Engineering Management in Production and Services Feed

Health-oriented responsibility as a mechanism for knowledge valorisation in professional service firms

Thu, 08 Oct 2026 00:00:00 GMT

This study examines health-oriented responsibility (HOR) as a distinct dimension of health-promoting leadership (HPL), identifying the mechanisms through which HOR contributes to knowledge valorisation in professional service firms (PSFs) operating within knowledge-intensive contexts. The qualitative study draws on 51 in-depth interviews conducted across 16 large private-sector organisations. Participants were purposefully selected to maximise information relevant to the study. Designed within a grounded theory framework, the study utilises open and selective coding as its primary analytical procedures. The findings reveal that health-oriented responsibility is a strategic organisational mechanism that mitigates the risk of operational discontinuity in human capital and reduces productivity losses associated with the depletion of cognitive resources. Furthermore, it enables the development of individual and team competences, thereby supporting the accumulation of organisational knowledge. This meta-dimension of health-promoting leadership is enacted across three interrelated domains of health: mental, physical, and social. The study provides novel insights into the role of health in business processes and its contribution to knowledge valorisation in professional service firms. It integrates knowledge management with change management through a health-centred lens, advancing a processual understanding of how health functions as a strategic mechanism in knowledge-intensive contexts. Consequently, the study establishes a foundation for leveraging long-term knowledge development as a source of competitive advantage in professional service firms. As a qualitative study, the research seeks analytical generalisation by identifying underlying conceptual mechanisms rather than statistical generalisation. The findings are based solely on the perspectives of employees perceived as leaders, which may limit insight into how other organisational members experience health-oriented responsibility.

Decision-making on R&D strategies of Big Data as a Service in a platform innovation ecosystem

Thu, 08 Oct 2026 00:00:00 GMT

This paper aims to identify the determinants and their dynamic impact on R&D strategic decisions regarding Big Data as a Service (BDaaS) within a platform innovation ecosystem (PIE). An evolutionary game model and simulation system comprising a platform-based firm and a complementor were developed to explore the causal relationships between key factors and BDaaS R&D strategies. The results indicate that changes in digital capability, absorptive capacity, incremental pay-offs, and penalties affect both actors’ R&D strategic choices between independent and collaborative approaches. Furthermore, a contractual agreement serves as a formal governance tool, and capability alignment is a key driver of successful joint BDaaS R&D. An appropriate balance between incremental pay-offs and penalties can help platform-based firms and complementors gain a competitive advantage and avoid opportunism. The study findings highlight the importance of understanding mutual capabilities and establishing a formal collaborative contract between a platform-based firm and a complementor before formulating R&D strategic decisions regarding BDaaS. This provides actionable insights for platform companies seeking to unlock data value and effectively govern collaboration.

Integration of Industry 4.0 and Circular Economy as a determinant of sustainable transformation of enterprise value creation chains

Thu, 08 Oct 2026 00:00:00 GMT

The contemporary global economy remains overwhelmingly influenced by the inventions of the Industrial Revolution 4.0 (IR 4.0), also referred to as Industry 4.0 (I 4.0). This article examines the potential impact of IR 4.0 inventions on changes in enterprise value-creation processes and their interface with the principles of the Circular Economy (CE) as a sine qua non for the functioning of Sustainable Value Creation Chains (SVCCs).This article is theoretical and conceptual. It is based on desk research. Predictive and prescriptive analyses played a fundamental role in the research due to the predominantly futurological nature of the considerations.The research identifies the benefits, threats, and barriers affecting how the IR 4.0 and CE interface is implemented, as well as its significance for establishing SVCCs. The research expands knowledge on this subject and may inspire entrepreneurs to undertake innovative, pro-ecological activities in their enterprises. These reflections may also serve as a basis for further theoretical and practical research.The originality of the research is demonstrated through its comprehensiveness, i.e. the predicted revolutionary changes arising from applied IR 4.0 inventions combined with CE principles in VCCs and the anticipated effects of this interface.

From digitalisation to AI readiness: an exploratory baseline study of youth labour-market adaptation and employment risks in Latvia

Thu, 08 Oct 2026 00:00:00 GMT

This paper examines whether digital familiarity among young people in Latvia can be interpreted as an analytical baseline for discussing future readiness for work in increasingly artificial intelligence (AI)-mediated labour markets. The study analyses the evidence from the 2023 survey on youth digital behaviour, labour values, and employment strategies as a pre-AI baseline for understanding emerging labour-market vulnerabilities. The study applies a risk-oriented analytical approach combining a literature review with the empirical analysis of two anonymous online surveys conducted in Latvia in 2023. The first survey, which serves as the primary empirical source, included 98 young respondents aged 15-29. The second survey, which is used as contextual evidence, included 113 respondents from Riga’s working population engaged in information technologies or the regular use of digital tools. The 2023 data are reinterpreted in light of labour-market, technological, and regulatory developments in 2024-2026.The findings show that the surveyed Latvian youth demonstrated strong everyday digital engagement and generally positive attitudes towards digitalisation. At the same time, this digital familiarity coexisted with pragmatic labour values, non-linear employment strategies, an education-employment mismatch, side employment, and limited use of educational or knowledge-oriented digital resources. These patterns do not provide direct evidence of AI readiness; rather, they indicate baseline conditions that may become more significant as labour markets become increasingly shaped by AI.The paper contributes by formally distinguishing between digital familiarity and AI readiness and by showing how pre-AI empirical data can be used as an exploratory baseline for analysing youth labour-market adaptation in a small European economy. The findings highlight the need for AI literacy, competence-oriented education reform, career resilience, and youth employment policies that address not only access to work but also employment quality, adaptability, and long-term professional sustainability.

Artificial Intelligence-driven Intelligent Transportation Systems applications as innovations in road freight transport enterprises

Thu, 08 Oct 2026 00:00:00 GMT

The paper presents research results on the current state of deployment of Intelligent Transportation System (ITS) applications as innovative technological solutions within road freight transport companies that could be modified through the use of Artificial Intelligence (AI). Eighteen ITS applications were proposed and examined for significant potential changes in their use resulting from AI implementation. During the empirical procedure, a quantitative study was undertaken using the survey research method on a sample of 152 road freight transport companies operating within three voivodships in the southern territory of Poland. The study addressed the recognition of motivations and capabilities of these enterprises in implementing innovations. It further examined the extent of the surveyed companies’ engagement in the implementation of selected innovative technological solutions, the utilised ITS applications that may be subject to modification as a result of AI integration, and the relationships between the use of modifiable ITS applications and the organisations’ capacity for innovation. The findings indicate that in these transport enterprises, most cases of advancement in the implementation of ITS technologies, potentially subject to modification through the application of AI, can be classified as either initial or preparatory. The research results also reveal a pronounced diversification of advancement among these ITS application deployments. The study offers a theoretical contribution by proposing an original conceptual framework that enables a systematic depiction of AI-enhanced ITS as drivers of technological innovation in freight transport enterprises.

Socio-Technical Adoption Framework for Operator 5.0

Thu, 08 Oct 2026 00:00:00 GMT

This paper develops and provides an ex-ante evaluation of the Socio-Technical Adoption Framework for Operator 5.0 (STAF-Op 5.0), a multi-level socio-technical construct system that structures adoption conditions and cross-level dependencies for implementing and scaling human-centric manufacturing technologies.The framework was developed abductively as a structured design artefact and evaluated through structured expert review with manufacturing and Industry 4.0/5.0 specialists, combining content-validity ratings and qualitative feedback to refine constructs, level anchoring, and cross-level relations.The framework separates socio-technical adoption conditions from staging and assessment routines, clarifies how adoption bottlenecks propagate across organisational levels, and provides a structured basis for managing Operator 5.0 technology deployment in manufacturing systems. Experts confirmed levelled construct relevance and clarity and acknowledged the need for explicit vertical relations to diagnose cross-level constraints that hinder stabilisation and scaling beyond pilot projects.The framework offers an explicit socio-technical adoption overlay for Operator 5.0 that integrates fragmented Industry 5.0 evidence into a coherent multi-level dependency logic, enabling technology-specific solutions to be managed without reducing adoption to isolated tool selection.The framework provides a readiness diagnostic checklist across four organisational levels, a sequencing logic that is intended to support identification of cross-level misalignment before scale-up, and a governance structure linking workstation interventions to factory-level data, strategy, and ecosystem conditions. This enables structured management of human-centric manufacturing technologies for Operator 5.0 rather than isolated implementation of solutions.

Impact of corporate actions on the parameters of selected innovation diffusion models

Tue, 30 Jun 2026 00:00:00 GMT

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.A case study method was applied, combined with an analysis of the mathematical structure of innovation diffusion models. A comparison was made between activities typical of management system improvement under the Kaizen methodology and, more broadly, Lean management.The study outcome is the linking of improvement activities with innovation diffusion models in mathematical terms. This provides the ability to rationally steer organisational impact and to develop management systems based on well-established innovation diffusion models.The proposed solution may be particularly useful for representatives of development and improvement departments, whose task is to increase employee engagement in reporting and adopting new methods, techniques, and ways of working. It also enables forecasting the impact of corporate actions on the outcomes related to the acceptance and application of selected methods.

Impact of e-commerce AI host characteristics on consumer purchase intentions: an investigation of Suzhou intangible cultural heritage products

Tue, 30 Jun 2026 00:00:00 GMT

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 research utilises a quantitative method, combining theoretical analysis with empirical data. A survey was conducted to assess the influence of AI host characteristics, including cuteness, vitality, professionalism, and responsiveness, on consumer purchase intentions. The study also examines the mediating roles of perceived novelty and social presence, and the moderating effect of consumer innovativeness, using structural equation modelling (SEM) and regression analysis.The findings indicate that AI host characteristics significantly affect consumer purchase intentions. Cuteness, vitality, professionalism, and responsiveness positively influence consumer attitudes and purchase decisions. Perceived novelty and social presence were identified as key mediators in this relationship. Consumer innovativeness was found to enhance the impact of AI host traits, particularly vitality and responsiveness, on consumer perceptions.This study extends research on AI-enabled livestream commerce and virtual influencers to the context of ICH product commercialisation. It proposes a dual-mechanism framework linking AI host characteristics to purchase intentions through perceived novelty and social presence, and identifies consumer innovativeness as a key boundary condition.The study offers practical insights for e-commerce platforms and businesses by identifying the AI host traits that drive consumer engagement. The findings can guide the optimisation of AI hosts to improve consumer experience and sales. It also highlights the need for businesses to tailor their AI-driven marketing strategies to different consumer segments based on innovativeness.

Organisational and individual determinants of human-AI collaboration

Tue, 30 Jun 2026 00:00:00 GMT

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.The theoretical framework draws on socio-technical systems theory, the socio-cognitive model of trust (SCMT), task-technology fit theory (TTF), and self-determination theory (SDT), which enabled a multidimensional analysis of attitudes towards AI. The research data consisted of survey results covering organisational practices, respondents’ personal attitudes and beliefs regarding digitalisation and work organisation, and their digital competencies. Three complementary methods were applied: exploratory factor analysis, cluster analysis, and linear regression, analysing responses from 614 respondents.The results revealed a difference between the strength of influence on openness to AI of individual factors - personal attitudes, beliefs, and digital competencies - and organisational factors. The bidirectional influence of formalisation on openness to AI was also confirmed.For the presented research, an original concept of “openness to AI collaboration” was developed, encompassing readiness to accept AI in a relatively broad range of organisational roles. In this regard, the study offers new insight into the conditions for an effective human-AI relationship.Based on the findings, recommendations were formulated for business practitioners interested in the effective implementation of AI.

Role of digitalisation in the internationalisation of polish agricultural machinery companies: results from a qualitative study

Tue, 30 Jun 2026 00:00:00 GMT

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 research results demonstrated that internationalisation processes can be supported by digitalisation at both the initial stage (e.g., gaining market knowledge and identifying new opportunities) and at the operational stage (e.g., managing foreign marketing activities and overcoming market entry barriers). These findings align with the results obtained by Etemad et al. (2010) and Neubert (2018). To date, research has primarily focused on the internationalisation of service sector firms, tech start-ups, born-global companies, and niche organisations utilising digital tools for expansion. In the context of industrial enterprises, there is a lack of detailed research on the use of digital tools for foreign expansion and on how digitalisation supports establishing and maintaining customer relationships. For business practice, the study suggests that managers should use digital tools to strengthen market intelligence, partner communication, and virtual relationship building, thereby increasing organisational readiness and agility for foreign market entry.

Human-AI collaboration in internal auditing: the moderating role of financial reporting quality

Tue, 30 Jun 2026 00:00:00 GMT

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.The study applies a quantitative, cross-sectional survey design. Data were collected from 150 employees working in accounting and audit-related roles in Jordanian industrial companies listed on the Amman Stock Exchange. The proposed model was tested using partial least squares structural equation modelling (PLS-SEM) in SmartPLS 4. AI capabilities were examined through four dimensions: expert systems (ES), algorithms (A), artificial neural networks (ANN), and intelligent agents (IA).All four AI capability dimensions have positive and statistically significant effects on QIA. Algorithms exert the strongest effect, followed by intelligent agents, expert systems, and artificial neural networks. The interaction between AI capabilities and QFR is also positive and statistically significant. This indicates that AI capabilities contribute more strongly to QIA when financial reports are accurate, complete, timely, and reliable.The study conceptualises internal auditing as a socio-technical process in which AI capabilities complement rather than replace auditors’ professional judgement. It extends the literature by identifying QFR as a contextual condition that shapes the effectiveness of AI-enabled internal auditing.Organisations should combine investment in AI tools with improvements in financial reporting systems, data governance, auditors’ digital competencies, and human-inthe-loop procedures. Reliable financial information and professional oversight are necessary to realise the benefits of AI-enabled internal auditing.

Building energy performance and housing market regimes: evidence from EU countries

Thu, 02 Apr 2026 00:00:00 GMT

In assessing developments in the real estate market, the energy efficiency of buildings is increasingly considered important from an environmental and economic perspectives. However, empirical evidence on the relationship between energy performance indicators and house price dynamics at the macro level remains mixed, suggesting that this relationship may vary across countries and contexts. This article examines whether and how building energy performance indicators are related to house price dynamics across European Union countries, focusing on short- and long-term relationships and cross-country heterogeneity. The study is based on panel data for 2010-2023, using house price indices as the dependent variable and several energy efficiency indicators, including final energy consumption in buildings and the share of renewable energy sources. Various econometric methods were applied, including panel cointegration analysis (FMOLS and DOLS), dynamic panel models (Arellano-Bond GMM), Granger causality tests, impulse-response analysis, quantile regression, threshold models, and exploratory country clustering. The results show that energy efficiency indicators are not strongly associated with house prices at the aggregate EU level, and their short-term effects are generally weak. Instead, the findings reveal significant heterogeneity across countries and income regimes, suggesting that the capitalisation of energy efficiency in house prices is highly context-dependent. By combining econometric analysis with exploratory segmentation approaches, the study helps structure heterogeneous energy housing interactions into distinct market environments relevant for investment and policy analysis. These results highlight the limitations of uniform macro-level approaches and underline the importance of differentiated market analysis.

Evolving AI models: adoption patterns of transformers and diffusers

Thu, 02 Apr 2026 00:00:00 GMT

This study investigates the development, adoption, and implications of artificial intelligence (AI) models by analysing a comprehensive dataset of over 316,000 models hosted on the Hugging Face platform. Focusing on two dominant model architectures - transformers and diffusion models - it examines their distribution across tasks, user engagement patterns, and practical applications in domains such as natural language processing, computer vision, audio processing, and generative media. The research highlights the growing prominence of generative AI, the role of open-source platforms in shaping model accessibility, and the divergence in use trends between foundational and emerging AI tools. Drawing on correlations between downloads, likes, citations, and model size, the paper discusses how each library’s community-driven dynamics shape their respective strengths. Finally, the paper discusses implications for business strategy and adoption, encompassing practical considerations like infrastructure requirements and ethical challenges, and underscores the potential for these evolving model ecosystems to drive innovative, human-centric AI solutions across diverse sectors.

Building safe organisations: using machine learning to decode safety habits of blue-collar workers in the construction industry

Thu, 02 Apr 2026 00:00:00 GMT

This study aims to provide a framework for categorising safety behaviours of construction workers, recognising the importance of employee safety in the competitive business environment. Employee safety is crucial to overall efficiency, productivity, and well-being, and the study seeks to contribute to understanding and managing workplace safety in the construction industry.This study utilises machine learning (ML) algorithms, like logistic regression, support vector machine, and decision trees, to develop a categorisation framework for the safety behaviours of construction workers. The framework is validated using frequent safety behaviours observed in a random sample of construction professionals.The study finds that workplace safety behaviours (WSB) are primarily influenced by supervisor support, reckless habits, and safety motivation. Limiting workplace accidents, enforcing safety laws, properly documenting safety processes, and organising sessions to educate staff are identified as critical sub-factors. Advancements in technology have resulted in significant improvements across construction organisations in allied domains. Additional considerations include education, preempting the possibility of accidents in different workplace situations, and enforcing strong disciplinary measures.The framework proposed can serve as a valuable tool for organisations to tailor safety interventions. By recognising the diverse influences on safety behaviours, companies can implement targeted measures to address specific root causes of unsafe practices. The practical implications of these findings for safety management in the construction industry are noteworthy.

Building business models of high-growth enterprises based on AI systems

Thu, 02 Apr 2026 00:00:00 GMT

To date, the use of artificial intelligence (AI) to shape the business models of high-growth enterprises (HGEs) remains an unexplored research topic. The article aims to examine the use of artificial intelligence to build business models for high-growth enterprises, considering the heterogeneity of these entities. The empirical research aimed to answer the following questions: (1) Does the use of artificial intelligence to build individual components of the business model depend on the size of the HGE? 2. Does the use of artificial intelligence to build individual components of the business model depend on the age of the HGE?The study was conducted in the second half of 2024 on a sample of 200 Polish high-growth enterprises that declared using AI in their business activities. Data were collected through a survey questionnaire. The survey questions were derived from the assumptions of building a business model based on three value components: value proposition, value creation and delivery, and value capture. The chi-square test, the Kruskal-Wallis test, and measures of dependence for immeasurable features were used to address the research questions.It has been shown that statistically significant relationships exist only between individual value components and the size of the enterprise when these components are perceived as the average of the values of the variables that comprise them. It has also been shown that the values of these components are differentiated by enterprise size.The novelty of the article is the research on the use of AI to build business models for high-growth enterprises, accounting for their heterogeneity.The article is addressed to scientific researchers and business practitioners, particularly those dealing with issues related to building business models and using AI to create value.

Strategic competency development for Industry 5.0 leaders: perspectives from the manufacturing sector

Thu, 02 Apr 2026 00:00:00 GMT

This paper aims to identify and define key leadership competencies required for Industry 5.0 in the manufacturing sector, emphasising the integration of advanced technologies with human-centric and sustainability values across different organisational levels.A two-round Delphi study was conducted with 30 manufacturing experts from multiple priority sub-sectors in Indonesia. Quantitative consensus was assessed using median ≥ 4.0 and IQR ≤ 1.0, complemented by Kendall’s W to measure agreement strength. The study generated a comprehensive Industry 5.0 Leadership Competency Framework consisting of five core dimensions: technical mastery, strategic leadership, people management, business acumen, and sustainability, supported by 24 validated competencies prioritised across senior, middle, and entry-level leadership roles.This study advances leadership theory by proposing a multilevel (macro-meso-micro) human-centric leadership model that integrates sustainability and ethical technological implementation, addressing a missing linkage in current Industry 5.0 literature.The framework guides manufacturing organisations in designing tiered leadership development pathways, performance evaluation instruments, and succession strategies aligned with Industry 5.0 transformation and sustainable operational excellence.

Impact of human resource training on supply chain efficiency in Guiyang’s enterprises: a structural equation modelling (SEM) analysis

Thu, 02 Apr 2026 00:00:00 GMT

This study investigates the impact of human resource (HR) training on supply chain efficiency (SCE) among enterprises in Guiyang, China. A quantitative approach was used to analyse data from 316 respondents across ten key industrial sectors. Structural equation modelling (SEM) was applied to analyse the relationships between HR training, organisational culture, technological adaptability, and supply chain efficiency. The measurement model demonstrated adequate convergent validity (average variance extracted > 0.50) and internal consistency (composite reliability > 0.70). Discriminant validity was confirmed through the Fornell-Larcker criterion and HTMT ratio (< 0.85). The structural model revealed a positive influence of HR training on organisational culture (β = 0.664, p < 0.01), technological adaptability (β = 0.399, p < 0.01), and SCE made directly (β = 0.262, p < 0.05) and indirectly through organisational culture and technological adaptability (β = 0.272, p < 0.05). The model fit indices (χ²/df = 1.295; CFI = 0.984; TLI = 0.976; RMSEA = 0.031) confirmed its robustness. The findings suggest that HR training enhances SCE by improving employee skills, fostering technological integration, and cultivating an adaptive organisational culture. This research contributes to the theoretical understanding of HR development in supply chain management. It provides policymakers and business leaders with practical insights on leveraging workforce training as a strategic tool to enhance supply chain performance in regions such as Guiyang.

Empirical insights into supply chain management integration with product lifecycle management: benefits and challenges

Thu, 02 Apr 2026 00:00:00 GMT

Integration of product lifecycle management systems with supply chain management processes has proven challenging for companies that attempt it, with limited empirical research available on this topic. Consequently, this study aimed to explore whether implementing product lifecycle management systems can enhance supply chain management within an organisation. To obtain relevant data, this study employed a single qualitative case study design, using in-depth semi-structured interviews and on-site observations conducted at ABC TECH to examine how Sovelia PLM supports supply chain processes. The data was collected from a company in Northern Europe that uses Sovelia lifecycle management software. Findings showed that this software generally aids in enhancing supply chain process integration, traceability, and structure. However, it is design-centric, which makes it more difficult to adequately address the supply chain management department’s operational requirements. These insights may enhance the decision-making processes and guide actionable strategies for seamless integration. The findings call for a more holistic product lifecycle management system that encapsulates both design and supply chain functionalities, thereby ensuring cohesive operations and improved communication within the company’s supply chain network.

Navigating servitisation in the GCC fashion sector: a comprehensive assessment of status and barriers

Wed, 31 Dec 2025 00:00:00 GMT

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.

Review of sentiment analysis in new product development: text, audio, visual, and multimodal data

Wed, 31 Dec 2025 00:00:00 GMT

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.