, such as those in the EU-27, this research is relevant and timely as it contributes to the landscape of literature, which can be utilized for the purpose of negotiating parameters of trade and integration arrangements.

This study adds to the literature on evaluating the direction for deepened integration of CARICOM economies, both with selected traditional and non-traditional trade partners as the region pilots recovery in a post-pandemic global space.

Policymakers can use the results of this study to leverage economic spillovers as a basis for determining which trade partners offer the most significant growth benefits as the region recovers from the COVID-19 pandemic and it will also assist in steering regional integration. This result also implies that over time, the comparative advantage structure of CARICOM member countries' export profile should change to reflect the import profile of its trade partners. To this end, this study can be used to inform and better position the respective trade and industrial development policies of countries in the Caribbean region as they attempt to deepen integration regionally and internationally. From the perspective of the partner, traditional trading relationships such as those which exist with European countries, such as the CARIFORUM-EU Economic Partnership Agreement, can be more deliberately utilized given the geographic benefits on offer with deepened relationships with economies in the Caribbean. Further, this research can also be a point of departure for future research.

This study is among the few empirical works that examine spillover effects as a strategy for rebuilding economic growth in the post-COVID 19 era. This study adds to the literature on evaluating the direction for deepened integration of CARICOM economies, both with selected traditional and non-traditional trade partners as the region navigates recovery in a post-pandemic global space.