This study examines how artificial intelligence (AI) capabilities influence the quality of internal auditing (QIA) within a human-AI collaborative framework. It also investigates whether the quality of financial reports (QFR) moderates this relationship.
This study explores the impact of AI host characteristics on consumer purchase intentions in livestreaming e-commerce, focusing on Suzhou’s intangible cultural heritage (ICH) products. It aims to develop a comprehensive framework for understanding how AI host traits influence consumer engagement and purchase behaviour in the context of cultural product promotion.
The study aims to identify relationships between organisational actions supporting Kaizen-based improvement initiatives and the parameters of selected innovation diffusion models. The paper presents the differences between innovation and the pursuit of excellence in the Kaizen approach, and the similarities in the dissemination of such ideas within organisations.
Digitalisation has revolutionised the way enterprises operate, influencing the development of business models, marketing communication, distribution, and customer relationship management. Over the last few years, internationalisation at the intersection of digitalisation research has become a significant topic in academic literature (Appiah et al., 2025). Researchers such as Birkinshaw (2022) and Katsikeas et al. (2020) call for studies on how digitalisation affects the internationalisation of companies. This article aims to identify the role of digitalisation in accelerating internationalisation processes within the agricultural machinery industry, with a particular focus on the industrial sector. A qualitative approach was adopted for this study. Individual in-depth interviews were conducted with six managers responsible for international strategies in agricultural machinery manufacturing companies in Poland.
The article examines organisational and individual factors influencing employees’ openness to collaborating with artificial intelligence (AI). The adopted understanding of “collaboration” refers to current practices in this field, where distinctions between human persons and AI are blurring. The study concerns the determinants of readiness to work within a hybrid, biological-silicon cooperation network.
In the face of the inevitable digitalisation of enterprises, limited research has investigated the impact of digital strategy, digital maturity, and AI capability on organisational performance. Drawing on the resource-based theory and recent work on AI in the organisational context, this research aims to uncover the configurations under which a firm’s digital strategy, digital maturity, and AI capability would jointly lead to higher performance. This study uses a unique fuzzy-set qualitative comparative analysis methodology to analyse data collected from 56 SMEs to investigate three domains of AI capability, along with digital strategy and digital maturity. The results suggest that high organisational performance does not depend on a single condition but rather on complex synergistic interactions among the studied conditions. The results indicate that three equifinal configurations lead to high performance of SMEs. The study suggests that AI technical resources are mandatory for any viable solution. This study provides pioneering insights into the empirical contributions of AI capability, digital strategy and digital maturity and their relationships to organisational performance in SMEs, by using a configurational approach. The adopted theoretical perspective addresses the need for a holistic approach to uncover the mechanisms underlying digital strategy and digital maturity in relation to AI capabilities in SMEs, and their mutual impact on organisational performance. These results have practical implications for decision-makers and owners of SMEs, providing new insights into the combination of factors that drive high performance.
This research provides a literature review on the application of sentiment analysis (SA) in the new product development (NPD) process. The literature review employs a systematic literature review methodology. The steps include selecting a review topic, searching and selecting relevant articles, assessing and synthesising the literature, and organising the writing of the review. Sentiment analysis is a subdomain of natural language processing (NLP) that examines user opinions. The sentiment analysis methodology has been employed in the new product development process to replace traditional methods. Sentiment analysis can be conducted across various modalities, including text, audio, image, and multimodal formats. Text modality for sentiment analysis has been used to enhance the lifetime of products and services. Audio data and image modalities represent alternative modalities; however, they receive significantly less attention. The limitation is that these modalities are predominantly executed in controlled environments, utilising open-source or benchmark datasets, and some still employ text modality sentiment analysis methods or lexicons. Multimodal data, conversely, aims to augment the informational dimension of the text modality and is typically executed using deep learning models. This modality encompasses numerous combinations with the primary objective of enhancing the performance of sentiment analysis, hence reducing bias. The findings suggest that future research in this domain should focus on improving multimodal sentiment analysis to improve the new product development process.
This article explores the intersection of artificial intelligence (AI) and marketing technologies (MarTech) by conducting a comprehensive bibliometric analysis. The aim is to identify dominant research themes, key contributors, and major gaps in the existing literature. MarTech is conceptualised as a system of digital tools that enables marketing transformation. Scopus and Web of Science were used to collect records. Following a preliminary comparison, the final analytical corpus of peer-reviewed scientific publications (n=492) was drawn solely from Scopus. The study is based on a dataset from 1987 to 2025. Using Biblioshiny, the analysis examined publication dynamics, citation patterns, co-authorship networks, and thematic clusters. The results indicate consistent growth in scholarly attention, with an annual publication increase of 7.39 % across the full period and 36.53 % between 2015 and 2025. Five primary thematic clusters were identified: (1) AI-Marketing Core and Innovation, this cluster acts as a motor theme, integrating innovation, AI applications, and marketing outcomes, and providing conceptual and methodological scaffolding for the field; (2) Technology Adoption, functioning as a basic theme, it connects sources of innovation with market outcomes; (3) Market Applications and Digital Commerce, this cluster reflects the operationalisation of value in commerce and digital marketing, exhibiting high centrality and moving towards motor-theme status; (4) Perception and Human-Centred Factors, representing a niche but strategically important human perspective; it moderates the relationship between adoption and outcomes. (5) Generative Artificial Intelligence (e.g., ChatGPT), this is the most emerging stream, acting as an accelerator for innovation, adoption, and applications, while simultaneously elevating the importance of quality, safety, and ethics. The United States, India, and China lead in publication volume, while the United Kingdom, France, and Australia demonstrate the highest citation impact. Despite the growing literature base, theoretical fragmentation persists, and limited studies address the ethical, social, and emotional implications of AI in marketing.
The study aims to explore how external support and the ability to understand systemic linkages (organisation’s capability for systems thinking) influence the relationship between organisational resilience and organisational performance.
This study aims to evaluate the current level of servitisation in the Gulf Cooperation Council (GCC) markets in the fashion sector and identify various internal and external obstacles that may hinder fashion organisations in the GCC region from fully adopting the servitisation strategy. An exploratory methodology was employed, using a qualitative approach with semi-structured interviews on a purposive sample. The study reveals that the implementation of the servitisation strategy in GCC is in its initial stages. While evidence of the dimensions underlying such a strategy was found, they were not employed and linked as suggested in the literature to generate the required results. Additionally, non-transparent and limited relationships with partners and unskilled employees were identified as the main barriers preventing fashion agents from fully embracing servitisation in the GCC fashion sector. This study uniquely explores servitisation in the GCC fashion sector, filling a significant gap in existing research that has largely overlooked this region and industry. Unlike previous works that broadly address servitisation in manufacturing, this paper delves into the specific challenges and adoption levels within the GCC’s culturally and economically distinct context. By offering nuanced insights from senior managers in leading fashion organisations, it provides valuable empirical evidence and practical implications for both academia and industry, marking a notable contribution to the literature on servitisation strategies in emerging markets.
This paper investigates how existing literature has approached the concept of sustainability in the context of digital transformation.
The research problem of this paper was whether medical image, behavioral pattern, and physiological data analysis further artificial intelligence-based disease progression prediction, big medical data analysis and processing, and treatment planning optimization, digital twin- and generative artificial intelligence-based disease progression prediction and medical process simulation, patient outcome and pathological condition improvement, and medical service efficiency and resource allocation. We show that physiological measurement indicator modeling and simulation and patient diagnosis and clinical workflow optimization necessitate generative artificial intelligence- and machine learning-based metaverse wearable and implantable medical devices. Our analyses debate on medical metaverse digital twin generative artificial intelligence and machine learning-based big clinical and medical imaging data interoperability and analysis harnessed in remote medical treatment and healthcare practices, healthcare delivery and patient outcome enhancement, real-time medical anomaly detection, timely medical treatment and response prediction, and immersive medical procedure and healthcare delivery simulation in blockchain Internet of Things wearable sensor and computer vision-based extended reality healthcare metaverse. Our results and contributions clarify that clinical decision support systems and generative artificial intelligence-based patient medical disease and health data processing and analysis configure clinical patient care and outcome prediction, health risk forecasting, medical abnormality detection, and remote patient vital sign and health issue monitoring.
This paper examines the effects of foreign direct investments (FDIs) on economic development and the transfer of knowledge and technology (technology spillover) in developing countries, specifically focusing on the Republic of Serbia.
Modern work in sales & service is increasingly enhanced and supported by digital technologies. As a result, frontline employees’ digital competencies are becoming a key success factor for sales & service work. Nevertheless, especially with regards to professional work, there is still a lack of knowledge about how to measure digital competencies. So far, specific empirical contribution focussing on professional digital work environments being increasingly knowledge-intense, collaborative, and virtualized are still very rare. In this article we seek to make a substantial contribution in that area of research. Based on the state-of-the art literature about digital competence among employees in professional work this article is one of the very few that introduces an empirically evaluated scale of digital competence based on a sample size of N=1,283. We suggest a context-related set of five dimensions of digital competence named (1) effective usage of technologies and tools, (2) farsighted & critical information handling, (3) sustained cooperation & communication, (4) integrative knowledge generation, and (5) co-creative problem solving. Evaluation of these five dimensions is conducted with the help of technostress, virtualization of work, space-time flexibility at work and availability for work-related issues. Finally, we present a critical reflection about the scale’s five dimensions.
With the rapid rise in labour costs in China’s hotel industry, service robots have emerged as a potential solution to enhance service efficiency and reduce operational expenses. However, their adoption rate in Chinese hotels remains low. While prior studies have primarily explored technical performance and costs from a managerial perspective, there is a lack of systematic methodologies examining adoption barriers from the lens of guests’ negative emotions. This study employs web-crawling technology to collect 20,900 low-rated reviews from six major Chinese online travel platforms. Using Latent Dirichlet Allocation (LDA) topic modelling combined with computational grounded theory, the authors identified ten key barriers to the adoption of service robots in hotels. Notably, this study introduces “Cultural Misfit”, “Frequent Malfunctions”, and “Inconvenient Operation” as distinct barriers. It also reveals a cascading effect involving service quality, functional utility, and expectation alignment, highlighting that multidimensional interactions drive technology acceptance. These findings provide theoretical and practical insights for optimising service robot deployment, offering new perspectives to improve service efficiency and user acceptance in China’s hotel industry.
High employee job performance is considered one of the key factors contributing to a company’s commercial success, especially in such service-oriented sectors as IT. Researchers recognise a significant role of employee organisational identification, work engagement, and organisational citizenship behaviour in improving job performance; however, a complex model showing the relationship between those variables has not been provided so far. Moreover, a discrepancy exists between the theoretical conceptualisation and definition of organisational identification and its empirically proven measurements. In this context, the article aims to develop a holistic measurement for organisational identification and analyse the roles of organisational identification, work engagement, and organisational citizenship behaviour in improving job performance of the IT sector employees.
In modern production systems, ensuring high product quality while minimising risk is a critical challenge. Traditional quality assessment methods often rely on expert judgment or complex models, which may introduce subjectivity or require large datasets. This study aims to develop a universal methodology for assessing product quality risks using a mathematically grounded approach that eliminates the need for expert-based evaluations and can be easily implemented in various industrial contexts. A qualimetric method based on nonlinear mathematical dependence using the error function “erf” is proposed. The method transforms measured quality indicators into a dimensionless scale and derives functionally dependent statistics under the assumption of a uniform distribution. The model is validated through analytical derivations and numerical experiments on piston components in precision mechanical engineering. A new mathematical model was established to calculate the probability density function of transformed quality indicators. The methodology enables the estimation of the probability that a quality indicator will fall within a risky range near tolerance limits. Numerical experiments confirmed the validity of the model, demonstrating its applicability to real-world production scenarios and its alignment with known principles of qualimetry. The proposed method provides a universal, objective, and practical tool for risk-based quality assessment. It can be applied across different industries, integrated into existing quality management systems, and used to support decision-making in production control. Future research should expand the model to accommodate nonuniform distributions and explore its integration with real-time quality monitoring systems.
The paper introduces a new multi-factor critical chain buffer estimation model and designs a dynamic monitoring method based on the project elements. A literature analysis determined a research gap and a research problem. It was found that the existing methods offer scarce collaborative studies on buffer setting and monitoring and insufficient research on buffer setting considering project economic indicators. However, these topics are often given priority consideration in practical engineering applications. Therefore, the study proposes a multi-factor critical chain buffer setting and its dynamic monitoring method. The planning stage analyses the impact of income, resources, and probability of success on buffer size setting and defines the calculation model of capacity constraint buffer. The execution stage dynamically sets buffer monitoring points according to the progress of project implementation, monitors the remaining buffer amount at the completion of each activity on the critical chain, and takes corresponding actions to ensure that the progress is controllable. The method was applied in a multi-project of a Chinese software enterprise. To further verify the effectiveness of this research, the method is compared with the traditional static buffer monitoring method (TBMM) and the relative buffer monitoring method (RBMM), and the construction period of the real project is simulated through the computer program for analysis. Results show that the research method can reduce unreasonable buffer settings, enhance the robustness of a buffer against complex environments, and reduce the probability of false warnings in the monitoring process.
This study aims to comprehensively review aviation forecasting research by identifying its bibliometric trends, evolving research areas, and thematic developments. It focuses on understanding the aviation industry’s research gaps, highlighting emerging trends, and offering insights into future forecasting innovations. A systematic literature review in the Scopus database used Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) and bibliometric analysis. It identified key patterns, influential publications, and emerging topics. A science mapping analysis was executed to pinpoint research trends in airline forecasting using Biblioshiny to visualise the network analysis and thematic evolution keywords mapping. The study categorised research trends and identified underexplored areas for future investigation. The findings reveal significant shifts in aviation forecasting research, with three distinct phases of publication growth and a surge in output from 2016 onwards. Passenger demand forecasting remains the most researched topic, though its growth has stabilised. Emerging issues such as customer behaviour, financial forecasting, and dynamic pricing have gained prominence, driven by advancements in machine learning and big data analytics. The study also highlights transitioning from traditional statistical methods to more advanced predictive techniques, emphasising real-time decision-making and operational efficiency. Established research areas, such as air cargo forecasting and fleet scheduling, have become more standardised, reducing the need for further innovation.
The effects of global change on development and the sustainability of the economy are visible in implementing innovations. Appropriate selection of experts, considering various knowledge areas, should use selected tools. The article introduces a research tool for selecting experts for innovation risk assessment. In particular, it aims to present individual measurement scales and their reliability assessment (Cronbach’s alpha). The article presents the factor structure of a potential expert’s competencies measured using a questionnaire. The questionnaire was developed for the appropriate selection of specialists from various industries, including production, mechanics, and management. The questionnaire constitutes a tool applicable for assessing and selecting people involved in the implementation and risk assessment of innovations. It is based on the following four scales (factors): open mind, closed mind, cognitive motivation, and response to uncertainty. The questionnaire’s effectiveness was studied using two research samples, n= 224 and n= 349, comprised of entrepreneurs and individuals professionally related to business and development and implementation of innovations. The interviewees were selected randomly. Factor analysis was used to reduce the test items in the questionnaire. The internal reliability of items was analysed using Cronbach’s alpha. The proposed questionnaire forms a new tool that can be used in selecting experts who deal with the risk assessment of innovations and in the broadly understood process of recruiting staff with appropriate competencies in terms of mindset characteristics. The article presents an analysis related to the conduct of typical research in Management and Quality Sciences, as well as practical principles guiding the use of the questionnaire, which may have wider application in the practice of risk management. The article presents the measurement tool with the answer key, which is a valuable guide for interpreting the results. The questionnaire facilitates the selection of individuals focused on independent and courageous problem-solving and the statement of evaluations. At the same time, an adequate level of caution should be respected, characterising people with risk aversion. Furthermore, creativity and openness are coupled with a considerable ability to develop new solutions and rationally respond to difficult and unpredictable situations.
