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Unconventional machining processes, particularly ultrasonic vibration cutting (UVC), can overcome such technical bottlenecks. However, the precise mechanism through which UVC affects the in-service functional performance of advanced aerospace materials remains obscure. This limits their industrial application and requires a deeper understanding.

The surface integrity and in-service functional performance of advanced aerospace materials are important guarantees for safety and stability in the aerospace industry. For advanced aerospace materials, which are difficult-to-machine, conventional machining processes cannot meet the requirements of high in-service functional performance owing to rapid tool wear, low processing efficiency and high cutting forces and temperatures in the cutting area during machining.

To address this literature gap, this study is focused on the quantitative evaluation of the in-service functional performance (fatigue performance, wear resistance and corrosion resistance) of advanced aerospace materials. First, the characteristics and usage background of advanced aerospace materials are elaborated in detail. Second, the improved effect of UVC on in-service functional performance is summarized. We have also explored the unique advantages of UVC during the processing of advanced aerospace materials. Finally, in response to some of the limitations of UVC, future development directions are proposed, including improvements in ultrasound systems, upgrades in ultrasound processing objects and theoretical breakthroughs in in-service functional performance.

This study provides insights into the optimization of machining processes to improve the in-service functional performance of advanced aviation materials, particularly the use of UVC and its unique process advantages.

Intangible capital (IC) is an important factor for economic growth and firm performance. The role IC has played has become even more crucial in recent decades, possibly influencing debt capacity and default risk assessment. This paper studies how entrepreneurial and employee-based IC affects financial leverage.

Employer–employee unbalanced panel data provided by Statistics Finland that refer to Finnish small and medium-sized enterprises (SMEs) are used. Intangibles are measured with an expenditure-based method. Employee-based IC and entrepreneurial knowledge are used to explain debt financing in SMEs.

The findings imply that IC-intensive firms have less debt capacity due to weak pledgeability and asymmetric information between borrower and lender. Entrepreneurs with managerial or financial knowledge increase the firm's debt capacity compared to other entrepreneurs, especially in knowledge-intensive services (KIS). One explanation is that the entrepreneurs are more competent in negotiating with lenders as the entrepreneurs possess better financial skills. Entrepreneurs with technical knowledge decrease the firm's debt capacity in all industries.

While some earlier research focused on the IC–financial leverage relationship, hardly any study has looked at entrepreneurial IC. This paper provides new insights by including entrepreneurial IC alongside employee-based IC.

Robotic process automation (RPA) has recently emerged as a technology focusing on the automation of repetitive, frequent, voluminous and rule-based tasks. Despite a few practical examples that document successful RPA deployments in organizations, evidence of its economic benefits has been mostly anecdotal. The purpose of this paper is to present a step-by-step method to RPA investment appraisal and a business case demonstrating how the steps can be applied to practice.

The methodology relies on design science research (DSR). The step-by-step method is a design artefact that builds on the mapping of processes and modelling of the associated costs. Due to the longitudinal nature of capital investments, modelling uses discounted cashflow and present value methods. Empirical grounding characteristic to DSR is achieved by field testing the artefact.

The step-by-step method is comprised of a preparatory step, three modelling steps and a concluding step. The modelling consists of compounding the interest rate, discounting the investment costs and establishing measures for comparison. These steps were applied to seven business processes to be automated by the case company, Estate Blend. The decision to deploy RPA was found to be trivial, not only based on the initial case data, but also based on multiple sensitivity analyses that showed how resistant RPA investments are to changing circumstances.

By following the provided step-by-step method, executives and managers can quantify the costs and benefits of RPA. The developed method enables any organization to directly compare investment alternatives against each other and against the probable status quo where many tasks in organizations are still carried out manually with little to no automation.

The paper addresses a growing new domain in the field of business process management by capitalizing on DSR and modelling-based approaches to RPA investment appraisal.

Prefabricated products are continually entering the building construction market; yet, the decision to use prefabricated products in a construction project is based mostly on personal preferences and the evaluation of direct costs. Researchers and practitioners have debated appropriate measurement systems for evaluating the impacts of prefabricated products and for comparing them with conventional on-site construction practices. The more advanced, cost–benefit approach to evaluating prefabricated products often inspires controversy because it may generate inaccurate results when converting non-monetary effects into costs. As prefabrication may affect multiple organisations and product subsystems, the method used to decide on production methods should consider multiple direct and indirect impacts, including nonmonetary ones. Thus, this study aims to develop a multi-criteria method to evaluate both the monetary and non-monetary impacts of prefabrication solutions to facilitate decision-making on whether to use prefabricated products.

Drawing upon a literature review, this research suggests a multi-criteria method that combines the choosing-by-advantage approach with a cost–benefit analysis. The method was presented for validation in focus group discussions and tested in a case involving a prefabricated bathroom.

The analysis indicates that the method helps a project’s stakeholders communicate about the relative merits of prefabrication and conventional construction while facilitating the final decision of whether to use prefabrication.

This research contributes a method of evaluating the monetary and non-monetary impacts of prefabricated products. The research underlines the need to evaluate the diverse benefits and sacrifices that stakeholder face when considering production methods in construction.

The objective of this research is to investigate the capabilities of the ChatGPT GPT-4 model, a form of artificial intelligence (AI), in comparison to human experts in the context of construction project risk management.

Employing a mixed-methods approach, the study draws a qualitative and quantitative comparison between 16 human risk management experts from Finnish construction companies and the ChatGPT AI model utilizing anonymous peer reviews. It focuses primarily on the areas of risk identification, analysis, and control.

ChatGPT has demonstrated a superior ability to generate comprehensive risk management plans, with its quantitative scores significantly surpassing the human average. Nonetheless, the AI model's strategies are found to lack practicality and specificity, areas where human expertise excels.

This study marks a significant advancement in construction project risk management research by conducting a pioneering blind-review study that assesses the capabilities of the advanced AI model, GPT-4, against those of human experts. Emphasizing the evolution from earlier GPT models, this research not only underscores the innovative application of ChatGPT-4 but also the critical role of anonymized peer evaluations in enhancing the objectivity of findings. It illuminates the synergistic potential of AI and human expertise, advocating for a collaborative model where AI serves as an augmentative tool, thereby optimizing human performance in identifying and managing risks.

This study aims to explore the formation of municipal risk management (RM) and the reasons for the differences of RM practices between the seven biggest cities in Finland.

The empirical data of this comparative qualitative case study comprises 33 interviews conducted with municipal managers. Supplementary material includes documentary material on municipal rules governing RM as well as annual reports and risk tools used in the municipalities.

This study found differences in cities with respect to when, how and why RM practices had evolved. The results indicate that differences in RM practices and development paths between cities are largely explained by the differences in the original reason to initiate RM, time span since its introduction, professional and educational backgrounds of risk managers, local risk events and accounting infrastructure such as RM tools developed in a city. These findings also suggest that even within the same municipality, different functions can be at different phases regarding RM.

This study reports on RM as a new form of accounting in the field of Finnish municipalities. This highlights how fairly uniform considerations at the field level lead to variation in the elaboration of RM practices at the municipal level. The study finds that different paths in the development of local RM involve iterative evolution between the phases of emergence, largely explained by contextual differences. This study contributes to understanding the emergence of new accounting forms in a municipal RM context.

While companies worldwide are largely comprised of small and medium-sized enterprises (SMEs), a significant amount of management accounting (MA) research focuses on larger organisations, thus leaving MA practice in SMEs relatively under-researched. This paper aims to examine MA adoption (MAA) and its interfaces with MA challenges and business performance from a small business perspective.

A sample of 502 small businesses is investigated with an embedded mixed methods research design comprised of qualitative content analysis, factor analysis and analysis of variance.

Up to 78% of small businesses are facing MA challenges that stem from organisation, systems, personnel and/or resources. Based on the present findings, MA challenges do motivate small businesses to at least consider investing in MAA as small businesses facing challenges are more likely to acquire systems and services than those reporting no issues at all. Hence, small business managers seem to not only recognise where their challenges lie, but also seek ways to improve the situation through MAA. The analysis also reveals that companies with the highest MA know-how have the best average solvency, suggesting that small businesses indeed benefit from MAA. Interestingly, the performance at medium levels of know-how declines while investments increase, revealing a “decreasing solvency phenomenon”. Potential explanations are, e.g. the MA not fitting the company’s exact needs, or information usability and use being limited by poor MA understanding.

The originality of the research lies in exploring the interfaces between MA challenges, MAA and small business performance using distinctive embedded mixed methods research design.

Successful commercialization is crucial to innovative firms, but further investigation is needed on how diverse stakeholders can contribute to the commercialization of a radical innovation that requires particular market creation support. This paper aims to, therefore, analyze the key stakeholders and their contributive activities in commercialization and market creation, particularly in the case of radical innovations.

This study relies on qualitative research design including interviews with key stakeholders, such as regulators, scientists, experts, licensing partners, core company representatives and extensive secondary data. This single-case study concerns a functional food product, which is a radical innovation requiring the development of a novel product category positioned between the food and medicine categories in global market settings. Since its market launch in 1995, the involvement of multiple stakeholders was needed for its successful commercialization in over 30 countries.

Results uncover the contributions of diverse stakeholders to commercialization and market creation, particularly of radical innovation. Stakeholders performed market creation activities such as regulating the marketing and labeling of food products, conducting safety assessments, revealing and validating the positive health effects of the novelty and raising awareness of healthy living and cardiovascular health. The commercialization activities included distributing the products overseas, applying the ingredient to different food products and making the products available for users.

This single-case study provides an overview of the positive stakeholder activities with contributions to market creation and commercialization of functional food innovations. Although the user perspective was not included in the empirical part of this study because of our focus on B2B actors, users of the innovation can contribute to R&D activities to a great extent.

The developed framework of stakeholders’ contributive activities in radical innovation commercialization and market creation contributes to literature discussing market creation as well as commercialization within the marketing and innovation management research fields. This work also generates practical advice for managers who commercialize (radical) innovations.

The purpose of this study is to examine the role of artificial intelligence (AI) in transforming the healthcare sector, with a focus on how AI contributes to entrepreneurship and value creation. This study also aims to explore the potential of combining AI with other technologies, such as cloud computing, blockchain, IoMT, additive manufacturing and 5G, in the healthcare industry.

Exploratory qualitative methodology was chosen to analyze 22 case studies from the USA, EU, Asia and South America. The data source was public and specialized podcast platforms.

The findings show that combining technologies can create a competitive advantage for technology entrepreneurs and bring about transitions from simple consumer devices to actionable healthcare applications. The results of this research identified three main entrepreneurship areas: 1. Analytics, including staff reduction, patient prediction and decision support; 2. Security, including protection against cyberattacks and detection of atypical cases; 3. Performance optimization, which, in addition to reducing the time and costs of medical procedures, includes staff training, reducing capital costs and working with new markets.

This study demonstrates how AI can be used with other technologies to cocreate value in the healthcare industry. This study provides a conceptual framework, “AI facilitators – AI achievers,” based on the findings and offer several theoretical contributions to academic literature in technology entrepreneurship and technology management and industry recommendations for practical implication.

This study aims to add to the understanding of the interactive nature of brand love by using a multilayer perspective that incorporates individual, group and societal contexts.

The qualitative empirical study uses abductive reasoning. Its theories and conclusions are grounded in naturally occurring data from an online brand community. The approach revealed new interactive processes of brand love.

This study extends our understanding of the interactive nature of brand love by adopting a layered perspective incorporating micro- (individual), meso- (in-group), macro- (in-group vs out-group) and mega-layer (societal) social dynamics that complements the predominant focus on individual psychological processes. It challenges the linear, monodirectional trajectory approach to brand love, suggesting that brand love is in constant flux as individuals move across the layers in their identification with the brand.

This study provides data from one destination brand in Finland. Future studies could consider other types of brands and contexts in other countries and cultures.

This study shows brand managers that brand lovers can be divided into subgroups with distinct drivers of their love to which brand managers should attend.

To the best of the authors’ knowledge, this is the first attempt to describe the interactive nature of brand love through interactions between and within four layers of brand love. Furthermore, this study enhances our understanding of the contradictory aspects of brand love.